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Computational Determination of the Relative Free Energy of Binding – Application to Alanine Scanning Mutagenesis

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Molecular Materials with Specific Interactions – Modeling and Design

Abstract

Protein-protein recognition and complex formation are key issues in understanding cellular functions. Therefore, having in mind that it is of extreme importance to detect the functional sites in proteins interfaces, the present review focuses on computational approaches used to calculate the binding free energy contributions of each of the interface residues. Usually these methods do not allow the calculation of the contribution of each residue for binding in the wild type complex, but instead the difference in binding free energy between the wild type and a given residue. Although the first would be more meaningful from a phenomenological point of view, the second is the only one that is possible to measure experimentally. A number of quantitative models with different levels of rigor and speed are available for determination of the relative binding energy upon alanine mutation of residues in protein-protein interfaces. These algorithms can be divided essentially in two types: (a) empirical functions or simple physical methods and (b) fully atomistic methods Computer simulations complement experimental analysis, and add molecular insight to the macroscopic properties, by allowing the decomposing of the binding free energy into contributions of the various energetic factors. The capacity of predicting protein-protein associations is essential in computational chemistry because it establishes the connecting bridge between structure and function of biomolecular systems, and it allows the characterization of the energetics of molecular complexes

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Moreira, I.S., Fernandes, P.A., Ramos, M.J. (2007). Computational Determination of the Relative Free Energy of Binding – Application to Alanine Scanning Mutagenesis. In: Sokalski, W.A. (eds) Molecular Materials with Specific Interactions – Modeling and Design. Challenges and Advances in Computational Chemistry and Physics, vol 4. Springer, Dordrecht. https://doi.org/10.1007/1-4020-5372-X_6

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