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A facile consensus ranking approach enhances virtual screening robustness and identifies a cell-active DYRK1? inhibitor.

ABSTRACT: BACKGROUND:Virtual screening is vital for contemporary drug discovery but striking performance fluctuations are commonly encountered, thus hampering error-free use. Results and Methodology: A conceptual framework is suggested for combining screening algorithms characterized by orthogonality (docking-scoring calculations, 3D shape similarity, 2D fingerprint similarity) into a simple, efficient and expansible python-based consensus ranking scheme. An original experimental dataset is created for comparing individual screening methods versus the novel approach. Its utilization leads to identification and phosphoproteomic evaluation of a cell-active DYRK1? inhibitor. CONCLUSION:Consensus ranking considerably stabilizes screening performance at reasonable computational cost, whereas individual screens are heavily dependent on calculation settings. Results indicate that the novel approach, currently available as a free online tool, is highly suitable for prospective screening by nonexperts.

SUBMITTER: Mavrogeni ME

PROVIDER: S-EPMC6479281 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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A facile consensus ranking approach enhances virtual screening robustness and identifies a cell-active DYRK1α inhibitor.

Mavrogeni Maria E ME Pronios Filippos F Zareifi Danae D Vasilakaki Sofia S Lozach Olivier O Alexopoulos Leonidas L Meijer Laurent L Myrianthopoulos Vassilios V Mikros Emmanuel E

Future medicinal chemistry 20181016 20

<h4>Background</h4>Virtual screening is vital for contemporary drug discovery but striking performance fluctuations are commonly encountered, thus hampering error-free use. Results and Methodology: A conceptual framework is suggested for combining screening algorithms characterized by orthogonality (docking-scoring calculations, 3D shape similarity, 2D fingerprint similarity) into a simple, efficient and expansible python-based consensus ranking scheme. An original experimental dataset is create ...[more]

PMID: 30325204

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Project description:BackgroundAccurate protein loop structure models are important to understand functions of many proteins. Identifying the native or near-native models by distinguishing them from the misfolded ones is a critical step in protein loop structure prediction.ResultsWe have developed a Pareto Optimal Consensus (POC) method, which is a consensus model ranking approach to integrate multiple knowledge- or physics-based scoring functions. The procedure of identifying the models of best quality in a model set includes: 1) identifying the models at the Pareto optimal front with respect to a set of scoring functions, and 2) ranking them based on the fuzzy dominance relationship to the rest of the models. We apply the POC method to a large number of decoy sets for loops of 4- to 12-residue in length using a functional space composed of several carefully-selected scoring functions: Rosetta, DOPE, DDFIRE, OPLS-AA, and a triplet backbone dihedral potential developed in our lab. Our computational results show that the sets of Pareto-optimal decoys, which are typically composed of approximately 20% or less of the overall decoys in a set, have a good coverage of the best or near-best decoys in more than 99% of the loop targets. Compared to the individual scoring function yielding best selection accuracy in the decoy sets, the POC method yields 23%, 37%, and 64% less false positives in distinguishing the native conformation, indentifying a near-native model (RMSD < 0.5A from the native) as top-ranked, and selecting at least one near-native model in the top-5-ranked models, respectively. Similar effectiveness of the POC method is also found in the decoy sets from membrane protein loops. Furthermore, the POC method outperforms the other popularly-used consensus strategies in model ranking, such as rank-by-number, rank-by-rank, rank-by-vote, and regression-based methods.ConclusionsBy integrating multiple knowledge- and physics-based scoring functions based on Pareto optimality and fuzzy dominance, the POC method is effective in distinguishing the best loop models from the other ones within a loop model set.

Dataset Information

A facile consensus ranking approach enhances virtual screening robustness and identifies a cell-active DYRK1? inhibitor.

Publications

A facile consensus ranking approach enhances virtual screening robustness and identifies a cell-active DYRK1α inhibitor.

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