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      Recent Developments and Applications of the MMPBSA Method

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          Abstract

          The Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) approach has been widely applied as an efficient and reliable free energy simulation method to model molecular recognition, such as for protein-ligand binding interactions. In this review, we focus on recent developments and applications of the MMPBSA method. The methodology review covers solvation terms, the entropy term, extensions to membrane proteins and high-speed screening, and new automation toolkits. Recent applications in various important biomedical and chemical fields are also reviewed. We conclude with a few future directions aimed at making MMPBSA a more robust and efficient method.

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          MMPBSA.py: An Efficient Program for End-State Free Energy Calculations.

          MM-PBSA is a post-processing end-state method to calculate free energies of molecules in solution. MMPBSA.py is a program written in Python for streamlining end-state free energy calculations using ensembles derived from molecular dynamics (MD) or Monte Carlo (MC) simulations. Several implicit solvation models are available with MMPBSA.py, including the Poisson-Boltzmann Model, the Generalized Born Model, and the Reference Interaction Site Model. Vibrational frequencies may be calculated using normal mode or quasi-harmonic analysis to approximate the solute entropy. Specific interactions can also be dissected using free energy decomposition or alanine scanning. A parallel implementation significantly speeds up the calculation by dividing frames evenly across available processors. MMPBSA.py is an efficient, user-friendly program with the flexibility to accommodate the needs of users performing end-state free energy calculations. The source code can be downloaded at http://ambermd.org/ with AmberTools, released under the GNU General Public License.
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            Classical electrostatics in biology and chemistry.

            A major revival in the use of classical electrostatics as an approach to the study of charged and polar molecules in aqueous solution has been made possible through the development of fast numerical and computational methods to solve the Poisson-Boltzmann equation for solute molecules that have complex shapes and charge distributions. Graphical visualization of the calculated electrostatic potentials generated by proteins and nucleic acids has revealed insights into the role of electrostatic interactions in a wide range of biological phenomena. Classical electrostatics has also proved to be successful quantitative tool yielding accurate descriptions of electrical potentials, diffusion limited processes, pH-dependent properties of proteins, ionic strength-dependent phenomena, and the solvation free energies of organic molecules.
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              Role of Repulsive Forces in Determining the Equilibrium Structure of Simple Liquids

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                Author and article information

                Contributors
                Journal
                Front Mol Biosci
                Front Mol Biosci
                Front. Mol. Biosci.
                Frontiers in Molecular Biosciences
                Frontiers Media S.A.
                2296-889X
                10 January 2018
                2017
                : 4
                : 87
                Affiliations
                [1] 1Chemical and Materials Physics Graduate Program, University of California, Irvine , Irvine, CA, United States
                [2] 2Department of Molecular Biology and Biochemistry, University of California, Irvine , Irvine, CA, United States
                [3] 3Department of Physics and Astronomy, University of California, Irvine , Irvine, CA, United States
                [4] 4Department of Biomedical Engineering, University of California, Irvine , Irvine, CA, United States
                [5] 5Department of Chemical Engineering and Materials Science, University of California, Irvine , Irvine, CA, United States
                Author notes

                Edited by: Chia-en Chang, University of California, Riverside, United States

                Reviewed by: Wei Chen, University of California, Riverside, United States; Haifeng Chen, Shanghai Jiao Tong University, China

                *Correspondence: Ray Luo rluo@ 123456uci.edu

                This article was submitted to Molecular Recognition, a section of the journal Frontiers in Molecular Biosciences

                Article
                10.3389/fmolb.2017.00087
                5768160
                29367919
                2782d8f7-1984-45eb-880f-8d05f85ae0c3
                Copyright © 2018 Wang, Greene, Xiao, Qi and Luo.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 18 September 2017
                : 30 November 2017
                Page count
                Figures: 0, Tables: 0, Equations: 11, References: 342, Pages: 18, Words: 18682
                Funding
                Funded by: National Institutes of Health 10.13039/100000002
                Award ID: GM093040
                Award ID: GM079383
                Categories
                Molecular Biosciences
                Review

                molecular recognition,binding affinity,free energy simulation,mmpbsa,continuum solvation model

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