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  • 29 = 20 amino acid state + 9 transition states (deletion insertion match)

Transcript

  • 1. Machine Learning as Applied to Structural Bioinformatics: Results and Challenges Philip E. Bourne University of California San Diego [email_address]
  • 2. The Current Situation
    • Structure contributes greatly to our understanding of living systems
    • We are locked into thinking about structure in specific ways which limits our view
      • All too often we consider structure as a static entity
      • The view at left is not how another protein or a small molecule ligand sees PKA
    • We are still not very good at certain problems …
  • 3. Example Unsolved Problems that Machine Learning Can Address
    • Predicting flexibility and disorder in protein structure
    • Predicting sites of protein-protein and protein-ligand interaction
    • Predicting protein function
    • Defining domain boundaries from sequence
    • Predicting secondary, tertiary and quaternary structure
    • Predicting what will crystallize
  • 4. Example Unsolved Problems that Machine Learning Can Address
    • Predicting flexibility and disorder in protein structure
    • Predicting sites of protein-protein and protein-ligand interaction
    • Predicting protein function
    • Defining domain boundaries from sequence
    • Predicting secondary, tertiary and quaternary structure
    • Predicting what will crystallize
    * Will talk about this * Will offer as a challenge
  • 5. The Current Situation: The Potential “Training Set” is Growing Quickly
    • High level of redundancy as measured by sequence or structure
    • Structure space is clearly very finite, but not clear how much is covered
    • Increase in functionally uncharacterized structures
    • Complexity is increasing, but still lack complexes
    • Structures predominantly 1 and 2 domains
    • Lack membrane proteins
    • In summary the training set is still not truly representative but structural genomics will improve this situation
  • 6. Predicting Functional Flexibility Jenny Gu Gu, Gribskov & Bourne PLoS Computational Biology 2006 Early On-line Release
  • 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. Bridging the Sequence-flexibility Gap Generalize sequence - flexibility relationship to identify local protein regions important for allostery
  • 9. The Training Dataset
    • The dataset contains the following qualities:
    • Non-redundant sequences
      • training set with sequences containing ≤ 10% identity.
    • With good quality structures
      • R-factor < 0.30
    • At high resolution
      • Resolution < 2.0 Å.
    • Total number of proteins in dataset: 1277 sequences
  • 10. Obtaining Protein Dynamic Information
    • Protein structures treated as a 3-D elastic network.
    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. Defining the Target Features
    • Gaussian Network Model:
    • Models protein structure as a 3-D elastic network.
      • Each Ca is a node in the network.
      • Each node undergoes Gaussian-distributed fluctuations influenced by neighboring interactions within a given cutoff distance. (7Å)
    • Decompose protein fluctuation into a summation of different modes.
    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. Side Note: Gaussian Network Model vs Molecular Dynamics
    • GNM relatively cause grained
    • GNM fast to compute vs MD
      • Look over larger time scales
      • Suitable for high throughput
  • 13. Functional Flexibility Score
    • Utilize correlated movements to help define regional flexibility with functional importance.
    • Functionally Flexible Score
    • For each residue:
    • Find Maximum and Minimum Correlation
    • Use to scale normalized fluctuation to determine functional importance
  • 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. Identifying Regions in Bovine Pancreatic Trypsin Inhibitor and Calmodulin
  • 16. How to Represent the Protein Sequence?
    • Residues characterized as FFs or not – approx 20% of residues with lengths typically 9+/-11
    • The longer the protein the longer the FFR
    • We use hidden Markov models to represent each protein sequence in the training dataset.
    • Hidden Markov models captures evolutionary information along with the probability of finding one of the 20 amino acids in each position of the sequence.
    • Use probability states as input features in the first layer of an architecture containing two SVM layers.
  • 17. Architecture of Wiggle Captures Evolutionary Effects Captures Local Effects (smoothing) 9*29 features used for each residue
  • 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. Architecture of Wiggle Captures Evolutionary Effects Captures Local Effects (smoothing) 9*29 features used for each residue
  • 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. Partition Training Set Based on Sequence Length
    • Prediction performance of SVM trained on a partitioned dataset (solid lines) is compared to that was trained on the entire dataset (dashed line).
    • 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.
    <200 AA Long >200 AA Long
  • 22. Performance of Wiggle Predictors
    • Wiggle
    • Accuracy: 66.01%
    • Precision: 37.11%
    • Recall: 70.49%
    • Wiggle 200
    • Accuracy: 76.46%
    • Precision: 48.99%
    • Recall: 78.27%
  • 23. Case Study: PvuII Endonuclease FF SCORE (homodimer for DNA specific cleavage) Wiggle 200
    • Identify known loop for minor grove recognition
    • Identify hinge residues not previously seen
    • Important result for mutagenesis studies
  • 24. Conclusions for Wiggle
    • FFRs can be measured from structure
    • With some empirical effort these data can be used as input to an SVM to predict FFRs from sequence alone
    • Useful for:
      • Improving docking studies
      • Better understand protein function
      • Engineer more or less stable proteins
      • ……
    Gu, Gribskov & Bourne 2006 PLoS Comp. Biol.. 2006 Early Release
  • 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. Methods to Identify Protein-protein Binding Sites
    • Docking
    • Threading and homology modeling
    • Evolutionary tracing
    • Correlated mutations
    • Properties of patches
    • Hydrophobicity
    • Neural networks and support vector machines (SVM)
  • 27.
    • None of the above methods consider the residues which are spatially conserved on the surfaces of structure homologs
    • These residues are reported to correspond to the energy hot spots on protein interfaces and can be derived from multiple structure alignments
    Structurally Conserved Surface Residues?
  • 28. Method: Incorporate Structural Conservation to Predict the Interface Residue Using SVM Support vector machine Sequence + structure information Binding site location
  • 29. Derive the Structurally Conserved Residues
    • 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)
    • Each position in the alignment has a structural conservation score, which represents the conservation in 3D space
    • A position has a high conservation score if the aligned residues are spatially conserved
  • 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. Training D ataset
    • 274 non-redundant chains of heterocomplexes (<30% sequence identity) extracted from the PDB
    • Each of these chains was accompanied with a structure alignment with at least 4 members
  • 32. SVM Training
    • A surface residue
    • Sequence profile + ASA + Structural conservation score
    • in a window of 13 residues
    • (The residue to be predicted and 12 spatially nearest surface residues)
    • Support vector machine classifier
    • Interface or non-interface residue ?
  • 33. SVM Training
    • 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)
    • Implemented using SVM light with the radial basis function as a kernel. (γ = 0.01, regularization parameter C =10)
    • A set of non-interface surface residues was randomly selected to make the ratio of positive and negative data 1:1
    • 3 fold cross-validation was performed
  • 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. 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.
    • Predicted Binding Sites - Example 1
    • Protein : domain 1 of the human coxsackie and adenovirus receptor (CAR D1)
    • Mediate adenoviruses and coxsackie virus B infection
    • 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
    • Binding partner : knob domain of the adenoviruses serotype 12 (Ad12)
  • 37.
    • Predicted Binding Sites - Example 2
    • Protein : adrendoxin (Adx)
    • 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
    • Binding partner : adrenodoxin reductase (AR)
  • 38.
    • Predicted Binding Sites - Example 3
    • Protein : fibroblast growth factor receptor 2 (FGFR2) Ser252Trp Mutant
    • Apert syndrome (AS) is caused by substitution of one of two adjacent residues, Ser252Trp or Pro253Arg
    • Binding partner : fibroblast growth factor (FGF2)
  • 39. Conclusions – Protein-protein Binding Sites
    • Incorporating the structural conservation score improved the prediction performance of SVM significantly
    • This study is an initial trial that exploits multiple structure alignment for the large scale prediction of functional regions
    • We need better algorithms for multiple structure alignment (we have one benchmark for anyone interested)
    • This method can be used to guide experiments, such as site-specific mutagenesis, or combined with docking procedures to limit the search space
  • 40. General Conclusions
    • Using known features of protein structure these can be mapped to the corresponding sequences and used to train an SVM
    • Having evaluated the SVM in a cross validation tests the performance can be determined
    • Good performance is shown in training for both flexibility and sites of protein-protein interaction
    • These predictors are currently being used to solve real biological problems
    • Can this approach be applied to other aspects of structure?
  • 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. Challenge – Defining Domain Boundaries from Sequence
    • A domain is the unit of currency of proteins – domain structures define function, indicate evolutionary relationships etc…
    • Domain prediction from structure easier than from sequence, but still not a solved problem
    • Recently developed an accurate test set of domain definitions and boundaries: http://pdomains.sdsc.edu
    • Good luck!
    Benchmark Data Available See: Holland et al 2006 JMB Early Release
  • 43. Acknowledgements
    • Functional Flexibility
      • Jenny Gu & Michael Gribskov
    • Protein-protein Interactions
      • JoLan Chung & Wei Wang
    • Domain Definitions
      • Stella Veretnik, Tim Holland, Ilya Shindalov, Nick Alexandrov, Abdur Sikur
    • Funding, NSF, NIH
  • 44. The structural conservation score
    • Raw structural conservation score
    • where
    • if a is not gap and b is not gap
    • otherwise
    • where N is the total number of aligned structures, s i ( x ) is the amino acid at position x
    • in the i th structure in the alignment, m is a modified PET substitution matrix calculated by Valdar et al.
  • 45. The structure conservation score
    • 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
    • Raw structural conservation scores were weighted by the normalized B-factors ( B norm, i ) to consider the structure flexibility
    • where