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      Activation pathway of Src kinase reveals intermediate states as novel targets for drug design

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          Abstract

          Unregulated activation of Src kinases leads to aberrant signaling, uncontrolled growth, and differentiation of cancerous cells. Reaching a complete mechanistic understanding of large scale conformational transformations underlying the activation of kinases could greatly help in the development of therapeutic drugs for the treatment of these pathologies. In principle, the nature of conformational transition could be modeled in silico via atomistic molecular dynamics simulations, although this is very challenging due to the long activation timescales. Here, we employ a computational paradigm that couples transition pathway techniques and Markov state model-based massively distributed simulations for mapping the conformational landscape of c-src tyrosine kinase. The computations provide the thermodynamics and kinetics of kinase activation for the first time, and help identify key structural intermediates. Furthermore, the presence of a novel allosteric site in an intermediate state of c-src that could be potentially utilized for drug design is predicted.

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          Most cited references61

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          Oncogenic kinase signalling.

          Protein-tyrosine kinases (PTKs) are important regulators of intracellular signal-transduction pathways mediating development and multicellular communication in metazoans. Their activity is normally tightly controlled and regulated. Perturbation of PTK signalling by mutations and other genetic alterations results in deregulated kinase activity and malignant transformation. The lipid kinase phosphoinositide 3-OH kinase (PI(3)K) and some of its downstream targets, such as the protein-serine/threonine kinases Akt and p70 S6 kinase (p70S6K), are crucial effectors in oncogenic PTK signalling. This review emphasizes how oncogenic conversion of protein kinases results from perturbation of the normal autoinhibitory constraints on kinase activity and provides an update on our knowledge about the role of deregulated PI(3)K/Akt and mammalian target of rapamycin/p70S6K signalling in human malignancies.
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            Protein kinases: evolution of dynamic regulatory proteins.

            Eukayotic protein kinases evolved as a family of highly dynamic molecules with strictly organized internal architecture. A single hydrophobic F-helix serves as a central scaffold for assembly of the entire molecule. Two non-consecutive hydrophobic structures termed "spines" anchor all the elements important for catalysis to the F-helix. They make firm, but flexible, connections within the molecule, providing a high level of internal dynamics of the protein kinase. During the course of evolution, protein kinases developed a universal regulatory mechanism associated with a large activation segment that can be dynamically folded and unfolded in the course of cell functioning. Protein kinases thus represent a unique, highly dynamic, and precisely regulated set of switches that control most biological events in eukaryotic cells. Copyright © 2010. Published by Elsevier Ltd.
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              Everything you wanted to know about Markov State Models but were afraid to ask.

              Simulating protein folding has been a challenging problem for decades due to the long timescales involved (compared with what is possible to simulate) and the challenges of gaining insight from the complex nature of the resulting simulation data. Markov State Models (MSMs) present a means to tackle both of these challenges, yielding simulations on experimentally relevant timescales, statistical significance, and coarse grained representations that are readily humanly understandable. Here, we review this method with the intended audience of non-experts, in order to introduce the method to a broader audience. We review the motivations, methods, and caveats of MSMs, as well as some recent highlights of applications of the method. We conclude by discussing how this approach is part of a paradigm shift in how one uses simulations, away from anecdotal single-trajectory approaches to a more comprehensive statistical approach. Copyright (c) 2010 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                101528555
                37539
                Nat Commun
                Nat Commun
                Nature communications
                2041-1723
                4 June 2014
                03 March 2014
                2014
                14 June 2015
                : 5
                : 3397
                Affiliations
                [1 ]Department of Chemistry, Stanford University, Stanford, CA 94305
                [2 ]SIMBIOS NIH center for biomedical computation, Stanford University, Stanford, CA 94305
                [3 ]Department of Biochemistry and Molecular Biology, Gordon Center for Integrative Science, The University of Chicago, Chicago, IL 60637
                [4 ]Biosciences Division, Argonne National Laboratory, Argonne, IL 60439
                Author notes
                Correspondence and requests for materials should be addressed to Vijay S. Pande. ( pande@ 123456stanford.edu )
                Article
                NIHMS566211
                10.1038/ncomms4397
                4465921
                24584478
                7a7f078e-73c9-4af9-aa7e-99fad8768221
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