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  1. 1. Machine Learning as Applied to Structural Bioinformatics: Results and Challenges Philip E. Bourne University of California San Diego [email_address]
  2. 2. The Current Situation <ul><li>Structure contributes greatly to our understanding of living systems </li></ul><ul><li>We are locked into thinking about structure in specific ways which limits our view </li></ul><ul><ul><li>All too often we consider structure as a static entity </li></ul></ul><ul><ul><li>The view at left is not how another protein or a small molecule ligand sees PKA </li></ul></ul><ul><li>We are still not very good at certain problems … </li></ul>
  3. 3. Example Unsolved Problems that Machine Learning Can Address <ul><li>Predicting flexibility and disorder in protein structure </li></ul><ul><li>Predicting sites of protein-protein and protein-ligand interaction </li></ul><ul><li>Predicting protein function </li></ul><ul><li>Defining domain boundaries from sequence </li></ul><ul><li>Predicting secondary, tertiary and quaternary structure </li></ul><ul><li>Predicting what will crystallize </li></ul>
  4. 4. Example Unsolved Problems that Machine Learning Can Address <ul><li>Predicting flexibility and disorder in protein structure </li></ul><ul><li>Predicting sites of protein-protein and protein-ligand interaction </li></ul><ul><li>Predicting protein function </li></ul><ul><li>Defining domain boundaries from sequence </li></ul><ul><li>Predicting secondary, tertiary and quaternary structure </li></ul><ul><li>Predicting what will crystallize </li></ul>* Will talk about this * Will offer as a challenge
  5. 5. The Current Situation: The Potential “Training Set” is Growing Quickly <ul><li>High level of redundancy as measured by sequence or structure </li></ul><ul><li>Structure space is clearly very finite, but not clear how much is covered </li></ul><ul><li>Increase in functionally uncharacterized structures </li></ul><ul><li>Complexity is increasing, but still lack complexes </li></ul><ul><li>Structures predominantly 1 and 2 domains </li></ul><ul><li>Lack membrane proteins </li></ul><ul><li>In summary the training set is still not truly representative but structural genomics will improve this situation </li></ul>
  6. 6. Predicting Functional Flexibility Jenny Gu Gu, Gribskov & Bourne PLoS Computational Biology 2006 Early On-line Release
  7. 7. Spectrum of Protein Order and Disorder Ordered Structures Disordered Structures If we believe that the 3-dimensional structure of a protein is defined by its 1-dimensional sequence then why not its flexibility?
  8. 8. Bridging the Sequence-flexibility Gap Generalize sequence - flexibility relationship to identify local protein regions important for allostery
  9. 9. The Training Dataset <ul><li>The dataset contains the following qualities: </li></ul><ul><li>Non-redundant sequences </li></ul><ul><ul><li>training set with sequences containing ≤ 10% identity. </li></ul></ul><ul><li>With good quality structures </li></ul><ul><ul><li>R-factor < 0.30 </li></ul></ul><ul><li>At high resolution </li></ul><ul><ul><li>Resolution < 2.0 Å. </li></ul></ul><ul><li>Total number of proteins in dataset: 1277 sequences </li></ul>
  10. 10. Obtaining Protein Dynamic Information <ul><li>Protein structures treated as a 3-D elastic network. </li></ul>Bahar, I., A.R. Atilgan, and B. Erman Direct evaluation of thermal fluctuations in proteins using a single-parameter harmonic potential. Folding & Design, 1997. 2(3): p. 173-181.
  11. 11. Defining the Target Features <ul><li>Gaussian Network Model: </li></ul><ul><li>Models protein structure as a 3-D elastic network. </li></ul><ul><ul><li>Each Ca is a node in the network. </li></ul></ul><ul><ul><li>Each node undergoes Gaussian-distributed fluctuations influenced by neighboring interactions within a given cutoff distance. (7Å) </li></ul></ul><ul><li>Decompose protein fluctuation into a summation of different modes. </li></ul>Bahar, I., A.R. Atilgan, and B. Erman Direct evaluation of thermal fluctuations in proteins using a single-parameter harmonic potential. Folding & Design, 1997. 2(3): p. 173-181.
  12. 12. Side Note: Gaussian Network Model vs Molecular Dynamics <ul><li>GNM relatively cause grained </li></ul><ul><li>GNM fast to compute vs MD </li></ul><ul><ul><li>Look over larger time scales </li></ul></ul><ul><ul><li>Suitable for high throughput </li></ul></ul>
  13. 13. Functional Flexibility Score <ul><li>Utilize correlated movements to help define regional flexibility with functional importance. </li></ul><ul><li>Functionally Flexible Score </li></ul><ul><li>For each residue: </li></ul><ul><li>Find Maximum and Minimum Correlation </li></ul><ul><li>Use to scale normalized fluctuation to determine functional importance </li></ul>
  14. 14. Example: Identifying Functional Flexible Regions (FFR) in HIV Protease Gu, Gribskov & Bourne PLoS Comp. Biol.. 2006 Early Release Correlated modes (yellow) Anti-correlated (blue) Normalized scores – single chain
  15. 15. Identifying Regions in Bovine Pancreatic Trypsin Inhibitor and Calmodulin
  16. 16. How to Represent the Protein Sequence? <ul><li>Residues characterized as FFs or not – approx 20% of residues with lengths typically 9+/-11 </li></ul><ul><li>The longer the protein the longer the FFR </li></ul><ul><li>We use hidden Markov models to represent each protein sequence in the training dataset. </li></ul><ul><li>Hidden Markov models captures evolutionary information along with the probability of finding one of the 20 amino acids in each position of the sequence. </li></ul><ul><li>Use probability states as input features in the first layer of an architecture containing two SVM layers. </li></ul>
  17. 17. Architecture of Wiggle Captures Evolutionary Effects Captures Local Effects (smoothing) 9*29 features used for each residue
  18. 18. Generating Additional Input Features Modified Bootstrapping – for Tripeptides – Accounts for Nearest Neighbors Effects Calculate Z score and P value for each pattern with respective null models Sample with replacement 44645 times Pooled Patterns (window size : 3) Null Model* for Non-FFR Regions Sample with replacement 199515 times * Generate 10,000 Null Models Null Model* for FFR Regions
  19. 19. Architecture of Wiggle Captures Evolutionary Effects Captures Local Effects (smoothing) 9*29 features used for each residue
  20. 20. Predictors Trained on the Entire Dataset Perform Poorly on Smaller Proteins. False Positive False Negative The characteristics of small proteins are different – eg percent of complexes
  21. 21. Partition Training Set Based on Sequence Length <ul><li>Prediction performance of SVM trained on a partitioned dataset (solid lines) is compared to that was trained on the entire dataset (dashed line). </li></ul><ul><li>Prediction quality improved when dataset is partitioned. Most notably for proteins up to 200 amino acid residues long. Slight improvements observed for proteins longer than 200 residues. </li></ul><200 AA Long >200 AA Long
  22. 22. Performance of Wiggle Predictors <ul><li>Wiggle </li></ul><ul><li>Accuracy: 66.01% </li></ul><ul><li>Precision: 37.11% </li></ul><ul><li>Recall: 70.49% </li></ul><ul><li>Wiggle 200 </li></ul><ul><li>Accuracy: 76.46% </li></ul><ul><li>Precision: 48.99% </li></ul><ul><li>Recall: 78.27% </li></ul>
  23. 23. Case Study: PvuII Endonuclease FF SCORE (homodimer for DNA specific cleavage) Wiggle 200 <ul><li>Identify known loop for minor grove recognition </li></ul><ul><li>Identify hinge residues not previously seen </li></ul><ul><li>Important result for mutagenesis studies </li></ul>
  24. 24. Conclusions for Wiggle <ul><li>FFRs can be measured from structure </li></ul><ul><li>With some empirical effort these data can be used as input to an SVM to predict FFRs from sequence alone </li></ul><ul><li>Useful for: </li></ul><ul><ul><li>Improving docking studies </li></ul></ul><ul><ul><li>Better understand protein function </li></ul></ul><ul><ul><li>Engineer more or less stable proteins </li></ul></ul><ul><ul><li>…… </li></ul></ul>Gu, Gribskov & Bourne 2006 PLoS Comp. Biol.. 2006 Early Release
  25. 25. Exploiting Sequence and Structure Homologs to Identify Protein-Protein Binding Sites JoLan Chung Chung, Wang & Bourne 2006 Proteins: Structure, Function and Bioinformatics, 62(3) 630-640
  26. 26. Methods to Identify Protein-protein Binding Sites <ul><li>Docking </li></ul><ul><li>Threading and homology modeling </li></ul><ul><li>Evolutionary tracing </li></ul><ul><li>Correlated mutations </li></ul><ul><li>Properties of patches </li></ul><ul><li>Hydrophobicity </li></ul><ul><li>Neural networks and support vector machines (SVM) </li></ul>
  27. 27. <ul><li>None of the above methods consider the residues which are spatially conserved on the surfaces of structure homologs </li></ul><ul><li>These residues are reported to correspond to the energy hot spots on protein interfaces and can be derived from multiple structure alignments </li></ul>Structurally Conserved Surface Residues?
  28. 28. Method: Incorporate Structural Conservation to Predict the Interface Residue Using SVM Support vector machine Sequence + structure information Binding site location
  29. 29. Derive the Structurally Conserved Residues <ul><li>The structural conservation scores were derived from multiple structural alignments and weighted by the normalized B-factors to consider the structure flexibility that will result in a bad alignment (could use FFRs in the future) </li></ul><ul><li>Each position in the alignment has a structural conservation score, which represents the conservation in 3D space </li></ul><ul><li>A position has a high conservation score if the aligned residues are spatially conserved </li></ul>
  30. 30. Structurally Conserved Residues and Interface Residues E.g. Residues with the top 20% of structure conservation scores (red) mapped to adrenodoxin (Adx, PDB code 1E6E:B) and known to bind adrenodoxin reductase (AR, blue).
  31. 31. Training D ataset <ul><li>274 non-redundant chains of heterocomplexes (<30% sequence identity) extracted from the PDB </li></ul><ul><li>Each of these chains was accompanied with a structure alignment with at least 4 members </li></ul>
  32. 32. SVM Training <ul><li>A surface residue </li></ul><ul><li>↓ </li></ul><ul><li>Sequence profile + ASA + Structural conservation score </li></ul><ul><li>in a window of 13 residues </li></ul><ul><li>(The residue to be predicted and 12 spatially nearest surface residues) </li></ul><ul><li>↓ </li></ul><ul><li>Support vector machine classifier </li></ul><ul><li>↓ </li></ul><ul><li>Interface or non-interface residue ? </li></ul>
  33. 33. SVM Training <ul><li>Each residue was encoded as a feature vector with 13×21 dimensions: (the surface residue to be predicted + 12 nearest neighbors) x (20 amino acids + accessible surface area) </li></ul><ul><li>Implemented using SVM light with the radial basis function as a kernel. (γ = 0.01, regularization parameter C =10) </li></ul><ul><li>A set of non-interface surface residues was randomly selected to make the ratio of positive and negative data 1:1 </li></ul><ul><li>3 fold cross-validation was performed </li></ul>
  34. 34. Predictor 1: Sequence profile + ASA. Predictor 2: Sequence profile + ASA + structural conservation score Predictor 3: Sequence profile + ASA + raw structural conservation score without weighted by the normalized B-factor Predictor 4: Sequence profile + ASA+ normalized B-factor The Performance of Various Predictors
  35. 35. Precise prediction: at least 70% interface residues were identified Correct prediction: at least 50 % interface residues were identified Partial prediction: some but less than 50 % interface residues were identified Wrong prediction: no interface residues were identified The Performances of the Predictors
  36. 36. <ul><li>Predicted Binding Sites - Example 1 </li></ul><ul><li>Protein : domain 1 of the human coxsackie and adenovirus receptor (CAR D1) </li></ul><ul><li>Mediate adenoviruses and coxsackie virus B infection </li></ul><ul><li>CAR is an integral membrane protein expressed in a broad range of human and murine cell type. CAR D1 is one of its two extracellular domains </li></ul><ul><li>Binding partner : knob domain of the adenoviruses serotype 12 (Ad12) </li></ul>
  37. 37. <ul><li>Predicted Binding Sites - Example 2 </li></ul><ul><li>Protein : adrendoxin (Adx) </li></ul><ul><li>In mitochondria of the adrenal cortex, the steroid hydroxylating system requires the transfer of electrons from the membrane-attached flavoprotein AR via the soluble Adx to the membrane-integrated cytochrome P450 of the CYP 11 family </li></ul><ul><li>Binding partner : adrenodoxin reductase (AR) </li></ul>
  38. 38. <ul><li>Predicted Binding Sites - Example 3 </li></ul><ul><li>Protein : fibroblast growth factor receptor 2 (FGFR2) Ser252Trp Mutant </li></ul><ul><li>Apert syndrome (AS) is caused by substitution of one of two adjacent residues, Ser252Trp or Pro253Arg </li></ul><ul><li>Binding partner : fibroblast growth factor (FGF2) </li></ul>
  39. 39. Conclusions – Protein-protein Binding Sites <ul><li>Incorporating the structural conservation score improved the prediction performance of SVM significantly </li></ul><ul><li>This study is an initial trial that exploits multiple structure alignment for the large scale prediction of functional regions </li></ul><ul><li>We need better algorithms for multiple structure alignment (we have one benchmark for anyone interested) </li></ul><ul><li>This method can be used to guide experiments, such as site-specific mutagenesis, or combined with docking procedures to limit the search space </li></ul>
  40. 40. General Conclusions <ul><li>Using known features of protein structure these can be mapped to the corresponding sequences and used to train an SVM </li></ul><ul><li>Having evaluated the SVM in a cross validation tests the performance can be determined </li></ul><ul><li>Good performance is shown in training for both flexibility and sites of protein-protein interaction </li></ul><ul><li>These predictors are currently being used to solve real biological problems </li></ul><ul><li>Can this approach be applied to other aspects of structure? </li></ul>
  41. 41. 1fohb PUU: 2 Experts: 3 A. B. C. D. E. Consider Domain Definitions: Holland et al. 2006 JMB Early Release Veretnik et al. 2004 JMB 339(3), 647-678 1ytf PUU: 1 Experts: 2 1d0gt PUU: 1 Experts: 3 1dgk PUU: 6 Experts: 4 1aoga PUU: 4 Experts: 3
  42. 42. Challenge – Defining Domain Boundaries from Sequence <ul><li>A domain is the unit of currency of proteins – domain structures define function, indicate evolutionary relationships etc… </li></ul><ul><li>Domain prediction from structure easier than from sequence, but still not a solved problem </li></ul><ul><li>Recently developed an accurate test set of domain definitions and boundaries: </li></ul><ul><li>Good luck! </li></ul>Benchmark Data Available See: Holland et al 2006 JMB Early Release
  43. 43. Acknowledgements <ul><li>Functional Flexibility </li></ul><ul><ul><li>Jenny Gu & Michael Gribskov </li></ul></ul><ul><li>Protein-protein Interactions </li></ul><ul><ul><li>JoLan Chung & Wei Wang </li></ul></ul><ul><li>Domain Definitions </li></ul><ul><ul><li>Stella Veretnik, Tim Holland, Ilya Shindalov, Nick Alexandrov, Abdur Sikur </li></ul></ul><ul><li>Funding, NSF, NIH </li></ul>
  44. 44. The structural conservation score <ul><li>Raw structural conservation score </li></ul><ul><li>where </li></ul><ul><li>if a is not gap and b is not gap </li></ul><ul><li>otherwise </li></ul><ul><li>where N is the total number of aligned structures, s i ( x ) is the amino acid at position x </li></ul><ul><li>in the i th structure in the alignment, m is a modified PET substitution matrix calculated by Valdar et al. </li></ul>
  45. 45. The structure conservation score <ul><li>The B-factors determined by X-ray crystallographic experiments provide an indication of the degree of mobility and disorder of an atom in a protein structure </li></ul><ul><li>Raw structural conservation scores were weighted by the normalized B-factors ( B norm, i ) to consider the structure flexibility </li></ul><ul><li>where </li></ul>