Artículo

Determining free energies of protein-ligand binding and association/dissociation processes using computer simulations

Gauto, Diego Fernando Icon; Modenutti, Carlos Pablo Icon; Dumas, Victoria Gisel Icon; Álvarez, Lucía Icon; Bustamante, Juan Pablo Icon; Turjanski, Adrian Icon; Marti, Marcelo Adrian Icon

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Fecha

01/2012

Editorial

Bioinfo Publications

Revista

World Research Journal of Peptide and Protein

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Institución organizadora

ISSN

2278-4586

eISSN

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Idioma

Inglés

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Resumen

The development of new drugs is one of the most important research areas in the bio-sciences, and where structural bioinformatics plays a central role. Over the last thirty years rational drug design has contributed to the introduction of many new drugs in the market and computational (or in-silico based) methods are an essential part of these programs having the great advantage of the potential delivery of new drug candidates faster and at lower cost when compared to high throughput experimental methods. At the heart of these methods, lies the determination of a given drug (or ligand) affinity for a given protein receptor, which includes determination or knowledge of the protein-ligand complex. Thus, the theoretical prediction of ligand binding free energies (ΔGB), is one of the most important and yet challenging problems in computational biochemistry, and therefore the subject of the current review. The review starts describing the so called End point methods for computing ligand binding free energies which rely on performing MD simulations of the complexes with post-processing analysis, and shows recent advances and improvements on ΔGB prediction using Quantum Mechanics and explicit solvation analysis techniques. Secondly we present free energy based methods that rely on the description of the binding process itself, reviewing first the use of biased non equilibrium based methods for small ligand binding to metalo proteins and second the recent advances to the study and free energy determination of big drug like ligand binding process with biased and free diffusion methods. Finally, we perform an overall comparison of the reviewed methods, and suggest which method (or methods) should be used in different ideal cases described as examples.

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