1. Protein Folding and Protein Threading Some slides from Tolga Can, CENG 465: Introduction to Bioinformatics, Middle East Technical University, Turkey Kristen Huber, EC 697S: Topics in Computational Biology, University of Massachusetts

2. Protein threading Structure is better conserved than sequence Structure can adopt a wide range of mutations. Physical forces favor certain structures. Number of folds is limited. Currently ~700 Total: 1,000 ~10,000 TIM barrel Tolga Can, METU, CENG 465

3. Protein Threading • Basic premise • Statistics from Protein Data Bank (~35,000 structures) The number of unique structural (domain) folds in nature is fairly small (possibly a few thousand) 90% of new structures submitted to PDB in the past three years have similar structural folds in PDB Tolga Can, METU, CENG 465

4. Concept of Threading • Thread (align or place) a query protein sequence onto a template structure in “optimal” way • Good alignment gives approximate backbone structure • Query sequence • MTYKLILNGKTKGETTTEAVDAATAEKVFQYANDNGVDGEWTYTE • Template set Tolga Can, METU, CENG 465

5. Protein Threading – energy function MTYKLILNGKTKGETTTEAVDAATAEKVFQYANDNGVDGEWTYTE how preferable to put two particular residues nearby: E_p how well a residue fits a structural environment: E_s alignment gap penalty: E_g total energy: E_p + E_s + E_g find a sequence-structure alignment to minimize the energy function Tolga Can, METU, CENG 465

6. Prediction of Protein Structures • Examples – a few good examples actual predicted predicted actual actual predicted actual predicted Tolga Can, METU, CENG 465

7. Prediction of Protein Structures • Not so good example Tolga Can, METU, CENG 465

8. CASP/CAFASP • CASP: Critical Assessment of Structure Prediction • CAFASP: Critical Assessment of Fully Automated Structure Prediction CASP Predictor CAFASP Predictor • Won’t get tired • High-throughput Tolga Can, METU, CENG 465

9. Protein Threading Kristen Huber, UMass, EC 697S

10. Protein Threading Kristen Huber, UMass, EC 697S

11. Protein Threading (RAPTOR) Jinbo Xu, Ying Xu, Dongsup Kim, Ming Li. RAPTOR: Optimal Protein Threading by Linear Programming Journal of Bioinformatics and Computational Biology, April 2003 Given a query sequenceS = (s1, s2, s3, …sn) and a template (library) sequenceT = (t1, t2, t3, …tm), pair up elements from S and T, by possibly inserting gaps, while minimizing an energy function Assumptions the template is a sequence of cores (conserved segments – α-helix or β-sheet) connected by loops gaps are allowed only within the loops only interactions between residues in the cores are considered; interaction between residues is assumed to exist if they are within 7 Ǻ and at least 4 positions away

12. Protein Threading (RAPTOR) Steps of the RAPTOR algorithm: build a contact map for the template structure find all possible alignments for each core within the query sequence build a contact map for the query sequence and template structure define energy function and carry out minimization Em – mutation score Es – environment fitness score Ep – pairwise interaction score Eg – gap penalty score Ess – secondary structure compatibility Wx – weights (determined experimentally)

13. Protein Threading (RAPTOR) Step 1: Build a contact map for the template structure contact map indicates interactions between cores, i.e.if any two residues within the cores interact Xu et al., JBCB, 2003

14. Protein Threading (RAPTOR) Step 3: Build a contact map for the query and template Xu et al., JBCB, 2003

15. Protein Threading (RAPTOR) Step 4: define energy function and carry out minimization Xu et al., JBCB, 2003