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Rapid, Accurate, Precise, and Reliable Relative Free Energy Prediction Using Ensemble Based Thermodynamic Integration.
Bhati, Agastya P; Wan, Shunzhou; Wright, David W; Coveney, Peter V.
Afiliación
  • Bhati AP; Centre for Computational Science, Department of Chemistry, University College London , 20 Gordon Street, London WC1H 0AJ, United Kingdom.
  • Wan S; Centre for Computational Science, Department of Chemistry, University College London , 20 Gordon Street, London WC1H 0AJ, United Kingdom.
  • Wright DW; Centre for Computational Science, Department of Chemistry, University College London , 20 Gordon Street, London WC1H 0AJ, United Kingdom.
  • Coveney PV; Centre for Computational Science, Department of Chemistry, University College London , 20 Gordon Street, London WC1H 0AJ, United Kingdom.
J Chem Theory Comput ; 13(1): 210-222, 2017 01 10.
Article en En | MEDLINE | ID: mdl-27997169
The accurate prediction of the binding affinities of ligands to proteins is a major goal in drug discovery and personalized medicine. The time taken to make such predictions is of similar importance to their accuracy, precision, and reliability. In the past few years, an ensemble based molecular dynamics approach has been proposed that provides a route to reliable predictions of free energies based on the molecular mechanics Poisson-Boltzmann surface area method which meets the requirements of speed, accuracy, precision, and reliability. Here, we describe an equivalent methodology based on thermodynamic integration to substantially improve the speed, accuracy, precision, and reliability of calculated relative binding free energies. We report the performance of the method when applied to a diverse set of protein targets and ligands. The results are in very good agreement with experimental data (90% of calculations agree to within 1 kcal/mol), while the method is reproducible by construction. Statistical uncertainties of the order of 0.5 kcal/mol or less are achieved. We present a systematic account of how the uncertainty in the predictions may be estimated.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Termodinámica / Proteínas / Simulación de Dinámica Molecular Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Chem Theory Comput Año: 2017 Tipo del documento: Article País de afiliación: Reino Unido Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Termodinámica / Proteínas / Simulación de Dinámica Molecular Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Chem Theory Comput Año: 2017 Tipo del documento: Article País de afiliación: Reino Unido Pais de publicación: Estados Unidos