Allosteric drug development holds promise for delivering medicines that are more selective and less toxic than those that target orthosteric sites. To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. Over the past decade, structural data has become more readily available for larger protein systems and more membrane protein classes (e.g., GPCRs and ion channels), which are common allosteric drug targets. In parallel, improved simulation methods now provide better atomistic understanding of the protein dynamics and cooperative motions that are critical to allosteric mechanisms. As a result of these advances, the field of predictive allosteric drug development is now on the cusp of a new era of rational structure-based computational methods. Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov state models and topology analyses provide insight into the relationship between protein dynamics and allosteric drug binding. In each section, we first provide an overview of the various method classes before describing relevant algorithms and software Emerging Computational packages for the Rational large scale chemical data mining allosteric drug discovery of relative exploration for a descriptor-based encoding of selected hits atom types to a multi-covalent fragment-based pharmaco-ligand against novel elucidated active β-amyloid Peptide binding sites.
Emerging Computational Methods; Rational Discovery; Allosteric Drugs; large scale; chemical data mining; drug discovery; relative exploration; descriptor-based; encoding of selected hits; atom types; multi-covalent; fragment-based; pharmaco-ligand; novel elucidated active β-amyloid Peptide; binding sites;