All successful state-of-the-art protein docking methods employ a so called multistage approach. At the first stage of such approaches a rough energy potential is used to score billions of conformations. At a second stage, thousands of conformations with the best scores are retained and clustered based on a certain similarity metric. Cluster centers correspond to putative predictions/models. Recent work by the proposing team demonstrated that greater prediction quality can be achieved by properly exploring these clusters through a process called refinement. This work resulted in the development of a prototype refinement approach - the Semi-Definite programming-based Underestimation method (SDU). The central goal of the project is to build on the SDU success and develop a new high-throughput refinement protocol able to produce predictions of near-crystallographic quality in the most computationally efficient manner. Efficiency will be achieved by leveraging the funnel-like shape that binding free energy potentials exhibit.
The specific aims are: (1) the development of a new clustering method that can classify the conformations retained from a first-stage method into clusters suitable for the proposed refinement strategy;(2) the characterization of the structure of the multi-dimensional funnel corresponding to each cluster and the development of an efficient refinement strategy to explore this funnel;(3) the development of a side-chain positioning algorithm appropriate for docking by leveraging Markov random field theory;and (4) the dissemination of the algorithms developed through the release to the research community of a software package and an automated refinement server. It is anticipated that the computational efficiency gains of the proposed refinement protocol over alternative Monte Carlo methods will exceed two orders of magnitude, while, at the same time, significantly improve upon the accuracy achieved by earlier refinement approaches. A novelty of the proposed work is in its use of sophisticated machinery from the fields of optimization and decision theory specially tailored to the biophysical properties of the docking problem. Techniques from convex and combinatorial optimization, machine learning, and Markov random fields are brought to bear on the refinement stage of multistage protein docking approaches. An important element of the work is the systematic characterization of multi-dimensional binding energy funnels. The existence of such funnels has been long conjectured but it has not led to new docking approaches so far. The proposed algorithms essentially achieve this goal by devising efficient strategies to identify, characterize, and explore these funnels.

Public Health Relevance

This work will substantially improve upon computational methods for characterizing and predicting protein- protein interactions. It will enable treating relatively weak protein complexes involving larger proteins than what is possible today. This will result in a better understanding of processes such as metabolic control, immune response, signal transduction, and gene regulation.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
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Macromolecular Structure and Function D Study Section (MSFD)
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Wehrle, Janna P
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Boston University
Engineering (All Types)
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Zarbafian, Shahrooz; Moghadasi, Mohammad; Roshandelpoor, Athar et al. (2018) Protein docking refinement by convex underestimation in the low-dimensional subspace of encounter complexes. Sci Rep 8:5896
Brisimi, Theodora S; Chen, Ruidi; Mela, Theofanie et al. (2018) Federated learning of predictive models from federated Electronic Health Records. Int J Med Inform 112:59-67
Bohnuud, Tanggis; Luo, Lingqi; Wodak, Shoshana J et al. (2017) A benchmark testing ground for integrating homology modeling and protein docking. Proteins 85:10-16
Vajda, Sandor; Yueh, Christine; Beglov, Dmitri et al. (2017) New additions to the ClusPro server motivated by CAPRI. Proteins 85:435-444
Mamonov, Artem B; Moghadasi, Mohammad; Mirzaei, Hanieh et al. (2016) Focused grid-based resampling for protein docking and mapping. J Comput Chem 37:961-70
Padhorny, Dzmitry; Kazennov, Andrey; Zerbe, Brandon S et al. (2016) Protein-protein docking by fast generalized Fourier transforms on 5D rotational manifolds. Proc Natl Acad Sci U S A 113:E4286-93
Zhao, Qi; Stettner, Arion I; Reznik, Ed et al. (2016) Mapping the landscape of metabolic goals of a cell. Genome Biol 17:109
Im, Wonpil; Liang, Jie; Olson, Arthur et al. (2016) Challenges in structural approaches to cell modeling. J Mol Biol 428:2943-64
Lensink, Marc F; Velankar, Sameer; Kryshtafovych, Andriy et al. (2016) Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment. Proteins 84 Suppl 1:323-48
Xia, Bing; Mamonov, Artem; Leysen, Seppe et al. (2015) Accounting for observed small angle X-ray scattering profile in the protein-protein docking server ClusPro. J Comput Chem 36:1568-72

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