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      Rational design of protein–protein interaction inhibitors

      1 , 2 , 3 , 4 , 5
      MedChemComm
      Royal Society of Chemistry (RSC)

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          Abstract

          Low molecular weight compound competing for the binding of the p53 tumor suppressor to the MDM2 oncoprotein.

          Abstract

          Protein–protein interactions are at the heart of most physiopathological processes. As such, they have attracted considerable attention for designing drugs of the future. Although initially considered as high-value but difficult to identify, low molecular weight compounds able to selectively and potently modulate protein–protein interactions have recently reached clinical trials. Along with high-throughput screening of compound libraries, combining structural and computational approaches has boosted this formerly minor area of research into a currently tremendously active field. This review highlights the very recent developments in the rational design of protein–protein interaction inhibitors.

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          Most cited references57

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          A fast flexible docking method using an incremental construction algorithm.

          We present an automatic method for docking organic ligands into protein binding sites. The method can be used in the design process of specific protein ligands. It combines an appropriate model of the physico-chemical properties of the docked molecules with efficient methods for sampling the conformational space of the ligand. If the ligand is flexible, it can adopt a large variety of different conformations. Each such minimum in conformational space presents a potential candidate for the conformation of the ligand in the complexed state. Our docking method samples the conformation space of the ligand on the basis of a discrete model and uses a tree-search technique for placing the ligand incrementally into the active site. For placing the first fragment of the ligand into the protein, we use hashing techniques adapted from computer vision. The incremental construction algorithm is based on a greedy strategy combined with efficient methods for overlap detection and for the search of new interactions. We present results on 19 complexes of which the binding geometry has been crystallographically determined. All considered ligands are docked in at most three minutes on a current workstation. The experimentally observed binding mode of the ligand is reproduced with 0.5 to 1.2 A rms deviation. It is almost always found among the highest-ranking conformations computed.
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            An empirical framework for binary interactome mapping

            Several attempts have been made at systematically mapping protein-protein interaction, or “interactome” networks. However, it remains difficult to assess the quality and coverage of existing datasets. We describe a framework that uses an empirically-based approach to rigorously dissect quality parameters of currently available human interactome maps. Our results indicate that high-throughput yeast two-hybrid (HT-Y2H) interactions for human are superior in precision to literature-curated interactions supported by only a single publication, suggesting that HT-Y2H is suitable to map a significant portion of the human interactome. We estimate that the human interactome contains ~130,000 binary interactions, most of which remain to be mapped. Similar to estimates of DNA sequence data quality and genome size early in the human genome project, estimates of protein interaction data quality and interactome size are critical to establish the magnitude of the task of comprehensive human interactome mapping and to illuminate a path towards this goal.
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              Estimating the size of the human interactome.

              After the completion of the human and other genome projects it emerged that the number of genes in organisms as diverse as fruit flies, nematodes, and humans does not reflect our perception of their relative complexity. Here, we provide reliable evidence that the size of protein interaction networks in different organisms appears to correlate much better with their apparent biological complexity. We develop a stable and powerful, yet simple, statistical procedure to estimate the size of the whole network from subnet data. This approach is then applied to a range of eukaryotic organisms for which extensive protein interaction data have been collected and we estimate the number of interactions in humans to be approximately 650,000. We find that the human interaction network is one order of magnitude bigger than the Drosophila melanogaster interactome and approximately 3 times bigger than in Caenorhabditis elegans.
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                Author and article information

                Journal
                MCCEAY
                MedChemComm
                Med. Chem. Commun.
                Royal Society of Chemistry (RSC)
                2040-2503
                2040-2511
                2015
                2015
                : 6
                : 1
                : 51-60
                Affiliations
                [1 ]Laboratory for Therapeutical Innovation
                [2 ]UMR7200 CNRS-Université de Strasbourg
                [3 ]MEDALIS Drug Discovery Center
                [4 ]67400 Illkirch
                [5 ]France
                Article
                10.1039/C4MD00328D
                6a6980e1-e7a2-43d8-bfb5-daede2a31c1b
                © 2015
                History

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