![12
LITERATURE REVIEW
[1] Graph Neural Network for Protein–Protein Interaction
Prediction: A Comparative Study
A comparative study for protein–protein interaction prediction using
multiple graph neural network-based models, including the GCN, GAT
and other GNN types. One of the future works of this study will be
combining global information to perform the link prediction on
graphs.
[2]DeepRank-GNN: a graph neural network framework to
learn patterns in protein–protein interfaces
We present a single GNN architecture in this article that can be
reused as such or derived and replaced. The DeepRank framework
offers many perspectives of extension such as the application to
single proteins (e.g. for genetic variant pathogenicity prediction), to
larger multimeric states (e.g. for larger complex(quality prediction).](https://image.slidesharecdn.com/upload-230309140232-284599d8/85/upload-pdf-12-320.jpg)
![24
REFERENCES
[1]Zhou, J. et al. Graph neural networks: A review of methods and applications. AI Open 1, 57-81
(2020).
[2]Zhou, H,;Wang, W.; Jin, J,; zheng, z,; Zhou,B. Graph Neural Network for Protein-Protein
Interaction Prediction: A comparative Study. Molecules 2022, 27, 6135.
[3]Xiaotian Hu, Cong Feng, Tianyi Ling and Ming Chena ,Deep learning frameworks for protein–
protein interaction prediction. Comput Struct Biotechnol J. 2022; 20: 3223–3233. doi:
10.1016/j.csbj.2022.06.025.
[4]Kipf, T. N. & Welling, M. Semi-supervised classification with graph convolutional networks. arXiv
preprint arXiv:1609.02907 (2016).
[5]Hashemifar, S., Neyshabur, B., Khan, A. A. & Xu, J. Predicting protein–protein interactions
through sequence-based deep learning. Bioinformatics 34, i802–i810 (2018)
[6]Yang F., Fan K., Song D., Lin H. Graph-based prediction of Protein-protein interactions with
attributed signed graph embedding. BMC Bioinf. 2020;21:1–16. doi: 10.1186/s12859-020-03646-
8.
[7]Uversky V.N., Oldfield C.J., Dunker A.K. Intrinsically disordered proteins in human diseases:
Introducing the D 2 concept. Annu Rev Biophys. 2008;37:215–246. doi:
10.1146/annurev.biophys.37.032807.125924.](https://image.slidesharecdn.com/upload-230309140232-284599d8/85/upload-pdf-24-320.jpg)
![25
REFERENCES
[8]Du X., Sun S., Hu C., Yao Y
., Yan Y
., Zhang Y
. DeepPPI: Boosting Prediction of Protein-Protein
Interactions with Deep Neural Networks. J Chem Inf Model. 2017;57:1499–1510. doi:
10.1021/acs.jcim.7b00028.
[9]Licata L., Briganti L., Peluso D., Perfetto L., Iannuccelli M., Galeota E., et al. MINT, the molecular
interaction database: 2012 Update. Nucleic Acids Res. 2012;40:D572–D574. doi:
10.1093/nar/gkr930.
[10]Bock J.R., Gough D.A. Predicting protein–protein interactions from primary structure.
Bioinformatics. 2001;17:455–460.
[11]Ding Z., Kihara D. Computational methods for predicting protein-protein interactions using
various protein features. Curr Protoc Protein Sci. 2018;93:e62.
[12]Rabbani G., Baig M.H., Ahmad K., Choi I. Protein-protein Interactions and their Role in Various
Diseases and their Prediction Techniques. Curr Protein Pept Sci. 2017;19:948–957. doi:
10.2174/1389203718666170828122927.
[13]Shen, J. et al. Predicting protein–protein interactions based only on sequences information.
Proc. Natl. Acad. Sci. 104, 4337–4341 (2007).
[14]Uzair, M. & Jamil, N. Effects of hidden layers on the efficiency of neural networks. In 2020 IEEE
23rd International Multitopic Conference (INMIC), 1–6 (IEEE, 2020).](https://image.slidesharecdn.com/upload-230309140232-284599d8/85/upload-pdf-25-320.jpg)
upload.pdf
- 1. 1 INTRODUCTION ● Protein-protein interactions (PPIs) participate in all important biological processes in living organisms, such as catalyzing metabolic reactions, DNA replication, DNA transcription, responding to stimuli and transporting molecules from one location to another. ● To reveal the function mechanisms in cells, it is important to identify PPIs that take place in the living organism.
- 2. 2 What is Protein-Protein Interaction ? Protein–protein interactions (PPIs) are physical contacts of high specificity established between two or more protein molecules as a result of biochemical events . https://media.geeksforgeeks.org/wp-content/uploads/20220704095158/C11.png
- 3. 3 Why PPI prediction? PPI prediction models can be a valuable tool for screening protein pairs while developing new drugs for targeted protein degradation. https://sj.jst.go.jp/news/202109/n0924-03k.html
- 4. 4 PPI prediction Techniques: Performed in a controlled environment outside a living organism. Ex: X-ray crystallography... Performed on the whole living organism itself. Ex: yeast two-hybrid (Y2H, Y3H)... performed on a computer (or) via computer simulation. Ex: sequence-based approaches... In Vitro Silico In Vivo
- 5. 5 Computational Biology: Computational biology and bioinformatics have the potential not only to speed up the drug discovery process, thus reducing the costs, but also to change the way drugs are designed. https://www.cell.com/cms/attachment/6dce762f-d8a4-4284-baec-bc0bfb5d9666/gr1_lrg.jpg
- 6. 6 Protein input for computational Biology: The starting point (input) of protein structure prediction or protein interaction prediction is the one- dimensional amino acid sequence of target protein. https://upload.wikimedia.org/wikipedia/commons/thumb/b/b5/Histone_Alignment.png/595px-Histone_Alignment.png
- 9. 9 Stage-I Result: Precision, F-measure and MCC obtained by model are 0.303, 0.397 and 0.206, respectively.
- 10. 10 Problem statement: The performance of model for protein-protein interaction site detection by combining local and global features can be improved by incorporating additional features or using ensemble methods, or exploring different model architectures such as graphical convolutional networks. Illustration of a Sars-cov2 virus particle structure https://images.novusbio.com/design/dw-nb-sars-cov-2-viral-particle-v3.png
- 11. 11 Gantt Chart: Implementaton Date Learning and literature review 5th Feb 2023 - 10th Mar 2023 Sampling the pre-processed data 12th Mar 2023 - 15th Mar 2023 Modifying the pre-processed data 15th Mar 2023 - 20th Mar 2023 Modifying the architecture 1st Apr 2023 - 15th Apr -2023 Training the model 16th Apr 2023 - 20th Apr 2023 Evaluating performance metrics 22nd Apr 2023 - 30th Apr 2023
- 12. 12 LITERATURE REVIEW [1] Graph Neural Network for Protein–Protein Interaction Prediction: A Comparative Study A comparative study for protein–protein interaction prediction using multiple graph neural network-based models, including the GCN, GAT and other GNN types. One of the future works of this study will be combining global information to perform the link prediction on graphs. [2]DeepRank-GNN: a graph neural network framework to learn patterns in protein–protein interfaces We present a single GNN architecture in this article that can be reused as such or derived and replaced. The DeepRank framework offers many perspectives of extension such as the application to single proteins (e.g. for genetic variant pathogenicity prediction), to larger multimeric states (e.g. for larger complex(quality prediction).
- 14. 14 Graph Convolutional network : A Graph Convolutional Network, or GCN, is one of the flavour of GNN used for learning on graph-structured data. It is based on an efficient variant of convolutional neural networks which operate directly on graphs. The graphs are sequentially passed to consecutive convolution/activation/pooling layers. The final graph representations are flattened using the mean value of each feature and merged before applying fully connected layers.
- 15. 15 Implementation: To predict PPI consists of three modules: protein graph construction, feature extraction, Concatenation and classifier to predict interactions between them. Graph representation of proteins: we will construct the molecular graph of proteins, also known as amino-acids/residues contact network, using the PDB files. The PDB file is a text file containing structural information such as 3D atomic coordinates.
- 16. 16 Graph Construction: Let G(V, E) be a graph representing the proteins, where each node ( v ∈ V ) is the residue and interaction between the residues is described by an edge ( e ∈ E ). Two residues are connected if they have any pair of atoms (one from each residue) having the Euclidean distance lessthan a threshold distance. Amino acids
- 17. 17 Graph Construction: Node features: The first step is to get each protein’s PDB sequence and molecular graph structure using a python script. Then the residue-level features are merged with a molecular graph, creating a final protein graph. PSSM DSSP RPS
- 18. 18 Features: Name of feature Full name Description No of parameters Type Secondary structure DSSP One-hot encoded 9 Node feature Evolutionary Inofrmation Position specific scoring matrix PSSM 20 Node feature Raw protein sequence Raw protein sequence One-hot encoded 20 Node feature distance Normalised distance (optional) 1 Edge feature
- 19. 19 Feature Extraction: The GCN is used to learn features from data represented as graphs. The connection between every pair of nodes is encoded in the adjacency matrix, A ∈ RL,L . The matrix, X ∈ RL,F, contains the residue-level features for all nodes of the given graph. Each layer of GCN takes the adjacency matrix (A) and node embeddings from the previous layer as input and outputs the node-level embeddings for the next layer. After the GCN layer, each residue feature vector is updated as a weighted sum of the features of neighboring nodes in the graph, including residue’s own feature.
- 20. 20 Classification: To ensure the fixed-size representations of all proteins, there will be a global pooling layer after the GNN layer. After the pooling layer, we add a fully connected (FC) layer with a LeakyReLU activation function to get the final representation of a protein from its pooled representation. On the other hand, we get the learned local feature vector. The concatenated feature vectors of protein are then fed to the classifier having two FC layers and an output layer with sigmoid activation. Note: The LeakyReLU is used as an activation function to add non-linearity to the output of a fully connected layer.
- 21. 21 Architecture: Local feature Extraction PDB file Features sequence Protein featured graph Global Feature vector Pooling layer GCN layer PSSM RPS DSSP Sliding window Local feature vector 2 Fully Connected layers Predic tion Global feature Extraction Classification
- 22. 22 Dataset: Dset_186 has been built from the PDB database and consists of 186 protein sequences. Dset_72 has 72 protein sequences and PDBset_164 consists of 164 protein sequences. Thus, we have 422 different annotated protein sequences.
- 23. 23 Performance Metrics: Accuracy = Correct Predictions / Total Predictions Precision = TruePositive / (TruePositive + FalsePositive) Recall = TruePositive / (TruePositive + FalseNegative) F-Measure = (2 * Precision * Recall) / (Precision + Recall) MCC = TP*TN-FP*FN / ((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN))^0.5
- 24. 24 REFERENCES [1]Zhou, J. et al. Graph neural networks: A review of methods and applications. AI Open 1, 57-81 (2020). [2]Zhou, H,;Wang, W.; Jin, J,; zheng, z,; Zhou,B. Graph Neural Network for Protein-Protein Interaction Prediction: A comparative Study. Molecules 2022, 27, 6135. [3]Xiaotian Hu, Cong Feng, Tianyi Ling and Ming Chena ,Deep learning frameworks for protein– protein interaction prediction. Comput Struct Biotechnol J. 2022; 20: 3223–3233. doi: 10.1016/j.csbj.2022.06.025. [4]Kipf, T. N. & Welling, M. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016). [5]Hashemifar, S., Neyshabur, B., Khan, A. A. & Xu, J. Predicting protein–protein interactions through sequence-based deep learning. Bioinformatics 34, i802–i810 (2018) [6]Yang F., Fan K., Song D., Lin H. Graph-based prediction of Protein-protein interactions with attributed signed graph embedding. BMC Bioinf. 2020;21:1–16. doi: 10.1186/s12859-020-03646- 8. [7]Uversky V.N., Oldfield C.J., Dunker A.K. Intrinsically disordered proteins in human diseases: Introducing the D 2 concept. Annu Rev Biophys. 2008;37:215–246. doi: 10.1146/annurev.biophys.37.032807.125924.
- 25. 25 REFERENCES [8]Du X., Sun S., Hu C., Yao Y ., Yan Y ., Zhang Y . DeepPPI: Boosting Prediction of Protein-Protein Interactions with Deep Neural Networks. J Chem Inf Model. 2017;57:1499–1510. doi: 10.1021/acs.jcim.7b00028. [9]Licata L., Briganti L., Peluso D., Perfetto L., Iannuccelli M., Galeota E., et al. MINT, the molecular interaction database: 2012 Update. Nucleic Acids Res. 2012;40:D572–D574. doi: 10.1093/nar/gkr930. [10]Bock J.R., Gough D.A. Predicting protein–protein interactions from primary structure. Bioinformatics. 2001;17:455–460. [11]Ding Z., Kihara D. Computational methods for predicting protein-protein interactions using various protein features. Curr Protoc Protein Sci. 2018;93:e62. [12]Rabbani G., Baig M.H., Ahmad K., Choi I. Protein-protein Interactions and their Role in Various Diseases and their Prediction Techniques. Curr Protein Pept Sci. 2017;19:948–957. doi: 10.2174/1389203718666170828122927. [13]Shen, J. et al. Predicting protein–protein interactions based only on sequences information. Proc. Natl. Acad. Sci. 104, 4337–4341 (2007). [14]Uzair, M. & Jamil, N. Effects of hidden layers on the efficiency of neural networks. In 2020 IEEE 23rd International Multitopic Conference (INMIC), 1–6 (IEEE, 2020).
- 26. THANK YOU..