Molecular Recognition and Drug-Lead Identification: What Can Molecular Simulations Tell Us?

Abstract

Molecular recognition and ligand binding involving proteins underlie the most important life processes within the cell, such as substrate transport, catalysis, signal transmission, receptor trafficking, gene regulation, switching on and off of biochemical pathways. Despite recent successes in predicting the structures of many protein-substrate complexes, the dynamic aspects of binding have been largely neglected by computational/theoretical investigations. Recently, several groups have started tackling these problems with the use of experimental and simulation methods and developed models describing the variation of protein dynamics upon complex formation, shedding light on how substrate or inhibitor binding can alter protein flexibility and function. The study of ligand-induced dynamic variations has also been exploited to review the concept of allosteric changes, in the absence of major conformational changes. In this context, the study of the influence of protein motions on signal transduction and on catalytic activities has been used to develop pharmacophore models based on ensembles of protein conformations. These models, taking flexibility explicitly into account, are able to distinguish active inhibitors versus nonactive drug-like compounds, to define new molecular motifs and to preferentially identify specific ligands for a certain protein target. The application of these methods holds great promise in advancing structure-based drug discovery and medicinal chemistry in general, opening up the possibility to explore broader chemical spaces than is normally done in an efficient way. In this review, examples illustrating the extent to which simulations can be used to understand these phenomena will be presented along with examples of methodological developments to increase physical understanding of the processes and improve the possibility to rationally design new molecules.

Ligand-guided optimization of CXCR4 homology models for virtual screening using a multiple chemotype approach

“…Therefore, this model might be used for virtual screening of new CXCR4 antagonists. The optimal virtual ligand screening performance obtained in this study with a multiple receptor representation can be related to a shift in the CXCR4 binding site upon ligand binding (commonly referred as the ''induced fit'' mechanism), highlighting the importance of flexibility and dynamics in protein-ligand docking [55]. According to this mechanism, different chemotypes can induce alternative conformational changes to the binding pocket, each one of these representing only a fraction of the total chemical molecular recognition properties.…”

Pairwise additivity of energy components in protein-ligand binding: The HIV II protease-Indinavir case

“…2 In addition, technological advancements have allowed for the development of various computational tools, which aid in different phases of a FBDD effort such as the generation of fragment libraries and in silico screening of these libraries against targets to identify potential hits. The hope that successful binding mode prediction would facilitate the evolution of a hit to a potent lead molecule underscores the need for molecular docking studies [4][5][6][7][8] and this has given rise to the use of computational screening as a starting point in FBDD. 9 The underlying assumption in FBDD is an additive (or nearly-additive) enhancement of binding affinity from each of the fragment molecules constituting the fully assembled inhibitor.…”

Limits of Free Energy Computation for Protein−Ligand Interactions

“…19,20 ΔΔG sol is given as: (4) and the individual terms can be obtained from explicit or implicit solvation models or experiment. 21,22 Rearranging and collecting terms together that represent the change in the total energy, enthalpy and entropy, respectively, we obtain: (5) which, when consolidated, leads us to a compact representation 23 of the four key terms we have to evaluate in order to obtain the free energy of binding of a ligand to a protein in aqueous solution: (6) where the first three of the individual terms are defined as: (7) For our purposes a key result of this simple analysis is that the master equation (equation 6) yields a ΔE term, which indicates the change in the electronic energy of a molecule in the gasphase. This term, along with the second term in equation 6 (the ΔH term), are quantities for which modern electronic structure theory can obtain highly reliable values for using appropriate methodologies.…”