ORCID Profile
0000-0002-4420-6401
Current Organisation
CSIRO
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Publisher: Elsevier BV
Date: 2020
Publisher: Oxford University Press (OUP)
Date: 30-04-2018
DOI: 10.1093/NAR/GKY300
Publisher: Springer Science and Business Media LLC
Date: 11-2022
DOI: 10.1038/S41594-022-00849-W
Abstract: Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods for protein structure predictions have reached the accuracy of experimentally determined models. Although this has been independently verified, the implementation of these methods across structural-biology applications remains to be tested. Here, we evaluate the use of AlphaFold2 (AF2) predictions in the study of characteristic structural elements the impact of missense variants function and ligand binding site predictions modeling of interactions and modeling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modeled when compared with homology modeling, identifying structural features rarely seen in the Protein Data Bank. AF2-based predictions of protein disorder and complexes surpass dedicated tools, and AF2 models can be used across erse applications equally well compared with experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life-science research.
Publisher: Cold Spring Harbor Laboratory
Date: 30-05-2020
DOI: 10.1101/2020.05.29.124610
Abstract: The emergence of the COVID-19 pandemic has spurred a global rush to uncover basic biological mechanisms, to inform effective vaccine and drug development. Despite viral novelty, global sequencing efforts have already identified genomic variation across isolates. To enable easy exploration and spatial visualization of the potential implications of SARS-CoV-2 mutations on infection, host immunity and drug development we have developed COVID-3D ( biosig.unimelb.edu.au/covid3d/ ).
Publisher: American Chemical Society (ACS)
Date: 11-2021
Publisher: Oxford University Press (OUP)
Date: 12-05-2020
DOI: 10.1093/BIOINFORMATICS/BTAA480
Abstract: EasyVS is a web-based platform built to simplify molecule library selection and virtual screening. With an intuitive interface, the tool allows users to go from selecting a protein target with a known structure and tailoring a purchasable molecule library to performing and visualizing docking in a few clicks. Our system also allows users to filter screening libraries based on molecule properties, cluster molecules by similarity and personalize docking parameters. EasyVS is freely available as an easy-to-use web interface at biosig.unimelb.edu.au/easyvs. douglas.pires@unimelb.edu.au or david.ascher@unimelb.edu.au Supplementary data are available at Bioinformatics online.
Publisher: FapUNIFESP (SciELO)
Date: 12-2019
DOI: 10.1590/0034-7167-2017-0537
Abstract: To evaluate the performance of the modified early warning score (Mews) in a nursing ward for patients in clinical deterioration. This is an analytical, quantitative and predictive study. Mews' parameters (systolic blood pressure, heart rate, respiratory rate, temperature and level of consciousness) were evaluated every six hours. The following events were reported: death, cardiopulmonary arrest and transfer to intensive care. The evaluations were performed in a hospital of reference in the state of São Paulo, Brazil. A total of 300 patients were included (57 ± 18 years old, males: 65%). There number of combined events was observed to be greater the higher the score's value (00% 00% 01 09% 19% 28% 89%, respectively, for Mews 0 1 2 3 4 5 and 6 p < 0.0001). Mews ≥ 4 was the most appropriate cut-off point for prediction of these events (sensitivity: 87%, specificity: 85% and accuracy: 0.86). Mews properly measured the occurrence of severe events in hospitalized patients of a Brazilian public hospital's nursing ward. Mews ≥ 4 seems to be the most appropriate cut-off point for prediction of these events.
Publisher: Springer Science and Business Media LLC
Date: 05-02-2020
DOI: 10.1038/S41598-020-58635-X
Abstract: Pyrazinamide plays an important role in tuberculosis treatment however, its use is complicated by side-effects and challenges with reliable drug susceptibility testing. Resistance to pyrazinamide is largely driven by mutations in pyrazinamidase (pncA), responsible for drug activation, but genetic heterogeneity has hindered development of a molecular diagnostic test. We proposed to use information on how variants were likely to affect the 3D structure of pncA to identify variants likely to lead to pyrazinamide resistance. We curated 610 pncA mutations with high confidence experimental and clinical information on pyrazinamide susceptibility. The molecular consequences of each mutation on protein stability, conformation, and interactions were computationally assessed using our comprehensive suite of graph-based signature methods, mCSM. The molecular consequences of the variants were used to train a classifier with an accuracy of 80%. Our model was tested against internationally curated clinical datasets, achieving up to 85% accuracy. Screening of 600 Victorian clinical isolates identified a set of previously unreported variants, which our model had a 71% agreement with drug susceptibility testing. Here, we have shown the 3D structure of pncA can be used to accurately identify pyrazinamide resistance mutations. SUSPECT-PZA is freely available at: biosig.unimelb.edu.au/suspect_pza/ .
Publisher: Cold Spring Harbor Laboratory
Date: 29-05-2019
DOI: 10.1101/654095
Abstract: In contrast to the situation with tuberculosis, rif in resistance in leprosy may remain undetected due to the lack of rapid and effective diagnostic methods. A quick and reliable method is essential to determine the impacts of emerging detrimental mutations. The functional consequences of missense mutations within the β-subunit of RNA polymerase in Mycobacterium leprae ( M. leprae ) contribute to phenotypic rif in resistance outcomes in leprosy. Here we report in-silico saturation mutagenesis of all residues in the β-subunit of RNA polymerase to all other 19 amino acid types and predict their impacts on overall thermodynamic stability, on interactions at subunit interfaces, and on β-subunit-RNA and rif in affinities using state-of-the-art structure, sequence and normal mode analysis-based methods. A total of 21,394 mutations were analysed, and it was noted that mutations in the conserved residues that line the active-site cleft show largely destabilizing effects, resulting in increased relative solvent accessibility and concomitant decrease in depth of the mutant residues. The mutations at residues S437, G459, H451, P489, K884 and H1035 are identified as extremely detrimental as they induce highly destabilizing effects on the overall stability, nucleic acid and rif in affinities. Destabilizing effects were predicted for all the experimentally identified rif in-resistant mutations in M. leprae indicating that this model can be used as a surveillance tool to monitor emerging detrimental mutations conferring rif in resistance in leprosy. Emergence of primary and secondary drug resistance to rif in in leprosy is a growing concern and poses threat to the leprosy control and elimination measures globally. In the absence of an effective in-vitro system to detect and monitor phenotypic rif in resistance in leprosy, most of the diagnosis relies on detecting mutations in the drug resistance determining regions of the rpoB gene that encodes the β subunit of RNA polymerase in M. leprae . Few labs in the world perform mouse food pad propagation of M. leprae in the presence of drugs (rif in) to determine growth patterns and confirm resistance, however the duration of these methods lasts from 8 to 12 months making them impractical for diagnosis. Understanding molecular mechanisms of drug resistance is vital to associating mutations to clinical resistance outcomes in leprosy. Here we propose an in-silico saturation mutagenesis approach to comprehensively elucidate the structural implications of any mutations that exist or can arise in the β subunit of RNA polymerase in M. leprae . Most of the predicted mutations may not occur in M. leprae due to fitness costs but the information thus generated by this approach help decipher the impacts of mutations across the structure and conversely enable identification of stable regions in the protein that are least impacted by mutations (mutation coolspots) which can be a choice for small molecule binding and structure guided drug discovery.
Publisher: Oxford University Press (OUP)
Date: 29-05-2020
DOI: 10.1093/NAR/GKAA416
Abstract: Significant efforts have been invested into understanding and predicting the molecular consequences of mutations in protein coding regions, however nearly all approaches have been developed using globular, soluble proteins. These methods have been shown to poorly translate to studying the effects of mutations in membrane proteins. To fill this gap, here we report, mCSM-membrane, a user-friendly web server that can be used to analyse the impacts of mutations on membrane protein stability and the likelihood of them being disease associated. mCSM-membrane derives from our well-established mutation modelling approach that uses graph-based signatures to model protein geometry and physicochemical properties for supervised learning. Our stability predictor achieved correlations of up to 0.72 and 0.67 (on cross validation and blind tests, respectively), while our pathogenicity predictor achieved a Matthew's Correlation Coefficient (MCC) of up to 0.77 and 0.73, outperforming previously described methods in both predicting changes in stability and in identifying pathogenic variants. mCSM-membrane will be an invaluable and dedicated resource for investigating the effects of single-point mutations on membrane proteins through a freely available, user friendly web server at biosig.unimelb.edu.au/mcsm_membrane.
Publisher: Informa UK Limited
Date: 11-05-2017
DOI: 10.1080/17460441.2017.1322579
Abstract: Mutations introduce ersity into genomes, leading to selective changes and driving evolution. These changes have contributed to the emergence of many of the current major health concerns of the 21st century, from the development of genetic diseases and cancers to the rise and spread of drug resistance. The experimental systematic testing of all mutations in a system of interest is impractical and not cost-effective, which has created interest in the development of computational tools to understand the molecular consequences of mutations to aid and guide rational experimentation. Areas covered: Here, the authors discuss the recent development of computational methods to understand the effects of coding mutations to protein function and interactions, particularly in the context of the 3D structure of the protein. Expert opinion: While significant progress has been made in terms of innovative tools to understand and quantify the different range of effects in which a mutation or a set of mutations can give rise to a phenotype, a great gap still exists when integrating these predictions and drawing causality conclusions linking variants. This often requires a detailed understanding of the system being perturbed. However, as part of the drug development process it can be used preemptively in a similar fashion to pharmacokinetics predictions, to guide development of therapeutics to help guide the design and analysis of clinical trials, patient treatment and public health policy strategies.
Publisher: Elsevier BV
Date: 2021
Publisher: Public Library of Science (PLoS)
Date: 29-05-2019
Publisher: Oxford University Press (OUP)
Date: 21-05-2018
DOI: 10.1093/NAR/GKY375
Publisher: Wiley
Date: 11-09-2020
DOI: 10.1002/PRO.3942
Publisher: MDPI AG
Date: 28-01-2023
DOI: 10.3390/PHARMACEUTICS15020431
Abstract: Biologics are one of the most rapidly expanding classes of therapeutics, but can be associated with a range of toxic properties. In small-molecule drug development, early identification of potential toxicity led to a significant reduction in clinical trial failures, however we currently lack robust qualitative rules or predictive tools for peptide- and protein-based biologics. To address this, we have manually curated the largest set of high-quality experimental data on peptide and protein toxicities, and developed CSM-Toxin, a novel in-silico protein toxicity classifier, which relies solely on the protein primary sequence. Our approach encodes the protein sequence information using a deep learning natural languages model to understand “biological” language, where residues are treated as words and protein sequences as sentences. The CSM-Toxin was able to accurately identify peptides and proteins with potential toxicity, achieving an MCC of up to 0.66 across both cross-validation and multiple non-redundant blind tests, outperforming other methods and highlighting the robust and generalisable performance of our model. We strongly believe the CSM-Toxin will serve as a valuable platform to minimise potential toxicity in the biologic development pipeline. Our method is freely available as an easy-to-use webserver.
Publisher: Elsevier BV
Date: 10-2021
Publisher: Springer New York
Date: 2019
DOI: 10.1007/978-1-4939-9161-7_9
Abstract: The ability to predict how mutations affect protein structure, folding, and flexibility can elucidate the molecular mechanisms leading to disruption of supersecondary structures, the emergence of phenotypes, as well guiding rational protein engineering. The advent of fast and accurate computational tools has enabled us to comprehensively explore the landscape of mutation effects on protein structures, prioritizing mutations for rational experimental validation.Here we describe the use of two complementary web-based in silico methods, DUET and DynaMut, developed to infer the effects of mutations on folding, stability, and flexibility and how they can be used to explore and interpret these effects on protein supersecondary structures.
Publisher: Oxford University Press (OUP)
Date: 26-10-2020
DOI: 10.1093/BIOINFORMATICS/BTZ779
Abstract: A lack of accurate computational tools to guide rational mutagenesis has made affinity maturation a recurrent challenge in antibody (Ab) development. We previously showed that graph-based signatures can be used to predict the effects of mutations on Ab binding affinity. Here we present an updated and refined version of this approach, mCSM-AB2, capable of accurately modelling the effects of mutations on Ab–antigen binding affinity, through the inclusion of evolutionary and energetic terms. Using a new and expanded database of over 1800 mutations with experimental binding measurements and structural information, mCSM-AB2 achieved a Pearson’s correlation of 0.73 and 0.77 across training and blind tests, respectively, outperforming available methods currently used for rational Ab engineering. mCSM-AB2 is available as a user-friendly and freely accessible web server providing rapid analysis of both in idual mutations or the entire binding interface to guide rational antibody affinity maturation at biosig.unimelb.edu.au/mcsm_ab2 Supplementary data are available at Bioinformatics online.
Publisher: Springer Science and Business Media LLC
Date: 04-01-2021
Publisher: Springer US
Date: 2020
Publisher: Oxford University Press (OUP)
Date: 22-05-2019
DOI: 10.1093/NAR/GKZ383
Abstract: Protein–protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predict the effects of missense mutations on protein–protein interaction binding affinity. mCSM-PPI2 uses graph-based structural signatures to model effects of variations on the inter-residue interaction network, evolutionary information, complex network metrics and energetic terms to generate an optimised predictor. We demonstrate that our method outperforms previous methods, ranking first among 26 others on CAPRI blind tests. mCSM-PPI2 is freely available as a user friendly webserver at biosig.unimelb.edu.au/mcsm_ppi2/.
Publisher: Oxford University Press (OUP)
Date: 09-12-2022
DOI: 10.1093/BIOINFORMATICS/BTAC797
Abstract: Over 300 000 protein–protein interaction (PPI) pairs have been identified in the human proteome and targeting these is fast becoming the next frontier in drug design. Predicting PPI sites, however, is a challenging task that traditionally requires computationally expensive and time-consuming docking simulations. A major weakness of modern protein docking algorithms is the inability to account for protein flexibility, which ultimately leads to relatively poor results. Here, we propose DockNet, an efficient Siamese graph-based neural network method which predicts contact residues between two interacting proteins. Unlike other methods that only utilize a protein’s surface or treat the protein structure as a rigid body, DockNet incorporates the entire protein structure and places no limits on protein flexibility during an interaction. Predictions are modeled at the residue level, based on a erse set of input node features including residue type, surface accessibility, residue depth, secondary structure, pharmacophore and torsional angles. DockNet is comparable to current state-of-the-art methods, achieving an area under the curve (AUC) value of up to 0.84 on an independent test set (DB5), can be applied to a variety of different protein structures and can be utilized in situations where accurate unbound protein structures cannot be obtained. DockNet is available at pwilliams09/docknet and an easy-to-use webserver at biosig.lab.uq.edu.au/docknet. All other data underlying this article are available in the article and in its online supplementary material. Supplementary data are available at Bioinformatics online.
Publisher: Cold Spring Harbor Laboratory
Date: 26-09-2021
DOI: 10.1101/2021.09.26.461876
Abstract: Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods have led to protein structure predictions that have reached the accuracy of experimentally determined models. While this has been independently verified, the implementation of these methods across structural biology applications remains to be tested. Here, we evaluate the use of AlphaFold 2 (AF2) predictions in the study of characteristic structural elements the impact of missense variants function and ligand binding site predictions modelling of interactions and modelling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modelled when compared to homology modelling, identifying structural features rarely seen in the PDB. AF2-based predictions of protein disorder and protein complexes surpass state-of-the-art tools and AF2 models can be used across erse applications equally well compared to experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life science research.
Publisher: Springer Science and Business Media LLC
Date: 09-09-2020
No related grants have been discovered for Carlos Henrique Miranda Rodrigues.