Predicting Protein Solvent Accessibility with Sequence, Evolutionary Information and Context-based Features 12/05/2013 Ashraf Yaseen Department of Mathematics & Computer Science Central State University Wilberforce, Ohio Yaohang Li Computer Science Department Old Dominion University Norfolk, Virginia BIOT 2013: Biotechnology and Bioinformatics Symposium

Contents  Introduction  Research Objective  Background  Method  Protein data sets  Context-based features  Neural Network model  Results  Summary 2

Introduction 3  The solvent-accessible surface area, or accessibility, of a residue is the surface area of the residue that is exposed to solvent.  The residue accessibility is a useful indicator to the residue's location, on the surface or in the core Surface area of a protein segment

Introduction-cont. 4  DSSP program calculates the absolute solvent accessibility values of proteins  Relative values are calculated as the ratio between the absolute solvent accessibility value and that in an extended tripeptide (Ala-X-Ala) conformation  To allow comparisons between the accessibility of the different amino acids in proteins  A threshold of 0.25 to define 2-state (exposed if >0.25, buried otherwise)

Prediction effectiveness 5  Residue solvent accessibility plays an important role in folding and enhancing proteins’ thermodynamic and mechanical stability  The burial of residues at core (hydrophobic residues) is a major driving force for folding  Active sites of proteins are located on its surface.  Reduce the conformational space to aid modeling protein structures in three dimensions  Help predict important protein functions

Predicting Structural Features in Protein Modeling 6 Protein Modeling  Correctly predicting structural features is a critical step stone to obtain correct 3D models Sequence 3D intermediate prediction steps

Protein Structural Features 7 Protein 1BOO Chain A Secondary Structure: General 3D form of local segments of residues Disulfide bond in protein chain Surface area of a protein segment Properties of the residues in proteins

Background 8  Many methods using different protein datasets and different computational methods,  Neural networks, support vector machines, nearest neighbor, information theory, and Bayesian statistics  The prediction is in a discrete fashion  Significant accuracy increase when using evolutionary information  2-state prediction accuracy of ~75% with 0.25 threshold  PSI-BLAST derived profiles  2-state prediction accuracy of ~78%

Background-cont. 9 Secondary Structure Prediction 3-state (helix, sheet, coil) 8-state ( α -helix, π -helix, helix, β -strand, β -bridge, turn, bend and others) Residue Solvent Accessibility Prediction 2-state (buried or exposed) Predictor Structural feature (state) of Ri Disulfide Bonding Prediction Stage1: Bonding state prediction (bonded/free) Stage2: Connectivity prediction (connected, not connected) Structural features prediction  classification Each residue is predicted to be in one of few states Machine Learning (ANN, SVM, HMM,...)

Statement of the Problem 10  The improvement of prediction methods benefits from the incorporation of effective features  MSA in machine learning  The accuracy of current prediction methods is stagnated for the past few years  2-state solvent accessibility ~78% 3-state secondary structure ~76-80% 8-state secondary structure ~68%

Statement of the Problem-cont. 11  How to continuously improve the accuracy of predicting protein structural features toward their theoretical upper bounds?  Reducing the inaccuracy of protein structural features prediction, will be very useful in improving the efficiency of protein tertiary structure prediction  the search space for finding a tertiary structure goes up super-linearly with the fraction of inaccuracy in structural feature prediction

HH X Our Approach 12  Extracting and selecting “good” features can significantly enhance the prediction performance  Probably the most effective features, when predicting the structural state of a residue, are the structural states of the neighboring residues  With true states >90% RiRi H H C C B Solvent Accessibility B: Buried E: Exposed Secondary Structure H: Helix E: Sheet C: Coil B B B B

Our Approach-cont. 13  Unfortunately, using the true structural states as features is not feasible  However, this inspires us that the favorability of a residue adopting a certain structural state can be also an effective feature  Statistical scores measuring the favorability of a residue adopting a certain structural state within its amino acid environment can be evaluated from the experimentally determined protein structures in (PDB)

Our Approach-cont. 14 Predictor Structural feature (state) of Ri Input encoding Sequence & evolutionary info (MSA) + Structure info (context-based scores) We expect that our approaches will improve the predictions of protein structural features with the goal of achieving high accuracy levels

Method Context-based features  potential scores  calculated based on the context- based statistics, derived from the protein datasets  estimate the favorability of residues in adopting specific structural states, within their amino acid environment. 15 Context-based Model

Context-based Statistics & Potentials 16 RiRi X RiRi CiCi CiCi YRiRi X CiCi

Encoding & Neural Network Model 17

Results 18 CASP9Manesh215Carugo338 NETASA Q2Q QBQB QEQE Sable t=0.2 Q2Q QBQB QEQE Sable t=0.3 Q2Q QBQB QEQE Netsurf Q2Q QBQB QEQE SPINE Q2Q QBQB QEQE ACCpro Q2Q QBQB QEQE Casa Q2Q QBQB QEQE C OMPARISON OF Q2 ACCURACY BETWEEN OUR AND OTHER POPULARLY USED S OLVENT A CCESSIBILITY PREDICTION SERVERS C OMPARISON OF PREDICTION PERFORMANCE OF S OLVENT A CCESSIBILITY USING PSSM ONLY AND PSSM WITH CONTEXT - BASED SCORES ON C ULL USING 7- FOLD CROSS VALIDATION QBQB QEQE Q2Q2 PSSM Only 78.44%80.61%79.50% PSSM+Score 79.21%82.00%80.76% Q B and Q E to measure the quality of predicting the buried state and the exposed state respectively Q 2 = total number of residues correctly predicted /total number of residues

Results-cont. 19 DAVMVFARQGDKGSVSVGDKHFRTQAFKVRLVNAAKSEISLKNSCLVAQSAAGQSFRLDTVDEELTADTLKPGASVEGDAIFASEDDAVYGASLVRLSDRCK 3NRF-A EEB.BEBEEEEEEEEEEEEEEEEBBBBEBEBBBEBEEEBEBEEEBBBBBBEEEEEBEEEEEEEEBEEEEBEEEEEBEBEBEBBBEEEBBEEBBBBBBBEEEE DSSP SA2 EEB.BBBBEEEEBBBBEEEEEEBBBEBEBBBBEBEEEEBEBEEBBBBBBBEEEEEBEBEEBEEEBEEEBBEEEEEBEBBBBBBBEEEEBBEBEBBEBBEEBE PSSM Only EEB.BBBEEEEEEEBEEEEEEBBBBEBEBBBBEEEEEEBEBEEBBBBBBBEEEEEBEBEEBEEEBEEEEBEEEEEBEBBBBBBBEEEBBBEBBBBEBBEEBE PSSM+Score Solvent Accessibility Prediction on protein 3NRF(A) Q2

20 Working with Casa Input title Input your sequence Input your Submit, then wait for the results... “Casa” available at:

21 Working with Casa Check your , Click the link provided The results are displayed

Summary  The effectiveness of using context-based features has been demonstrated in our computational results in N-fold cross validation as well as on benchmarks, where enhancements of prediction accuracies in secondary structures, disulfide bond and solvent accessibility are observed.  Web servers implementing our prediction methods are currently available.  Dinosolve, available at  C3-Scorpion, available at:  C8-Scorpion, available at:  Casa, available at: 22

Publications 23 Publication 1 Ashraf Yaseen and Yaohang Li “Enhancing Protein Disulfide Bonding Prediction Accuracy with Context- based Features”, Proceedings of Biotechnology and Bioinformatics Symposium, (BIOT2012), Provo, Ashraf Yaseen and Yaohang Li, "Dinosolve: A Protein Disulfide Bonding Prediction Server using Context- based Features to Enhance Prediction Accuracy". Accepted, BMC Bioinformatics Ashraf Yaseen and Yaohang Li “Template-based Prediction of Protein 8-state Secondary structures”. 3 rd IEEE International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), New Orleans, April Accepted, BMC Bioinformatics 4 Ashraf Yaseen and Yaohang Li “Predicting Protein Solvent Accessibility with Sequence, Evolutionary Information and Context-based Features”, Accepted at BIOT Ashraf Yaseen and Yaohang Li “Context-based features can enhance protein secondary structure prediction accuracy”. Submitted to Bioinformatics. 6 Ashraf Yaseen and Yaohang Li, “Accelerating Knowledge-based Energy Evaluation in Protein Structure Modeling with Graphics Processing Units,” Journal of Parallel and Distributed Computing, 72(2): , 2012

Acknowledgement  This work is partially supported by NSF through grant and ODU SEECR grant 24

Questions? Thank You 25