Utilizing Experimental Data for Reducing Ensemble Size in Flexible-Protein Docking

Abstract

Efficient and sufficient incorporation of protein flexibility into docking is still a challenging task. Docking to an ensemble of protein structures has proven its utility for docking, but using a large ensemble of structures can reduce the efficiency of docking and can increase the number of false positives in virtual screening. In this paper, we describe the application of our new methodology, Limoc, to generate an ensemble of holo-like protein structures in combination with the relaxed complex scheme (RCS), to virtual screening. We describe different schemes to reduce the ensemble of protein structures to increase efficiency and enrichment quality. Utilizing experimental knowledge about actives for a target protein allows the reduction of ensemble members to a minimum of three protein structures increasing enrichment quality and efficiency simultaneously.

Understanding the challenges of protein flexibility in drug design

“…For example, ensembles derived from NMA or Monte Carlo can be filtered using ligand information to select representative protein conformations [75]. Using a dynamic virtual ligand (represented by a collection of functional groups), an MD simulation of the binding event can generate promising protein conformations [94], [95]. Another method generates an ensemble of protein conformations by docking flexible ligands to a flexible receptor, using known active compounds [96].…”

Induced fit docking, and the use of QM/MM methods in docking

“…Xu et al [18] tested two novel schemes to select protein structures for ensemble docking using experimental ligand data. In the first scheme, protein structures were selected that best reproduced the native binding mode of 1–3 known protein-ligand complex structures of the target protein.…”

Rational Prediction with Molecular Dynamics for Hit Identification

“…While other methods, such as normal mode analysis [85], can be used to generate conformational diversity, MD has the benefit of approximating the conformational ensemble expected at thermal equilibrium, which may facilitate virtual screening success. Contemporary MD simulations can easily result in tens of thousands to hundreds of thousands of receptor conformations, and a virtual screen can be performed against anyone of these [86], all of them [87, 88], or any subset [40, 41, 89]. Moreover, there are multiple ways to combine the docking scores from each receptor.…”

“…For example, when ensembles are constructed from 20 unique receptor conformations, there are a total of 1,048,575 ensembles to consider. In practice, performance can be plotted as a function of size by extracting the top-performing member for each size [89, 94]. For example, Fig.…”