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Fine tuning for success in structure-based virtual screening

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Abstract

Structure-based virtual screening plays a significant role in drug-discovery. The method virtually docks millions of compounds from corporate or public libraries into a binding site of a disease-related protein structure, allowing for the selection of a small list of potential ligands for experimental testing. Many algorithms are available for docking and assessing the affinity of compounds for a targeted protein site. The performance of affinity estimation calculations is highly dependent on the size and nature of the site, therefore a rationale for selecting the best protocol is required. To address this issue, we have developed an automated calibration process, implemented in a Knime workflow. It consists of four steps: preparation of a protein test set with structures and models of the target, preparation of a compound test set with target-related ligands and decoys, automatic test of 24 scoring/rescoring protocols for each target structure and model, and graphical display of results. The automation of the process combined with execution on high performance computing resources greatly reduces the duration of the calibration phase, and the test of many combinations of algorithms on various target conformations results in a rational and optimal choice of the best protocol. Here, we present this tool and exemplify its application in setting-up an optimal protocol for SBVS against Retinoid X Receptor alpha.

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Data availability

All data generated or analysed during this study are included in this published article and its supplementary information files.

Code availability

The Knime workflow Evotec_StructureBasedVirtualScreening_Calibration generated during the current study is available on Knime HUB https://hub.knime.com/.

Abbreviations

AUC:

Area under the curve

EF:

Enrichment factor

FMO:

Fragment molecular orbital

HBAcc:

Hydrogen-bond acceptor

HBDon:

Hydrogen-bond donor

HM:

Homology model

HTS:

High throughput screening

MC:

Monte Carlo

MD:

Molecular dynamics

MW:

Molecular weight

PDB:

Protein Data Bank

RMSD:

Root mean square deviation

ROC:

Receiver operating characteristic

SBVS:

Structure-based virtual screening

SF:

Scoring function

VS:

Virtual screening

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Acknowledgements

This work was part of a Lean Initiative at Evotec. We thank Danielle De Boyer-Montegut for her support during all the phases of this lean project, aiming to easy the calibration of SBVS projects. We also warmly thank Jon Ainsley for the manuscript revision.

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No funding was received to assist with the preparation of this manuscript.

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Correspondence to Emilie Pihan.

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Pihan, E., Kotev, M., Rabal, O. et al. Fine tuning for success in structure-based virtual screening. J Comput Aided Mol Des 35, 1195–1206 (2021). https://doi.org/10.1007/s10822-021-00431-4

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