Consensus virtual screening approaches to predict protein ligands

Abstract

-In order to exploit the advantages of receptor-based virtual screening, namely time/cost saving and specificity, it is important to rely on algorithms that predict a high number of active ligands at the top ranks of a small molecule database. Towards that goal consensus methods combining the results of several docking algorithms were developed and compared against the individual algorithms. Furthermore, a recently proposed rescoring method based on drug efficiency indices was evaluated. Among AutoDock Vina 1.0, AutoDock 4.2 and GemDock, AutoDock Vina was the best performing single method in predicting high affinity ligands from a database of known ligands and decoys. The rescoring of predicted binding energies with the water/butanol partition coeffcient did not lead to an improvement averaged over all receptor targets. Various consensus algorithms were investigated and a simple combination of AutoDock and AutoDock Vina results gave the most consistent performance that showed early enrichment of known ligands for all receptor targets investigated. In case a number ligands is known for a specific target, every method proposed in this study should be evaluated.

Evaluation of a novel virtual screening strategy using receptor decoy binding sites

“…Overall, the majority of targets did not show any improvement in enrichment at the top 1 % or 2 % of the list after applying the receptor decoy method. Five targets (Comt, Ache, CDK2, HIVrt and Pparg) show improved ROCE factors compared to those obtained in the previous study [11], (see footnotes in Tables 1 and 2) when considering at least the top 15 % of the decoy site list. Beyond 15 % the enrichment for all targets (except HIVrt and Parp) either remained constant or dropped to a lower value.…”

“…The complexes were selected from several different protein categories in the database such as hormone receptors, kinases, proteases and other enzymes to represent a wide range of targets, including 10 targets which had previously been evaluated [11]. Virtual screening for all fifteen targets was performed using Autodock Vina version 1.1.1 with the default parameters [12].…”

“…Furthermore, methods to increase the accuracy of virtual screening have been suggested, for example considering receptor flexibility to reduce the numbers of false positive molecules [9], consensus docking to predict correct binding pose [10], and a consensus virtual screening method that combined the rank lists of ligands from different algorithms [11]. However, these improved methods can still result in a low number of correct predictions for some receptors [11]. In the work described here the novel strategy of using receptor decoy sites was developed and evaluated for the first time together with the docking software AutoDock Vina [12].…”

Acoplamiento Molecular: Avances Recientes y Retos

“…These methods allow pose enrichment and better ranking results (Du, Bleylevens, Bitorina, Wichapong & Nicolaes, 2014;Kukol, 2011) and have the advantage that do not require other input or calculation beyond statistics. Successful applications include hit identification towards HIV reverse-transcriptase (Samanta & Das, 2017), tubulin inhibitors (Fani, Sattarinezhad & Bordbar, 2017), and Zika virus (Onawole, Sulaiman, Adegoke & Kolapo, 2017).…”

VinaMPI: Facilitating multiple receptor high-throughput virtual docking on high-performance computers

“…In addition to the added technical benefits, Vina yields better Receiver Operator Characteristics Enrichment than the Autodock program. [24] To scale Vina on supercomputing architectures, an all worker scheme was chosen to overcome the communication bottleneck of the parent-child distribution scheme. VinaMPI is a compiled C program that can be submitted as one job on leadership-class computing resources, obtaining a large number of processors in order to reduce the time-to-completion of very large screens.…”