Mapping Monomeric Threading to ProteinâProtein Structure

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Mapping Monomeric Threading to Protein-Protein Structure Prediction Aysam Guerler, Brandon Govindarajoo, and Yang Zhang J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/ci300579r • Publication Date (Web): 17 Feb 2013 Downloaded from http://pubs.acs.org on February 19, 2013

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Journal of Chemical Information and Modeling

Mapping Monomeric Threading to Protein-Protein Structure Prediction Aysam Guerler, Brandon Govindarajoo and Yang Zhang*

Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, 48109, USA *To whom correspondence should be addressed. Tel: +1-734-647-1549; Fax: +1-734-6156553; Email: [email protected]

Running Title: Protein complex structure recognition

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ABSTRACT

The key step of template-based protein-protein structure prediction is the recognition of complexes from experimental structure libraries that have similar quaternary fold. Maintaining two monomer and dimer structure libraries is however laborious and inappropriate library construction can degrade template recognition coverage. We propose a novel strategy SPRING to identify complexes by mapping monomeric threading alignments to protein-protein interactions based on the original oligomer entries in the PDB, which does not rely on library construction and increases the efficiency and quality of complex template recognitions. SPRING is tested on 1,838 non-homologous protein complexes which can recognize correct quaternary template structures with a TM-score >0.5 in 1,115 cases after excluding homologous proteins. The average TM-score of the first model is 60% and 17% higher than that by HHsearch and COTH, respectively, while the number of targets with an interface RMSD 13 to predict the correct template alignments, the false-positive and false-negative rates for TM-score >0.5 are 0.22 and 0.27, respectively. A similar tendency was also seen when using other criterions (see Fig. 7). Nevertheless, there is a considerable fraction of proteins which have low specificity, i.e. the proteins that have a high-scoring prediction but with poor model qualities when compared to the native, or vice versa. For instance, we identified overall 53 structures which have a SPRING-score above 20 but with a TM-score 30% have been excluded in the test. Nevertheless, considering the large number of possible protein-protein interactions in genomes, high accuracy predictions for even less than half of all interactions would yield highly valuable new insights, not saying that a higher successful rate should not be possible if homologous templates are included. As a threading-based modeling approach, SPRING only provides partial structures on the target sequences, with Cα structural models derived from the complex templates. The full16


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length atomic structural models need to be generated using separate assembly and refinement procedures such as TACOS (http://zhanglab.ccmb.med.umich.edu/TACOS/). Moreover, in the presented version, SPRING only considers pair-wise protein sequences known to interact. The extension of the method to the high-order complex prediction is straightforward since no additional template library and monomer-complex lookup table are needed. We are working on addressing these issues and plan to apply the SPRING mapping technique to the construction of genome-wide structural networks.

ACKNOWLEDGMENTS This work was supported by the National Science Foundation [0903629, DBI1027394]; and the National Institute of General Medical Sciences [GM083107, GM084222].

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Figure 1. Flowchart of SPRING pipeline. Target Figure 2. Cumulative fraction of TM-score, sequences A and B are first threaded against the native contacts, and interface and global RMSD monomer template library, which yields two lists at different threshold cutoffs, for models on of templates TA (black) and TB (gray). For chain 1,838

proteins

predicted

by

SPRING-M,

A, we retrieve all binding partners PA (light SPRING-H, SPRING-C, COTH and NAIVE-M, gray) from the original template entry in the respectively. The shown data are from the bestPDB. Each binding partner is associated to its out-of-five top ranked models for each protein closest homologue (e.g. P2 and B2) by a pre- target. calculated look-up table using PSI-BLAST. The complex models are then constructed by structurally aligning the top-rank monomer models to the template/binding partner and ranked by SPRING-score (Eq. 1).

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Figure 3. Head-to-head comparisons of 1,838 SPRING-M models with that by the control methods. The left column shows TM-score of the best in top five complex models and the right column is the fraction of the correctly predicted interface contacts. (A, B) SPRING-M vs. COTH; (C, D) SPRING-M vs. NAIVE-M.

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Figure 4. Predicted dimer models (dark color) of NAIVE-M, COTH and SPRING-M for target protein superposed with the native structure of the 1-cys peroxiredoxin complex (light color, PDBID: 1XCC). The values below each superposition are TM-score, I-RMSD and fraction of aligned interface residues.

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Figure 5. Complex models (dark color) are Figure 6. Head-to-head comparisons of the superposed with the native crystal structure of SPRING models using different monomeric putative kinase complex (light color, PDBID: threading methods on 1,838 test proteins. The left 2AN1). (A) COTH; (B) SPRING-M.

column shows TM-score of the best in top five complex models and the right column is the fraction of the correctly predicted interface contacts. (A, B) SPRING-C vs. SPRING-H; (C, D) SPRING-C vs. SPRING-M.

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Figure 7. Fraction of predicted models above Figure 8. Comparison of SPRING and ZDOCK and below specific quality thresholds within a models at different target-template sequence given SPRING-score interval for the top ranked similarity thresholds (30, 50 and 70%) on 77 models. The depicted quality measures are TM- heterodimeric protein complexes. The number of score, fraction of native interface contacts, correct predictions, i.e. with I-RMSD

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