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      Structure-Based Function Prediction of Uncharacterized Protein Using Binding Sites Comparison

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          Abstract

          A challenge in structural genomics is prediction of the function of uncharacterized proteins. When proteins cannot be related to other proteins of known activity, identification of function based on sequence or structural homology is impossible and in such cases it would be useful to assess structurally conserved binding sites in connection with the protein's function. In this paper, we propose the function of a protein of unknown activity, the Tm1631 protein from Thermotoga maritima, by comparing its predicted binding site to a library containing thousands of candidate structures. The comparison revealed numerous similarities with nucleotide binding sites including specifically, a DNA-binding site of endonuclease IV. We constructed a model of this Tm1631 protein with a DNA-ligand from the newly found similar binding site using ProBiS, and validated this model by molecular dynamics. The interactions predicted by the Tm1631-DNA model corresponded to those known to be important in endonuclease IV-DNA complex model and the corresponding binding free energies, calculated from these models were in close agreement. We thus propose that Tm1631 is a DNA binding enzyme with endonuclease activity that recognizes DNA lesions in which at least two consecutive nucleotides are unpaired. Our approach is general, and can be applied to any protein of unknown function. It might also be useful to guide experimental determination of function of uncharacterized proteins.

          Author Summary

          For a substantial proportion of proteins, their functions are not known since these proteins are not related in sequence to any other known proteins. Binding sites are evolutionarily conserved across very distant protein families, and finding similar binding sites between known and unknown proteins can provide clues as to functions of the unknown proteins. We choose one of the “unknown function” proteins, and found, using a novel strategy of binding site comparison to construct a hypothetical protein-ligand complex, subsequently validated by molecular dynamics that this protein most likely binds and repairs the damaged DNA similar to known DNA-repair enzymes. Our methodology is general and enables one to determine functions of other proteins currently labelled as “unknown function”. We envision that the methodology presented herein, the binding sites comparisons enhanced by molecular dynamics, will stimulate the function prediction of other uncharacterized proteins with structures in the Protein Data Bank and boost experimental functional studies of proteins of unknown functions.

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          Most cited references28

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          Flexible structure alignment by chaining aligned fragment pairs allowing twists.

          Protein structures are flexible and undergo structural rearrangements as part of their function, and yet most existing protein structure comparison methods treat them as rigid bodies, which may lead to incorrect alignment. We have developed the Flexible structure AlignmenT by Chaining AFPs (Aligned Fragment Pairs) with Twists (FATCAT), a new method for structural alignment of proteins. The FATCAT approach simultaneously addresses the two major goals of flexible structure alignment; optimizing the alignment and minimizing the number of rigid-body movements (twists) around pivot points (hinges) introduced in the reference protein. In contrast, currently existing flexible structure alignment programs treat the hinge detection as a post-process of a standard rigid body alignment. We illustrate the advantages of the FATCAT approach by several examples of comparison between proteins known to adopt different conformations, where the FATCAT algorithm achieves more accurate structure alignments than current methods, while at the same time introducing fewer hinges.
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            New analytic approximation to the standard molecular volume definition and its application to generalized Born calculations.

            In a recent article (Lee, M. S.; Salsbury, F. R. Jr.; Brooks, C. L., III. J Chem Phys 2002, 116, 10606), we demonstrated that generalized Born (GB) theory provides a good approximation to Poisson electrostatic solvation energy calculations if one uses the same definitions of molecular volume for each. In this work, we present a new and improved analytic method for reproducing the Lee-Richards molecular volume, which is the most common volume definition for Poisson calculations. Overall, 1% errors are achieved for absolute solvation energies of a large set of proteins and relative solvation energies of protein conformations. We also introduce an accurate SASA approximation that uses the same machinery employed by our GB method and requires a small addition of computational cost. The combined methodology is shown to yield an efficient and accurate implicit solvent representation for simulations of biopolymers. Copyright 2003 Wiley Periodicals, Inc.
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              ProBiS algorithm for detection of structurally similar protein binding sites by local structural alignment

              Motivation: Exploitation of locally similar 3D patterns of physicochemical properties on the surface of a protein for detection of binding sites that may lack sequence and global structural conservation. Results: An algorithm, ProBiS is described that detects structurally similar sites on protein surfaces by local surface structure alignment. It compares the query protein to members of a database of protein 3D structures and detects with sub-residue precision, structurally similar sites as patterns of physicochemical properties on the protein surface. Using an efficient maximum clique algorithm, the program identifies proteins that share local structural similarities with the query protein and generates structure-based alignments of these proteins with the query. Structural similarity scores are calculated for the query protein's surface residues, and are expressed as different colors on the query protein surface. The algorithm has been used successfully for the detection of protein–protein, protein–small ligand and protein–DNA binding sites. Availability: The software is available, as a web tool, free of charge for academic users at http://probis.cmm.ki.si Contact: dusa@cmm.ki.si Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                November 2013
                November 2013
                14 November 2013
                : 9
                : 11
                : e1003341
                Affiliations
                [1 ]National Institute of Chemistry, Ljubljana, Slovenia
                [2 ]University of Primorska, Faculty of Mathematics, Natural Sciences and Information Technologies, Koper, Slovenia
                University of Maryland, Baltimore, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: JK MH MO JTK DJ. Performed the experiments: JK MH MO JTK DJ. Analyzed the data: JK MH MO JTK DJ. Wrote the paper: JK MH MO JTK DJ. Designed the software used in analysis: JK MH MO JTK DJ.

                Article
                PCOMPBIOL-D-13-01249
                10.1371/journal.pcbi.1003341
                3828134
                24244144
                4a4f61f9-44e4-436f-8379-3abb1313ed97
                Copyright @ 2013

                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
                : 15 July 2013
                : 1 October 2013
                Page count
                Pages: 9
                Funding
                Financial support was provided by grant P1-0002 of the Ministry of Higher Education, Science, and Technology of Slovenia and the Slovenian Research Agency. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article

                Quantitative & Systems biology
                Quantitative & Systems biology

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