Template-based Prediction of Protein 8-state Secondary Structures June 12 th 2013 Ashraf Yaseen and Yaohang Li DEPARTMENT OF COMPUTER SCIENCE OLD DOMINION UNIVERSITY, NORFOLK, VA 3rd IEEE International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)

Contents  Introduction  Secondary Structure Definition & Representation  Secondary Structure Prediction  C8-Scorpion  Materials & Methods  Data Sets, Template Construction, and Encoding  Neural Network Model  Results & Discussions  Summary 2

Protein Secondary Structure Prediction in Protein Modeling 3  Proteins; Proteios, “primary”, “of prime importance.” The primary components of living things  In nature, proteins fold into specific 3D structures  critical to their functions Protein Modeling  Correctly predicting protein secondary structure is a critical step stone to obtain correct 3D models Sequen ce 3D intermediate prediction steps

Secondary Structures - Definition Protein 1BOO Chain A π -helix α -helix helix Turn Bend Other β -strand 4 General 3D form of local segments of residues Identified from determined protein 3D DSSP

Secondary Structures - Representation helix (G) α -helix (H) π -helix (I) β -stand (E) bridge (B) turn (T) bend (S) others (C)

Secondary Structure Prediction - Effectiveness 6  Correctly predicting secondary structure  Reduce the degrees of freedom in protein structure modeling  reduce the difficulty of obtaining high resolution 3D models  Derive a much smaller range of possible torsion angles

Secondary Structure Prediction - Background 7 Secondary Structure Prediction 3-state (helix, sheet, coil) 8-state ( α -helix, π -helix, helix, β -strand, β - bridge, turn, bend and others) Predictor Structural state of Ri Secondary Structure Prediction  classification Each residue is predicted to be in one of few states Machine Learning (ANN, SVM, HMM,...)  3-state Examples:  GOR4, PSI-Pred, PHD, SAM, Porter, JPred, SPINE, SSPRO, NETSURF, and many others.  ~80% (Q3)  8-state Examples:  SSpro8, 62-63% Q8  RaptorXss8, 67.9% Q8

Secondary Structure Prediction - 8-state 8 Prediction Accuracy of RaptorXss8 on Benchmarks of CB513, CASP9, Manesh215, and Carugo338. Prediction accuracies for 3-10 helices (G), π -helices (I), β -bridges (B), and bends (T) are particularly low due to their low appearance frequencies Distribution of 3-10 helices (G), α -helices (H), π -helices (I), β -sheets (E), β -bridges (B), turns (T), bends (S), and coils (C) in Cull5547

Secondary Structure Prediction - Template-based 9  Most current methods for secondary structure predictions are ab initio  However, many protein sequences have some degree of similarity among themselves  Latest version of Porter (in 3-state)  Improvement in prediction accuracy with >30% sequence similarity  Decline in efficiency with low sequence similarity <20%

Template-based C8-SCORPION 10 Predictor Structural feature (state) of Ri Input encoding Sequence & evolutionary info (PSSM) + Structure info. from (templates Or context-based scores) Is an extension of our previous method C3-SCORPION

Materials & Methods 11 Cull5547 PISCES server 25% (at most) sequence identity, 2.0A resolution CASP9 Manesh215 Carugo338 CB513 Data Sets Template Construction Encoding Context-based scores: potential scores, based on statistics, derived from the protein datasets, estimate the favorability of residues in adopting specific structural states, within their amino acid environment.

Materials & Methods -cont. 12 Two phases of template-based 8-state secondary structure prediction (architecture and encoding) Neural Network Model

Results & Discussions 13 Q8Q8 SOV 8 G H I 0.00 E B S T C Overall fold cross-validation accuracy in template-based 8-state prediction Q8Q8 SOV 8 No TemplateWith TemplateNo TemplateWith Template CB CASP Manesh Carugo Comparison between 8-state predictions with and without template on Benchmarks Distribution of 8-state secondary structure prediction accuracy (Q8) as a function of sequence similarity- the first group of bars corresponds to template-less predictions

Results & Discussions -cont. 14 (0, 10](10, 20](20, 40](40, 70](70, 95] # of chains 4,4264,2153,2041,4371,133 QHQH QGQG QIQI 0.00 QEQE QBQB QTQT QSQS QCQC Q8Q Comparison of 7-fold cross validation prediction accuracies in eight states when templates with different sequence similarities are used

Results & Discussions -cont. 15 Comparison between template-less and template-based predictions on 1BTN chain A

16 Working with C8-Scorpion Input title Input your sequence Input your Submit, then wait for the results... “C8-Scorpion” available at:

17 Working with C8-Scorpion Check your , Click the link provided The results are displayed

Summary  The effectiveness of using structural information in templates has been demonstrated in our computational results in 7-fold cross validation as well as on benchmarks, where enhancements of prediction accuracies are observed.  Overall, 78.85% Q8 accuracy and 80.10% SOV8 accuracy are achieved in 7-fold cross validation  More importantly, when good templates are available, the prediction accuracy of less frequent secondary structure states, such as 3-10 helices, turns, and bends, are highly improved, which are suitable for practical use in applications.  A webserver (C8-Scorpion) implementing template-less 8-state secondary structure prediction is currently available at The integration of template- based prediction into the C8-Scorpion webserver is currently under development 18

Acknowledgement 19 This work is partially supported by NSF grant and ODU 2013 Multidisciplinary Seed grant

Questions? Thank You 20