Biology subjects

Leach, A. R.

Publications and source records attributed to Leach, A. R..

7 recordsLinked to original sources

bioRxiv2026

Chemical Probes in Scientific Literature: Expanding and Validating Target-Disease Evidence

Chemical probes are indispensable tools for validating therapeutic hypotheses, yet their broader impact on early-stage drug discovery remains unquantified. To our knowledge, this study represents the first systematic, large-scale investigation of the chemical probe literature. By screening over 18 million articles using a high-quality dictionary of 561 chemical probes, we identified 20,000 articles mentioning a chemical probe which resulted in 5,558 unique target-disease (T-D) associations. Our analysis yields four principal findings that redefine the utility of these chemicals: First, we show that chemical probe evidence typically precedes the appearance of structured data in major knowledge bases by 1-7 years, providing a crucial lead time for target prioritisation. Second, we identified 353 T-D pairs (6.4%) with no prior evidence in the Open Targets Platform, highlighting the approachs discovery potential. Third, the application of strict novelty filters uncovered 135 new high-confidence associations between targets and diseases, revealing distinct opportunities for therapeutic repurposing in non-oncological, rare autoimmune diseases, and diseases without effective therapies due to complex biology or high treatment resistance. Finally, we demonstrate that chemical probes are essential for strengthening evidence, providing functional validation for associations previously supported only by weaker, correlative data such as RNA expression or animal models. Collectively, these findings illustrate that chemical probes catalyse early therapeutic discovery, emphasising the importance of cataloguing existing probes and identifying new ones.

bioinformatics↗

bioRxiv2025

RP3Net: a deep learning model for predicting recombinant protein production in Escherichia coli

Recombinant protein expression can be a limiting step in the production of protein reagents for drug discovery and other biotechnology applications. We introduce RP3Net (Recombinant Protein Production Prediction Network), an AI model of small-scale heterologous soluble protein expression in Escherichia coli. RP3Net utilizes the most recent protein and genomic foundational models. A curated dataset of internal experimental results from AstraZeneca (AZ) and publicly available data from the Structural Genomics Consortium (SGC) was used for training, validation and testing of RP3Net. Set Transformer Pooling (STP) aggregation and Meta Label Correction (MLC) with large scale purification data enabled RP3Net to improve Area Under Receiver Operator Curve (AUROC) by 0.15, compared to the baseline model. When experimentally validated on an independent, manually selected set of 97 constructs, RP3Net outperformed currently available models, with an AUROC of 0.83, delivering accurate predictions in 77% of the cases, and correctly identifying successfully expressing constructs in 92% of cases.

bioinformatics↗

bioRxiv2024

EnsembleFlex: Protein Structure Ensemble Analysis Made Easy

EnsembleFlex is a novel tool designed to perform dual-scale flexibility analysis of protein structure ensembles, encompassing both backbone and side-chain dynamics. It integrates dedicated superposition methodologies, two dimension reduction techniques, customizable clustering, an automated ligand binding site analysis and analysis of conserved water molecules, enhancing the accuracy of ensemble flexibility analysis and visualisation. With a user-friendly, no-coding graphical interface as well as a code-based option, EnsembleFlex is accessible to a broad range of researchers. The tool complements experimental ensemble data analysis with predictive methods based on elastic network models, bridging the gap between experimental and computational approaches in protein flexibility analysis, and does so from a global backbone-based perspective, as well as from a focused side-chain and binding site-centred point of view. The need for reliable, streamlined, fast, and user-friendly tools for structural ensemble analysis is critical in todays research environment, and EnsembleFlex addresses these needs efficiently, offering an accessible solution that integrates powerful analysis methods in a single package. Its wide applicability across various domains of molecular design underscores the importance of streamlined flexibility analysis in scientific research.

molecular biology↗

bioRxiv2022

Illuminating the Druggable Genome through Patent Bioactivity Data

The patent literature is a potentially valuable source of bioactivity data. The SureChEMBL database (https://www.surechembl.org/) is a publicly available large-scale resource that contains compounds extracted on a daily basis from the full text, images and attachments of patent documents, through an automated text and image-mining pipeline. In this paper we describe a process to prioritise 3.7 million life science relevant patents obtained from SureChEMBL, according to how likely they were to contain bioactivity data for potent small molecules on less-studied targets, according to the classification developed by the Illuminating the Druggable Genome (IDG) project. The overall goal was to select a smaller number of patents that could be manually curated and incorporated into the ChEMBL database. We describe the approach taken, the results obtained, and provide some illustrative examples.

bioinformatics↗

bioRxiv2020

Influence of HLA class II polymorphism on predicted cellular immunity against SARS-CoV-2 at the population and individual level

Development of effective adaptive immune responses after coronavirus disease 2019 (COVID-19) and after vaccination against SARS-CoV-2 is predicated on recognition of viral peptides, presented in the context of HLA class II molecules, by CD4+ T-cells. We capitalised on extensive high resolution HLA data deposited in the National Marrow Donor Program registry to obtain detailed information on human HLA haplotype frequencies of twenty five human populations and used a bioinformatics approach to investigate the role of HLA polymorphism on SARS-CoV-2 immunogenicity at the population and at the individual level. Within populations, we identify wide inter-individual variability in predicted CD4+ T-cell reactivity against structural, non-structural and accessory SARS-CoV-2 proteins, according to expressed HLA genotype. However, we find similar potential for anti-SARS-CoV-2 cellular immunity at the population level, across all ethnic groups examined, suggesting that HLA polymorphism is unlikely to account for observed disparities in clinical outcomes after COVID-19 among different race and ethnic groups. We predict robust immune reactivity against SARS-CoV-2 Spike protein, the basis for the majority of current vaccination efforts, both at the population and individual level, despite significant variation in Spike-derived peptide presentation by individual HLA genotypes. Finally, we provide comprehensive maps of SARS-CoV-2 proteome immunogenicity accounting for population coverage in major ethnic groups. Our findings provide important insight on the potential role of HLA polymorphism on development of protective immunity after SARS-CoV-2 infection and after vaccination and a firm basis for further experimental studies in this field.

immunology↗

bioRxiv2020

Ribosome fingerprinting with a solid-state nanopore

Nanopores hold great potential for the analysis of complex biological molecules at the single entity level. One particularly interesting macromolecular machine is the ribosome, responsible for translating mRNA into proteins. In this study, we use a solid-state nanopore to fingerprint 80S ribosomes and polysomes from a human neuronal cell line and, Drosophila melanogaster cultured cells and ovaries. Specifically, we show that the peak amplitude and dwell time characteristics of 80S ribosomes are distinct from polysomes and can be used to discriminate ribosomes from polysomes in mixed samples. Moreover, we are able to distinguish large polysomes, containing more than 7 ribosomes, from those containing 2-3 ribosomes, and demonstrate a correlation between polysome size and peak amplitude. This study highlights the application of solid-state nanopores as a rapid analytical tool for the detection and characterization of ribosomal complexes.

biophysics↗

bioRxiv2020

Drug mechanism-of-action discovery through the integration of pharmacological and CRISPR screens

Low success rates during drug development are due in part to the difficulty of defining drug mechanism-of-action and molecular markers of therapeutic activity. Here, we integrated 199,219 drug sensitivity measurements for 397 unique anti-cancer drugs and genome-wide CRISPR loss-of-function screens in 484 cell lines to systematically investigate in cellular drug mechanism-of-action. We observed an enrichment for positive associations between drug sensitivity and knockout of their nominal targets, and by leveraging protein-protein networks we identified pathways that mediate drug response. This revealed an unappreciated role of mitochondrial E3 ubiquitin-protein ligase MARCH5 in sensitivity to MCL1 inhibitors. We also estimated drug on-target and off-target activity, informing on specificity, potency and toxicity. Linking drug and gene dependency together with genomic datasets uncovered contexts in which molecular networks when perturbed mediate cancer cell loss-of-fitness, and thereby provide independent and orthogonal evidence of biomarkers for drug development. This study illustrates how integrating cell line drug sensitivity with CRISPR loss-of-function screens can elucidate mechanism-of-action to advance drug development.

systems biology↗