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      Ligand Clouds around Protein Clouds: A Scenario of Ligand Binding with Intrinsically Disordered Proteins

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          Abstract

          Intrinsically disordered proteins (IDPs) were found to be widely associated with human diseases and may serve as potential drug design targets. However, drug design targeting IDPs is still in the very early stages. Progress in drug design is usually achieved using experimental screening; however, the structural disorder of IDPs makes it difficult to characterize their interaction with ligands using experiments alone. To better understand the structure of IDPs and their interactions with small molecule ligands, we performed extensive simulations on the c-Myc 370–409 peptide and its binding to a reported small molecule inhibitor, ligand 10074-A4. We found that the conformational space of the apo c-Myc 370–409 peptide was rather dispersed and that the conformations of the peptide were stabilized mainly by charge interactions and hydrogen bonds. Under the binding of the ligand, c-Myc 370–409 remained disordered. The ligand was found to bind to c-Myc 370–409 at different sites along the chain and behaved like a ‘ligand cloud’. In contrast to ligand binding to more rigid target proteins that usually results in a dominant bound structure, ligand binding to IDPs may better be described as ligand clouds around protein clouds. Nevertheless, the binding of the ligand and a non-ligand to the c-Myc 370–409 target could be clearly distinguished. The present study provides insights that will help improve rational drug design that targets IDPs.

          Author Summary

          Intrinsically disordered proteins (IDPs) exist as conformational ensembles that change rapidly. They are an important and common class of proteins in all kingdoms of life. IDPs are widely associated with human diseases and may serve as potential drug design targets. However, drug design targeting IDPs is difficult and only limited examples have been reported. One example is the oncoprotein, c-Myc, for which seven inhibitors were discovered by experimental screening. Understanding how small inhibitor molecules bind to c-Myc may help in understanding the binding mechanism of IDPs with ligands. In the present study, we conducted extensive molecular dynamics simulations to explore the binding mechanism for the c-Myc peptide with an inhibitor 10074-A4. We found that 10074-A4 could bind to c-Myc 370–409 at different sites along the peptide chain and its binding behavior could be described as a ‘ligand cloud’. Even in the bound state, the structure of the c-Myc 370–409 peptide remained a dynamic ensemble. Compared to c-Myc peptides that do not bind to 10074-A4, c-Myc 370–409 binds selectively with 10074-A4, but the specificity of binding was not high. The interactions of IDPs with ligands can perhaps be described as a scenario in which ligand clouds around protein clouds.

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          Most cited references34

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          Small molecule selected to disrupt oncogenic protein EWS-FLI1 interaction with RNA Helicase A inhibits Ewing's Sarcoma

          Many sarcomas and leukemias carry non-random chromosomal translocations encoding mutant fusion transcription factors that are essential to their molecular pathogenesis. These novel, tumor-specific proteins provides a unique opportunity for the development of highly selective anticancer drugs that has yet to be exploited. A particularly clear example is provided by Ewing's Sarcoma Family Tumors (ESFT) which contain a characteristic t(11;22) translocation leading to expression of the oncogenic fusion protein EWS-FLI1. EWS-FLI1 is a disordered protein that precluded standard structure-based small molecule inhibitor design. Using surface plasmon resonance screening, we discovered a lead compound, NSC635437. A derivative compound, YK-4-279, blocks RHA binding to EWS-FLI1, induces apoptosis in ESFT cells, and reduces the growth of ESFT orthotopic xenografts. These findings provide proof of principle that inhibiting the interaction of mutant cancer-specific transcription factors with the normal cellular binding partners required for their oncogenic activity provides a promising strategy for the development of uniquely effective, tumor-specific anticancer agents.
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            Predicting intrinsic disorder in proteins: an overview.

            The discovery of intrinsically disordered proteins (IDP) (i.e., biologically active proteins that do not possess stable secondary and/or tertiary structures) came as an unexpected surprise, as the existence of such proteins is in contradiction to the traditional "sequence-->structure-->function" paradigm. Accurate prediction of a protein's predisposition to be intrinsically disordered is a necessary prerequisite for the further understanding of principles and mechanisms of protein folding and function, and is a key for the elaboration of a new structural and functional hierarchy of proteins. Therefore, prediction of IDPs has attracted the attention of many researchers, and a number of prediction tools have been developed. Predictions of disorder, in turn, are playing major roles in directing laboratory experiments that are leading to the discovery of ever more disordered proteins, and thereby leading to a positive feedback loop in the investigation of these proteins. In this review of algorithms for intrinsic disorder prediction, the basic concepts of various prediction methods for IDPs are summarized, the strengths and shortcomings of many of the methods are analyzed, and the difficulties and directions of future development of IDP prediction techniques are discussed.
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              Comparing and combining predictors of mostly disordered proteins.

              Intrinsically disordered proteins and regions carry out varied and vital cellular functions. Proteins with disordered regions are especially common in eukaryotic cells, with a subset of these proteins being mostly disordered, e.g., with more disordered than ordered residues. Two distinct methods have been previously described for using amino acid sequences to predict which proteins are likely to be mostly disordered. These methods are based on the net charge-hydropathy distribution and disorder prediction score distribution. Each of these methods is reexamined, and the prediction results are compared herein. A new prediction method based on consensus is described. Application of the consensus method to whole genomes reveals that approximately 4.5% of Yersinia pestis, 5% of Escherichia coli K12, 6% of Archaeoglobus fulgidus, 8% of Methanobacterium thermoautotrophicum, 23% of Arabidopsis thaliana, and 28% of Mus musculus proteins are mostly disordered. The unexpectedly high frequency of mostly disordered proteins in eukaryotes has important implications both for large-scale, high-throughput projects and also for focused experiments aimed at determination of protein structure and function.
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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 2013
                October 2013
                3 October 2013
                : 9
                : 10
                : e1003249
                Affiliations
                [1 ]BNLMS, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
                [2 ]Center for Quantitative Biology, Peking University, Beijing, China
                [3 ]Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
                University of Uppsala, Sweden
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: FJ LL ZL. Performed the experiments: FJ. Analyzed the data: FJ CY LL ZL. Contributed reagents/materials/analysis tools: FJ LL ZL. Wrote the paper: FJ LL ZL.

                Article
                PCOMPBIOL-D-13-00558
                10.1371/journal.pcbi.1003249
                3789766
                24098099
                634eed76-5bb0-4e90-b12c-6ecc7f850adb
                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
                : 3 April 2013
                : 15 August 2013
                Page count
                Pages: 11
                Funding
                This work was supported by the Ministry of Science and Technology of China (Grant Nos. 2009CB918500 and 2012AA020308)and the National Natural Science Foundation of China (Grant Nos. 20973016, 90913021 and 11021463). 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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