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      Insights into Protein–Ligand Interactions: Mechanisms, Models, and Methods

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          Abstract

          Molecular recognition, which is the process of biological macromolecules interacting with each other or various small molecules with a high specificity and affinity to form a specific complex, constitutes the basis of all processes in living organisms. Proteins, an important class of biological macromolecules, realize their functions through binding to themselves or other molecules. A detailed understanding of the protein–ligand interactions is therefore central to understanding biology at the molecular level. Moreover, knowledge of the mechanisms responsible for the protein-ligand recognition and binding will also facilitate the discovery, design, and development of drugs. In the present review, first, the physicochemical mechanisms underlying protein–ligand binding, including the binding kinetics, thermodynamic concepts and relationships, and binding driving forces, are introduced and rationalized. Next, three currently existing protein-ligand binding models—the “lock-and-key”, “induced fit”, and “conformational selection”—are described and their underlying thermodynamic mechanisms are discussed. Finally, the methods available for investigating protein–ligand binding affinity, including experimental and theoretical/computational approaches, are introduced, and their advantages, disadvantages, and challenges are discussed.

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          Most cited references 189

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          A nonequilibrium equality for free energy differences

           C Jarzynski (1996)
          An expression is derived for the classical free energy difference between two configurations of a system, in terms of an ensemble of finite-time measurements of the work performed in parametrically switching from one configuration to the other. Two well-known equilibrium identities emerge as limiting cases of this result.
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            Escaping free-energy minima

             M. Parrinello,  A Laio (2002)
            We introduce a novel and powerful method for exploring the properties of the multidimensional free energy surfaces of complex many-body systems by means of a coarse-grained non-Markovian dynamics in the space defined by a few collective coordinates.A characteristic feature of this dynamics is the presence of a history-dependent potential term that, in time, fills the minima in the free energy surface, allowing the efficient exploration and accurate determination of the free energy surface as a function of the collective coordinates. We demonstrate the usefulness of this approach in the case of the dissociation of a NaCl molecule in water and in the study of the conformational changes of a dialanine in solution.
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              Funnels, pathways, and the energy landscape of protein folding: a synthesis.

              The understanding, and even the description of protein folding is impeded by the complexity of the process. Much of this complexity can be described and understood by taking a statistical approach to the energetics of protein conformation, that is, to the energy landscape. The statistical energy landscape approach explains when and why unique behaviors, such as specific folding pathways, occur in some proteins and more generally explains the distinction between folding processes common to all sequences and those peculiar to individual sequences. This approach also gives new, quantitative insights into the interpretation of experiments and simulations of protein folding thermodynamics and kinetics. Specifically, the picture provides simple explanations for folding as a two-state first-order phase transition, for the origin of metastable collapsed unfolded states and for the curved Arrhenius plots observed in both laboratory experiments and discrete lattice simulations. The relation of these quantitative ideas to folding pathways, to uniexponential vs. multiexponential behavior in protein folding experiments and to the effect of mutations on folding is also discussed. The success of energy landscape ideas in protein structure prediction is also described. The use of the energy landscape approach for analyzing data is illustrated with a quantitative analysis of some recent simulations, and a qualitative analysis of experiments on the folding of three proteins. The work unifies several previously proposed ideas concerning the mechanism protein folding and delimits the regions of validity of these ideas under different thermodynamic conditions.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Int J Mol Sci
                Int J Mol Sci
                ijms
                International Journal of Molecular Sciences
                MDPI
                1422-0067
                26 January 2016
                February 2016
                : 17
                : 2
                Affiliations
                [1 ]Laboratory for Conservation and Utilization of Bio-Resources, Yunnan University, Kunming 650091, China; duxingok@ 123456gmail.com (X.D.); liyi.gerry@ 123456gmail.com (Y.L.); xiayl@ 123456ynu.edu.cn (Y.-L.X.); aishimann@ 123456gmail.com (S.-M.A.); liangjingyn90@ 123456gmail.com (J.L.); speng431@ 123456gmail.com (P.S.); rich@ 123456ynu.edu.cn (X.-L.J.)
                [2 ]Department of Applied Mathematics, Yunnan Agricultural University, Kunming 650201, China
                [3 ]Laboratory of Molecular Cardiology, Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming 650032, China
                [4 ]Key Laboratory for Tumor molecular biology of High Education in Yunnan Province, School of Life Sciences, Yunnan University, Kunming 650091, China
                Author notes
                [* ]Correspondence: shuqunliu@ 123456ynu.edu.cn ; Tel.: +86-871-6503-5257
                [†]

                These authors contributed equally to this work.

                Article
                ijms-17-00144
                10.3390/ijms17020144
                4783878
                26821017
                © 2016 by the authors; licensee MDPI, Basel, Switzerland.

                This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license ( http://creativecommons.org/licenses/by/4.0/).

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