Special Issue: Selected Papers from Computational Structural Bioinformatics Workshop 2011; Guest Editors: Jing He, Amarda Shehu, Nurit Haspel and Brian Chen – Research PapersNo Access

GUIDING PROTEIN DOCKING WITH GEOMETRIC AND EVOLUTIONARY INFORMATION

IRINA HASHMI

Department of Computer Science, George Mason University, Fairfax, VA, 22030, USA

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BAHAR AKBAL-DELIBAS

Department of Computer Science, University of Massachusetts at Boston, Boston, MA, 02125, USA

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NURIT HASPEL

Department of Computer Science, University of Massachusetts at Boston, Boston, MA, 02125, USA

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, and

AMARDA SHEHU

Department of Computer Science, George Mason University, Fairfax, VA, 22030, USA

Department of Bioinformatics and Computational Biology, George Mason University, Fairfax, VA, 22030, USA

Department of Bioengineering, George Mason University, Fairfax, VA, 22030, USA

Corresponding author.

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https://doi.org/10.1142/S0219720012420085Cited by:10 (Source: Crossref)

Abstract

Structural modeling of molecular assemblies promises to improve our understanding of molecular interactions and biological function. Even when focusing on modeling structures of protein dimers from knowledge of monomeric native structure, docking two rigid structures onto one another entails exploring a large configurational space. This paper presents a novel approach for docking protein molecules and elucidating native-like configurations of protein dimers. The approach makes use of geometric hashing to focus the docking of monomeric units on geometrically complementary regions through rigid-body transformations. This geometry-based approach improves the feasibility of searching the combined configurational space. The search space is narrowed even further by focusing the sought rigid-body transformations around molecular surface regions composed of amino acids with high evolutionary conservation. This condition is based on recent findings, where analysis of protein assemblies reveals that many functional interfaces are significantly conserved throughout evolution. Different search procedures are employed in this work to search the resulting narrowed configurational space. A proof-of-concept energy-guided probabilistic search procedure is also presented. Results are shown on a broad list of 18 protein dimers and additionally compared with data reported by other labs. Our analysis shows that focusing the search around evolutionary-conserved interfaces results in lower lRMSDs.

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