Biology subjects

Wlodarchak, N.

Publications and source records attributed to Wlodarchak, N..

5 recordsLinked to original sources

bioRxiv2026

BoltzMol-1: Towards Reliable Virtual Screening for Fast and Cost-Effective Hit Discovery

We present BoltzMol-1, a small-molecule hit discovery pipeline, centered on an optimized version of Boltz-2, explicitly adapted for prospective discovery. Reliable hit discovery that generalizes across target classes (rather than only the well-characterized families that dominate existing ligand data) would broaden the range of biology accessible to small-molecule intervention and reduce reliance on resource-intensive high-throughput screening. Towards this goal, the system prioritizes compounds for rapid experimental validation by coupling model-driven ranking with streamlined procurement from commercial catalogs. To improve developability at the point of selection, we introduce a suite of ADMET models for kinetic solubility (logS), lipophilicity (logD), and Caco-2 permeability. These models act as an early triage layer, systematically filtering out compounds with unfavorable physicochemical and absorption properties prior to synthesis or purchase. Across a panel of ten targets (most with no representation in the underlying affinity training data) we observe strong prospective performance on challenging systems. Functional actives or binders were identified for 6 of 10 targets, despite modest experimental budgets of 28-96 compounds per target. These results include successes on receptors and enzymes traditionally considered difficult for structure- or ligand-based approaches. Collectively, this work establishes a practical framework for low-throughput, cost-constrained discovery campaigns capable of delivering chemically tractable binders with favorable property profiles. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=124 SRC="FIGDIR/small/736485v1_fig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@1d7ff06org.highwire.dtl.DTLVardef@1a7fc7aorg.highwire.dtl.DTLVardef@1b0dc98org.highwire.dtl.DTLVardef@62b978_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1:C_FLOATNO Overview of the prospective virtual-screening campaigns across all targets. For each target, the panel shows the predicted protein-ligand complex together with the number of compounds tested, the number of confirmed actives/binders, and the assays used for screening and follow-up. C_FIG

biochemistry↗

bioRxiv2019

Inherited duplications of PPP2R3B promote naevi and melanoma via a novel C21orf91-driven proliferative phenotype

The majority of the heredity of melanoma remains unexplained, however inherited copy number changes have not yet been systematically studied. The genetic environment is highly relevant to treatment stratification, and new gene discovery is therefore desirable. Using an unbiased whole genome screening approach for copy number we identify here a novel melanoma predisposing factor, familial duplications of gene PPP2R3B, encoding a regulatory unit of critical phosphatase PP2A. Significant correlation between expression of PPP2R3B in tumour tissue and survival in a large melanoma cohort was confirmed, and associated with a non-immunological expression profile. Mechanistically, construction and extensive characterization of a stable, inducible cellular model for PPP2R3B overexpression revealed induction of pigment cell switching towards proliferation and away from migration. Importantly, this was independent of the known microphthalmia-associated transcription factor (MITF)-controlled pigment cell phenotype switch, and was instead driven by uncharacterised gene C21orf91. Bioinformatic studies point to C21orf91as a novel target of MITF, and therefore a potential hub in the control of phenotype switching in melanoma. This study identifies novel germline copy number variants in PPP2R3B predisposing to melanocytic neoplasia, and uncovers a new potential therapeutic target C21orf91 in the control of pigment cell proliferation.

genomics↗

bioRxiv2019

Predicting kinase inhibitors using bioactivity matrix derived informer sets

Prediction of compounds that are active against a desired biological target is a common step in drug discovery efforts. Virtual screening methods seek some active-enriched fraction of a library for experimental testing. Where data are too scarce to train supervised learning models for compound prioritization, initial screening must provide the necessary data. Commonly, such an initial library is selected on the basis of chemical diversity by some pseudo-random process (for example, the first few plates of a larger library) or by selecting an entire smaller library. These approaches may not produce a sufficient number or diversity of actives. An alternative approach is to select an informer set of screening compounds on the basis of chemogenomic information from previous testing of compounds against a large number of targets. We compare different ways of using chemogenomic data to choose a small informer set of compounds based on previously measured bioactivity data. We develop this Informer-Based-Ranking (IBR) approach using the Published Kinase Inhibitor Sets (PKIS) as the chemogenomic data to select the informer sets. We test the informer compounds on a target that is not part of the chemogenomic data, then predict the activity of the remaining compounds based on the experimental informer data and the chemogenomic data. Through new chemical screening experiments, we demonstrate the utility of IBR strategies in a prospective test on two kinase targets not included in the PKIS. Using limited training data in both retrospective and prospective tests, bioactivity fingerprints based on chemogenomic data outperform chemical fingerprints in predicting active compounds in both standard virtual screening metrics and accurate identification of hits from novel chemical classes. Author SummaryIn the early stages of drug discovery efforts, computational models are used to predict activity and prioritize compounds for experimental testing. New targets commonly lack the data necessary to build effective models, and the screening needed to generate that experimental data can be costly. We seek to improve the efficiency of the initial screening phase, and of the process of prioritizing compounds for subsequent screening. We choose a small informer set of compounds based on publicly available prior screening data on distinct (though related) targets. We then use experimental data on these informer compounds to predict the activity of other compounds in the set against the target of interest. Computational and statistical tools are needed to identify informer compounds and to prioritize other compounds for subsequent phases of screening. Using limited training data, we find that selection of informer compounds on the basis of bioactivity data from previous screening efforts is superior to the traditional approach of selection of a chemically diverse subset of compounds. We demonstrate the success of this approach in retrospective tests on the Published Kinase Inhibitor Sets (PKIS) chemogenomic data and in prospective experimental screens against two additional non-human kinase targets.

pharmacology and toxicology↗

bioRxiv2018

Comparative analysis of PknB inhibitors for reactivity and toxicity

Bacterial serine/threonine kinases are increasingly sought after as drug targets for new antibiotics. PknB, an essential kinase in Mycobacteria tuberculosis, is intensely targeted, and many inhibitors are in the developmental pipeline. These inhibitors typically are derived from screens of known kinase inhibitors and most share similar chemical properties as their parent compounds were all designed for optimal pharmacokinetic properties in the human body. Here, we investigate the reactivity and toxicity of a proposed PknB inhibitor, YH-8, which does not follow traditional drug design rules. We found that the compound is highly reactive with thiolating agents and has appreciable toxicity in a zebrafish animal model. Furthermore, we find minimal anti-mycobacterial activity with non-tubercular mycobacteria strains. These data suggest that further investigation is needed into its efficacy and physiochemical properties if it is to be further developed as an effective antibiotic.

microbiology↗

bioRxiv2017

Repurposed kinase inhibitors and β-lactams as a novel therapy for antibiotic resistant bacteria

Antibiotic resistant bacteria are an increasing global problem, and pathogenic actinomycetes and firmicutes are particularly challenging obstacles. These pathogens share several eukaryotic-like kinases that present antibiotic development opportunities. We used computational modelling to identify human kinase inhibitors that could be repurposed towards bacteria as part of a novel combination therapy. The computational model suggested a family of inhibitors, the imidazopyridine aminofurazans (IPAs), bind PknB with high affinity. We found that these inhibitors biochemically inhibit PknB, with potency roughly following the predicted models. A novel x-ray structure confirmed that the inhibitors bind as predicted and made favorable protein contacts with the target. These inhibitors also have antimicrobial activity towards Mycobacteria and Nocardia, and normally ineffective {beta}-lactams can potentiate IPAs to more efficiently inhibit growth of these pathogens. Collectively, our data show that in silico modeling can be used as a tool to discover promising drug leads, and the inhibitors we discovered can synergize with clinically relevant antibiotics to restore their efficacy against bacteria with limited treatment options.

microbiology↗