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      Protein Docking by the Underestimation of Free Energy Funnels in the Space of Encounter Complexes

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          Abstract

          Similarly to protein folding, the association of two proteins is driven by a free energy funnel, determined by favorable interactions in some neighborhood of the native state. We describe a docking method based on stochastic global minimization of funnel-shaped energy functions in the space of rigid body motions ( SE(3)) while accounting for flexibility of the interface side chains. The method, called semi-definite programming-based underestimation (SDU), employs a general quadratic function to underestimate a set of local energy minima and uses the resulting underestimator to bias further sampling. While SDU effectively minimizes functions with funnel-shaped basins, its application to docking in the rotational and translational space SE(3) is not straightforward due to the geometry of that space. We introduce a strategy that uses separate independent variables for side-chain optimization, center-to-center distance of the two proteins, and five angular descriptors of the relative orientations of the molecules. The removal of the center-to-center distance turns out to vastly improve the efficiency of the search, because the five-dimensional space now exhibits a well-behaved energy surface suitable for underestimation. This algorithm explores the free energy surface spanned by encounter complexes that correspond to local free energy minima and shows similarity to the model of macromolecular association that proceeds through a series of collisions. Results for standard protein docking benchmarks establish that in this space the free energy landscape is a funnel in a reasonably broad neighborhood of the native state and that the SDU strategy can generate docking predictions with less than 5 Å ligand interface C α root-mean-square deviation while achieving an approximately 20-fold efficiency gain compared to Monte Carlo methods.

          Author Summary

          Protein–protein interactions play a central role in various aspects of the structural and functional organization of the cell, and their elucidation is crucial for a better understanding of processes such as metabolic control, signal transduction, and gene regulation. Genomewide proteomics studies, primarily yeast two-hybrid assays, will provide an increasing list of interacting proteins, but only a small fraction of the potential complexes will be amenable to direct experimental analysis. Thus, it is important to develop computational docking methods that can elucidate the details of specific interactions at the atomic level. Protein–protein docking generally starts with a rigid body search that generates a large number of docked conformations with good shape, electrostatic, and chemical complementarity. The conformations are clustered to obtain a manageable number of models, but the current methods are unable to select the most likely structure among these models. Here we describe a refinement algorithm that, applied to the individual clusters, improves the quality of the models. The better models are suitable for higher-accuracy energy calculation, thereby increasing the chances that near-native structures can be identified, and thus the refinement increases the reliability of the entire docking algorithm.

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          Most cited references46

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          A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae.

          Two large-scale yeast two-hybrid screens were undertaken to identify protein-protein interactions between full-length open reading frames predicted from the Saccharomyces cerevisiae genome sequence. In one approach, we constructed a protein array of about 6,000 yeast transformants, with each transformant expressing one of the open reading frames as a fusion to an activation domain. This array was screened by a simple and automated procedure for 192 yeast proteins, with positive responses identified by their positions in the array. In a second approach, we pooled cells expressing one of about 6,000 activation domain fusions to generate a library. We used a high-throughput screening procedure to screen nearly all of the 6,000 predicted yeast proteins, expressed as Gal4 DNA-binding domain fusion proteins, against the library, and characterized positives by sequence analysis. These approaches resulted in the detection of 957 putative interactions involving 1,004 S. cerevisiae proteins. These data reveal interactions that place functionally unclassified proteins in a biological context, interactions between proteins involved in the same biological function, and interactions that link biological functions together into larger cellular processes. The results of these screens are shown here.
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            A comprehensive two-hybrid analysis to explore the yeast protein interactome.

            Protein-protein interactions play crucial roles in the execution of various biological functions. Accordingly, their comprehensive description would contribute considerably to the functional interpretation of fully sequenced genomes, which are flooded with novel genes of unpredictable functions. We previously developed a system to examine two-hybrid interactions in all possible combinations between the approximately 6,000 proteins of the budding yeast Saccharomyces cerevisiae. Here we have completed the comprehensive analysis using this system to identify 4,549 two-hybrid interactions among 3,278 proteins. Unexpectedly, these data do not largely overlap with those obtained by the other project [Uetz, P., et al. (2000) Nature (London) 403, 623-627] and hence have substantially expanded our knowledge on the protein interaction space or interactome of the yeast. Cumulative connection of these binary interactions generates a single huge network linking the vast majority of the proteins. Bioinformatics-aided selection of biologically relevant interactions highlights various intriguing subnetworks. They include, for instance, the one that had successfully foreseen the involvement of a novel protein in spindle pole body function as well as the one that may uncover a hitherto unidentified multiprotein complex potentially participating in the process of vesicular transport. Our data would thus significantly expand and improve the protein interaction map for the exploration of genome functions that eventually leads to thorough understanding of the cell as a molecular system.
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              Using SeDuMi 1.02, A Matlab toolbox for optimization over symmetric cones

              Jos Sturm (1999)
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                October 2008
                October 2008
                10 October 2008
                : 4
                : 10
                : e1000191
                Affiliations
                [1 ]BioMolecular Engineering Research Center, Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
                [2 ]Center for Information and Systems Engineering, Boston University, Boston, Massachusetts, United States of America
                [3 ]Division of Systems Engineering, Boston University, Boston, Massachusetts, United States of America
                [4 ]Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts, United States of America
                [5 ]Department of Mechanical Engineering, Boston University, Boston, Massachusetts, United States of America
                University of California San Francisco, United States of America
                Author notes

                Conceived and designed the experiments: YS ICP PV SV. Performed the experiments: YS. Analyzed the data: YS. Wrote the paper: YS ICP PV SV.

                Article
                08-PLCB-RA-0447R2
                10.1371/journal.pcbi.1000191
                2538569
                18846200
                b42208a9-22da-4694-99cc-6adf1dbca603
                Shen et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 2 June 2008
                : 22 August 2008
                Page count
                Pages: 12
                Categories
                Research Article
                Biophysics/Biomacromolecule-Ligand Interactions
                Biophysics/Structural Genomics
                Biophysics/Theory and Simulation
                Computational Biology/Macromolecular Structure Analysis
                Computer Science/Applications
                Computer Science/Numerical Analysis and Theoretical Computing

                Quantitative & Systems biology
                Quantitative & Systems biology

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