Andr á s Fiser Department of Biochemistry and Seaver Center for Bioinformatics

1. Multiple Mapping Method with Multiple Templates (M4T): optimizing sequence-to-structure alignments and combining unique information from multiple templates András Fiser Department of Biochemistry and Seaver Center for Bioinformatics Albert Einstein College of Medicine Bronx, New York, USA

2. Comparative protein structure modeling START Template Search Multiple Templates Target – Template Alignment Multiple Mapping Method Model Building Loop, side chain modeling Model Evaluation Statistical potential END

3. Why do we need sequence alignments? #Sequence vs. sequence: Establishing residue equivalencies between two proteins to locate conserved/variable regions #Sequence vs. databases: Querying sequence databases • #Sequence vs. structure • To generate input alignment for comparative modeling / threading

4. Ranking of models built on alternative alignments Template: 1a6m; Target: 1spg, chain B ~21% sequence identity Example: Template VLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDRFKHLKTEAEMKASEDLKKHGVTVLTALGAILKKK Target CLW DWTDAERAAIKALWGKIDVGEIGP—-QALSRLLIVYPWTQRHFKGFGNISTNAAILGNAKVAEHGKTVMGGLDRAVQNM Target A2D DWTDAERAAIKALWGKI—-DVGEIGPQALSRLLIVYPWTQRHFKGFGNISTNAAILGNAKVAEHGKTVMGGLDRAVQNM Template GHHEAELKPLAQSHATKHKIPIKYLEFISEAIIHVLHSRH-PGDFGADAQGAMNKALELFRKDIAAKYKELGY Target CLW DNIKNVYKQLSIKHSEKIHVDPDNFRLLGEIITMCVGAKFGPSAFTPEIHEAWQKFLAVVVSALGRQYH---- Target A2D DNIKNVYKQLSIKHSEKIHVDPDNFRLLGEIITMCVGAKF-G---PSAFTPEIHEAWQKFLAVVVSALGRQYH Problem: None of the currently available methods produce consistentlysuperior results in all cases

5. Alternative solutions vs. sequence similarity Instead of relying on just one alignment method, one should combine results of several alternative techniques

6. Multiple Mapping Method • Idea: • Improve the accuracy of sequence-to-structure alignment by optimally splicing alternative inputs. • Three components: - Sampling - Algorithm - Scoring function

7. MMM scoring function: increasing the dimensionality of input information Template VLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDRFKHLKTEAEMKASEDLKKHGVTVLTALGAIL Target CLW DWTDAERAAIKALWGKIDVGEIGP—-QALSRLLIVYPWTQRHFKGFGNISTNAAILGNAKVAEHGKTVMGGLDRAV Template KKKGHHEAELKPLAQSHATKHKIPIKYLEFISEAIIHVLHSRH-PGDFGADAQGAMNKALELFRKDIAAKYKELGY Target CLWQNMDNIKNVYKQLSIKHSEKIHVDPDNFRLLGEIITMCVGAKFGPSAFTPEIHEAWQKFLAVVVSALGRQYH---- Template VLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDRFKHLKTEAEMKASEDLKKHGVTVLTALGAIL Target A2D DWTDAERAAIKALWGKI—-DVGEIGPQALSRLLIVYPWTQRHFKGFGNISTNAAILGNAKVAEHGKTVMGGLDRAV Template KKKGHHEAELKPLAQSHATKHKIPIKYLEFISEAIIHVLHSRH-PGDFGADAQGAMNKALELFRKDIAAKYKELGY Target A2D QNMDNIKNVYKQLSIKHSEKIHVDPDNFRLLGEIITMCVGAKF-G---PSAFTPEIHEAWQKFLAVVVSALGRQYH 1 2 1 2 Different mapping identifies a different environment for each residue to align Assess the “fitness” of each mapping

8. Multiple Mapping Method: Algorithm Step 1: Identify variable regions from the consensus alignment of the input set Step 2: Select the best scoring variable segments, and combine them with with the core region of the alignment. Example: Template 1a6m; Target 1spg, chain B 21% sequence id Template VLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDRFKHLKTEAEMKASEDLKKHGVTVLTALGAILKKK Target CLW DWTDAERAAIKALWGKIDVGEIGP—-QALSRLLIVYPWTQRHFKGFGNISTNAAILGNAKVAEHGKTVMGGLDRAVQNM Target A2D DWTDAERAAIKALWGKI—-DVGEIGPQALSRLLIVYPWTQRHFKGFGNISTNAAILGNAKVAEHGKTVMGGLDRAVQNM Template GHHEAELKPLAQSHATKHKIPIKYLEFISEAIIHVLHSRH-PGDFGADAQGAMNKALELFRKDIAAKYKELGY Target CLW DNIKNVYKQLSIKHSEKIHVDPDNFRLLGEIITMCVGAKFGPSAFTPEIHEAWQKFLAVVVSALGRQYH---- Target A2D DNIKNVYKQLSIKHSEKIHVDPDNFRLLGEIITMCVGAKF-G---PSAFTPEIHEAWQKFLAVVVSALGRQYH

9. CLUSTALW 4.6 Å ALIGN2D 1.1 Å MMM example using ideal scoring function Experimental ClustalW, RMSD 2.0 Å Align2D, RMSD 2.7 Å CLUSTALW 2.6 Å ALIGN2D 6.1 Å Template VLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDRFKHLKTEAEMKASEDLKKHGVTVLTALGAILKKK Target MMM DWTDAERAAIKALWGKI—-DVGEIGPQALSRLLIVYPWTQRHFKGFGNISTNAAILGNAKVAEHGKTVMGGLDRAVQNM Template GHHEAELKPLAQSHATKHKIPIKYLEFISEAIIHVLHSRH-PGDFGADAQGAMNKALELFRKDIAAKYKELGY Target MMM DNIKNVYKQLSIKHSEKIHVDPDNFRLLGEIITMCVGAKFGPSAFTPEIHEAWQKFLAVVVSALGRQYH---- Experimental MMM, RMSD 1.8 Å

10. Multiple Mapping Method: scoring function (1) A composite scoring function to assess the compatibility/fit of alternative variable segments in the template structural environment. • The composite scoring function consists of three mostly non-overlapping components. • Environment-specific substitution matrices (FUGUE1). • A scoring scheme based on a comparison (PHD vs. DSSP) of the secondary structure types (H3P22). • Statistically derived residue-residue contact energy (Rykunov and Fiser3). 1Shi et al. J. Mol. Biol. (2001) 310, 243-257 2Rice et al., J. Mol. Biol (1997) 267, 1026-1038 3Rykunov & Fiser., Proteins. (2007) 67, 559-68

11. MMM performance on 1400 pairs

12. MMM performance on 87 pairs, meta-servers ESypred3D Consensus

13. Sampling vs. Scoring

14. Summary • Multiple Mapping Method optimally combines alternative alignments obtained from different methods or scoring function: On a benchmark dataset of 6635 protein pair structural alignments, comparative models built using MMM alignments are approximately 0.3 Ǻ and 0.5 Å more accurate on average in the whole spectrum and in the <30% target-template sequence identity regions, respectively, than the average accuracy of models built using the alternative input alignments ( ~3 and ~4 Å).

15. Optimally combining multiple templates

17. Selecting multiple templates • Target sequence: by PSI-BLAST. • Hits selected if sequence overlap with the target is > 60% of the actual SCOP domain length or more than 75% of the PDB chain length in case of a missing SCOP classification. • Iterative clustering procedure identifies the most suitable templates to combine. Templates are selected or discarded according to a hierarchical selection procedure that accounts for • sequence identity between templates and target sequence, • sequence identity among templates, • crystal resolution of the templates, • contribution of templates to the target sequence (i.e. if a region is covered by several templates or by a single template only).

18. Single versus multiple templates Using a dataset of 765 proteins with known structure two sets of models were built: (1) using one template (best E-value hit; light bars), (2) using multiple templates (grey bars)

19. And…increased coverage Histogram of models’ difference length. Each query sequence is modeled using single and multiple templates. The histogram shows the frequency of (Lm–Ls). Lm: length of model built using multiple templates, and Ls length of the model built using a single template.

20. The x-ray structure, the model with multiple templates and with a single template are shown in grey, red, and blue, respectively. Multiple templates agree much better in two exposed regions: A and B, than the model built using single template.

21. Increased CoverageThe x-ray structure, the model with multiple templates, and model with single templates are shown in grey, red, and blue, respectively. The addition of extra templates allowed obtaining a longer model that include a beta-turn-beta-turn extra region (20 amino acids), depicted in ribbon.

22. Acknowledgement • Lab members: • Dmitrij Rykunov • Rotem Rubinstein • J. Eduardo Fajardo • Carlos J. Madrid-Aliste • Veena Venkatagiriyappa • Joseph Dybas • Mario Pujato • Brajesh Rai • Narcis Fernandez-Fuentes • Elliot Sternberger

23. Http://www.fiserlab.org/servers