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WIREs Comput Mol Sci
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Interaction entropy for computational alanine scanning in protein–protein binding

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Protein–protein interactions (PPIs) are at the heart of signal transduction and are central to the function of protein machine in biology. The highly specific protein–protein binding is quantitatively characterized by the binding free energy whose accurate calculation from first principle is a grand challenge in computational biology. Accurate prediction of critical residues along with their specific and quantitative contributions to protein–protein binding free energy is extremely helpful to reveal binding mechanisms and identify drug‐like molecules that alter PPIs. In this overview, we describe an interaction entropy (IE) approach combined with the MM/GBSA method for solvation to compute residue‐specific protein–protein binding free energy. In this approach, the entropic contribution to binding free energy of individual residue is explicitly computed by using the IE method from a single MD trajectory. Studies for an extensive set of realistic protein–protein interaction systems demonstrated that by including the entropic contribution, the agreement between the computed residue‐specific binding free energies and the corresponding experimental data is systematically improved. We also show application of the current approach to the important major histocompatibility complex (MHC)‐antigen binding to provide important information on hot spots with potential application for use in cancer vaccine. WIREs Comput Mol Sci 2018, 8:e1342. doi: 10.1002/wcms.1342 This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Free Energy Methods Software > Simulation Methods
Illustration of protein–protein interaction (PPI) systems. The numbers Np,Np′ and Npp’ denote, respectively, the number of water molecules in protein P, protein P′ and the complex system PP’ with the relation Npp’ = Np + Np′.
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Fluctuations of residue‐specific interaction energies and their interaction entropies (IEs) calculated for essential residues in antigen 5C0F and 5C0G.
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Structure of the class I major histocompatibility complex (MHC)‐antigen complex with MHC protein shown as white surface (a). Red colored residues in the antigen are predicted hotspots that interact strongly with MHC and green colored residues mainly interact with TCR (T‐cell receptor). (b) Red colored residues in MHC are predicted to be hot spots that interact strongly with the antigen (shown in green color).
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Gas‐phase interaction energies of three residues as a function of simulation time (left figure), distribution of the interaction energies of these three residues (middle figure), and time‐averaged interaction entropy or interaction entropy (IE) of these three residues (right figure).
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Error estimates (deviation from experimental values) in the calculated binding free energies for each of the 24 systems. The blue bars represent the errors of MM/GBSA result of energy only and red bars represent the errors of IE calculation with entropic contribution to the free energy. (a) mean signed error (MSE); (b) root mean square error (RMSE); (c) mean absolute error (MAE).
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Residue‐specific binding free energies calculated for some specific systems used in this work and compared to the experimental values.
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Definition of binding free energy difference in alanine scanning mutagenesis process.
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Changes of interaction entropy (IE) as a function of integration time for protein–protein interaction (PPI) systems 2NU4 and 2NU0 (left figure) and for 2NU1 and 2NU2 (right figure). The zero time corresponds to the starting of production MD.
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Fluctuation and distribution of the interaction energy and the corresponding fitted Gaussian curve for protein–protein interaction (PPI) system 2NU4.
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Software > Simulation Methods
Molecular and Statistical Mechanics > Free Energy Methods
Structure and Mechanism > Computational Biochemistry and Biophysics

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