MM-GB(PB)SA Calculations of Protein-Ligand Binding Free Energies
Joseph M., Georgios Archontis · InTech eBooks · 2012
The importance of computational chemistry in modern scientific research is well established. Continuous improvement in software and algorithms for the modeling of chemical interactions has transformed molecular modeling into a powerful tool for many current day research projects. From a medical perspective, one of the ultimate goals in computer-aided drug design (CADD) is the accurate prediction of ligand-binding affinities to a macromolecular target, which can facilitate and speed the routine identification of new candidates in early stage drug discovery projects (Gilson & Zhou, 2007; Hayes & Leonidas, 2010). In particular, structure-based modeling provides an efficient pathway towards exploiting known three-dimensional structural data in the design and proposal of new molecules for experimental evaluation. Docking calculations are now widely used in highthroughput virtual screening of structurally diverse molecules from available compound libraries/databases against specific targets. Once initial “hits” or “lead” molecules are identified (normally low μM inhibitors), modification of their chemical features in the “lead optimization” phase can improve their binding affinities and fine-tune other desirable druglike properties. However, docking calculations currently have limited success beyond the lead identification stage, where more accurate lower-throughput computational methods are needed. In this regard, the Molecular Mechanics/Generalized Born Surface Area (MMGBSA) and Molecular Mechanics/Poisson-Boltzmann Surface Area (MM-PBSA) methods calculate binding free energies using molecular mechanics (forcefields) and continuum (implicit) solvation models (Kollman et al., 2000).They have been successfully applied across a range of targets and are implemented in software programs such as Amber (Case et al., 2005), Delphi (Rocchia et al., 2001) and Schrodinger (Du et al., 2011). With a target readership from beginner to expert, the current chapter provides an extensive and critical overview of MM-GB(PB)SA methods and their applications. The theoretical foundation of the MM-GB(PB)SA method is first described. We then discuss key aspects which improve the accuracy of results, and highlight potential caveats due to the approximations inherent in the methods. The chapter concludes with a review of recent representative applications, which illustrate both successes and limitations. The emphasis of this chapter is on structure-