Towards accurate high-throughput ligand affinity prediction by exploiting structural ensembles, docking metrics and ligand similarity

Abstract

Motivation Nowadays, virtual screening (VS) plays a major role in the process of drug development. Nonetheless, an accurate estimation of binding affinities, which is crucial at all stages, is not trivial and may require target-specific fine-tuning. Furthermore, drug design also requires improved predictions for putative secondary targets among which is Estrogen Receptor alpha (ERα). Results VS based on combinations of Structure-Based VS (SBVS) and Ligand-Based VS (LBVS) is gaining momentum to help characterizing secondary targets of xenobiotics (including drugs and pollutants). In this study, we propose an integrated approach using ligand docking based on multiple structural en-sembles to reflect the conformational flexibility of the receptor. Then, we investigate the impact of the two different types of features (structure-based docking descriptors and ligand-based molecular descriptors) for affinity predictions based on a random forest algorithm. We find that ligand-based features have limited predictive power (rP =0.69, R2=0.47), compared to structure-based features (rP =0.78, R2=0.60) while their combination maintains the overall accuracy (rP =0.77, R2=0.56). Extending the training dataset to include xenobiotics, leads to a novel high-throughput affinity prediction method for ERα ligands (rP =0.85, R2=0.71). Method’s robustness is tested on several ligand databases and performances are compared with existing rescoring procedures. The presented prediction tool is provided to the community as a dedicated satellite of the @TOME server. Availability http://atome4.cbs.cnrs.fr/ATOME_V3/SERVER/EDMon_v3.html Contact schneider@cbs.cnrs.fr, labesse@cbs.cnrs.fr

In Silico Predictions of Endocrine Disruptors Properties

“…This dedicated server called EDMon (Endocrine Disruptor Monitoring; available at: http://edmon.cbs.cnrs.fr/) is now available to screen for ER α , ER β , and peroxisome proliferator–activated receptor- γ (PPAR γ ). It predicts for affinities using a rescoring approach based on machine learning (38). However, the problem is still severe for promiscuous proteins, such as the nuclear receptors CAR (constitutive androstane receptor) and PXR (pregnane X receptor) (39).…”

In Vitro and In Silico Study of 5-[(4-Methylpiperazin-1-yl) methyl]dehydrozingerone and 5-(Morpholin-4-ylmethyl) dehydrozingerone as Lipoxygenase and Xanthine Oxidase Inhibitor

“…Nevertheless, the in silico study results indicated that the binding scores did not show a good correlation with the in vitro experiment obtained. The comparative study found that the binding affinity's predictive power is relatively low than a structurebased feature [32] . Fig.…”

GM-DockZn: a geometry matching-based docking algorithm for zinc proteins

“…Many zinc proteins are established, or potential, drug targets (Anzellotti and Farrell, 2008;Krezel and Maret, 2016;Parkin, 2004). While progresses were made in molecular docking algorithms (Ballester and Mitchell, 2010;Boyles, et al, 2020;Cang and Wei, 2017;Johansson-Akhe, et al, 2020;Lu, et al, 2019;Schneider, et al, 2020;Velazquez-Libera, et al, 2020;Wang, et al, 2019;Zhang and Sanner, 2019) according to recent assessment (Li, et al, 2014;Su, et al, 2019), metalloproteins were found more challenging than nonmetalloproteins for docking because of additional interactions involving with metal ions. Hu et al (Hu, et al, 2004) showed that a correct zinc-coordination geometry is essential for the state-of-the-art docking software FlexX, Autodock, and GOLD (Jones, et al, 1995;Jones, et al, 1997;Kramer, et al, 1999;Morris, et al, 2009;Rarey, et al, 1996;Trott and Olson, 2010) to achieve a reasonable prediction.…”