Per-Residue Energy Footprints-Based Pharmacophore Modeling as an Enhanced In Silico Approach in Drug Discovery: A Case Study on the Identification of Novel β-Secretase1 (BACE1) Inhibitors as Anti-Alzheimer Agents

Abstract

A further refinement to our previously published “target-bound pharmacophore modeling approach” is reported herein. Instead of relying on “random” pharmacophores—which is the most commonly used approach in literature—phrmacophoric “hot spots” were selected based on highly contributing amino acid residues to inhibitor binding. Highly contributing amino acid residues were identified based on energy footprints obtained from molecular dynamics and thermodynamic calculations. To our knowledge, this is the first attempt in the literature to use this approach. Previously, docking energy was used to create energy-based pharmacophores, however, docking scores, in many cases, are artifacts and questionable. Hence, we introduce the concept of per-residue energy decomposition footprints obtained from molecular dynamics ensembles in order to create more reliable pharmacophore modeling which can be implemented in drug discovery workflows. The purpose of this work is not to compare the validity of this approach against previously reported ones, but rather to introduce a more rational approach to the scientific domain. Further computational and experimental validations would be advised. Herein, we applied the developed protocol in order to identify novel BACE1 inhibitors and results were validated against experimentally determined inhibitors with known activity against BACE1. This protocol was found promising to identify hits with higher binding affinities when compared against experimentally determined inhibitors. Other post-dynamic binding analysis for the identified hits was also presented in this report as a roadmap towards the design of potential BACE1 inhibitors as anti-Alzheimer Agents.