Application of the ESMACS Binding Free Energy Protocol to a Multi‐Binding Site Lactate Dehydogenase A Ligand Dataset. Issue 1 (18th November 2019)

• Record Type: Journal Article
• Authors: Wright, David W.; Husseini, Fouad; Wan, Shunzhou; Meyer, Christophe; van Vlijmen, Herman; Tresadern, Gary; Coveney, Peter V.
• Is Part Of: Advanced theory and simulations. Volume 3:Issue 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-11-18
• Subjects: binding free energy calculations -- fragment‐based drug design -- molecular dynamics -- molecular mechanics Poisson–Boltzmann surface area (MMPBSA); Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.201900194
• Languages: English
• ISSNs: 2513-0390

Abstract

Abstract: Over the past two decades, the use of fragment‐based lead generation has become a common, mature approach to identify tractable starting points in chemical space for the drug discovery process. This approach naturally involves the study of the binding properties of highly heterogeneous ligands. Such datasets challenge computational techniques to provide comparable binding free energy estimates from different binding modes. The performance of a range of statistically robust ensemble‐based binding free energy calculation protocols, called ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent), is evaluated. Ligands designed to target two binding pockets in the lactate dehydogenase, a target protein, which vary in size, charge, and binding mode, are studied. When compared to experimental results, excellent statistical rankings are obtained across this highly diverse set of ligands. In addition, three approaches to account for entropic contributions are investigated: 1) normal mode analysis, 2) weighted solvent accessible surface area (WSAS), and 3) variational entropy. Normal mode analysis and WSAS correlate strongly with each other—although the latter is computationally far cheaper—but do not improve rankings. Variational entropy corrects exaggerated discrimination of ligands bound in different pockets but creates three outliers which reduce the quality of the overall ranking. Abstract : A challenging application of ensemble‐based binding free energy calculations is reported in which the dataset exhibits substantial chemical diversity in ligands and their binding modes. Excellent correlation is obtained between predictions and experimental results, notwithstanding that the ligands vary in size, charge, and binding pockets targeted. Entropic contributions are evaluated and compared using three different methods, none of which improve the overall ranking.