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      Evidence of Conformational Selection Driving the Formation of Ligand Binding Sites in Protein-Protein Interfaces

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          Abstract

          Many protein-protein interactions (PPIs) are compelling targets for drug discovery, and in a number of cases can be disrupted by small molecules. The main goal of this study is to examine the mechanism of binding site formation in the interface region of proteins that are PPI targets by comparing ligand-free and ligand-bound structures. To avoid any potential bias, we focus on ensembles of ligand-free protein conformations obtained by nuclear magnetic resonance (NMR) techniques and deposited in the Protein Data Bank, rather than on ensembles specifically generated for this study. The measures used for structure comparison are based on detecting binding hot spots, i.e., protein regions that are major contributors to the binding free energy. The main tool of the analysis is computational solvent mapping, which explores the surface of proteins by docking a large number of small “probe” molecules. Although we consider conformational ensembles obtained by NMR techniques, the analysis is independent of the method used for generating the structures. Finding the energetically most important regions, mapping can identify binding site residues using ligand-free models based on NMR data. In addition, the method selects conformations that are similar to some peptide-bound or ligand-bound structure in terms of the properties of the binding site. This agrees with the conformational selection model of molecular recognition, which assumes such pre-existing conformations. The analysis also shows the maximum level of similarity between unbound and bound states that is achieved without any influence from a ligand. Further shift toward the bound structure assumes protein-peptide or protein-ligand interactions, either selecting higher energy conformations that are not part of the NMR ensemble, or leading to induced fit. Thus, forming the sites in protein-protein interfaces that bind peptides and can be targeted by small ligands always includes conformational selection, although other recognition mechanisms may also be involved.

          Author Summary

          Many protein-protein interfaces (PPIs) are biologically compelling drug targets. Disrupting the interaction between two large proteins by a small inhibitor requires forming a high affinity binding site in the interface that generally can bind both peptides and drug-like compounds. Here we investigate whether such sites are induced by peptide or ligand binding, or already exist in the unbound state. The analysis requires comparing ligand-free and ligand-bound structures. To avoid any potential bias, we study ensembles of ligand-free protein conformations obtained by nuclear magnetic resonance (NMR) rather than generated by simulations. The analysis is based on computational solvent mapping, which explores the surface of the target protein by docking a large number of small “probe” molecules. Results show that ensembles of ligand-free models always include conformations that are fairly similar to some peptide-bound or ligand-bound structure in terms of the properties of the binding site. The analysis also identifies the models that are the most similar to a bound state, and shows the maximum level of similarity that is achieved without any influence from a ligand. While forming the binding site may require a combination of recognition mechanisms, there is preference for the spontaneous formation of bound-like structures.

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          Most cited references31

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          Structure of the MDM2 oncoprotein bound to the p53 tumor suppressor transactivation domain.

          The MDM2 oncoprotein is a cellular inhibitor of the p53 tumor suppressor in that it can bind the transactivation domain of p53 and downregulate its ability to activate transcription. In certain cancers, MDM2 amplification is a common event and contributes to the inactivation of p53. The crystal structure of the 109-residue amino-terminal domain of MDM2 bound to a 15-residue transactivation domain peptide of p53 revealed that MDM2 has a deep hydrophobic cleft on which the p53 peptide binds as an amphipathic alpha helix. The interface relies on the steric complementarity between the MDM2 cleft and the hydrophobic face of the p53 alpha helix and, in particular, on a triad of p53 amino acids-Phe19, Trp23, and Leu26-which insert deep into the MDM2 cleft. These same p53 residues are also involved in transactivation, supporting the hypothesis that MDM2 inactivates p53 by concealing its transactivation domain. The structure also suggests that the amphipathic alpha helix may be a common structural motif in the binding of a diverse family of transactivation factors to the TATA-binding protein-associated factors.
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            A hot spot of binding energy in a hormone-receptor interface.

            The x-ray crystal structure of the complex between human growth hormone (hGH) and the extracellular domian of its first bound receptor (hGHbp) shows that about 30 side chains from each protein make contact. Individual replacement of contact residues in the hGHbp with alanine showed that a central hydrophobic region, dominated by two tryptophan residues, accounts for more than three-quarters of the binding free energy. This "functional epitope" is surrounded by less important contact residues that are generally hydrophilic and partially hydrated, so that the interface resembles a cross section through a globular protein. The functionally important residues on the hGHbp directly contact those on hGH. Thus, only a small and complementary set of contact residues maintains binding affinity, a property that may be general to protein-protein interfaces.
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              PIPER: an FFT-based protein docking program with pairwise potentials.

              The Fast Fourier Transform (FFT) correlation approach to protein-protein docking can evaluate the energies of billions of docked conformations on a grid if the energy is described in the form of a correlation function. Here, this restriction is removed, and the approach is efficiently used with pairwise interaction potentials that substantially improve the docking results. The basic idea is approximating the interaction matrix by its eigenvectors corresponding to the few dominant eigenvalues, resulting in an energy expression written as the sum of a few correlation functions, and solving the problem by repeated FFT calculations. In addition to describing how the method is implemented, we present a novel class of structure-based pairwise intermolecular potentials. The DARS (Decoys As the Reference State) potentials are extracted from structures of protein-protein complexes and use large sets of docked conformations as decoys to derive atom pair distributions in the reference state. The current version of the DARS potential works well for enzyme-inhibitor complexes. With the new FFT-based program, DARS provides much better docking results than the earlier approaches, in many cases generating 50% more near-native docked conformations. Although the potential is far from optimal for antibody-antigen pairs, the results are still slightly better than those given by an earlier FFT method. The docking program PIPER is freely available for noncommercial applications. (c) 2006 Wiley-Liss, Inc.
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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
                October 2014
                2 October 2014
                : 10
                : 10
                : e1003872
                Affiliations
                [1 ]Program in Bioinformatics, Boston University, Boston, Massachusetts, United States of America
                [2 ]Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
                [3 ]Department of Chemistry, Boston University, Boston, Massachusetts, United States of America
                University of Houston, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: DK SV. Performed the experiments: TB DK. Analyzed the data: TB DK SV. Contributed to the writing of the manuscript: TB DK SV.

                Article
                PCOMPBIOL-D-14-00911
                10.1371/journal.pcbi.1003872
                4183424
                25275445
                b719f1ad-84e0-4e24-b4d4-e4f436440997
                Copyright @ 2014

                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
                : 23 May 2014
                : 21 August 2014
                Page count
                Pages: 9
                Funding
                This work was supported by grants from the National Institute of Genera Medical Sciences (1R01GM064700 and 1R01GM061867, both to SV, and 1R01GM093147 to DK), and from the National Science Foundation (DBI1147082 to SV and DK). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Biochemistry
                Biomacromolecule-Ligand Interactions
                Biophysics
                Biophysical Simulations
                Biophysics Theory
                Computational Biology
                Molecular Biology
                Molecular Complexes
                Custom metadata
                The authors confirm that all data underlying the findings are fully available without restriction. Input data are from the Protein Data Bank. The accession codes are 1z1m, 1ycr, 1rv1, 2lzg, 1iu2, 1rgr, 2kpk, 2kpl, 4a53, 4a54, 2m03, 2yxj, and 1bxl. All other relevant data are within the paper and its Supporting Information files.

                Quantitative & Systems biology
                Quantitative & Systems biology

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