Biology subjects

Blundell, T.

Publications and source records attributed to Blundell, T..

5 recordsLinked to original sources

bioRxiv2025

Insights from aquaporin structures into drug-resistant sleeping sickness

Trypanosoma brucei is the causal agent of African trypanosomiasis in humans and animals, the latter resulting in significant negative economic impacts in afflicted areas of the world. Resistance has arisen to the trypanocidal drugs pentamidine and melarsoprol through mutations in the aquaglyceroporin TbAQP2 that prevent their uptake. Here we use cryogenic electron microscopy to determine the structure of TbAQP2 from Trypanosoma brucei, bound to either the substrate glycerol or to the sleeping sickness drugs, pentamidine or melarsoprol. The drugs bind within the AQP2 channel at a site completely overlapping that of glycerol. Mutations leading to a drug-resistant phenotype were found in the channel lining. Molecular dynamics simulations showed the channel can be traversed by pentamidine, with a low energy binding site at the centre of the channel, flanked by regions of high energy association at the extracellular and intracellular ends. Drug-resistant TbAQP2 mutants are still predicted to bind pentamidine, but the much weaker binding in the centre of the channel observed in the MD simulations would be insufficient to compensate for the high energy processes of ingress and egress, hence impairing transport at pharmacologically relevant concentrations. The structures of drug-bound TbAQP2 represent a novel paradigm for drug-transporter interactions that could provide new mechanisms for targeting drugs into pathogens and human cells.

molecular biology↗

bioRxiv2022

Ionic control of porin permeability in bacteria.

Bacterial porins permit permeation of hydrophilic nutrients and antibiotics across the outer membrane but also contribute to proton leak from the periplasmic space, suggesting that their activity might be dynamically regulated. Here we show, in Escherichia coli, that porin permeability is controlled by changes in periplasmic ions, inhibited by periplasmic acidification, thereby limiting proton loss during electron transport chain activity, and enhanced during starvation, promoting nutrient uptake. Growth in glucose increases periplasmic potassium through activating the voltage-gated channel Kch, triggering enhanced porin permeation and membrane action potentials. This metabolic control of porin permeability explains the recognized decrease in antibiotic susceptibility when bacteria are grown in lipid media and the impact of mutations in central metabolism genes on drug resistance, identifying Kch as a therapeutic target to improve bacterial killing by antibiotics. One sentence summaryThe permeability of bacterial porin is dynamically regulated by periplasmic pH and potassium levels, altering antibiotic resistance.

microbiology↗

bioRxiv2021

The pathobiology of Mycobacterium abscessus revealed through phenogenomic analysis

The medical and scientific response to emerging pathogens is often severely hampered by ignorance of the genetic determinants of virulence, drug resistance, and clinical outcomes that could be used to identify therapeutic drug targets and forecast patient trajectories 1-5. Taking the newly emergent multidrug-resistant bacteria Mycobacterium abscessus as an example 6, we show that combining high dimensional phenotyping with whole genome sequencing in a phenogenomic analysis can rapidly reveal actionable systems-level insights into bacterial pathobiology. Using in vitro and in vivo phenotyping, we discovered three distinct clusters of isolates, each associated with a different clinical outcome. We combined genome-wide association studies (GWAS) with proteome-wide computational structural modelling 7 to define likely causal variants, and employed direct coupling analysis (DCA) 8 to identify co-evolving, and therefore potentially epistatic, gene networks. We then used in vivo CRISPR-based silencing to validate our findings, defining a novel secretion system controlling virulence in M. abscessus, and illustrating how phenogenomics can reveal critical pathways within emerging pathogenic bacteria.

microbiology↗

bioRxiv2020

Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Protein Structures

Geometric deep learning has well-motivated applications in the context of biology, a domain where relational structure in datasets can be meaningfully leveraged. Currently, efforts in both geometric deep learning and, more broadly, deep learning applied to biomolecular tasks have been hampered by a scarcity of appropriate datasets accessible to domain specialists and machine learning researchers alike. However, there has been little exploration of how to best to integrate and construct geometric representations of these datatypes. To address this, we introduce Graphein as a turn-key tool for transforming raw data from widely-used bioinformatics databases into machine learning-ready datasets in a high-throughput and flexible manner. Graphein is a Python library for constructing graph and surface-mesh representations of protein structures and biological interaction networks for computational analysis. Graphein provides utilities for data retrieval from widely-used bioinformatics databases for structural data, including the Protein Data Bank, the recently-released AlphaFold Structure Database, and for biomolecular interaction networks from STRINGdb, BioGrid, TRRUST and RegNetwork. The library interfaces with popular geometric deep learning libraries: DGL, PyTorch Geometric and PyTorch3D though remains framework agnostic as it is built on top of the PyData ecosystem to enable inter-operability with scientific computing tools and libraries. Graphein is designed to be highly flexible, allowing the user to specify each step of the data preparation, scalable to facilitate working with large protein complexes and interaction graphs, and contains useful pre-processing tools for preparing experimental files. Graphein facilitates network-based, graph-theoretic and topological analyses of structural and interaction datasets in a high-throughput manner. As example workflows, we make available two new protein structure-related datasets, previously unused by the geometric deep learning community. We envision that Graphein will facilitate developments in computational biology, graph representation learning and drug discovery. Availability and implementationGraphein is written in Python. Source code, example usage and tutorials, datasets, and documentation are made freely available under the MIT License at the following URL: graphein.ai

bioinformatics↗

bioRxiv2019

Inhibiting Mycobacterium tuberculosis CoaBC by targeting a new allosteric site.

Coenzyme A (CoA) is a fundamental co-factor for all life, involved in numerous metabolic pathways and cellular processes, and its biosynthetic pathway has raised substantial interest as a drug target against multiple pathogens including Mycobacterium tuberculosis. The biosynthesis of CoA is performed in five steps, with the second and third steps being catalysed in the vast majority of prokaryotes, including M. tuberculosis, by a single bifunctional protein, CoaBC. Depletion of CoaBC was found to be bactericidal in M. tuberculosis. Here we report the first structure of a full-length CoaBC, from the model organism Mycobacterium smegmatis, describe how it is organised as a dodecamer and regulated by CoA thioesters. A high-throughput biochemical screen focusing on CoaB identified two inhibitors with different chemical scaffolds. Hit expansion led to the discovery of potent inhibitors of M. tuberculosis CoaB, which we show to bind to a novel cryptic allosteric site within CoaB.

biochemistry↗