![Molecular Docking Analysis of Selected
Phytochemicals for the Treatment
of Proteus Syndrome
Tanwar Reeya and Das Asmita
Abstract Proteus syndrome is a rare hamartomatous disorder that is characterized
by the overgrowth of tissues in a mosaic manner. Since drug therapy was not seen
to be a component of standard care for Proteus syndrome, this paper focuses on
finding phytochemicals against AKT-1 protein whose mutation is responsible for
Proteus syndrome. Lipinski’s rule of 5 was applied to check the drug-likeliness of
the selected phytochemicals followed by 3 rounds of molecular docking, computation
of bioavailability radar and MD simulations. Simulations revealed Tanshinone-II A
to be a potent inhibitor of AKT-1. Further, in-vivo studies can be performed on
Tanshinone-II A for clinical use of the compound. In the above study, Miransertib
(ARQ 092) was used as a positive control since it has recently shown to have a
therapeutic effect on a teenager with Proteus syndrome and ovarian carcinoma.
Keywords Proteus syndrome · Molecular docking · AKT-1 · Miransertib ·
Phytocompounds
1 Introduction
Proteus syndrome is a rare disorder which involves an atypical skeletal growth [1].
This disease was first reported in the year 1979 followed by similar reports in the year
1983 by Wiedemann et al. It begins postnatally and progresses in a rapid and dispro-
portionate manner which usually results in the distortion of the normal tissue [2].
There are cases in which many affected individuals are born without any perceptible
symptoms, and the overgrowth usually begins during the time frame of 6–18 months.
The severity and extent to which this disease can affect a patient vary greatly from
one to another, but a few common manifestations of this disease are asymmetric,
T. Reeya (B) · D. Asmita
Department of Biotechnology, Delhi Technological University, Main Bawana Road, Delhi
110042, India
D. Asmita
e-mail: asmitadas1710@dce.ac.in
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022
S. Smys et al. (eds.), Computational Vision and Bio-Inspired Computing,
Advances in Intelligent Systems and Computing 1420,
https://doi.org/10.1007/978-981-16-9573-5_1
1](https://image.slidesharecdn.com/56541-250317165258-28d6deb8/85/Computational-Vision-and-Bio-Inspired-Computing-Proceedings-of-ICCVBIC-2021-Advances-in-Intelligent-Systems-and-Computing-1420-S-Smys-Editor-18-320.jpg)
![2 T. Reeya and D. Asmita
distorting bony overgrowth, CCTN or cerebriform connective tissue nevi, dysregu-
lation of fatty tissue and vascular anomalies [3]. The patients affected by this disease
are susceptible to mesothelioma, breast cancer and papillary thyroid carcinoma since
they have predisposition to benign and malignant tumours [4]. Although the exact
cause of Proteus syndrome is not very clear, it has been seen that mutations in the
AKT-1 gene that occurs after fertilization of the embryo (somatic mutation) are an
important cause. AKT-1 is a part of the PI3K/AKT signalling pathway which is
responsible for the regulation of various cellular processes such as the cell growth,
proliferation and apoptosis [5]. The activation of AKT takes place due to its PH
domain’s interaction with a lipid or a secondary messenger called the phosphatidyli-
nositol 3,4,5-trisphosphate [PI (3,4,5) P3]. Following this interaction, AKT-1 is
then phosphorylated at threonine 308 (T308) by phosphoinositide-dependent kinase
which also binds to [PI (3,4,5) P3]. AKT is fully activated when it is phosphorylated
by mTORC2 complex at position 473 (Serine 473) [6]. Since bioinformatics helps in
the reduction of cost of designing experiments, their execution and laboratory trials,
we use it with the aim to produce results which would help us bring about the use of
phytocompounds extracted from plants like Sanguinaria canadensis, Evodia rutae-
carpa, Salvia miltiorrhizae, Trigonella foenum, etc. These plants have been selected
due to their ability to participate in targeted therapeutic activity against AKT-1 protein
in cancer. Miransertib has been used as a positive control for conducting molecular
docking studies since the use of Miransertib (ARQ 092) has recently shown to have
a therapeutic effect on a teenager with Proteus syndrome and ovarian carcinoma
in 2019 [7]. Miransertib is an allosteric inhibitor of our target protein AKT-1 which
binds to the combined interface of PH domain and the N and C lobe of kinase domain
(Fig. 1). This binding enables the target protein to be locked in a closed conforma-
tion which results in the blocking of the phospholipid binding site by the kinase
domain. The consequence of this conformation enables the allosterically inhibited
AKT-1 to remain cytosolic and inactivated (activation occurs through phosphory-
lation) (Fig. 2). The nature of inhibition of Miransertib poses an advantage since a
Fig. 1 a The following figure represents the orientation of the PH domain (orange) relative to the
N-lobe (pink) and C-lobe (yellow) of the kinase domain and Inhibitor VIII shown in green. b In the
same orientation as Panel A, the kinase domain is surface rendered. c Structure of AKT1(1–443):
Inhibitor VIII rotated approximately 180° compared to Panel B. Courtesy: https://doi.org/10.1371/
journal.pone.0012913.g003 [8]](https://image.slidesharecdn.com/56541-250317165258-28d6deb8/85/Computational-Vision-and-Bio-Inspired-Computing-Proceedings-of-ICCVBIC-2021-Advances-in-Intelligent-Systems-and-Computing-1420-S-Smys-Editor-19-320.jpg)
![Molecular Docking Analysis of Selected Phytochemicals … 3
Fig. 2 Model of AKT activation and inhibition. In the cytoplasm, the ‘PH-in’ and ‘PH-out’ confor-
mations of AKT are in equilibrium. AKT is recruited to the plasma membrane via interactions with
the products of PI3K and is subsequently phosphorylated on two sites, T308 and S473 in AKT1,
which results in kinase activation. The allosteric inhibitor stabilizes the ‘PH-in’ form of the inactive
enzyme (top left); whereas the ATP-competitive inhibitor binds to the activated form of the kinase
(bottom left). Surface representations derived from the following structures: PDB code: 3CQW
(active and ATP-competitive inhibitor bound kinase), PDB code: 1UNQ (membrane-bound PH
domain) and PDB code: 3O96 (cytoplasmic PH and kinase domains and membrane-bound kinase
domain). Colouring as follows: kinase domain (yellow), PH domain (orange), IP4 binding residues
(cyan), phospho-T308 (red), allosteric inhibitor (green), ATP-competitive inhibitor (blue) and PI3K
products (violet). Phospho-S473 is not visible in these orientations of AKT. Courtesy: https://doi.
org/10.1371/journal.pone.0012913.g003 [8]
study conducted by Wu et al. in 2010 [8] hypothesized allosteric inhibitors to have a
higher efficacy than small ATP-competitive molecules. They proposed this since the
competitive inhibitor is not able to close the PH domain fully upon its binding due
to which the kinase domain and the phospholipid binding site remain exposed [8].
This exposure enhances the ability of AKT to localize to the membrane, whereas
the allosteric inhibitors restrict both membrane association and activation by phos-
phorylation. Although, a clinical perspective with respect to this observation is still
needed [8]. Lastly, it is the first drug to go through clinical trials (Phase-II) in May,
2021 for Proteus syndrome. Hence, this research explores the properties of selected
phytocompounds with respect to our target protein; AKT-1 keeping Miransertib as
the positive control.](https://image.slidesharecdn.com/56541-250317165258-28d6deb8/85/Computational-Vision-and-Bio-Inspired-Computing-Proceedings-of-ICCVBIC-2021-Advances-in-Intelligent-Systems-and-Computing-1420-S-Smys-Editor-20-320.jpg)
![4 T. Reeya and D. Asmita
2 Methods and Materials
Building of Phytochemical Library: NPCARE (silver.sejong.ac.kr/npcancer/) or
the Natural Products CARE is a database built by the Department of Bioscience and
Biotechnology, Sejong University, consisting of a list of natural products that can
regulate specific genes involved in cancer [9]. Each entry in this database is annotated
with the name of genus and species of the biological source, the type of cancer it is
involved with, the name of the cell line used to determine its anti-cancerous property,
its PubChem ID and lastly a myriad of information about the target gene/protein.
Since our research focusses on down regulating the effects AKT-1 gene for the
treatment of Proteus syndrome, a list of 21 phytochemicals were obtained for AKT-1.
(as shown in Table 1).
Protein/macromolecule and Ligands: Protein Data Bank or PDB (https://www.
rcsb.org/) is a database that contains three-dimensional structural information of large
biological molecules such as nucleic acids and proteins [10]. Our target protein AKT-
1wasretrievedfromthefollowingdatabasebyusingthePDBID:3O96in.pdbformat.
The structure contained a single A chain with an amino acid length of 446 along with
a Covalent-Allosteric AKT Inhibitor. The publicly accessible PubChem repository
has served as a resource of chemical information to the scientific community since
2004 (https://pubchem.ncbi.nlm.nih.gov/), and it contains information regarding the
chemical and physical properties of the substance along with its structural repre-
sentation (2-D,3-D, etc.) [11]. Three-dimensional structures of the ligands were
obtained from PubChem repository using their respective PubChem IDs obtained
from NPCARE database in.sdf format followed by their conversion to.pdb format
using Biovia Discovery Studio.
ADME Analysis: In order to begin the screening of ligands, ADME analysis
was conducted by applying Lipinski’s rule of 5 [12]. The factors involved (molec-
ular weight (<500 Da), high lipophilicity (LogP <5), hydrogen bonds donors (<5)
and hydrogen bond acceptors (<10)) in conducting the same were retrieved from
PubChem repository. According to this analysis, any ligand that violates more than
2 of the above stated parameters will be debarred from further analysis.
Molecular Docking Analysis: It is a regularly used computational tool which aids
the process of designing drugs since it provides information regarding the binding
mode of the known ligands, identification of potent drug candidates and their binding
affinity. The following tool was used in three rounds to identify the binding affinity
and the type of interactions our selected phytochemicals displayed. The first round
of molecular docking was conducted by PyRx. It is an open-source (https://pyrx.sou
rceforge.io/) virtual screening tool which contains a combination of software’s such
as AutoDock Vina, AutoDock 4.2 and Open Babel [13]. For the following round of
docking studies, AutoDock Vina tool compiled in PyRx was used. The dimensions
used for this screening were centre (x, y, z) = (9.3494, −7.9442, 10.1538) and
dimensions in angstrom (x, y, z) = (18.0256, 25.000, 24.4774). The water molecules
and the allosteric inhibitor were removed beforehand. Lastly, an exhaustiveness of 8
was set to give the output of the lowest energy possible with the help of AutoDock](https://image.slidesharecdn.com/56541-250317165258-28d6deb8/85/Computational-Vision-and-Bio-Inspired-Computing-Proceedings-of-ICCVBIC-2021-Advances-in-Intelligent-Systems-and-Computing-1420-S-Smys-Editor-21-320.jpg)
![10 T. Reeya and D. Asmita
Table 1 (continued)
S.No. Species Compound name PubChem
ID
2-D diagram ADME analysis
21 Sanguinaria
canadensis
Sanguinarine CID 5154 Molecular
weight
(500 Da):
332.33 g/mol
Lipophilicity
(LogP 5): 2.72
H bond donor
(5): 0
H bond acceptor
(10): 4
Violations: 0
Vina-PyRx, and the ligands were then filtered out on the basis of their binding energy.
Theselectedligandswerethendockedfurtherintwomoreroundsusinganotheropen-
source docking tool, AutoDock 4.2 (http://autodock.scripps.edu/) [14]. Optimization
of both the protein and the ligand was carried out by eliminating water, including
polar hydrogens followed by adding Kollman and Gasteiger charges. The final step
of optimization was carried out by removing the native ligand of the protein. The
output utilized for docking studies with an exhaustiveness set to 10 was Lamarckian
GA, and the best results out of the two were then used for further analysis.
Bioavailability Radar: SwissADME is an online tool (http://www.swissa
dme.ch/) which was used to obtain a bioavailability radar, and this radar scrutinized
the phytocompounds on the basis of six parameters (saturation, flexibility, solubility,
size, polarity and lipophilicity). The region coloured in pink defines the limit within
which the parameters should lie, and any deviation from the region indicated that the
drug is not orally bioavailable.
Molecular Dynamics: The docked complex was subjected to molecular dynamics
simulation by the use of Demond-Maestro module [15]. Since the software provides
high performance algorithms in its default settings itself, they were used to obtain
high speed and precise results. The docked complex was subjected to submersion
in TIP3P water model in an orthorhombic shape, after which the entire system was
neutralized by addition of seven chlorine ions at 0.15 M concentration. All the atoms
in the system were aligned by optimized potentials for liquid simulations-AA (OPLS-
AA) 2005 force field. SHAKE/RATTLE algorithm along with NVT as the ensemble
class was used to limit the movement of the atoms covalently bonded. The conditions
set in order to start the simulations were temperature: 300 K and pressure: 1 bar for
100 ns. Integration of all the parameters of dynamic simulation was carried out using
the RESPA integrator. Finally, to analyse the component stability and dynamic nature
of interaction, a trajectory of 100 ns was set to show in 1000 frames.](https://image.slidesharecdn.com/56541-250317165258-28d6deb8/85/Computational-Vision-and-Bio-Inspired-Computing-Proceedings-of-ICCVBIC-2021-Advances-in-Intelligent-Systems-and-Computing-1420-S-Smys-Editor-27-320.jpg)
![Molecular Docking Analysis of Selected Phytochemicals … 11
3 Results and Discussion
ADME Analysis: 21 compounds were screened with respect to Lipinski’s rule of 5,
and the parameters used for the same were molecular weight (500 Da), hydrogen
bonds donors (5), high lipophilicity (LogP 5) and hydrogen bond acceptors (10).
This screening helped in determining the drug-likeliness of the selected phytocom-
pounds. Usually, compounds are eliminated if they violate more than two parameters
of Lipinski’s rule of five. Out of the 21 compounds, 20 showed 0 violations leaving
Tetrandrine with PubChem ID: 73078 with one violation (due to its molecular weight
being greater than 500) as shown in Table 1.
Since all 21 of them fell within the parameters, it helped us conclude that all 21
compounds do not show any poor absorption or permeation, and hence, they were
all used for further investigation.
Docking Analysis: The 21 selected phytochemicals were then introduced into
a virtual space to conduct molecular docking studies. This was conducted in three
rounds, first being conducted by PyRx and the last two by AutoDock 4.2. The active
site residues for AKT-1 were obtained from a research study of the crystal structure
of AKT-1 protein with an allosteric inhibitor by Wen-I Wu et al. in [8]. The residues
at the active site of AKT-1 were VAL201, SER205, VAL270, THR82, CYS296,
VAL83, GLU85, ILE84, ARG273, ASN54, ASP274, VAL271, TYR272, THR211,
THR291, ILE290, ASP292, LEU210, LEU264, TRP80 and LYS268. These active
sites are present at the linkage of PH domain and the N and C lobe of kinase domain.
Docking Analysis using AutoDock Vina-PyRx: The first round of docking anal-
ysis was conducted to identify the phytocompounds that have the ability to compete
with Miransertib as a potent inhibitor by comparing their resultant vina binding affini-
ties. As depicted by Table 2, results obtained by PyRx revealed that the binding ener-
gies of Diosgenin (−12.6 kcal/mol), Sanguinarine (−12.2 kcal/mol) and Tanshinone-
IIA (−11.7 kcal/mol) are to be greater than that of Miransertib (−11.6 kcal/mol)
which was taken as a positive control due to its therapeutic properties. Due to their
depiction of greater binding strength than the positive control, these three phytochem-
icals, Disogenin, Sanguinarine and Tanshinone-IIA, were then selected for further
docking.
Docking Analysis using AutoDock 4.2: The three phytocompounds, namely
Disogenin, Tanshinone-II and Sanguinarine, were selected for two more rounds of
molecular docking studies which were conducted by Autodock 4.2. The residues
used for this round were the same as mentioned above.
The value of the best confirmation of Disogenin out of the 20 obtained was seen
to be −11.39 kcal/mol. The four different types of interactions observed were pi-
alkyl conventional hydrogen bond, van der Waals, and alkyl, and TRP80, LEU210,
LEU264 and VAL270 were seen to be interacting via alkyl and pi-alkyl bond while
LYS268, THR211 were seen to be interacting via conventional hydrogen bond,
respectively. The remaining residues weakly interacted with the ligand via van der
Waals interaction. (Fig. 1).](https://image.slidesharecdn.com/56541-250317165258-28d6deb8/85/Computational-Vision-and-Bio-Inspired-Computing-Proceedings-of-ICCVBIC-2021-Advances-in-Intelligent-Systems-and-Computing-1420-S-Smys-Editor-28-320.jpg)
Computational Vision and Bio Inspired Computing Proceedings of ICCVBIC 2021 Advances in Intelligent Systems and Computing 1420 S. Smys (Editor)
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- 6. Editors S. Smys Department of ECE RVS Technical Campus Coimbatore, Tamil Nadu, India Valentina Emilia Balas Faculty of Engineering Aurel Vlaicu University of Arad Arad, Romania João Manuel R. S. Tavares Departamento de Engenharia Mecanica Faculdade de Engenharia Universidade do Porto Porto, Portugal ISSN 2194-5357 ISSN 2194-5365 (electronic) Advances in Intelligent Systems and Computing ISBN 978-981-16-9572-8 ISBN 978-981-16-9573-5 (eBook) https://doi.org/10.1007/978-981-16-9573-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
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- 8. Preface This conference proceedings volume contains the written versions of most of the contributions presented during the conference of ICCVBIC 2021. This conference provided a setting for discussing the recent developments in a wide variety of topics includingcomputationalvision,fuzzy,imageprocessingandbio-inspiredcomputing. This conference has been a good opportunity for participants coming from various destinations to present and discuss topics in their respective research areas. ICCVBIC 2021 conference tends to collect the latest research results and appli- cations on computational vision and bio-inspired computing. It includes a selection of 63 papers from 252 papers submitted to the conference from universities and industries all over the world. All of the accepted papers were subjected to strict peer- reviewing by 2–4 expert referees. The papers have been selected for this volume because of quality and the relevance to the conference. ICCVBIC 2021 would like to express our sincere appreciation to all authors for their contributions to this book. We would like to extend our thanks to all the referees for their constructive comments on all papers, and especially, we would like to thank the organizing committee for their hard working. Finally, we would like to thank the Springer publications for producing this volume. Coimbatore, India Porto, Portugal Arad, Romania Dr. S. Smys Dr. João Manuel R. S. Tavares Dr. Valentina Emilia Balas vii
- 9. Acknowledgements We would like to acknowledge the excellent work of our conference organising committee and keynote speakers for their presentations on November 25–26, 2021. The organizers also wish to acknowledge publicly the valuable services provided by the reviewers. On behalf of the editors, organizers, authors and readers of this conference, we wish to thank the keynote speakers and the reviewers for their time, hard work and dedication to this conference. The organizers also wish to acknowledge speakers and participants who attended this conference. Many thanks for all persons who helped and supported this conference. ICCVBIC 2021 would like to acknowledge the contribution made to the organization by its many volunteers. Members contributed their time, energy and knowledge at a local, regional and international level. We also thank all the chairpersons and conference committee members for their support. ix
- 10. Contents Molecular Docking Analysis of Selected Phytochemicals for the Treatment of Proteus Syndrome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Tanwar Reeya and Das Asmita A Deep Learning-Based Detection of Wrinkles on Skin . . . . . . . . . . . . . . . . 25 H. Deepa, S. Gowrishankar, and A. Veena Image Transmission Using Leach and Security Using RSA in Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 S. Aruna Deepthi, V. Aruna, and R. Leelavathi Code Injection Prevention in Content Management Systems Using Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 C. Kavithamani, R. S. Sankara Subramanian, Srinevasan Krishnamurthy, Jayakrishnan Chathu, and Gayatri Iyer A Review of Hyperspectral Image Classification with Various Segmentation Approaches Based on Labelled Samples . . . . . . . . . . . . . . . . 69 Sneha and Ajay Kaul Improvements in User Targeted Offline Advertising Using CNN and Deviation-Based Queue Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Ruchika Malhotra, Samarth Gupta, Sarthak Katyal, and Ronak Sakhuja Movie Recommendation System Using Hybrid Collaborative Filtering Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Rohit Kale, Saurabh Rudrawar, and Nikhil Agrawal Hybrid Pipeline Infinity Laplacian Plus Convolutional Stage Applied to Depth Completion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Vanel Lazcano and Felipe Calderero A Novel Approach of DEMOO with SLA Algorithm to Predict Protein Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 P. Lakshmi and D. Ramyachitra xi
- 11. xii Contents Economic Load Dispatch Problem with Valve-Point Loading Effect Using DNLP Optimization Using GAMS . . . . . . . . . . . . . . . . . . . . . . 149 P. Dinakara Prasad Reddy, Ch. Devisree, M. Vijaya Kumar Naik, and K. Guna Prasad Solar Radio Spectrum Classification Based on ConvLSTM . . . . . . . . . . . . 161 Ruru Cheng and Guowu Yuan Particle Swarm Optimization-Based Neural Network for Wireless Heterogeneous Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Divya Y. Chirayil Impact Analysis of COVID-19 on Various Indian Sectors . . . . . . . . . . . . . . 181 Shreya Nayak, Govind Thakur, and Narendra Shekokar Emotion Recognition in Speech Using MFCC and Classifiers . . . . . . . . . . 197 G. Ajitha, Addagatla Prashanth, Chelle Radhika, and Kancharapu Chaitanya A Comparative Analysis on Image Caption Generator Using Deep Learning Architecture—ResNet and VGG16 . . . . . . . . . . . . . . . . . . . . . . . . . 209 V. Sri Neha, B. Nikhila, K. Deepika, and T. Subetha Corona Warrior Smart Band . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 Soham S. Methul, Shubhangee K. Varma, and Ashok S. Chandak Cellular Learning Automata: Review and Future Trend . . . . . . . . . . . . . . . 229 Mohammad Khanjary Computer Vision and Machine Learning-Based Techniques for Detecting the Safety Violations of COVID-19 Scenarios: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 K. S. Kavitha and Megha P.Arakeri Stigmergy-Based Collision-Avoidance Algorithm for Self-Organising Swarms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 Paolo Grasso and Mauro Sebastián Innocente Handling Security Issues in Software-defined Networks (SDNs) Using Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 Deepak Kumar and Jawahar Thakur Multi-purpose Web Application Honeypot to Detect Multiple Types of Attacks and Expose the Attacker’s Identity . . . . . . . . . . . . . . . . . . 279 P. Sri Latha and S. Prasanth Vaidya An Empirical Approach for Tuning an Autonomous Mobile Robot in Gazebo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 Naveen Bharathiraman, Adwait Kaundanya, Jaiesh Singhal, Yash Wadalkar, and Kiran Talele
- 12. Contents xiii An Investigation on Computational Intelligent Solutions for Highly Dynamic Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 R. Haripriya, C. B. Vinutha, and M. Nagaraja A Study of Underwater Image Pre-processing and Techniques . . . . . . . . . 313 Pooja Prasenan and C. D. Suriyakala Multilabel Text Classification of Scientific Abstract . . . . . . . . . . . . . . . . . . . 335 T. R. Srinivas, A. V. S. Rithvik, and Saswati Mukherjee Spirochaeta Bacteria Detection Using an Effective Semantic Segmentation Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355 Apeksha Kulkarni, P. Sai Dinesh Reddy, Rishabh Bassi, Suryakant Kumar Kashyap, and M. Vijayalakshmi An IoT-Based Intelligent Air Quality Monitoring System . . . . . . . . . . . . . . 367 K. R. Chetan Machine Learning-Based Sentiment Analysis Towards Indian Ministry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381 K. Bhargavi, Pratish Mashankar, Pamidimukkala Vasista Sreevarsh, Radhika Bilolikar, and Preethi Ranganathan Detection and Prediction for Obstructive Sleep Apnea Recognition . . . . . 393 T. Srinivas Reddy, A. Pradeep Kumar, M. Mahesh, and J. Prabhakar A Comprehensive Study of Advances in Oral Cancer Detection Using Image Processing Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 S. M. Sagari and Vindhya P. Malagi Training a Multilayer Perception for Modeling Stock Price Index Predictions Using Modified Whale Optimization Algorithm . . . . . . . . . . . 415 Nebojsa Bacanin, Miodrag Zivkovic, Luka Jovanovic, Milica Ivanovic, and Tarik A. Rashid Energy Saving Mechanism Using Extensive Game Theory Technique in Wireless Body Area Network (ES-EG) . . . . . . . . . . . . . . . . . . 431 M. Ayeesha Nasreen and Selvi Ravindran Performance Analysis of Routing Methods for Unmanned Aerial Vehicle Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449 Supriya Kamble and Sanjay Pardeshi Study of Classification Algorithms for Handwritten Character Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461 R. Sanjay Krishna, E. Jaya Suriya, and J. Shana A Hybrid Multiclass Classifier Approach for the Detection of Malicious Domain Names Using RNN Model . . . . . . . . . . . . . . . . . . . . . . 471 B. Aarthi, N. Jeenath Shafana, Judy Flavia, and Balika J. Chelliah
- 13. xiv Contents Brain Tumor Detection and Classification Using Transfer Learning Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483 Addepalli Venkatanand Ram, Harish Kuchulakanti, and Tarla Sai Raj A Comprehensive Survey of AI Methods to Predict Adverse Drug-Drug Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495 P. Margaret Savitha and M. Pushpa Rani Feature Selection Using PSO Optimized-Framework with Machine Learning Classification System via Breast Cancer Survival Data . . . . . . . 513 Anusha Papasani, Nagaraju Devarakonda, Zdzislaw Polkowski, Madhavi Thotakura, and N. Bhagya Lakshmi Inception-Based CNN for Low-Light Image Enhancement . . . . . . . . . . . . 533 Moomal Panwar and Sanjay B. C. Gaur Quantum Grid: Toward Future Energy Transformation . . . . . . . . . . . . . . 547 N. Samanvita, Sowmya Raman, Shruti Gatade, Anil Kumar, and Shreeram Kulkarni Computer Vision in Autoimmune Diseases Diagnosis—Current Status and Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 571 Viktoria N. Tsakalidou, Pavlina Mitsou, and George A. Papakostas Covariance Features Improve Low-Resource Reservoir Computing Performance in Multivariate Time Series Classification . . . . . . . . . . . . . . . 587 Sofía Lawrie, Rubén Moreno-Bote, and Matthieu Gilson Wireless Sensor-Based Enhanced Security Protocol to Prevent Node Cloning Attack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603 S. Meganathan, N. Rajesh Kumar, S. Sheik Mohideen Shah, A. Sumathi, and S. Santhoshkumar A Deep Convolutional Neural Network-Based Speech-to-Text Conversion for Multilingual Languages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 617 S. Venkatasubramanian and R. Mohankumar A Novel Dual Model Approach for Categorization of Unbalanced Skin Lesion Image Classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 635 Shrey Dedhia, Siddharth Trivedi, Siddharth Salvi, Jay Jani, and Lynette D’mello A Study of Green Information Technology Using the Bibliometric Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 651 Agung Purnomo, Evaristus Didik Madyatmadja, Albert Verasius Dian Sano, Hendro Nindito, and Corinthias P. M. Sianipar
- 14. Contents xv Node Sleep Strategy for Improvement of Energy Efficiency in Wireless Multimedia Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 667 Minaxi Doorwar and P. Malathi Analysis of Greenness in Urban Cities Using Supervised and Unsupervised Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 675 Nita Nimbarte, Shraddha Sainis, and Sanjay Balamwar Sequence Models for Crop Yield Prediction Using Satellite Imagery . . . . 687 M. Sarith Divakar, M. Sudheep Elayidom, and R. Rajesh A Comparative Study of Word Embedding Techniques in Natural Language Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 701 Syed Abdul Basit Andrabi and Abdul Wahid A Taxonomy on Strategic Viewpoint and Insight Towards Multi-Cloud Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 713 S. Alangaram and S. P. Balakannan Instant Recipe Generation from Food Images . . . . . . . . . . . . . . . . . . . . . . . . 721 Amogh Rajesh Desai, Sakshi Goel, Tanvi Karennavar, and Preet Kanwal Breast Cancer Diagnosis Using Quantum-Inspired Classifier . . . . . . . . . . 737 S. R. Sannasi Chakravarthy and Harikumar Rajaguru Predictive Analysis Model for Mental Health . . . . . . . . . . . . . . . . . . . . . . . . . 749 Fazal Rehman, M. Lakshmi, K. Aditya Shastry, Syed Ismail, and Wasif Irshad Detection and Counting of Fruit from UAV RGB Images Using Computer Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 761 Adel Mokrane, Abenasser Kadouci, Amal Choukchou-Braham, and Brahim Cherki Efficient Segmentation of Tumor and Edema MR Images Using Optimized FFNN Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 779 Rehna Kalam and M. Abdul Rahiman A Survey on Brain Computer Interface: A Computing Intelligence . . . . . 795 A. Shanmugapriya and A. Grace Selvarani A Survey on Security and Privacy in Social Networks . . . . . . . . . . . . . . . . . 807 B. Jayaram and C. Jayakumar A Novel Video Reconstruction of Randomized Frames Using ORB Descriptor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 823 M. Rohith, P. Priyanka, M. Kamalakar, and T. Kavitha Prediction of the Wind Turbine Performances Using BEM Model Coupled to CFD Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 837 Samah Laalej, Abdelfattah Bouatem, Ahmed Al Mers, and Rabii Elmaani
- 15. xvi Contents Development of Novel Face Recognition Techniques for VGG Model by Using Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 849 A. Arunraja, V. Mahendran, C. Mukesh, and M. Mahinesh Enhanced Deep Hierarchical Classification Model for Smart Home-Based Alzheimer Disease Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . 863 C. Dhanusha and A. V. Senthil Kumar SSO: A Hybrid Swarm Intelligence Optimization Algorithm . . . . . . . . . . 879 Arjun Nelikanti, G. Venkata Rami Reddy, and G. Karuna Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 891
- 16. About the Editors Dr. S. Smys received his M.E. and Ph.D. degrees all in Wireless Communication and Networking from Anna University and Karunya University, India. His main area of research activity is localization and routing architecture in wireless networks. He servesasAssociateEditorofComputersandElectricalEngineering(C&EE)Journal, Elsevier and Guest Editor of MONET Journal, Springer. He is serving as Reviewer for IET, Springer, Inderscience and Elsevier journals. He has published many research articles in refereed journals and IEEE conferences. He has been General Chair, Session Chair, TPC Chair and Panelist in several conferences. He is Member of IEEE and Senior Member of IACSIT wireless research group. He has been serving as Organizing Chair and Program Chair of several International conferences, and in the Program Committees of several International conferences. Currently, he is working as professor in the Department of Information Technology at RVS technical Campus, Coimbatore, India. João Manuel R. S. Tavares graduated in Mechanical Engineering at the Universi- dade do Porto, Portugal, in 1992. He also earned his M.Sc. degree and Ph.D. degree in Electrical and Computer Engineering from the Universidade do Porto in 1995 and 2001 and attained his Habilitation in Mechanical Engineering in 2015. He is Senior Researcher at the Instituto de Ciência e Inovação em Engenharia Mecânica e Engen- haria Industrial (INEGI) and Associate Professor at the Department of Mechanical Engineering (DEMec) of the Faculdade de Engenharia da Universidade do Porto (FEUP). João Tavares is Co-Editor of more than 60 books, Co-Author of more than 50 book chapters, 650 articles in international and national journals and confer- ences and 3 international and 3 national patents. He has been Committee Member of several international and national journals and conferences, is Co-founder and Co- editor of the book series “Lecture Notes in Computational Vision and Biomechanics” published by Springer, Founder and Editor-in-Chief of the journal Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization published by Taylor & Francis, Editor-in-Chief of the journal Computer Methods in Biome- chanics and Biomedical Engineering published by Taylor & Francis and Co-Founder and Co-chair of the international conference series: CompIMAGE, ECCOMAS xvii
- 17. xviii About the Editors VipIMAGE, ICCEBS and BioDental. Additionally, he has been (Co-)Supervisor of several M.Sc. and Ph.D. thesis and Supervisor of several post-doc projects and has participated in many scientific projects both as Researcher and as Scientific Coor- dinator. His main research areas include computational vision, medical imaging, computational mechanics, scientific visualization, human–computer interaction and new product development. Dr. Valentina Emilia Balas is currently Full Professor at “Aurel Vlaicu” University of Arad, Romania. She is Author of more than 300 research papers. Her research interests are in intelligent systems, fuzzy control and soft computing. She is Editor- in-Chief to International Journal of Advanced Intelligence Paradigms (IJAIP) and to IJCSE. Dr. Balas is Member of EUSFLAT, ACM and a SM IEEE, Member in TC—EC and TC-FS (IEEE CIS), TC—SC (IEEE SMCS) and Joint Secretary FIM.
- 18. Molecular Docking Analysis of Selected Phytochemicals for the Treatment of Proteus Syndrome Tanwar Reeya and Das Asmita Abstract Proteus syndrome is a rare hamartomatous disorder that is characterized by the overgrowth of tissues in a mosaic manner. Since drug therapy was not seen to be a component of standard care for Proteus syndrome, this paper focuses on finding phytochemicals against AKT-1 protein whose mutation is responsible for Proteus syndrome. Lipinski’s rule of 5 was applied to check the drug-likeliness of the selected phytochemicals followed by 3 rounds of molecular docking, computation of bioavailability radar and MD simulations. Simulations revealed Tanshinone-II A to be a potent inhibitor of AKT-1. Further, in-vivo studies can be performed on Tanshinone-II A for clinical use of the compound. In the above study, Miransertib (ARQ 092) was used as a positive control since it has recently shown to have a therapeutic effect on a teenager with Proteus syndrome and ovarian carcinoma. Keywords Proteus syndrome · Molecular docking · AKT-1 · Miransertib · Phytocompounds 1 Introduction Proteus syndrome is a rare disorder which involves an atypical skeletal growth [1]. This disease was first reported in the year 1979 followed by similar reports in the year 1983 by Wiedemann et al. It begins postnatally and progresses in a rapid and dispro- portionate manner which usually results in the distortion of the normal tissue [2]. There are cases in which many affected individuals are born without any perceptible symptoms, and the overgrowth usually begins during the time frame of 6–18 months. The severity and extent to which this disease can affect a patient vary greatly from one to another, but a few common manifestations of this disease are asymmetric, T. Reeya (B) · D. Asmita Department of Biotechnology, Delhi Technological University, Main Bawana Road, Delhi 110042, India D. Asmita e-mail: asmitadas1710@dce.ac.in © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. Smys et al. (eds.), Computational Vision and Bio-Inspired Computing, Advances in Intelligent Systems and Computing 1420, https://doi.org/10.1007/978-981-16-9573-5_1 1
- 19. 2 T. Reeya and D. Asmita distorting bony overgrowth, CCTN or cerebriform connective tissue nevi, dysregu- lation of fatty tissue and vascular anomalies [3]. The patients affected by this disease are susceptible to mesothelioma, breast cancer and papillary thyroid carcinoma since they have predisposition to benign and malignant tumours [4]. Although the exact cause of Proteus syndrome is not very clear, it has been seen that mutations in the AKT-1 gene that occurs after fertilization of the embryo (somatic mutation) are an important cause. AKT-1 is a part of the PI3K/AKT signalling pathway which is responsible for the regulation of various cellular processes such as the cell growth, proliferation and apoptosis [5]. The activation of AKT takes place due to its PH domain’s interaction with a lipid or a secondary messenger called the phosphatidyli- nositol 3,4,5-trisphosphate [PI (3,4,5) P3]. Following this interaction, AKT-1 is then phosphorylated at threonine 308 (T308) by phosphoinositide-dependent kinase which also binds to [PI (3,4,5) P3]. AKT is fully activated when it is phosphorylated by mTORC2 complex at position 473 (Serine 473) [6]. Since bioinformatics helps in the reduction of cost of designing experiments, their execution and laboratory trials, we use it with the aim to produce results which would help us bring about the use of phytocompounds extracted from plants like Sanguinaria canadensis, Evodia rutae- carpa, Salvia miltiorrhizae, Trigonella foenum, etc. These plants have been selected due to their ability to participate in targeted therapeutic activity against AKT-1 protein in cancer. Miransertib has been used as a positive control for conducting molecular docking studies since the use of Miransertib (ARQ 092) has recently shown to have a therapeutic effect on a teenager with Proteus syndrome and ovarian carcinoma in 2019 [7]. Miransertib is an allosteric inhibitor of our target protein AKT-1 which binds to the combined interface of PH domain and the N and C lobe of kinase domain (Fig. 1). This binding enables the target protein to be locked in a closed conforma- tion which results in the blocking of the phospholipid binding site by the kinase domain. The consequence of this conformation enables the allosterically inhibited AKT-1 to remain cytosolic and inactivated (activation occurs through phosphory- lation) (Fig. 2). The nature of inhibition of Miransertib poses an advantage since a Fig. 1 a The following figure represents the orientation of the PH domain (orange) relative to the N-lobe (pink) and C-lobe (yellow) of the kinase domain and Inhibitor VIII shown in green. b In the same orientation as Panel A, the kinase domain is surface rendered. c Structure of AKT1(1–443): Inhibitor VIII rotated approximately 180° compared to Panel B. Courtesy: https://doi.org/10.1371/ journal.pone.0012913.g003 [8]
- 20. Molecular Docking Analysis of Selected Phytochemicals … 3 Fig. 2 Model of AKT activation and inhibition. In the cytoplasm, the ‘PH-in’ and ‘PH-out’ confor- mations of AKT are in equilibrium. AKT is recruited to the plasma membrane via interactions with the products of PI3K and is subsequently phosphorylated on two sites, T308 and S473 in AKT1, which results in kinase activation. The allosteric inhibitor stabilizes the ‘PH-in’ form of the inactive enzyme (top left); whereas the ATP-competitive inhibitor binds to the activated form of the kinase (bottom left). Surface representations derived from the following structures: PDB code: 3CQW (active and ATP-competitive inhibitor bound kinase), PDB code: 1UNQ (membrane-bound PH domain) and PDB code: 3O96 (cytoplasmic PH and kinase domains and membrane-bound kinase domain). Colouring as follows: kinase domain (yellow), PH domain (orange), IP4 binding residues (cyan), phospho-T308 (red), allosteric inhibitor (green), ATP-competitive inhibitor (blue) and PI3K products (violet). Phospho-S473 is not visible in these orientations of AKT. Courtesy: https://doi. org/10.1371/journal.pone.0012913.g003 [8] study conducted by Wu et al. in 2010 [8] hypothesized allosteric inhibitors to have a higher efficacy than small ATP-competitive molecules. They proposed this since the competitive inhibitor is not able to close the PH domain fully upon its binding due to which the kinase domain and the phospholipid binding site remain exposed [8]. This exposure enhances the ability of AKT to localize to the membrane, whereas the allosteric inhibitors restrict both membrane association and activation by phos- phorylation. Although, a clinical perspective with respect to this observation is still needed [8]. Lastly, it is the first drug to go through clinical trials (Phase-II) in May, 2021 for Proteus syndrome. Hence, this research explores the properties of selected phytocompounds with respect to our target protein; AKT-1 keeping Miransertib as the positive control.
- 21. 4 T. Reeya and D. Asmita 2 Methods and Materials Building of Phytochemical Library: NPCARE (silver.sejong.ac.kr/npcancer/) or the Natural Products CARE is a database built by the Department of Bioscience and Biotechnology, Sejong University, consisting of a list of natural products that can regulate specific genes involved in cancer [9]. Each entry in this database is annotated with the name of genus and species of the biological source, the type of cancer it is involved with, the name of the cell line used to determine its anti-cancerous property, its PubChem ID and lastly a myriad of information about the target gene/protein. Since our research focusses on down regulating the effects AKT-1 gene for the treatment of Proteus syndrome, a list of 21 phytochemicals were obtained for AKT-1. (as shown in Table 1). Protein/macromolecule and Ligands: Protein Data Bank or PDB (https://www. rcsb.org/) is a database that contains three-dimensional structural information of large biological molecules such as nucleic acids and proteins [10]. Our target protein AKT- 1wasretrievedfromthefollowingdatabasebyusingthePDBID:3O96in.pdbformat. The structure contained a single A chain with an amino acid length of 446 along with a Covalent-Allosteric AKT Inhibitor. The publicly accessible PubChem repository has served as a resource of chemical information to the scientific community since 2004 (https://pubchem.ncbi.nlm.nih.gov/), and it contains information regarding the chemical and physical properties of the substance along with its structural repre- sentation (2-D,3-D, etc.) [11]. Three-dimensional structures of the ligands were obtained from PubChem repository using their respective PubChem IDs obtained from NPCARE database in.sdf format followed by their conversion to.pdb format using Biovia Discovery Studio. ADME Analysis: In order to begin the screening of ligands, ADME analysis was conducted by applying Lipinski’s rule of 5 [12]. The factors involved (molec- ular weight (<500 Da), high lipophilicity (LogP <5), hydrogen bonds donors (<5) and hydrogen bond acceptors (<10)) in conducting the same were retrieved from PubChem repository. According to this analysis, any ligand that violates more than 2 of the above stated parameters will be debarred from further analysis. Molecular Docking Analysis: It is a regularly used computational tool which aids the process of designing drugs since it provides information regarding the binding mode of the known ligands, identification of potent drug candidates and their binding affinity. The following tool was used in three rounds to identify the binding affinity and the type of interactions our selected phytochemicals displayed. The first round of molecular docking was conducted by PyRx. It is an open-source (https://pyrx.sou rceforge.io/) virtual screening tool which contains a combination of software’s such as AutoDock Vina, AutoDock 4.2 and Open Babel [13]. For the following round of docking studies, AutoDock Vina tool compiled in PyRx was used. The dimensions used for this screening were centre (x, y, z) = (9.3494, −7.9442, 10.1538) and dimensions in angstrom (x, y, z) = (18.0256, 25.000, 24.4774). The water molecules and the allosteric inhibitor were removed beforehand. Lastly, an exhaustiveness of 8 was set to give the output of the lowest energy possible with the help of AutoDock
- 22. Molecular Docking Analysis of Selected Phytochemicals … 5 Table 1 ADME analysis of selected phytochemical S.No. Species Compound name PubChem ID 2-D diagram ADME analysis 1 Cordyceps militaris Cordycepin 3-deoxyadenosine CID 6303 Molecular weight (500 Da) 251.24 g/mol Lipophilicity (LogP 5) − 1.94 H bond donor (5) 3 H bond acceptor (10) 6 Violations 0 2 Citrus spp. Hesperetin CID 72281 Molecular weight (500 Da): 302.28 g/mol Lipophilicity (LogP 5): 0.41 H bond donor (5): 3 H bond acceptor (10): 6 Violations: 0 3 Dendrobium loddigesii Moscatilin CID 176096 Molecular weight (500 Da): 304.34 g/mol Lipophilicity (LogP 5): 1.94 H bond donor (5): 2 H bond acceptor (10): 5 Violations: 0 4 Larrea divaricata Nordihydroguaiaretic acid CID 4534 Molecular weight (500 Da): 302.36 g/mol Lipophilicity (LogP 5): 2.74 H bond donor (5): 4 H bond acceptor (10): 4 Violations: 0 (continued)
- 23. 6 T. Reeya and D. Asmita Table 1 (continued) S.No. Species Compound name PubChem ID 2-D diagram ADME analysis 5 Oridonin Oridonin CID 5321010 Molecular weight (500 Da): 364.43 g/mol Lipophilicity (LogP 5): 0.86 H bond donor (5): 4 H bond acceptor (10): 6 Violations: 0 6 Rabdosia coetsa Rabdocoetsin B CID 10452999 Molecular weight (500 Da): 390.47 g/mol Lipophilicity (LogP 5): 2.01 H bond donor (5): 2 H bond acceptor (10): 6 Violations: 0 7 Silybum marianum Silibinin CID 16211710 Molecular weight (500 Da): 482.44 g/mol Lipophilicity (LogP 5):-0.40 H bond donor (5): 5 H bond acceptor (10): 10 Violations: 0 8 Andrographis paniculata Andrographolide CID 5318517 Molecular weight (500 Da): 350.45 g/mol Lipophilicity (LogP 5): 1.98 H bond donor (5): 3 H bond acceptor (10): 5 Violations: 0 (continued)
- 24. Molecular Docking Analysis of Selected Phytochemicals … 7 Table 1 (continued) S.No. Species Compound name PubChem ID 2-D diagram ADME analysis 9 Capsicum spp. Capsaicin CID 1548943 Molecular weight (500 Da): 305.41 g/mol Lipophilicity (LogP 5): 2.69 H bond donor (5): 2 H bond acceptor (10): 3 Violations: 0 10 Salvia miltiorrhizae Cryptotanshinone CID 160254 Molecular weight (500 Da): 296.36 g/mol Lipophilicity (LogP 5): 2.36 H bond donor (5):0 H bond acceptor (10):3 Violations:0 11 Allium sativum Diallyl trisulfide CID 16315 Molecular weight (500 Da): 178.34 g/mol Lipophilicity (LogP 5): 2.35 H bond donor (5): 0 H bond acceptor (10): 0 Violations: 0 12 Isaria sinclairii FTY720 CID 107969 Molecular weight (500 Da): 343.93 g/mol Lipophilicity (LogP 5): 3.24 H bond donor (5): 3 H bond acceptor ( 10):3 Violations: 0 (continued)
- 25. 8 T. Reeya and D. Asmita Table 1 (continued) S.No. Species Compound name PubChem ID 2-D diagram ADME analysis 13 Arctium lappa Arctigenin CID 64981 Molecular weight (500 Da): 372.41 g/mol Lipophilicity (LogP 5): 2.12 H bond donor (5): 1 H bond acceptor (10): 6 Violations: 0 14 Plumbago zeylanica Plumbagin CID 10205 Molecular weight (500 Da): 188.18 g/mol Lipophilicity (LogP 5): 0.59 H bond donor (5): 1 H bond acceptor (10): 3 Violations: 0 15 Pterocarpus marsupium Pterostilbene CID 5281727 Molecular weight (500 Da): 256.30 g/mol Lipophilicity (LogP 5): 2.76 H bond donor (5): 1 H bond acceptor (10): 3 Violations: 0 16 Stephania tetrandra Tetrandrine CID 73078 Molecular weight (500 Da): 622.75 g/mol Lipophilicity (LogP 5): 3.73 H bond donor (5): 0 H bond acceptor (10):8 Violations: 1 (continued)
- 26. Molecular Docking Analysis of Selected Phytochemicals … 9 Table 1 (continued) S.No. Species Compound name PubChem ID 2-D diagram ADME analysis 17 Punica granatum Punicic_acid CID 5281126 Molecular weight (500 Da): 278.43 g/mol Lipophilicity (LogP 5): 4.38 H bond donor (5): 1 H bond acceptor (10): 2 Violations: 0 18 Salvia miltiorrhizae Tanshinone-IIA CID 164676 Molecular weight (500 Da): 294.34 g/mol Lipophilicity (LogP 5): 2.24 H bond donor (5): 0 H bond acceptor (10): 3 Violations: 0 19 Trigonella foenum Diosgenin CID 99474 Molecular weight (500 Da): 414.62 g/mol Lipophilicity (LogP 5): 4.94 H bond donor (5): 1 H bond acceptor (10): 3 Violations: 0 20 Evodia rutaecarpa Isoevodiamine CID 151289 Molecular weight (500 Da): 303.36 g/mol Lipophilicity (LogP 5): 3.16 H bond donor (5): 1 H bond acceptor (10): 1 Violations: 0 (continued)
- 27. 10 T. Reeya and D. Asmita Table 1 (continued) S.No. Species Compound name PubChem ID 2-D diagram ADME analysis 21 Sanguinaria canadensis Sanguinarine CID 5154 Molecular weight (500 Da): 332.33 g/mol Lipophilicity (LogP 5): 2.72 H bond donor (5): 0 H bond acceptor (10): 4 Violations: 0 Vina-PyRx, and the ligands were then filtered out on the basis of their binding energy. Theselectedligandswerethendockedfurtherintwomoreroundsusinganotheropen- source docking tool, AutoDock 4.2 (http://autodock.scripps.edu/) [14]. Optimization of both the protein and the ligand was carried out by eliminating water, including polar hydrogens followed by adding Kollman and Gasteiger charges. The final step of optimization was carried out by removing the native ligand of the protein. The output utilized for docking studies with an exhaustiveness set to 10 was Lamarckian GA, and the best results out of the two were then used for further analysis. Bioavailability Radar: SwissADME is an online tool (http://www.swissa dme.ch/) which was used to obtain a bioavailability radar, and this radar scrutinized the phytocompounds on the basis of six parameters (saturation, flexibility, solubility, size, polarity and lipophilicity). The region coloured in pink defines the limit within which the parameters should lie, and any deviation from the region indicated that the drug is not orally bioavailable. Molecular Dynamics: The docked complex was subjected to molecular dynamics simulation by the use of Demond-Maestro module [15]. Since the software provides high performance algorithms in its default settings itself, they were used to obtain high speed and precise results. The docked complex was subjected to submersion in TIP3P water model in an orthorhombic shape, after which the entire system was neutralized by addition of seven chlorine ions at 0.15 M concentration. All the atoms in the system were aligned by optimized potentials for liquid simulations-AA (OPLS- AA) 2005 force field. SHAKE/RATTLE algorithm along with NVT as the ensemble class was used to limit the movement of the atoms covalently bonded. The conditions set in order to start the simulations were temperature: 300 K and pressure: 1 bar for 100 ns. Integration of all the parameters of dynamic simulation was carried out using the RESPA integrator. Finally, to analyse the component stability and dynamic nature of interaction, a trajectory of 100 ns was set to show in 1000 frames.
- 28. Molecular Docking Analysis of Selected Phytochemicals … 11 3 Results and Discussion ADME Analysis: 21 compounds were screened with respect to Lipinski’s rule of 5, and the parameters used for the same were molecular weight (500 Da), hydrogen bonds donors (5), high lipophilicity (LogP 5) and hydrogen bond acceptors (10). This screening helped in determining the drug-likeliness of the selected phytocom- pounds. Usually, compounds are eliminated if they violate more than two parameters of Lipinski’s rule of five. Out of the 21 compounds, 20 showed 0 violations leaving Tetrandrine with PubChem ID: 73078 with one violation (due to its molecular weight being greater than 500) as shown in Table 1. Since all 21 of them fell within the parameters, it helped us conclude that all 21 compounds do not show any poor absorption or permeation, and hence, they were all used for further investigation. Docking Analysis: The 21 selected phytochemicals were then introduced into a virtual space to conduct molecular docking studies. This was conducted in three rounds, first being conducted by PyRx and the last two by AutoDock 4.2. The active site residues for AKT-1 were obtained from a research study of the crystal structure of AKT-1 protein with an allosteric inhibitor by Wen-I Wu et al. in [8]. The residues at the active site of AKT-1 were VAL201, SER205, VAL270, THR82, CYS296, VAL83, GLU85, ILE84, ARG273, ASN54, ASP274, VAL271, TYR272, THR211, THR291, ILE290, ASP292, LEU210, LEU264, TRP80 and LYS268. These active sites are present at the linkage of PH domain and the N and C lobe of kinase domain. Docking Analysis using AutoDock Vina-PyRx: The first round of docking anal- ysis was conducted to identify the phytocompounds that have the ability to compete with Miransertib as a potent inhibitor by comparing their resultant vina binding affini- ties. As depicted by Table 2, results obtained by PyRx revealed that the binding ener- gies of Diosgenin (−12.6 kcal/mol), Sanguinarine (−12.2 kcal/mol) and Tanshinone- IIA (−11.7 kcal/mol) are to be greater than that of Miransertib (−11.6 kcal/mol) which was taken as a positive control due to its therapeutic properties. Due to their depiction of greater binding strength than the positive control, these three phytochem- icals, Disogenin, Sanguinarine and Tanshinone-IIA, were then selected for further docking. Docking Analysis using AutoDock 4.2: The three phytocompounds, namely Disogenin, Tanshinone-II and Sanguinarine, were selected for two more rounds of molecular docking studies which were conducted by Autodock 4.2. The residues used for this round were the same as mentioned above. The value of the best confirmation of Disogenin out of the 20 obtained was seen to be −11.39 kcal/mol. The four different types of interactions observed were pi- alkyl conventional hydrogen bond, van der Waals, and alkyl, and TRP80, LEU210, LEU264 and VAL270 were seen to be interacting via alkyl and pi-alkyl bond while LYS268, THR211 were seen to be interacting via conventional hydrogen bond, respectively. The remaining residues weakly interacted with the ligand via van der Waals interaction. (Fig. 1).
- 29. 12 T. Reeya and D. Asmita Table 2 Molecular docking results of 20 compounds with 3O96 using AutoDock Vina tool compiled in PyRx S. No. Compound name Vina binding affinity (kcal/mol) 1 Cordycepin 3’-deoxyadenosine -7.7 2 Hesperetin −9.6 3 Moscatilin −8.4 4 Nordihydroguaiaretic acid −9.4 5 Oridonin −8.6 6 Rabdocoetsin B −8.5 7 Silibinin −7.7 8 Andrographolide −9.9 9 Capsaicin −8.2 10 Cryptotanshinone −11.6 11 Diallyl trisulfide −4.1 12 FTY720 −7.8 13 Arctigenin −10.1 14 Plumbagin −7.9 15 Pterostilbene −8.4 16 Tetrandrine −6.3 17 Punicic_acid −7.2 18 Tanshinone-IIA −11.7 19 Diosgenin −12.6 20 Isoevodiamine −9.2 21 Sanguinarine −12.2 22 Miransertib (Positive Control) −11.6 The value of the best confirmation of Sanguinarine out of the 20 obtained was seen to be −9.57 kcal/mol. A total of eight interactions were observed including van der Waals, alkyl, pi-alkyl, conventional hydrogen bond, pi-sigma, pi-pi stacked, pi-anion and carbon hydrogen bond. LEU 264 was seen to be interacting via pi-stigma bond, TRP80 was seen to be interacting via pi-pi stacked bond, LEU210, VAL270 were seen to be interacting via alkyl and pi-alkyl bond, ASP 292 was seen to be interacting via pi-anion bond, ILE 290 was seen to be interacting via carbon hydrogen bond, lastly LYS268 and SER 205 were seen to be interacting via conventional hydrogen bond, and the rest weakly interacted with the ligand via van der Waals interaction. (Fig. 2). The value of the best confirmation of Tanshinone-IIA out of the 20 obtained was seen to be −9.19 kcal/mol. A total of six interactions were observed including van der Waals, alkyl, pi-alkyl, conventional hydrogen bond, pi-sigma and pi-pi stacked.
- 30. Molecular Docking Analysis of Selected Phytochemicals … 13 VAL270 was seen to interacting via pi-sigma bond, TRP80 was seen to be interacting via pi-pi stacked, LEU210 and LEU264 were seen to be interacting via alkyl and pi-alkyl, lastly LYS268 was seen to be interacting via conventional hydrogen bond, and the rest weakly interacted with the ligand via van der Waals interaction. (Figs. 3 and 4). Even though the three compounds displayed a lesser binding energy than that of our positive control they were further investigated since they had an added advantage of being derived from natural resources, we have further created the bioavailability Fig. 3 Visualization of the docked complex Disogenin-3O96 a 3O96 is represented in the form of surface, and the ligand coloured in black is represented in the form of spheres illustrating the binding pocket. b A closer look at the interactions can be observed wherein the structure coloured in metallic purple represents the protein, the blue coloured sticks represent the protein groups, the green coloured ball and stick representation depicts the ligand: Disogenin, and the light green coloured surface around the ligand represents the binding pocket Fig. 4 2-D interaction diagram of Disogenin
- 31. 14 T. Reeya and D. Asmita Table 3 Results based on different techniques S. No. Name Binding energy (G) (Kcal/mol) Ligand efficiency Inhibition constant (NM) Intermolecular energy Vdw H bond desolvation 1 Diosgenin −11.39 −0.38 4.5 −11.69 −11.55 2 Tanshinone-IIA −9.19 −0.37 182.78 −9.36 −9.11 3 Sanguinarine −9.57 −0.44 97.04 −9.57 −9.29 4 Miransertib (positive control) −11.47 −0.35 3.94 −13.25 −12.77 Fig. 5 Visualization of the docked complex Tanshinone-IIA-3O96 a 3O96 is represented in the form of surface, and the ligand coloured in black is represented in the form of spheres illustrating the binding pocket. b A closer look at the interactions can be observed wherein the structure coloured in metallic purple represents the protein, the blue coloured sticks represent the protein groups, the green coloured ball and stick representation depicts the ligand: Tanshinone-IIA, and the light green coloured surface around the ligands represents the binding pocket radar and conducted molecular dynamics which is shown in Table 3 (Figs. 5, 6, 7 and 8). Bioavailability Radar: The three selected compounds, Disogenin, Tanshinone-II A and Sanguinarine, were then further investigated by computing the bioavailability radar, and this helped us in giving a closer look at the drug-likeness of the phyto- compound. The area in pink depicts the optimal range for each property; size: MW between 150 and 500 g/mol, lipophilicity: XLOGP3 between −0.7 and +5.0, solu- bility: log S not higher than 6, polarity: TPSA between 20 and 130 Å 2, saturation: fraction of carbons in the sp 3 hybridization not less than 0.25, and flexibility: no more than nine rotatable bonds. This analysis found that Disogenin and Tanshinone-IIA were both orally bioavailable. Sanguinarine on the other hand was not found to be orally bioavailable since it very clearly disobeyed the saturation parameter (Fig. 9). Molecular Dynamics: The stability of the protein upon binding of a small molecule is the most integral property to be explored, and in order to do the same, the docked complexes of the two compounds: Disogenin and Tanshinone-IIA were further subjected to molecular dynamics for 100 ns using Demond-Maestro
- 32. Molecular Docking Analysis of Selected Phytochemicals … 15 Fig. 6 2-D interaction diagram of Tanshinone-II A Fig. 7 Visualization of the docked complex Sanguinarine-3O96 a 3O96 is represented in the form of surface, and the ligand coloured in black is represented in the form of spheres illustrating the binding pocket. b A closer look at the interactions can be observed wherein the structure coloured in metallic purple represents the protein, the blue coloured sticks represent the protein groups, the green coloured ball and stick representation depicts the ligand: Sanguinarine, and the light green coloured surface around the ligands represents the binding pocket module. The properties that have been studied include root mean square devia- tion (RMSD), root mean square fluctuation (RMSF), radius of gyration (rGyr) and solvent-accessible surface area (SASA). Deviation in Structure and Compactness: The average RMSD or root mean square deviation values for the docked complexes of Disogenin and Tanshinone-IIA were found to be 3.11 Å and 2.77 Å. Figure 10 depicts a comparative graph of the RMSD values of both the docked complexes. An RMSD value lying anywhere between 2 and 3 is known to keep a good orientation despite of the fluctuations
- 33. 16 T. Reeya and D. Asmita Fig. 8 2-D interaction diagram of Sanguinarine Fig. 9 Bioavailability radar of Disogenin observed, on the other hand an RMSD value greater than 3 does not correspond to a stable conformation, and hence, Tanshinone has displayed a better orientation than Disogenin. RMSF plots of proteins indicate the fluctuations observed during the period of simulation. In most cases, it is observed that the unstructured part of the protein or the tails (N- and C-terminal) demonstrates fluctuations more than the structured part of the protein or the alpha helices and beta strands. This phenomenon is seen since the alpha helices and beta strands have a higher rigidity than the unstructured part of the protein (tails (N- and C-terminal)). RMSF plot of both Disogenin and Tanshinone- IIA with 3O96 has adhered to the phenomenon mentioned above, with its peaks
- 34. Molecular Docking Analysis of Selected Phytochemicals … 17 Fig. 10 Bioavailability radar of Tanshinone-IIA depicting the unstructured parts of the protein. But, RMSF plot of Tanshinone-IIA with 3O96 has shown all the residues it’s binding pocket to have lower values in the graph which depict a lower conformational change at the binding pocket (Fig. 11). Radius of gyration is another very crucial factor that is associated with the tertiary structure and general conformation representing information regarding the compact- ness and folding of protein. The average values of Rg or radius of gyration for Disogenin-3O96 complex and Tanshinone-IIA-3O96 complex were seen to be 4.79 Å and 3.49 Å indicating the stability of the protein folding of the complex formed by the latter. The left side of Fig. 12 shows the values of Tanshinone-IIA, whereas the right side shows the values of Disogenin. Solvent-accessible surface area or SASA is the surface area of the molecule acces- sible by water molecule, and average values of the same were observed to be 77.70 Å2 for the Disogenin-3O96 complex and 61.72 Å2 for Tanshinone-IIA-3O96 complex. The values suggest the interaction of inner residues with the solvent to be greater Fig. 11 Bioavailability radar of Sanguinarine
- 35. 18 T. Reeya and D. Asmita 0 0.5 1 1.5 2 2.5 3 3.5 4 0 4.4 8.8 13.2 17.6 22 26.4 30.8 35.2 39.6 44 48.4 52.8 57.2 61.6 66 70.4 74.8 79.2 83.6 88 92.4 96.8 RMSD Å TIME (NS) Disogenin Tanshinone-IIA Fig. 12 Comparative RMSD graph of docked complexes of Disogenin-AKT and Tanshinone-IIA- AKT in Disogenin due to the higher value of SASA indicating a lesser stability of the conformation (Fig. 13). Interactional dynamics and secondary structural analysis Figures 13 and 14 provide a closer look at the type of interactions that occur between Disogenin and 3O96 and the time period of the respective interactions, and apart from the interactions shown in Fig. 1.1, Disogenin has interacted with 12 more residues of 3O96. Majority of the residues: ASN 53, ASN 54, GLN 79, THR 82, GLN 203, ASN 204, SER 205, HIS 207, LEU 210, LEU 213, TYR 263, LYS 268, VAL 271, ARG 273 and ASP 292 have interacted via water bridge formation. ASP 54, GLN 0 1 2 3 4 5 6 7 8 9 0 16 32 48 64 80 96 112 128 144 160 176 192 208 224 240 256 272 288 304 320 336 352 368 RMSF Å Residue Index Disogenin Tanshinone-IIA Fig. 13 Comparative RMSF graph of docked complexes of Disogenin-AKT and Tanshinone-IIA- AKT
- 36. Molecular Docking Analysis of Selected Phytochemicals … 19 4.5 4.55 4.6 4.65 4.7 4.75 4.8 4.85 3.35 3.4 3.45 3.5 3.55 3.6 0 4.6 9.2 13.8 18.4 23 27.6 32.2 36.8 41.4 46 50.6 55.2 59.8 64.4 69 73.6 78.2 82.8 87.4 92 96.6 rGyr Å Tanshinone-IIA Disogenin Fig. 14 Comparative rGyr graph of docked complexes of Disogenin-AKT and Tanshinone-IIA- AKT 79, GLN 203, SER 205, THR 211, LEU 213, TYR 263 and LYS 268 have interacted with Disogenin via H bond formation. TRP 80, ILE 84, LEU 210, LEU 264 and TYR 272 have shown to interact by hydrophobic interactions. Lastly, GLN 203, LEU 213 and LYS 268 have also interacted via ionic bond formation. It can also be inferred that many of the residues have interacted via more than one type of interaction. The interactions between Tanshinone-IIA and 3O96 are represented via Figs. 15, 16, 17, 18 and 19, and it can be seen that Tanshinone-IIA has interacted with six more residues of 3O96 other than the ones shown in Fig. 2.1. TRP 80 has interacted for the entire time period of simulation via hydrophobic interactions. Another residue 0 20 40 60 80 100 120 140 160 0 4.2 8.4 12.6 16.8 21 25.2 29.4 33.6 37.8 42 46.2 50.4 54.6 58.8 63 67.2 71.4 75.6 79.8 84 88.2 92.4 96.6 SASA Å TIME (NS) Disogenin Tanshinone-IIA Fig. 15 Comparative SASA graph of docked complexes of Disogenin-AKT and Tanshinone-IIA- AKT
- 37. 20 T. Reeya and D. Asmita Fig. 16 Protein–ligand contacts (purple: hydrophobic, green: H bonds, pink: ionic, blue: water bridges) for Disogenin-3O96 complex Fig. 17 Timeline of the ligand contacts Disogenin-3O96 complex Fig. 18 Protein–ligand contacts (purple: hydrophobic, green: H bonds, blue: water bridges) for Tanshinone-IIA-3O96 complex
- 38. Molecular Docking Analysis of Selected Phytochemicals … 21 Fig. 19 Timeline of the ligand contacts for Tanshinone-IIA-3O96 complex that has interacted for the entire time period is TYR 272, it has shown a number of interactions such as: H bond, hydrophobic interactions and water bridges, and for majority of the time, it has interacted via the last type of interaction mentioned above. THR 211 has also shown a complete interaction for the entire simulation time period, and it has interacted via H bond formation and by water bridge formation, although it has majorly interacted via H bond formation. LYS268, ILE 290, THR 291 and ASP 292 have also shown interactions via water bridge formation although, for a shorter period of time. ILE 84, LEU 210, TYR 263, LEU 264 and VAL 270 have been seen to interact via hydrophobic interaction for a very short period of time. There have been 2–3 instances in the entire timeline when Tanshinone-II A has had a total of 0 number of contacts, and this value is seen to be less than that of Disogenin, having 8–9 such instances, depicting Tanshinone-IIA to be more stable than Disogenin. 4 Conclusion and Future Prospects As a part of this study, 21 compounds were selected from a variety of plants and scrutinized using a number of in-silico platforms. They were first analysed by using the Lipinski’s rule of 5 in order to check the drug-likeliness of the phytocompound. Since all 21 obeyed the Lipinski’s rule of 5, they were subjected to three rounds of molecular docking with 3O96. The first round of molecular docking with the help of AutoDock Vina-PyRx revealed the binding energies of three compounds, namely Diosgenin, Tanshinone-IIA and Sanguinarine, to be greater than our positive control Miransertib (chosen as positive control since it was already used as an effective therapeutic drug). These three were then subjected to two more rounds of molecular docking studies with the help of AutoDock 4.2, and the binding energies obtained were quite comparable to the positive control. While our results showed that the three compounds displayed comparable binding efficiency as compared to the positive control, they had the added advantage of being derived from natural sources which leads to a possibility of eliciting lesser side effects. Another advantage that they
- 39. 22 T. Reeya and D. Asmita possess is that they have displayed the property of being allosteric inhibitors of our target protein AKT-1, this is advantageous since a study on the crystal structure of the following protein hypothesized the efficacy of allosteric inhibitors to be better than that of small ATP-competitive molecules. Although, this observation is yet to be confirmed by clinical experiments. Further, the computation of bioavailability radar of the three chosen compounds revealed Diosgenin and Tanshinone-IIA to be orally bioavailable. These two were then subjected to a simulation of 100 ns which showed Tanshinone-II A to be a better candidate than Disogenin. The unavailability of enough drugs for this disorder calls for further studies, and hence, this research proposes the use Tanshinone-II A for further in-vivo assays to validate its effectiveness as a therapeutically active novel compound from a natural source. One of the drawbacks for this study includes the fact that it is a very rare disorder with an incidence of less than 1 in 1 million people worldwide. Only a few hundred affected individuals have been reported in the medical literature which has resulted in lesser research on it. Hence, further studies need to be conducted to produce a robust solution for Proteus syndrome. References 1. Lindhurst, M.J., et al.: A mosaic activating mutation in AKT1 associated with the proteus syndrome. N. Engl. J. Med. 365(7), 611–619 (2011) 2. Talari, K., et al.: Proteus syndrome: a rare case report. Indian J. Hum. Genet. 18(3), 356–358 (2012) 3. Wiedemann, H.R., et al.: The proteus syndrome. Partial gigantism of the hands and/or feet, nevi, hemihypertrophy, subcutaneous tumors, macrocephaly or other skull anomalies and possible accelerated growth and visceral affections. Eur. J. Pediatr. 140(1), 5–12 (1983) 4. Xu, F., et al.: Roles of the PI3K/AKT/mTOR signalling pathways in neurodegenerative diseases and tumours. Cell Biosci. 10, 54 (2020) 5. Manning, B.D., Toker, A.: AKT/PKB signaling: navigating the network. Cell 169(3), 381–405 (2017) 6. Fruman, D.A., et al.: The PI3K pathway in human disease. Cell 170(4), 605–635 (2017) 7. Biesecker, L.G., et al.: Clinical report: one year of treatment of Proteus syndrome with miransertib (ARQ 092). Cold Spring Harb. Mol Case Stud. 6(1), (2020) 8. Wu, W.I., et al.: Crystal structure of human AKT1 with an allosteric inhibitor reveals a new mode of kinase inhibition. PLoS ONE 5(9), e12913 (2010) 9. Choi, H., et al.: NPCARE: database of natural products and fractional extracts for cancer regulation. J. Cheminform. 9, 2 (2017) 10. Berman, H.M., et al.: The protein data bank. Nucleic. Acids Res. 28(1), 235–242 (2000) 11. Kim, S., et al.: PubChem substance and compound databases. Nucleic Acids Res. 44(D1), D1202–D1213 (2016) 12. Lipinski,C.A.: Lead-anddrug-like compounds: the rule-of-five revolution.DrugDiscov.Today Technol. 1(4), 337–341 (2004) 13. Dallakyan, S., Olson, A.J.: Small-molecule library screening by docking with PyRx. Methods Mol. Biol. 1263, 243–250 (2015) 14. Morris, G.M., et al.: AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J. Comput. Chem. 30(16), 2785–2791 (2009)
- 40. Molecular Docking Analysis of Selected Phytochemicals … 23 15. Bowers, K.J., et al.: Scalable algorithms for molecular dynamics simulations on commodity clusters. In: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing, pp. 84–es. Association for computing machinery, Tampa, Florida, (2006)
- 41. A Deep Learning-Based Detection of Wrinkles on Skin H. Deepa, S. Gowrishankar, and A. Veena Abstract Aging is a natural process that affects the human body. The primary focus of this research work is to study the appearance of face wrinkles, which is considered as one of the most noticeable changes that happen as people become older. In any medical cosmetology, skin analysis becomes an important procedure for the wrinkle detection or any other medical problems. Maximum of the conventional wrinkles examination schemes is semi-automatic. Also, these methods require a lot of human interference. Since different applications are available for estimating the age based on the facial images and other skin-related factors, the main aim of this research study is to use deep CNN for detecting the wrinkles in the human skin. The proposed work describes a novel method for predicting age and wrinkles by using image processing and other advanced technologies. The proposed method is more focused on the wrinkles detection based on convolution neural network. The wrinkles on the skin, which gets increased based on the age, are being used as the discriminating factor to predict the age of the human being by using the images. AI, deep learning and CNN techniques are incorporated to achieve fast performance system. Also, this research work will provide a detailed description about the selected test images and database. The software design of the front end and the backend details is displayed along with the result screenshots. The proposed method initially detects the wrinkles by using facial images. Based on noticed wrinkles on the skin, the facial structures are removed to find ROI. Previously, wrinkles in the ROI were identified by using a pattern recognition algorithm. A classifier is intended to offer improved accuracy for identification when it is targeted at a specific problem. The proposed technique can efficiently diagnose the skin illness using restricted features mined as ROI, assess the stage of wrinkles and analyze the stage of wrinkles. Keywords Deep learning · Image segmentation · Edge detection · Hough transformer · Region of interest · Neural networks · Texture features H. Deepa (B) · S. Gowrishankar · A. Veena Department of Computer Science and Engineering, Dr. Ambedkar Institute of Technology, Bengaluru, Karnataka 560056, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. Smys et al. (eds.), Computational Vision and Bio-Inspired Computing, Advances in Intelligent Systems and Computing 1420, https://doi.org/10.1007/978-981-16-9573-5_2 25
- 42. Discovering Diverse Content Through Random Scribd Documents
- 43. Kylläpä silloin opettajat juoksivat. He menivät sellaista kyytiä, että oikein piti ihmetellä. Tietysti rehtori löytyi karsserista, mutta aivan uupuneena, sillä hän ei ollut lauantaista asti syönyt. Rehtori olisi tahtonut erottaa pojat, mutta hänet oli vallannut kummallinen pelko. Hän ei uskaltanut tulla enää koulullekaan, vaan päästyään sieltä pois kurkisteli kadun nurkan takaa koululle päin. Toiset opettajat alkoivat myös pelätä ja jättivät koulun. Pojat eivät tienneet, mitä heidän pitäisi tehdä. He tulivat kouluun joka aamu säännölliseen aikaan, ja kun ei opettajia ollut, niin pitivät he itse tuntia. Ja kyllä ne olivat meluisia tunteja, arvaahan sen. Iltaisin opettajat uskalsivat tulla koululle neuvottelemaan, mihin toimenpiteisiin oli ryhdyttävä. Päätettiin ottaa Kiljusen pojat kiinni ja toimittaa heidät pois kaupungista. Tätä toimeenpanemaan pyydettiin poliiseja. Kaikki Helsingin poliisit olivat liikkeellä, ne saarsivat koulun. Rohkeimmat tohtivat mennä ovesta sisään. He etsivät joka luokan ja viimein löysivät pojat. Kyllähän Mökö ja Luru ennestään olivat siihen tottuneet, että heitä suurella kunnialla kohdeltiin poliisiviranomaisten puolelta; niin nytkin he päät pystyssä astelivat suuren poliisilauman saattamina asemalle päin, missä suljettu vaunu odotti heitä. Pojat oli päätetty toimittaa Viipuriin. VI Heidän tultuaan Viipurin lyseoon ei sielläkään kukaan voinut ymmärtää, miksi he olivat joutuneet Helsingistä pois, niin tavattoman
- 44. kiltin vaikutuksen he tekivät. He osasivat läksynsä aivan erinomaisesti, vaikka omalla tavallaan, väittäen muun muassa maantieteen tunnilla, että koko kaupunki oli alkujaan ollut iso rinkilä. Tälle naurettiin, mutta siitä ei kukaan pahastunut, sillä onhan Viipuri vieläkin kuuluisa rinkeleistään. Toisilta pojilta he kuulivat, että maailmassa on sellaisia laivoja, jotka kulkevat veden alla ja joita sen vuoksi sanotaan vedenalaisiksi veneiksi. Eräänä päivänä kävellessään sillalla, joka vie linnan luona olevan salmen yli, sattui Mökö näkemään veden alta pistävän kepin. — Tuossa on sellaisen laivan masto, sanoi hän. — Tietysti se on laivan masto, sanoi siihen Luru. He palasivat kaupungille, tapasivat kadulla tuttuja koulupoikia ja kertoivat niille, että olivat nähneet linnan sillan luona vedenalaisen laivan. Huhu levisi hirveällä vauhdilla. Muutamat riensivät heti sinne, toiset menivät kotiinsa ilmoittamaan, joista taas puhelimella kerrottiin asiat tuttaville. Ei ollut kulunut tuntiakaan, kun koko kaupunki tiesi, että linnan luona oli vedenalainen vene. Ja kaikki riensivät tietysti sitä katsomaan. Mikä tungos ja mikä ahdinko olikaan linnan lähellä! Ihmisiä oli niin paljon, että olivat tippua veteen. Ja kaikki katsoivat tuohon seipääseen ja uskoivat hekin, että se oli laivan maston huippu. Ja nyt odotettiin vain, että laiva nousisi veden pinnalle. Kun tästä viimein oli selvitty ja koulun rehtori saanut tietää, mistä huhu oli saanut alkunsa, toimitti hän pojat junaan ja lähetti Kuopioon.
- 45. Täällä pojat olivat kolme päivää. Toisena päivänä he menivät Puijon mäelle kävelemään. Tämä on korkea mäki kaupungin lähellä. Oli syksy, ja eräs tummanruskeassa puvussa oleva muija oli sieniä poimimassa metsässä. Hui, kuinka pojat pelästyivät nähdessään hänet takaapäin! — Se on karhu, sanoi Luru, varmasti se on karhu! Tulista vauhtia he menivät kaupungille ja huusivat pitkin katuja mennessään: — Puijolla on karhu. Puijolla on karhu. Aijai, mikä hätä syntyi kaupungissa! Muutamat panivat kiireesti ovensa lukkoon ja tukkivat ikkunansa siinä pelossa, että karhu voisi tulla heidän asuntoonsa. Mutta rohkeimmat läksivät karhua tappamaan. Suuri armeija järjestettiin. Kun kaupungissa ei ollut aseita, niin otti kukin mitä sattui löytämään, seipäitä ja hiilihankoja, muutamilla oli aseina ainoastaan vanhoja kalosseja, joilla he uskoivat voivansa lyödä karhun kuoliaaksi, niin urhoollisia he olivat. Kaupungin pormestari kulki suuri lippu kädessään palokunnan edessä kehoittaen sitä pontevuuteen. Poliisit, joilla oli aseita, paljastivat miekkansa ja koettivat näyttää urhoollisilta. Ja nyt tämä joukko lähti Puijon mäkeä kohden. Kun he olivat sen huipulle päässeet, järjestäytyivät he rintamaan taistellakseen karhua vastaan. Poliisit, jotka alussa olivat näyttäneet urhoollisilta, pelkäsivät nyt aivan tavattomasti, vapisivat kuin haavan lehdet. Pormestari oli kiivennyt mäellä olevaan näkötorniin ja tarkasteltuaan kaukoputkella seutua näki tuon saman
- 46. ruskeapukuisen muijan ja erehtyi hänkin. Huusi tornista alas: — Peto on tuolla! Hyökätkää sinnepäin! Kaikki lykkivät edelleen poliiseja, jotka eivät mitenkään tahtoneet uskaltaa lähteä karhua tappamaan. Kun heitä lykättiin, niin täytyihän heidän mennä. Kun he saapuivat muijan lähelle, niin silloin kaikki heittivät sitä kohden aseitaan, keppejä, hiilihankoja, vanhoja kalosseja ynnä mitä kullakin sattui olemaan. Muija tietysti säikähtyi hirveästi ja hyppäsi pystyyn lähtien täyttä voimaa juoksemaan pakoon. Ja silloin nähtiin, ettei siellä ollutkaan mitään karhua, ainoastaan vanha vaimo. Seuraavana päivänä toimitettiin Kiljusen pojat pois kaupungista ja lähetettiin Turkuun. Tänne oli levinnyt tieto Kiljusen poikien vaarallisuudesta. Heti annettiin määräys, ettei kaupunkiin saa tulla kukaan vieras ihminen. Kun siis Kiljusen pojat junalla tulivat sinne, ei heitä päästettykään asemahuoneesta minnekään, vaan heti otettiin uusi juna ja lähetettiin pojat Tampereelle, jonne oli mukavin heidät toimittaa. Ja tänne he viimein tulivat, joutuivat kouluun ja olivat niin siivosti, ettei opettajilla ollut mitään heitä vastaan sanottavaa. He käyttivät siellä alkuperäistä nimeään, joka oli Kiljander, eikä kukaan silloin pannut mitään erikoista huomiota heihin, vaan jokainen piti heitä aivan tavallisina poikina. Rehtori, joka oli ottanut heidät vastaan, katseli kyllä toisinaan heihin pelokkain silmin, mutta rauhoittui, kun
- 47. sai kuulla, että pojat olivat olleet aivan siivosti siinä asunnossakin, minkä hän heille oli toimittanut. VII Mutta isä ja äiti Kiljusen tuli poikiaan ikävä. He päättivät lähteä Helsinkiin katsomaan, miten nämä voivat. Kun he tulivat lyseolle, jonne pojat ensin oli viety, saivat he kuulla, että Mökö ja Luru oli siirretty yhteiskouluun. — Se onkin heille oikein sopiva koulu, sanoi äiti — sillä juuri tyttöseuraa he tarvitsivat. Yhteiskoulussa käskettiin heitä menemään toiseen lyseoon. Täällä rehtori ilmoitti lähettäneensä pojat Viipuriin. — Mitä te niitä niin lähettelette kuin paketteja? kysyi isä. — Siellä on heille sopivampi koulu, vastasi rehtori. — Vai niin, vastasi isä Kiljunen. He nousivat junaan ja tulivat Viipuriin. Täällä oli levinnyt tieto heidän tulostaan. Koko kaupunki oli liikkeellä, sillä tahtoihan jokainen nähdä, millaisia he olivat. Koko aseman tori oli aivan mustanaan kansaa, ja hurraahuudoin otettiin isä ja äiti Kiljunen vastaan. — Missä ovat meidän poikamme? huusi isä Kiljunen kokoontuneelle kansalle. — Ne on viety Kuopioon, vastasi poliisi, joka piti järjestystä yllä. — Mennään sitten Kuopioon, sanoi isä Kiljunen.
- 48. He joutuivat laivaan ja läksivät Saimaan kanavan kautta kulkemaan Kuopiota kohden. Matkalla ei heille tapahtunut mitään merkillistä, ei mitään muuta kuin että eräässä kohdassa isä Kiljunen tahtoi koettaa peränpitämistaitoaan, tarttui ruoteliin ja väänsi sen niin pitkälle sivullepäin kuin suinkin mahdollista. Silloin jostain tuntemattomasta syystä se ei enää liikkunutkaan. Kun oli täysi höyry päällä, niin alkoi laiva mennä keskellä selkää ympäri, ei yhtään eteenpäin, vaan aina vain ympäri. Täytyi pysäyttää kone, jotta voitiin korjata ruoteli. Tietysti Kuopion satamassa oli väkeä hirveästi Kiljusten sinne tullessa. Kun ei poikia siellä ollut, niin menivät vanhemmat rautatielle ja läksivät Turkuun, jossa uskoivat poikainsa olevan. Kun tieto heidän tulostaan saapui Turkuun, niin koottiin sotalaivoja satamaan ja kaikki kanuunat käännettiin kaupunkiin päin, sillä eihän tiennyt, mitä tapahtuisi, kun tämä herrasväki aina sai aikaan epäjärjestystä. Mutta eihän siellä mitään tapahtunut. Isä ja äiti Kiljunen läksivät heti Tampereelle. Sen koulun rehtori, jonne Kiljusen pojat olivat tulleet Kiljanderin nimellä, oli lähtenyt matkalle. Kun siis isä ja äiti Kiljunen etsivät poikiaan, ei kukaan voinut sanoa, missä he olivat. Kaupungin valtuusto ei keksinyt mitään muuta keinoa tässä hädässä, kuin että kaikki Tampereen koulujen lapset pantiin riviin molemmin puolin katua asemalta alkaen, kosken yli vievän sillan poikki, pitkin Hämeenkatua aina Pyynikille asti. Ja sitten isä ja äiti Kiljunen läksivät etsimään poikiaan. Tietysti kaikki liikenne oli kadulla seisautettu, liikkeet olivat suljetut, ihmisiä oli joka paikassa katsomassa ja ihmettelemässä. Se oli oikea juhlapäivä Tampereella.
- 49. — Täällä on hyvä järjestys, muuta ei voi sanoa, lausui isä Kiljunen äidille seisoessaan aseman portailla ja katsellessaan tätä hirveän pitkää lapsijonoa, joka ulottui pitkin katua niin pitkälle, kuin suinkin näki. Tehtaiden pillit puhalsivat merkiksi, että kaikki oli kunnossa, ja silloin isä sanoi äidille: — Nyt travaamaan eteenpäin, mamma! Mene sinä toista puolta katua, minä toista! He läksivät juoksemaan kumpikin omaa puoltaan etsien poikiaan. Kyllä he saivat juosta, sillä lyseon oppilaat olivat joutuneet aivan Pyynikin lähelle asti. Täällä Mökö ja Luru näkivät isänsä ja äitinsä juoksevan ja huusivat: — Hurraa, minne te nyt kippaatte sellaista kyytiä? Ja sitten he kaikki huusivat ilosta. Mutta isä ja äiti Kiljunen ottivat lapsensa pois koulusta ja veivät kotiinsa, josta he sitten uudelleen veivät heidät Helsinkiin ja saivat heidät sijoitetuksi erääseen kouluun, jossa oli niin pahankurisia lapsia, ettei siellä huomattu laisinkaan erotusta Kiljusen poikien ja muiden välillä. Ja kun ei kukaan välittänyt heistä, niin eivät he enää sen jälkeen saaneet koulussa mitään merkillistä aikaan. Kiljusen poikien jouluaatto
- 50. Kiljusen molemmat pojat, Mökö ja Luru, olivat joululomaksi tulleet kotiin kaupungista, jossa he kävivät koulua. Pulla, tuo heidän pyöreä ja paksu villakoiransa, oli ollut aivan hurjana ilosta nähdessään taas molemmat pojat. Mökö ja Luru tuumivat kovasti, mitä he antaisivat äidilleen joululahjaksi, sillä jotain aivan erikoista he tahtoivat antaa. Kauan tuumailtuaan päätti Mökö antaa elävän kissan ja Luru pienen koiran. Kissa saisi pyydellä hiiriä ja koira olla äidin seurana, kun Pulla vietäisiin poikien mukaan. Kylästä he olivat ostaneet nämä molemmat elukat, ja jouluaattona siinä hämärän tultua meni kumpikin lahjaansa noutamaan. Kiljusen kotona oli kaikki rauhallista, sillä eihän kukaan edeltäpäin tiennyt, mikä merkillinen jouluaatto oli tulossa, niin merkillinen, että kaikki Kiljusen seikkailut Helsingissä eivät olleet mitään sen rinnalla. Mökö meni korilla noutamaan kissaa, ja toisella hiukan suuremmalla korilla meni Luru koiraa noutamaan, ja samaan aikaan he kumpikin palasivat matkaltaan. Yhdessä he sitten astuivat saliin, jossa pöytä oli katettu jouluateriaa varten. Koko talon väki oli koolla, oli vain poikia odotettu. — Tässä on äidille joululahja, sanoi Mökö ja avasi korin, jossa kissa oli. Kissa hyppäsi lattialle, ja Kiljusen rouva parkaisi pahasti. Ei hän kissaa pelännyt, mutta kun se tuli niin äkkiä, niin hän ei voinut olla huutamatta. Ja kun Kiljusen rouva huusi, niin kyllä se kuului. — Tässä on toinen lahja, sanoi Luru ja päästi koiran korista.
- 51. Koira näki kissan ja kissa näki koiran, ja kun ne kaksi eivät koskaan ole olleet hyvässä sovussa, niin mitä ne nytkään olisivat sopineet. Kissa hyppäsi pöydälle ja koira hyppäsi haukkuen jäljessä. Ja kun pöydällä oli paljon tavaraa, niin mitä ne sitä olisivat joutaneet varomaan. Kissa astui ensin keskelle voiastiaa, koira kaatoi maitokannun. Sitten hyppäsi kissa suureen liemimaljaan, josta se aivan märkänä loikkasi pois. Koira kaatui puurovatiin ja pääsi vihdoin pöydälle, vaikka aivan valkoisena riisiryynipuurosta. Ja koira haukkui ja kissa sähisi ja koko Kiljusen perhe huusi. Oli siinä taas elämää yhdeksi kertaa! Isä Kiljunen meni ajamaan näitä eläimiä pöydältä pois ja siinä ajaessaan kaatoi kaiken sen mitä kissa ja koira eivät vielä olleet ennättäneet kaataa. Kissa hyppäsi pakoon ja suoraan isä Kiljusen pään päälle, mikä ei suinkaan tuntunut hauskalta, kun se painoi kyntensä päähän kiinni. Äiti Kiljunen oli ottanut hiilihangon ja koetti sillä lyödä koiraa, mutta ei osannutkaan koiraan, vaan sen sijaan kaikkiin muihin. Hän löi isä Kiljusta vatsaan, Mököä päähän, Lurua selkään ja itseään hyvin moneen paikkaan. Kissa hyppäsi nyt permannolle ja kiipesi suuren vaatekaapin päälle. Pojat koettivat ajaa sitä sieltä pois ja kaatoivat kaapin, jolloin kaikki, mitä sen päällä ja sisällä oli, vieri pitkin permantoa. Tällä ajalla löi äiti Kiljunen kattolampun säpäleiksi, jolloin huoneessa tuli aivan pimeä. Pimeässä meni kissa äiti Kiljusen hameitten alle piiloon, josta tämä niin pelästyi, että kiljaisten kaatui kumoon. Koira juoksi permannolla poikia pakoon. Mökö ja Luru kompastuivat äitiinsä ja isä Kiljunen heihin. Ja siinä nyt koko herrasväki makasi yhdessä kasassa. Silloin avautui ovi ja kissa ja koira pääsivät pakenemaan. Oli siinä huoneessa oikea joulujärjestys, kun taas valoa tuotiin, huonekalut kumossa, kaikki astiat pöydältä maahan vedettynä ja säpäleinä.
- 52. Tällä aikaa pakenivat kissa ja koira keittiöön. Siellä oli Pulla, joka tietysti nosti heti aika mellakan. Kissa pakeni avoimeen uuniin ja joutui paistinpannuun, ja kun palvelijatar samassa pani uunin kiinni, niin sinne se kissa jäikin. Pulla pakeni sitä uutta koiraa pöydälle, ja kun pöydällä oli taikina suurta kaakkua varten, niin kompastui se siihen, ja kun se siinä pyöriskeli, niin tarttui se yhä enemmän taikinaan ja lopulta se jäi aivan sen sisään. Palvelijatar ei siinä hädässä mitään huomannut, vaan otti käärön ja pisti sen uuniin paistumaan. Nyt talossa oli äkkiä aivan hiljaista. Kaikki ihmettelivät minne se uusi kissa ja Pulla olivat kadonneet, mutta niitä ei löydetty mistään. Kun se uusi koira oli pesty puurosta puhtaaksi ja pöytä uudelleen katettu, niin alkoi Kiljusen herrasväki syödä päivällistään. Kun paisti tuotiin sisään, niin huomattiinkin, minne se uusi kissa oli joutunut: se oli kokonaan paistunut paistinpannussa. Ja kun suuri joulukakku kannettiin pöytään, niin löytyi Pulla sen sisältä. Se oli aivan pyörryksissä, mutta virkosi jälleen henkiin, josta poikien ilo oli hyvin suuri. — Pullasta oli vähällä tulla oikea pulla, sanoivat pojat. Ja siinä he olivatkin aivan oikeassa. Tällainen oli Kiljusten jouluaatto.
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