An Overview to Protein bioinformatics
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InterPro is a database that classifies proteins into families, domains, and sequence features based on their structural and functional properties. It integrates predictive models from several member databases to annotate unknown protein sequences. Protein signatures like patterns, profiles, fingerprints and hidden Markov models are generated from multiple sequence alignments and used by InterPro for classification. AlphaFold is an artificial intelligence system that can predict protein three-dimensional structures directly from amino acid sequences, representing a major advance in solving the protein folding problem.
![A L V K L I S G K
A I V H E S A T K
C H V R D L S C K
C P V E S T I S K
A F V T I Y E F R
C G V R Y T D K R
[AC]-x-V-x(4)-{ED}-[KR]
motif
MSA
Pattern/Regular
expression
MSA
1 2 3 4 5 6 7 ..
.
n
A - - 0.2 - - - - - -
R - - 0.3 - - - - - -
F - - 0.5 - - - - - -
G - - 0 - - - - - -
.. - - 0.8 - - - - - -
Y - - 1.2 - - - - - -
sequence positions
amino
acids
PSSM
1) Patterns 2) Profiles
position-specific scoring matrix
Regular expressions to model:
⚙ → Sequence features
MSA to PSSM:
⚙ → Familes and Domains](https://image.slidesharecdn.com/proteinbioinformatics-220207043535/85/An-Overview-to-Protein-bioinformatics-9-320.jpg)
An Overview to Protein bioinformatics
- 1. What is a protein? Anticuerpo ATP sintetasa Hemoglobina Catalasa RNA Polimerasa RNA Polimerasa Actina Insulina Colágeno Rodopsina p53 GFP 1
- 2. InterPro Protein classification resources at the EBI • InterPro is the main resource for protein classification at the EBI. • Is a single searchable resource for: • Pa2erns • Profiles • Fingerprints • HMMs • From a number of different member databases
- 3. Protein classification Proteins can be classified into groups • The FAMILIES to which they belong • The DOMAINS they contain • The SEQUENCE FEATURES they possess Structural properties: • Domains • Sequence Features Tools/Models: • Protein signatures Evaluated through • Sequence similarity • Structural similarity according to their Based on
- 4. What are protein FAMILIES? Protein Family Group of proteins that share a common evolutionary origin: • Related functions • Sequence similarity • Structural similarity Superfamily Family Subfamily Arranged into hierarchies Set of homologous proteins
- 5. What are protein DOMAINS? Are structural units in a protein. • Continuous or discontinous (multiple motifs) • Often associated with different functions. • Between 40 and 500 residues. Domains may exist in a variety of biological contexts, where similar domains can be found in proteins with different functions. Catali&c ac&vity Subcellular targe&ng DNA binding ACP-binding domain Catalytic domain E. coli - Malonyl-CoA:acyl carrier protein PDB: 1MLA
- 6. SEQUENCE ANNOTATIONS: Groups of amino acids that confer certain characteristics upon a protein. • A few aminoacids long. • O!en nested within domains Examples: • Active sites • Binding sites • Signals • Post-translational modification • Repeats • Motifs What are SEQUENCE FEATURES? Active site Signal Repeats Domains PDZ-binding domain
- 7. Predic.ve Models to classify proteins into families predict the presence of important domains or sequence features What are PROTEIN SIGNATURES? MGAFGHGFG TYHKLAALED GTLKHHAKLQ PHLSLLCMSKL DCKLPQGFFF MGAFGHGFG TYHKLAALED GTLKHHAKLQ PHLSLLCMSKL DCKLPQGFFF Protein family/domain MSA Build Predictive Model Database Search Model refinement - maduration Protein signature Significant match Unknown protein
- 8. Different approaches can be used to generate signatures. 1. Patterns 2. Profiles 3. Fingerprints 4. Hidden Markov Models (HMMs) Signature Types Single motif methods Multiple motif methods Full alingment methods
- 9. A L V K L I S G K A I V H E S A T K C H V R D L S C K C P V E S T I S K A F V T I Y E F R C G V R Y T D K R [AC]-x-V-x(4)-{ED}-[KR] motif MSA Pattern/Regular expression MSA 1 2 3 4 5 6 7 .. . n A - - 0.2 - - - - - - R - - 0.3 - - - - - - F - - 0.5 - - - - - - G - - 0 - - - - - - .. - - 0.8 - - - - - - Y - - 1.2 - - - - - - sequence positions amino acids PSSM 1) Patterns 2) Profiles position-specific scoring matrix Regular expressions to model: ⚙ → Sequence features MSA to PSSM: ⚙ → Familes and Domains
- 10. Motif 1 Motif 2 Motif 3 1 2 3 MSA Profiles Fingerprint signature Correct Order and space "Probabilistics profiles" Convert multiple sequence alignments into position-specific scoring matrix (PSSMs). 1 2 3 4 5 6 7 .. . n A - - 0.2 - - - - - - R - - 0.3 - - - - - - F - - 0.5 - - - - - - G - - 0 - - - - - - .. - - 0.8 - - - - - - Y - - 1.2 - - - - - - 3) Fingerprints 4) HMMs MSA to PSSM to model: ⚙ → Homologous sequences MSA → Profiles → Fingerprint : ⚙ → Families
- 11. When use InterPro? You can use InterPro if you have an amino acid sequence or set of sequences and you want to know: www.ebi.ac.uk/interpro/ • What they are, what family they belong to • What is their function and how it can be explained in structural terms • You can also use InterPro for a variety of other purposes, such as examining the structural or functional predictions for any sequence already in the UniProt database.
- 12. PROFILE Hidden Markov Models Profiles Pa<ers Composi?on Predic?on Domain & Families CATH Pfam TIGRFAM HAMAP PRINTS Prosite pa$ers MobiDB SUPER FAMILY SMART PIRSF Prosite Profiles SFLD CDD PANTHER Homologous Superfamilies Features & Sites Intrinsic Disorder InterPro UniProt PDB 4) Protein 5) Taxonomy 6) Proteome 3) Structure 1) Interpro entries 2) Database signatures 13 InterPro Member databases Biological En;ty Signature method
- 13. UniProt: Universal Protein resource 1. UniProtKB (Knowledgebase): 1. Swiss-Prot ⭐: Manually annotated entries 2. TrEMBL: Automatically annotated entries 2. UniParc (archive): • Non-redundant database • Protein sequences from public databases 3. UniRef (Reference Clusters): • Three databases • 100, 90, 50 • Sequences are clustered by its identity (%) UniProt
- 14. Protein Data Bank Database for the three-dimensional structural data of large biological molecules. • X-ray crystallography • Nuclear Magnetic Resonance Spectroscopy • Cryogenic electron microscopy ~ 175, 000 structures
- 15. UCSF Chimera • Structure visualiza;on • Molecular graphics • Topology and structural Analysis • UniProt annota;on • MSA and Blastp • Interface: • 3D-modelling • Pocket data • Molecular docking • Molecular Dynamics RecommendaCon
- 16. 3d - Protein structure prediction PotenCal energy landscape Accuracy Difficulty • Compara.ve modelling – Requires: Known fold + clear homology • Fold recogni.on (threading) – Requires: Known fold • Ab ini.o / new fold methods – Requires: only target sequence
- 17. Homology modeling Threading & Fragment assembly Molecular dynamics INPUT: query sequence Q INPUT: query sequence Q INPUT: query sequence Q INPUT: Database of known folds or structure fragments INPUT: Database of protein structures 1. find protein P high sequence similarity to Q 2. return P’s structure as an approxima=on to Q’s structure 1. Laws of physics to simulate folding of Q 1. find a set of fragments that Q can be aligned with 2. return F as an approxima=on to Q’s structure
- 18. Protein structure prediction Database similarity search (BLAST) Does sequence align with a protein of known structure? Protein family Sequence search (InterPro) Rele7onship to known structure? Secondary structure predicCon 3D-structure predicCon Homology modeling Predicted 3D Structural model 3D structural analysis in laboratory • Hints for domain assignment? • Func7on? NO NO YES YES Protein Sequence
- 20. AlphaFold MGAFGHGFG TYHKLAALED GTLKHHAKLQ PHLSLLCMF… What is it? - AF is an Artificial intelligence program - Google’s DeepMind The Goal: - Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence It “solves” two main problems: 1. Sequence-Structure gap 2. Protein folding Why solving these problems? Jumper, J., Evans, R., Pritzel, A. et al. Nature 596, 583–589 (2021).
- 21. Sequence-Structure gap - 1958: determination of the first protein structure. - John Kendrew & Max Perutz - Structure determination (experimental): - NMR - X-ray crystallography - Cryo-Electron microscopy - Protein Data Bank: - Total: ~170,000 - Unique: ~100,000
- 22. AlphaFold 1 The protein folding problem - 1972: Christian Anfisen, Nobel Prize in Chemistry. - “It should be possible to determine a protein’s three-dimensional shape based solely on its sequence” - A typical protein could adopt 10^300 different configurations - Longer than the age of the universe - However, in nature, proteins spontaneously fold into their functional shape. - Cyrus Levinthal’s paradox (1969) - 50 years open research problem
- 23. The protein folding problem CASP Critical Assessment of Techniques for Protein Structure prediction • The protein folding Olympics • The state of the art in protein structure prediction - The competition: - Since 1994 - Takes place every two years - Last competition: CASP14 – 2020 - Organizers: - Known both the sequence and the structure Participants: - Receive only the protein’s sequence - Must blindly predict the structure of the proteins - Predictions: compared with the experimental data
- 24. Homology modeling Threading & Fragment assembly Molecular dynamics INPUT: query sequence Q INPUT: query sequence Q INPUT: query sequence Q INPUT: Database of known folds or structure fragments INPUT: Database of protein structures 1. find protein P high sequence similarity to Q 2. return P’s structure as an approxima=on to Q’s structure 1. Laws of physics to simulate folding of Q 1. find a set of fragments that Q can be aligned with 2. return F as an approxima=on to Q’s structure • Force field • Molecular mechanics
- 25. CASP before AlphaFold The metric: - How well is the prediction compared with the experimental data? GDT: Global Distance Test - Compares two structures - From 0 to 100 (%) - Greater is better - Uses distance cutoffs - Uses alpha Carbons - More accurate than RMSD Homology modeling Threading & Fragment assembly Molecular dynamics
- 26. CASP and AlphaFold CASP14: 152 targets Jumper, J., Evans, R., Pritzel, A. et al. Nature 596, 583–589 (2021).
- 27. AlphaFold 1 The model: • CASP13 (2018) • Convolutional-based Neural Network Training: • Structures: 31,247 domains • Sequences: UniClust30 Senior, et al. (2020). Nature, 577(7792), 706–710. X y Sequence Structure MGAFGHGFG TYHKLAALED GTLKHHAKLQ PHLSLLCMF…
- 28. AlphaFold 1 • Input: • Protein amino acid sequence • Multiple Sequence Alignments (MSA): • Profile features MSA 1 2 3 4 5 6 7 .. . n A - - 0.2 - - - - - - R - - 0.3 - - - - - - F - - 0.5 - - - - - - G - - 0 - - - - - - .. - - 0.8 - - - - - - Y - - 1.2 - - - - - - sequence positions amino acids PSSM position-specific scoring matrix MSA to Profiles - PSSM: ⚙ → Familes and Domains Senior, et al. (2020). Nature, 577(7792), 706–710. MSA Profile Structure optimization
- 29. AlphaFold 1 Senior, et al. (2020). Nature, 577(7792), 706–710.
- 30. MGAFGHGFG TYHKLAALED GTLKHHAKLQ PHLSLLCMF… Input Sequence MSA Profile The ML model The distogram y X Convolu=onal Neural Network The central component: • A convolutional neural network • Trained on PDB structures • It predicts the distances dij between the Cβ atoms of pairs, ij, of residues of a protein. AlphaFold 1 Senior, et al. (2020). Nature, 577(7792), 706–710.
- 31. AlphaFold 1 The distogram Resiudue 29 The predicted probability distribu?ons for distances of residue 29 to all other residues (41) Senior, et al. (2020). Nature, 577(7792), 706–710.
- 32. MGAFGHGFG TYHKLAALED GTLKHHAKLQ PHLSLLCMF… Input Sequence MSA Profile The ML model The distogram y X Convolu=onal Neural Network Gradient descent: • Rotate the phi and psi angles • Match the predicted Cβ atoms distances AlphaFold 1 Protein folding Senior, et al. (2020). Nature, 577(7792), 706–710.
- 33. Senior, et al. (2020). Nature, 577(7792), 706–710.
- 34. AlphaFold References: 1. Senior, et al. (2020). Improved protein structure prediction using potentials from deep learning. Nature, 577(7792), 706–710. 2. Jumper, J., Evans, R., Pritzel, A. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).