{"id":452,"date":"2015-06-09T14:15:29","date_gmt":"2015-06-09T18:15:29","guid":{"rendered":"https:\/\/sites.bu.edu\/paschalidis\/?page_id=452"},"modified":"2015-06-11T23:55:45","modified_gmt":"2015-06-12T03:55:45","slug":"final-stage-optimization-methods-for-protein-docking-exploiting-energy-funnels","status":"publish","type":"page","link":"https:\/\/sites.bu.edu\/paschalidis\/research\/final-stage-optimization-methods-for-protein-docking-exploiting-energy-funnels\/","title":{"rendered":"Final-Stage Optimization Methods for Protein Docking Exploiting Energy Funnels"},"content":{"rendered":"<p align=\"justify\"><span>Funding Agency:<\/span> National Institute of General Medical Sciences, National Institutes of Health (NIGMS\/NIH).<\/p>\n<p align=\"justify\"><span>Award Number:<\/span> 1-R21-GM079396-01.<\/p>\n<p align=\"justify\"><span>Principal Investigators:<\/span> Yannis Paschalidis and Pirooz Vakili, Boston University.<\/p>\n<h5 align=\"justify\">Project Summary<\/h5>\n<p align=\"justify\">All recent successful methods for protein&#8211;protein docking are based on a multistage approach. Such an approach first applies a coarse grain search, and then isolates a number of regions (clusters) in the conformational space that need to be further explored. <em>Final-stage<\/em> exploration involves <em>cluster refinement<\/em> and <em>cluster discrimination<\/em> steps and poses a number of challenges: a multitude of clusters to explore, an extremely rugged energy landscape, and the need to account for the <em>flexibility<\/em> of the proteins and to incorporate<em>entropy<\/em> metrics in otherwise quite sophisticated energy potentials.<\/p>\n<p align=\"justify\"><em>The central goal of this proposal is to develop novel high-throughput optimization methods that can efficiently explore a multitude of conformational clusters and produce high-quality predictions of the bound structure<\/em>. To that end, the work will leverage a new global optimization method developed by the proposing team, the Semi-Definite programming-based Underestimation (SDU) method, which can exploit the funnel-like shape of energy functions. Specific aims include: (1) the development of a final-stage optimization method that can efficiently explore conformational clusters; (2) the extension of the final-stage optimization method developed under Specific Aim 1 to allow full flexibility for the side-chains in the interface between the two proteins; and (3) the development of a cluster-discrimination algorithm that combines stochastic search approaches with estimates of funnel volume as a surrogate for the entropy of complexes in the funnel.<\/p>\n<p align=\"justify\">Novel aspects of the proposed work include: <em>(i)<\/em> the identification and efficient exploration of multi-dimensional energy funnels in the translation\/orientational subspaces defined by the movement of the ligand towards the receptor, <em>(ii)<\/em> the coordination of translational and orientational movements of the ligand, which can potentially reveal information about dominant association pathways, <em>(iii)<\/em> the development of an algorithm for fast re-packing of the interface side-chains using ideas from combinatorial optimization, and <em>(iv)<\/em> the incorporation of a surrogate entropy metric in cluster discrimination leveraging stochastic search approaches.<\/p>\n<p align=\"justify\">This work will substantially improve upon docking results for relatively weak protein complexes and enable the flexible docking of larger proteins than what is possible today, resulting in a better understanding of processes such as metabolic control, signal transduction, and gene regulation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Funding Agency: National Institute of General Medical Sciences, National Institutes of Health (NIGMS\/NIH). Award Number: 1-R21-GM079396-01. Principal Investigators: Yannis Paschalidis and Pirooz Vakili, Boston University. Project Summary All recent successful methods for protein&#8211;protein docking are based on a multistage approach. Such an approach first applies a coarse grain search, and then isolates a number of [&hellip;]<\/p>\n","protected":false},"author":2469,"featured_media":0,"parent":10,"menu_order":17,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/sites.bu.edu\/paschalidis\/wp-json\/wp\/v2\/pages\/452"}],"collection":[{"href":"https:\/\/sites.bu.edu\/paschalidis\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.bu.edu\/paschalidis\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/paschalidis\/wp-json\/wp\/v2\/users\/2469"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/paschalidis\/wp-json\/wp\/v2\/comments?post=452"}],"version-history":[{"count":2,"href":"https:\/\/sites.bu.edu\/paschalidis\/wp-json\/wp\/v2\/pages\/452\/revisions"}],"predecessor-version":[{"id":733,"href":"https:\/\/sites.bu.edu\/paschalidis\/wp-json\/wp\/v2\/pages\/452\/revisions\/733"}],"up":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/paschalidis\/wp-json\/wp\/v2\/pages\/10"}],"wp:attachment":[{"href":"https:\/\/sites.bu.edu\/paschalidis\/wp-json\/wp\/v2\/media?parent=452"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}