Biochemistry and Pharmacology - Research Publications
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ItemCSM-Potential: mapping protein interactions and biological ligands in 3D space using geometric deep learning(OXFORD UNIV PRESS, 2022-05-24)Recent advances in protein structural modelling have enabled the accurate prediction of the holo 3D structures of almost any protein, however protein function is intrinsically linked to the interactions it makes. While a number of computational approaches have been proposed to explore potential biological interactions, they have been limited to specific interactions, and have not been readily accessible for non-experts or use in bioinformatics pipelines. Here we present CSM-Potential, a geometric deep learning approach to identify regions of a protein surface that are likely to mediate protein-protein and protein-ligand interactions in order to provide a link between 3D structure and biological function. Our method has shown robust performance, outperforming existing methods for both predictive tasks. By assessing the performance of CSM-Potential on independent blind tests, we show that our method was able to achieve ROC AUC values of up to 0.81 for the identification of potential protein-protein binding sites, and up to 0.96 accuracy on biological ligand classification. Our method is freely available as a user-friendly and easy-to-use web server and API at http://biosig.unimelb.edu.au/csm_potential.
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ItemcropCSM: designing safe and potent herbicides with graph-based signatures(OXFORD UNIV PRESS, 2022-02-24)Herbicides have revolutionised weed management, increased crop yields and improved profitability allowing for an increase in worldwide food security. Their widespread use, however, has also led to a rise in resistance and concerns about their environmental impact. Despite the need for potent and safe herbicidal molecules, no herbicide with a new mode of action has reached the market in 30 years. Although development of computational approaches has proven invaluable to guide rational drug discovery pipelines, leading to higher hit rates and lower attrition due to poor toxicity, little has been done in contrast for herbicide design. To fill this gap, we have developed cropCSM, a computational platform to help identify new, potent, nontoxic and environmentally safe herbicides. By using a knowledge-based approach, we identified physicochemical properties and substructures enriched in safe herbicides. By representing the small molecules as a graph, we leveraged these insights to guide the development of predictive models trained and tested on the largest collected data set of molecules with experimentally characterised herbicidal profiles to date (over 4500 compounds). In addition, we developed six new environmental and human toxicity predictors, spanning five different species to assist in molecule prioritisation. cropCSM was able to correctly identify 97% of herbicides currently available commercially, while predicting toxicity profiles with accuracies of up to 92%. We believe cropCSM will be an essential tool for the enrichment of screening libraries and to guide the development of potent and safe herbicides. We have made the method freely available through a user-friendly webserver at http://biosig.unimelb.edu.au/crop_csm.
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ItemSystematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures(OXFORD UNIV PRESS, 2022-03-10)Changes in protein sequence can have dramatic effects on how proteins fold, their stability and dynamics. Over the last 20 years, pioneering methods have been developed to try to estimate the effects of missense mutations on protein stability, leveraging growing availability of protein 3D structures. These, however, have been developed and validated using experimentally derived structures and biophysical measurements. A large proportion of protein structures remain to be experimentally elucidated and, while many studies have based their conclusions on predictions made using homology models, there has been no systematic evaluation of the reliability of these tools in the absence of experimental structural data. We have, therefore, systematically investigated the performance and robustness of ten widely used structural methods when presented with homology models built using templates at a range of sequence identity levels (from 15% to 95%) and contrasted performance with sequence-based tools, as a baseline. We found there is indeed performance deterioration on homology models built using templates with sequence identity below 40%, where sequence-based tools might become preferable. This was most marked for mutations in solvent exposed residues and stabilizing mutations. As structure prediction tools improve, the reliability of these predictors is expected to follow, however we strongly suggest that these factors should be taken into consideration when interpreting results from structure-based predictors of mutation effects on protein stability.
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ItemOxidative desulfurization pathway for complete catabolism of sulfoquinovose by bacteria(NATL ACAD SCIENCES, 2022-01-25)Catabolism of sulfoquinovose (SQ; 6-deoxy-6-sulfoglucose), the ubiquitous sulfosugar produced by photosynthetic organisms, is an important component of the biogeochemical carbon and sulfur cycles. Here, we describe a pathway for SQ degradation that involves oxidative desulfurization to release sulfite and enable utilization of the entire carbon skeleton of the sugar to support the growth of the plant pathogen Agrobacterium tumefaciens SQ or its glycoside sulfoquinovosyl glycerol are imported into the cell by an ATP-binding cassette transporter system with an associated SQ binding protein. A sulfoquinovosidase hydrolyzes the SQ glycoside and the liberated SQ is acted on by a flavin mononucleotide-dependent sulfoquinovose monooxygenase, in concert with an NADH-dependent flavin reductase, to release sulfite and 6-oxo-glucose. An NAD(P)H-dependent oxidoreductase reduces the 6-oxo-glucose to glucose, enabling entry into primary metabolic pathways. Structural and biochemical studies provide detailed insights into the recognition of key metabolites by proteins in this pathway. Bioinformatic analyses reveal that the sulfoquinovose monooxygenase pathway is distributed across Alpha- and Betaproteobacteria and is especially prevalent within the Rhizobiales order. This strategy for SQ catabolism is distinct from previously described pathways because it enables the complete utilization of all carbons within SQ by a single organism with concomitant production of inorganic sulfite.
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ItemNo Preview AvailableKnown allosteric proteins have central roles in genetic disease(PUBLIC LIBRARY SCIENCE, 2022-02-01)Allostery is a form of protein regulation, where ligands that bind sites located apart from the active site can modify the activity of the protein. The molecular mechanisms of allostery have been extensively studied, because allosteric sites are less conserved than active sites, and drugs targeting them are more specific than drugs binding the active sites. Here we quantify the importance of allostery in genetic disease. We show that 1) known allosteric proteins are central in disease networks, contribute to genetic disease and comorbidities much more than non-allosteric proteins, and there is an association between being allosteric and involvement in disease; 2) they are enriched in many major disease types like hematopoietic diseases, cardiovascular diseases, cancers, diabetes, or diseases of the central nervous system; 3) variants from cancer genome-wide association studies are enriched near allosteric proteins, indicating their importance to polygenic traits; and 4) the importance of allosteric proteins in disease is due, at least partly, to their central positions in protein-protein interaction networks, and less due to their dynamical properties.
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ItemEstimating tuberculosis drug resistance amplification rates in high-burden settings(BMC, 2022-01-24)BACKGROUND: Antimicrobial resistance develops following the accrual of mutations in the bacterial genome, and may variably impact organism fitness and hence, transmission risk. Classical representation of tuberculosis (TB) dynamics using a single or two strain (DS/MDR-TB) model typically does not capture elements of this important aspect of TB epidemiology. To understand and estimate the likelihood of resistance spreading in high drug-resistant TB incidence settings, we used epidemiological data to develop a mathematical model of Mycobacterium tuberculosis (Mtb) transmission. METHODS: A four-strain (drug-susceptible (DS), isoniazid mono-resistant (INH-R), rifampicin mono-resistant (RIF-R) and multidrug-resistant (MDR)) compartmental deterministic Mtb transmission model was developed to explore the progression from DS- to MDR-TB in The Philippines and Viet Nam. The models were calibrated using data from national tuberculosis prevalence (NTP) surveys and drug resistance surveys (DRS). An adaptive Metropolis algorithm was used to estimate the risks of drug resistance amplification among unsuccessfully treated individuals. RESULTS: The estimated proportion of INH-R amplification among failing treatments was 0.84 (95% CI 0.79-0.89) for The Philippines and 0.77 (95% CI 0.71-0.84) for Viet Nam. The proportion of RIF-R amplification among failing treatments was 0.05 (95% CI 0.04-0.07) for The Philippines and 0.011 (95% CI 0.010-0.012) for Viet Nam. CONCLUSION: The risk of resistance amplification due to treatment failure for INH was dramatically higher than RIF. We observed RIF-R strains were more likely to be transmitted than acquired through amplification, while both mechanisms of acquisition were important contributors in the case of INH-R. These findings highlight the complexity of drug resistance dynamics in high-incidence settings, and emphasize the importance of prioritizing testing algorithms which allow for early detection of INH-R.
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ItemmmCSM-NA: accurately predicting effects of single and multiple mutations on protein nucleic acid binding affinity(OXFORD UNIV PRESS, 2021-11-17)While protein-nucleic acid interactions are pivotal for many crucial biological processes, limited experimental data has made the development of computational approaches to characterise these interactions a challenge. Consequently, most approaches to understand the effects of missense mutations on protein-nucleic acid affinity have focused on single-point mutations and have presented a limited performance on independent data sets. To overcome this, we have curated the largest dataset of experimentally measured effects of mutations on nucleic acid binding affinity to date, encompassing 856 single-point mutations and 141 multiple-point mutations across 155 experimentally solved complexes. This was used in combination with an optimized version of our graph-based signatures to develop mmCSM-NA (http://biosig.unimelb.edu.au/mmcsm_na), the first scalable method capable of quantitatively and accurately predicting the effects of multiple-point mutations on nucleic acid binding affinities. mmCSM-NA obtained a Pearson's correlation of up to 0.67 (RMSE of 1.06 Kcal/mol) on single-point mutations under cross-validation, and up to 0.65 on independent non-redundant datasets of multiple-point mutations (RMSE of 1.12 kcal/mol), outperforming similar tools. mmCSM-NA is freely available as an easy-to-use web-server and API. We believe it will be an invaluable tool to shed light on the role of mutations affecting protein-nucleic acid interactions in diseases.
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ItempdCSM-GPCR: predicting potent GPCR ligands with graph-based signatures.(Oxford University Press (OUP), 2021)MOTIVATION: G protein-coupled receptors (GPCRs) can selectively bind to many types of ligands, ranging from light-sensitive compounds, ions, hormones, pheromones and neurotransmitters, modulating cell physiology. Considering their role in many essential cellular processes, they are one of the most targeted protein families, with over a third of all approved drugs modulating GPCR signalling. Despite this, the large diversity of receptors and their multipass transmembrane architectures make the identification and development of novel specific, and safe GPCR ligands a challenge. While computational approaches have the potential to assist GPCR drug development, they have presented limited performance and generalization capabilities. Here, we explored the use of graph-based signatures to develop pdCSM-GPCR, a method capable of rapidly and accurately screening potential GPCR ligands. RESULTS: Bioactivity data (IC50, EC50, Ki and Kd) for individual GPCRs were curated. After curation, we used the data for developing predictive models for 36 major GPCR targets, across 4 classes (A, B, C and F). Our models compose the most comprehensive computational resource for GPCR bioactivity prediction to date. Across stratified 10-fold cross-validation and blind tests, our approach achieved Pearson's correlations of up to 0.89, significantly outperforming previous methods. Interpreting our results, we identified common important features of potent GPCRs ligands, which tend to have bicyclic rings, leading to higher levels of aromaticity. We believe pdCSM-GPCR will be an invaluable tool to assist screening efforts, enriching compound libraries and ranking candidates for further experimental validation. AVAILABILITY AND IMPLEMENTATION: pdCSM-GPCR predictive models and datasets used have been made available via a freely accessible and easy-to-use web server at http://biosig.unimelb.edu.au/pdcsm_gpcr/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online.
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ItemGlutathione transferase P1-1 as an arsenic drug-sequestering enzyme(WILEY, 2017-02-01)Arsenic-based compounds are paradoxically both poisons and drugs. Glutathione transferase (GSTP1-1) is a major factor in resistance to such drugs. Here we describe using crystallography, X-ray absorption spectroscopy, mutagenesis, mass spectrometry, and kinetic studies how GSTP1-1 recognizes the drug phenylarsine oxide (PAO). In conditions of cellular stress where glutathione (GSH) levels are low, PAO crosslinks C47 to C101 of the opposing monomer, a distance of 19.9 Å, and causes a dramatic widening of the dimer interface by approximately 10 Å. The GSH conjugate of PAO, which forms rapidly in cancerous cells, is a potent inhibitor (Ki = 90 nM) and binds as a di-GSH complex in the active site forming part of a continuous network of interactions from one active site to the other. In summary, GSTP1-1 can detoxify arsenic-based drugs by sequestration at the active site and at the dimer interface, in situations where there is a plentiful supply of GSH, and at the reactive cysteines in conditions of low GSH.
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ItemNo Preview AvailableUnveiling six potent and highly selective antileishmanial agents via the open source compound collection 'Pathogen Box' against antimony-sensitive and -resistant Leishmania braziliensis(ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER, 2021-01-01)Despite all efforts to provide new chemical entities to tackle leishmaniases, we are still dependent on a the limited drug arsenal, together with drawbacks like toxicity and drug-resistant parasites. Collaborative drug discovery emerged as an option to speed up the way to find alternative antileishmanial agents. This is the case of Medicines for Malaria Ventures - MMV, that promotes an open source drug discovery initiative to fight diseases worldwide. Here, we screened 400 compounds from 'Pathogen Box' (PBox) collection against Leishmania braziliensis, the main etiological agent of cutaneous leishmaniasis in Brazil. Twenty-three compounds were able to inhibit ≥ 80 % L. braziliensis growth at 5 μM. Six out of the PBox selected 23 compounds were found to be highly selective against L. braziliensis intracellular amastigotes with selectivity index varying from > 104 to > 746 and IC50s ranging from 47 to 480 nM. The compounds were also active against antimony-resistant L. braziliensis isolated from the field or laboratory selected mutants, revealing the potential on treating patients infected with drug resistant parasites. Most of the selected compounds were known to be active against kinetoplastids, however, two compounds (MMV688703 and MMV676477) were part of toxoplasmosis and tuberculosis 'PBox' disease set, reinforcing the potential of phenotyping screening to unveil drug repurposing. Here we applied a computational prediction of pharmacokinetic properties using the ADMET predictor pkCSM (http://biosig.unimelb.edu.au/pkcsm/). The tool offered clues on potential drug development needs and can support further in vivo studies. Molecular docking analysis identified CRK3 (LbrM.35.0660), CYP450 (LbrM.30.3580) and PKA (LbrM.18.1180) as L. braziliensis targets for MMV676604, MMV688372 and MMV688703, respectively. Compounds from 'Pathogen Box' thus represents a new hope for novel (or repurposed) small molecules source to tackle leishmaniases.