Protein Secondary Structure Prediction Based on Position-specific Scoring Matrices Yan Liu Sep 29, 2003.

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Presentation transcript:

Protein Secondary Structure Prediction Based on Position-specific Scoring Matrices Yan Liu Sep 29, 2003

Protein Secondary Structure Dictionary of Secondary Structure Prediction (DSSP) based on hydrogen bonding patterns and geometrical constraints 7 DSSP labels for PSS: Helix types: H (alpha-helix) G ( 3 / 10 helix) Sheet types: B (extended strand, participates in beta ladder) E (isolated beta-bridge strand) Coil types: T _ S (Coil)

Protein Secondary Structure Prediction Given a protein sequence: APAFSVSPASGA Predict its secondary structure sequence: CCEEEEECCCC Application Provide constraints for tertiary structure predictions or as part of fold recognition

Related Work Standard SS prediction methods: PHD (Rost & Sander 1993) Multiple sequence profiles Based on the observations that conserved regions are functional important, and (or) buried in the protein core Benner & Gerloff demonstrated that the degree of solvent accessibility can be predicted with reasonable accuracy Two-layered feed-forward Neural networks

PSIPRED: Generation of a sequence profile Position-specific score matrices Prediction of initial secondary structure Standard feed-forward back-propagation networks Filtering the predicted structures

Position-specific scoring matrices (PSSM) -1 PSSM (Altschul et al., 1997), or profiles Given a protein sequence with length N, together with its multiple sequence alignment Construct a Nx20 matrix Score definition Different methods for estimating Qi Alpha = Nc-1, beta = 10 Fi: weighted observed frequencies Other estimation:

Position-specific scoring matrices (PSSM) -2 Advantage A more sensitive scoring system Improved estimation of the probabilities of which amino acids occur at pattern position Relatively precise definition of the boundaries of important motifs Disadvantage Too sensitive to biases in the sequence data banks Prone to erroneously incorporating repetitive sequences into the profiles

PSSM in PSIPRED Input to neural networks: The PSSM from PSI-BLAST after three iterations Set to window size to 15 Scaled to the 0-1 range by standard logistic function

Neural network architecture-1 Two stage neural networks 1 st stage: Sequence to structure mapping 315 inputs: 21 * hidden units: 3 * 15 2 nd stage: Structure to structure mapping 60 inputs: 4 * hidden variable: 4 * 15 (extra input to indicate the window spans a chain terminus)

Neural network architecture-2 Training parameters Momentum term: 0.9 Learning rate: Prevent overfitting: leave 10% of the training set for validation

Experimental results Training and testing data Collected to remove structural similarity Apply CATH to detect homologous protein sequences A total of 187 protein sequences: 62, 62, 63 Three-way cross-validation

Experimental results Per-chain results Distribution of Q3 and SOV (left) Avg Q3: 76.0% Avg SOV: 73.5% Per-residue results Q3: 76.5%

Experimental results Rank top 1 in CASP –3 Avg Q3: 73.4% (69.0% by top 2, 66.7% by PHD) Avg SOV: 71.9% (65.7% by top 2, 63.8% by PHD) Also rank top 1 in CASP –4 (Dec, 2000)

Conclusion PSIPRED is by far the best method for secondary structure prediction The difference between PHD and PSIPRED: Position-specific scoring matrices Training data