A Pipeline to Enhance Ligand Virtual Screening: Integrating Molecular Dynamics and Fingerprints for Ligand and Proteins

The importance of taking into account protein flexibility in drug design and virtual ligand screening (VS) has been widely debated in the literature, and molecular dynamics (MD) has been recognized as one of the most powerful tools for investigating intrinsic protein dynamics. Nevertheless, deciphering the amount of information hidden in MD simulations and recognizing a significant minimal set of states to be used in virtual screening experiments can be quite complicated. Here we present an integrated MD-FLAP (Molecular Dynamics - Fingerprint for Ligand and Proteins) approach, comprising a pipeline of Molecular Dynamics, Clustering and Linear Discriminant Analysis, for enhancing accuracy and efficacy in VS campaigns. We first extracted a limited number of representative structures from tens of ns of MD trajectories by means of the k-medoids clustering algorithm as implemented in the BiKi Life Science Suite.1 Then, instead of applying arbitrary selection criteria, i.e. RMSD, pharmacophore properties, enrichment performances, we let the Linear Discriminant Analysis algorithm, implemented in FLAP,2 to automatically choose the best performing conformational states among medoids and X-ray structures. Retrospective virtual screenings confirmed that ensemble receptor protocols outperform single rigid receptor approaches, proved that computationally generated conformations comprise the same quantity/quality of information included in X-ray structures, and pointed to the MD-FLAP approach as a valuable tool for improving VS performances.