Biology subjects

Skwark, M. J.

Publications and source records attributed to Skwark, M. J..

5 recordsLinked to original sources

bioRxiv2022

Peptide-MHC Structure Prediction With Mixed Residue and Atom Graph Neural Network

Neoantigen-targeting vaccines have achieved breakthrough success in cancer immunotherapy by eliciting immune responses against neoantigens, which are proteins uniquely produced by cancer cells. During the immune response, the interactions between peptides and major histocompatibility complexes (MHC) play an important role as peptides must be bound and presented by MHC to be recognised by the immune system. However, only limited experimentally determined peptide-MHC (pMHC) structures are available, and in-silico structure modelling is therefore used for studying their interactions. Current approaches mainly use Monte Carlo sampling and energy minimisation, and are often computationally expensive. On the other hand, the advent of large high-quality proteomic data sets has led to an unprecedented opportunity for deep learning-based methods with pMHC structure prediction becoming feasible with these trained protein folding models. In this work, we present a graph neural network-based model for pMHC structure prediction, which takes an amino acid-level pMHC graph and an atomic-level peptide graph as inputs and predicts the peptide backbone conformation. With a novel weighted reconstruction loss, the trained model achieved a similar accuracy to AlphaFold 2, requiring only 1.7M learnable parameters compared to 93M, representing a more than 98% reduction in the number of required parameters.

bioinformatics↗

bioRxiv2021

Early Computational Detection of Potential High Risk SARS-CoV-2 Variants

The ongoing COVID-19 pandemic is leading to the discovery of hundreds of novel SARS-CoV-2 variants on a daily basis. While most variants do not impact the course of the pandemic, some variants pose a significantly increased risk when the acquired mutations allow better evasion of antibody neutralisation in previously infected or vaccinated subjects or increased transmissibility. Early detection of such high risk variants (HRVs) is paramount for the proper management of the pandemic. However, experimental assays to determine immune evasion and transmissibility characteristics of new variants are resource-intensive and time-consuming, potentially leading to delays in appropriate responses by decision makers. Here we present a novel in silico approach combining spike (S) protein structure modelling and large protein transformer language models on S protein sequences to accurately rank SARS-CoV-2 variants for immune escape and fitness potential. These metrics can be combined into an automated Early Warning System (EWS) capable of evaluating new variants in minutes and risk-monitoring variant lineages in near real-time. The system accurately pinpoints the putatively dangerous variants by selecting on average less than 0.3% of the novel variants each week. With only the S protein nucleotide sequence as input, the EWS detects HRVs earlier and with better precision than baseline metrics such as the growth metric (which requires real-world observations) or random sampling. Notably, Omicron BA.1 was flagged by the EWS on the day its sequence was made available. Additionally, our immune escape and fitness metrics were experimentally validated using in vitro pseudovirus-based virus neutralisation test (pVNT) assays and binding assays. The EWS flagged as potentially dangerous all 16 variants (Alpha-Omicron BA.1/2/4/5) designated by the World Health Organisation (WHO) with an average lead time of more than one and a half months ahead of them being designated as such. One-Sentence SummaryA COVID-19 Early Warning System combining structural modelling with machine learning to detect and monitor high risk SARS-CoV-2 variants, identifying all 16 WHO designated variants on average more than one and a half months in advance by selecting on average less than 0.3% of the weekly novel variants.

bioinformatics↗

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↗

bioRxiv2019

Computational Saturation Mutagenesis of the β subunit of RNA polymerase in Mycobacterium leprae

In contrast to the situation with tuberculosis, rifampin 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 {beta}-subunit of RNA polymerase in Mycobacterium leprae (M. leprae) contribute to phenotypic rifampin resistance outcomes in leprosy. Here we report in-silico saturation mutagenesis of all residues in the {beta}-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 {beta}-subunit-RNA and rifampin 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 rifampin affinities. Destabilizing effects were predicted for all the experimentally identified rifampin-resistant mutations in M. leprae indicating that this model can be used as a surveillance tool to monitor emerging detrimental mutations conferring rifampin resistance in leprosy.\n\nAUTHOR SUMMARYEmergence of primary and secondary drug resistance to rifampin 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 rifampin resistance in leprosy, most of the diagnosis relies on detecting mutations in the drug resistance determining regions of the rpoB gene that encodes the {beta} 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 (rifampin) 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 {beta} 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.

bioinformatics↗

bioRxiv2019

Comparative analyses of metabolic pathways in mycobacteria: Insights into the identification and characterisation of new drug targets in M. tuberculosis, M. leprae and M. abscessus

Three related mycobacteria are the cause of widespread infections in man and are the focus of intense research and drug-discovery efforts in the face of growing antimicrobial resistance. Mycobacterium tuberculosis, the causative agent of tuberculosis, is currently one of the top ten causes of death in the world according to WHO; M. abscessus, a group of non-tuberculous mycobacteria causes lung infections and other opportunistic infections in humans; and M. leprae, the causative agent of leprosy, remains endemic in tropical countries. There is an urgent need to design alternatives to conventional treatment strategies, due to the increase in resistance to standard antibacterials. In this study, we present a comparative analysis of chokepoint and essentiality datasets that will provide insight into the development of new treatment regimes. We illustrate the key metabolic pathways shared between these three organisms and identify drug targets with a wide metabolic impact that are common to the three species. We demonstrate that 72% of the chokepoint enzymes are proteins essential to Mycobacterium tuberculosis. We show also that 78% of the drug targets, prioritized based on their presence in multiple paths on the metabolic network, are present in pathways shared by M. tuberculosis, M. leprae and M. abscessus, including biosynthesis of amino acids, carbohydrates, cell structures, fatty acid and lipid biosynthesis. A further 17% is found in the prioritised pathways shared between M. tuberculosis and M. abscessus. We have performed comparative structure modelling of potential drug targets identified using our analysis in order to assess druggability and demonstrate the importance of chokepoint analysis in terms of drug target identification. AUTHOR SUMMARYComputer simulation studies to design new drugs against mycobacteria

bioinformatics↗