Knowledge-Based Strategy to Improve Ligand Pose Prediction Accuracy for Lead Optimization

Accurately predicting how a small molecule binds to its target protein is an essential requirement for structure-based drug design (SBDD) efforts. In structurally enabled medicinal chemistry programs, binding pose prediction is often applied to ligands after a related compound’s crystal structure bound to the target protein has been solved. In this article, we present an automated pose prediction protocol that makes extensive use of existing X-ray ligand information. It uses spatial restraints during docking based on maximum common substructure (MCS) overlap between candidate molecule and existing X-ray coordinates of the related compound. For a validation data set of 8784 docking runs, our protocol’s pose prediction accuracy (80–82%) is almost two times higher than that of one unbiased docking method software (43%). To demonstrate the utility of this protocol in a project setting, we show its application in a chronological manner for a number of internal drug discovery efforts. The accuracy and applicability of this algorithm (>70% of cases) to medicinal chemistry efforts make this the approach of choice for pose prediction in lead optimization programs

Beyond PAINs: Chemotype Sensitivity of Protein Methyltransferases in Screens

Screening of the relatively new target class, the lysine and arginine methyltransferases (MTases), presents unique challenges in the identification and confirmation of active chemical matter. Examination of high throughput screening data generated using Scintillation Proximity Assay (SPA) format for a number of protein MTase targets reveals sensitivity to both the known pan assay interference compounds (PAINS) and also other scaffolds not currently precedented as assay interferers. We find that, in general, true actives show significant selectivity within the MTase family. With the exception of slight modifications of SAM-like compounds, scaffolds that are observed frequently in multiple MTase assays should be viewed with caution and should be carefully validated before following up

Predicting the Conformational Variability of Abl Tyrosine Kinase using Molecular Dynamics Simulations and Markov State Models

Understanding protein conformational variability remains a challenge in drug discovery. The issue arises in protein kinases, whose multiple conformational states can affect the binding of small-molecule inhibitors. To overcome this challenge, we propose a comprehensive computational framework based on Markov state models (MSMs). Our framework integrates the information from explicit-solvent molecular dynamics simulations to accurately rank-order the accessible conformational variants of a target protein. We tested the methodology using Abl kinase with a reference and blind-test set. Only half of the Abl conformational variants discovered by our approach are present in the disclosed X-ray structures. The approach successfully identified a protein conformational state not previously observed in public structures but evident in a retrospective analysis of Lilly in-house structures: the X-ray structure of Abl with WHI-P154. Using a MSM-derived model, the free energy landscape and kinetic profile of Abl was analyzed in detail highlighting opportunities for targeting the unique metastable states

Selectivity Data: Assessment, Predictions, Concordance, and Implications

Could high-quality in silico predictions in drug discovery eventually replace part or most of experimental testing? To evaluate the agreement of selectivity data from different experimental or predictive sources, we introduce the new metric concordance minimum significant ratio (cMSR). Empowered by cMSR, we find the overall level of agreement between predicted and experimental data to be comparable to that found between experimental results from different sources. However, for molecules that are either highly selective or potent, the concordance between different experimental sources is significantly higher than the concordance between experimental and predicted values. We also show that computational models built from one data set are less predictive for other data sources and highlight the importance of bias correction for assessing selectivity data. Finally, we show that small-molecule target space relationships derived from different data sources and predictive models share overall similarity but can significantly differ in details

Exploiting an Allosteric Binding Site of PRMT3 Yields Potent and Selective Inhibitors

Protein arginine methyltransferases (PRMTs) play an important role in diverse biological processes. Among the nine known human PRMTs, PRMT3 has been implicated in ribosomal biosynthesis via asymmetric dimethylation of the 40S ribosomal protein S2 and in cancer via interaction with the DAL-1 tumor suppressor protein. However, few selective inhibitors of PRMTs have been discovered. We recently disclosed the first selective PRMT3 inhibitor, which occupies a novel allosteric binding site and is noncompetitive with both the peptide substrate and cofactor. Here we report comprehensive structure–activity relationship studies of this series, which resulted in the discovery of multiple PRMT3 inhibitors with submicromolar potencies. An X-ray crystal structure of compound <b>14u</b> in complex with PRMT3 confirmed that this inhibitor occupied the same allosteric binding site as our initial lead compound. These studies provide the first experimental evidence that potent and selective inhibitors can be created by exploiting the allosteric binding site of PRMT3

An Orally Bioavailable Chemical Probe of the Lysine Methyltransferases EZH2 and EZH1

EZH2 or EZH1 is the catalytic subunit of the polycomb repressive complex 2 that catalyzes methylation of histone H3 lysine 27 (H3K27). The trimethylation of H3K27 (H3K27me3) is a transcriptionally repressive post-translational modification. Overexpression of EZH2 and hypertrimethylation of H3K27 have been implicated in a number of cancers. Several selective inhibitors of EZH2 have been reported recently. Herein we disclose UNC1999, the first orally bioavailable inhibitor that has high <i>in vitro</i> potency for wild-type and mutant EZH2 as well as EZH1, a closely related H3K27 methyltransferase that shares 96% sequence identity with EZH2 in their respective catalytic domains. UNC1999 was highly selective for EZH2 and EZH1 over a broad range of epigenetic and non-epigenetic targets, competitive with the cofactor SAM and non-competitive with the peptide substrate. This inhibitor potently reduced H3K27me3 levels in cells and selectively killed diffused large B cell lymphoma cell lines harboring the EZH2^Y641N mutant. Importantly, UNC1999 was orally bioavailable in mice, making this inhibitor a valuable tool for investigating the role of EZH2 and EZH1 in chronic animal studies. We also designed and synthesized UNC2400, a close analogue of UNC1999 with potency >1,000-fold lower than that of UNC1999 as a negative control for cell-based studies. Finally, we created a biotin-tagged UNC1999 (UNC2399), which enriched EZH2 in pull-down studies, and a UNC1999–dye conjugate (UNC2239) for co-localization studies with EZH2 in live cells. Taken together, these compounds represent a set of useful tools for the biomedical community to investigate the role of EZH2 and EZH1 in health and disease