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

Keywords: CREB1; NCI diversity set-II; NSC379099; analysis of residuals; docking-scoring calculations; fingerprint similarity; p53; phosphoproteomics; screening enrichment; shape-based similarity.

Figure 1.. The workflow for the proposed consensus ranking approach.

Three orthogonal primary VS methods are involved. Four protein–ligand complexes are utilized, giving rise to equal numbers of protein templates for docking calculations and ligand queries for similarity screening. Templates and settings are varied so as to increase sampling and information content. Results are averaged, normalized, whereas the log transformation ensures that compounds attaining top ranks in individual screens will be favored upon incorporation in consensus ranking. The tool is freely available at www.consensus-calculator.online. VS: Virtual screening.

Figure 2.. Screening results and consensus ranking evaluation.

(A) A comparison of the ROC curves and metrics determined for the mean performance of each primary VS method (Glide, blue; ROCS, orange; Canvas, green) versus the two optimal consensus variations (Norm. Cons., red; Norm. Log. Cons., purple marked). In all cases except GSK3β consensus outperforms individual screening, whereas in GSK3β consensus retains a good early recovery of true actives and a considerably improved stability and performance over ligand-based methods. (B) The cumulative performance comparison of consensus ranking against individual methods over all kinases. ROC: Receiver operating characteristic; VS: Virtual screening.

Figure 3.. Screening results and consensus ranking evaluation.

(A) Box plots showing performance of individual screens versus consensus ranking as illustrated by the ROC-AUC metric. (B) A graph showing the enrichment factors calculated at several levels of the screened library. The gain in early enrichment achieved by the consensus approach is evident particularly with respect to the ligand-based VS methods. ROC-AUC: Area under the ROC curve; ROC: Receiver operating characteristic; VS: Virtual screening.

Figure 4.. Screening results and consensus ranking evaluation.

(A) Typical graphs of residuals (experimental–predicted ranks) as determined for the four kinases. The plots depicted herein were derived by implementing the Norm. Log. Cons. consensus approach but similar random distribution of residuals was identified for all screens performed in this study. (B) A heat map of the RMSD values calculated by the residual analysis for all screens reported in the study.

Figure 4.. Screening results and consensus ranking evaluation.

(A) Typical graphs of residuals (experimental–predicted ranks) as determined for the four kinases. The plots depicted herein were derived by implementing the Norm. Log. Cons. consensus approach but similar random distribution of residuals was identified for all screens performed in this study. (B) A heat map of the RMSD values calculated by the residual analysis for all screens reported in the study.

Figure 5.. Screening results and consensus ranking evaluation.

(A) Results from phosphoproteomic analysis on the effect of 15 in different cell lines. Administration of the compound induces significant upregulation of specific gene products such as the tumor-suppressor p53 and the transcription factor CREB1 in a consistent fashion over three of four studied systems. An increase of kinases MAPK3, MAPK12, MEK1, transcription factor c-JUN, NF-κβ inhibitor-α and metalloproteinase ADAM-TS1 was also observed in melanoma and hepatoblastoma cell lines. The only statistically significant downregulated gene product was tyrosine kinase LCK on PC3 prostate cells (n = 6; *, p < 0.5; **, p < 0.01; ***, p < 0.001).