Biochemistry and Pharmacology - Research Publications

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    Widespread remodeling of proteome solubility in response to different protein homeostasis stresses
    Sui, X ; Pires, DEV ; Ormsby, AR ; Cox, D ; Nie, S ; Vecchi, G ; Vendruscolo, M ; Ascher, DB ; Reid, GE ; Hatters, DM (National Academy of Sciences, 2020-02-04)
    The accumulation of protein deposits in neurodegenerative diseases has been hypothesized to depend on a metastable subproteome vulnerable to aggregation. To investigate this phenomenon and the mechanisms that regulate it, we measured the solubility of the proteome in the mouse Neuro2a cell line under six different protein homeostasis stresses: 1) Huntington’s disease proteotoxicity, 2) Hsp70, 3) Hsp90, 4) proteasome, 5) endoplasmic reticulum (ER)-mediated folding inhibition, and 6) oxidative stress. Overall, we found that about one-fifth of the proteome changed solubility with almost all of the increases in insolubility were counteracted by increases in solubility of other proteins. Each stress directed a highly specific pattern of change, which reflected the remodeling of protein complexes involved in adaptation to perturbation, most notably, stress granule (SG) proteins, which responded differently to different stresses. These results indicate that the protein homeostasis system is organized in a modular manner and aggregation patterns were not correlated with protein folding stability (ΔG). Instead, distinct cellular mechanisms regulate assembly patterns of multiple classes of protein complexes under different stress conditions.
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    HARP: a database of structural impacts of systematic missense mutations in drug targets of Mycobacterium leprae
    Vedithi, SC ; Malhotra, S ; Skwark, MJ ; Munir, A ; Acebron-Garcia-De-Eulate, M ; Waman, VP ; Alsulami, A ; Ascher, DB ; Blundell, TL (ELSEVIER, 2020)
    Computational Saturation Mutagenesis is an in-silico approach that employs systematic mutagenesis of each amino acid residue in the protein to all other amino acid types, and predicts changes in thermodynamic stability and affinity to the other subunits/protein counterparts, ligands and nucleic acid molecules. The data thus generated are useful in understanding the functional consequences of mutations in antimicrobial resistance phenotypes. In this study, we applied computational saturation mutagenesis to three important drug-targets in Mycobacterium leprae (M. leprae) for the drugs dapsone, rifampin and ofloxacin namely Dihydropteroate Synthase (DHPS), RNA Polymerase (RNAP) and DNA Gyrase (GYR), respectively. M. leprae causes leprosy and is an obligate intracellular bacillus with limited protein structural information associating mutations with phenotypic resistance outcomes in leprosy. Experimentally solved structures of DHPS, RNAP and GYR of M. leprae are not available in the Protein Data Bank, therefore, we modelled the structures of these proteins using template-based comparative modelling and introduced systematic mutations in each model generating 80,902 mutations and mutant structures for all the three proteins. Impacts of mutations on stability and protein-subunit, protein-ligand and protein-nucleic acid affinities were computed using various in-house developed and other published protein stability and affinity prediction software. A consensus impact was estimated for each mutation using qualitative scoring metrics for physicochemical properties and by a categorical grouping of stability and affinity predictions. We developed a web database named HARP (a database of Hansen's Disease Antimicrobial Resistance Profiles), which is accessible at the URL - https://harp-leprosy.org and provides the details to each of these predictions.
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    Combining structure and genomics to understand antimicrobial resistance
    Tunstall, T ; Portelli, S ; Phelan, J ; Clark, TG ; Ascher, DB ; Furnham, N (ELSEVIER, 2020)
    Antimicrobials against bacterial, viral and parasitic pathogens have transformed human and animal health. Nevertheless, their widespread use (and misuse) has led to the emergence of antimicrobial resistance (AMR) which poses a potentially catastrophic threat to public health and animal husbandry. There are several routes, both intrinsic and acquired, by which AMR can develop. One major route is through non-synonymous single nucleotide polymorphisms (nsSNPs) in coding regions. Large scale genomic studies using high-throughput sequencing data have provided powerful new ways to rapidly detect and respond to such genetic mutations linked to AMR. However, these studies are limited in their mechanistic insight. Computational tools can rapidly and inexpensively evaluate the effect of mutations on protein function and evolution. Subsequent insights can then inform experimental studies, and direct existing or new computational methods. Here we review a range of sequence and structure-based computational tools, focussing on tools successfully used to investigate mutational effect on drug targets in clinically important pathogens, particularly Mycobacterium tuberculosis. Combining genomic results with the biophysical effects of mutations can help reveal the molecular basis and consequences of resistance development. Furthermore, we summarise how the application of such a mechanistic understanding of drug resistance can be applied to limit the impact of AMR.
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    COVID-3D: An online resource to explore the structural distribution of genetic variation in SARS-CoV-2 and its implication on therapeutic development
    Portelli, S ; Olshansky, M ; Rodrigues, CHM ; D’Souza, E ; Myung, Y ; Silk, M ; Alavi, A ; Pires, DEV ; Ascher, D (Cold Spring Harbor Laboratory, 2020)

    SUMMARY

    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 ( http://biosig.unimelb.edu.au/covid3d/ ).
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    Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches.
    Portelli, S ; Myung, Y ; Furnham, N ; Vedithi, SC ; Pires, DEV ; Ascher, DB (Nature Publishing Group, 2020-10-22)
    Rifampicin resistance is a major therapeutic challenge, particularly in tuberculosis, leprosy, P. aeruginosa and S. aureus infections, where it develops via missense mutations in gene rpoB. Previously we have highlighted that these mutations reduce protein affinities within the RNA polymerase complex, subsequently reducing nucleic acid affinity. Here, we have used these insights to develop a computational rifampicin resistance predictor capable of identifying resistant mutations even outside the well-defined rifampicin resistance determining region (RRDR), using clinical M. tuberculosis sequencing information. Our tool successfully identified up to 90.9% of M. tuberculosis rpoB variants correctly, with sensitivity of 92.2%, specificity of 83.6% and MCC of 0.69, outperforming the current gold-standard GeneXpert-MTB/RIF. We show our model can be translated to other clinically relevant organisms: M. leprae, P. aeruginosa and S. aureus, despite weak sequence identity. Our method was implemented as an interactive tool, SUSPECT-RIF (StrUctural Susceptibility PrEdiCTion for RIFampicin), freely available at https://biosig.unimelb.edu.au/suspect_rif/ .
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    mmCSM-AB: guiding rational antibody engineering through multiple point mutations
    Myung, Y ; Pires, DE ; Ascher, DB (OXFORD UNIV PRESS, 2020-07-02)
    While antibodies are becoming an increasingly important therapeutic class, especially in personalized medicine, their development and optimization has been largely through experimental exploration. While there have been many efforts to develop computational tools to guide rational antibody engineering, most approaches are of limited accuracy when applied to antibody design, and have largely been limited to analysing a single point mutation at a time. To overcome this gap, we have curated a dataset of 242 experimentally determined changes in binding affinity upon multiple point mutations in antibody-target complexes (89 increasing and 153 decreasing binding affinity). Here, we have shown that by using our graph-based signatures and atomic interaction information, we can accurately analyse the consequence of multi-point mutations on antigen binding affinity. Our approach outperformed other available tools across cross-validation and two independent blind tests, achieving Pearson's correlations of up to 0.95. We have implemented our new approach, mmCSM-AB, as a web-server that can help guide the process of affinity maturation in antibody design. mmCSM-AB is freely available at http://biosig.unimelb.edu.au/mmcsm_ab/.
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    Computational saturation mutagenesis to predict structural consequences of systematic mutations in the beta subunit of RNA polymerase in Mycobacterium leprae
    Vedithi, SC ; Rodrigues, CHM ; Portelli, S ; Skwark, MJ ; Das, M ; Ascher, DB ; Blundell, TL ; Malhotra, S (ELSEVIER, 2020)
    UNLABELLED: Rifampin resistance in leprosy may remain undetected due to the lack of rapid and effective diagnostic tools. A quick and reliable method is essential to determine the impacts of emerging detrimental mutations in the drug targets. The functional consequences of missense mutations in the β-subunit of RNA polymerase (RNAP) in Mycobacterium leprae (M. leprae) contribute to phenotypic resistance to rifampin in leprosy. Here, we report in-silico saturation mutagenesis of all residues in the β-subunit of RNAP to all other 19 amino acid types (generating 21,394 mutations for 1126 residues) and predict their impacts on overall thermodynamic stability, on interactions at subunit interfaces, and on β-subunit-RNA and rifampin affinities (only for the rifampin binding site) using state-of-the-art structure, sequence and normal mode analysis-based methods. Mutations in the conserved residues that line the active-site cleft show largely destabilizing effects, resulting in increased relative solvent accessibility and a concomitant decrease in residue-depth (the extent to which a residue is buried in the protein structure space) of the mutant residues. The mutations at residue positions S437, G459, H451, P489, K884 and H1035 are identified as extremely detrimental as they induce highly destabilizing effects on the overall protein stability, and nucleic acid and rifampin affinities. Destabilizing effects were predicted for all the clinically/experimentally identified rifampin-resistant mutations in M. leprae indicating that this model can be used as a surveillance tool to monitor emerging detrimental mutations that destabilise RNAP-rifampin interactions and confer rifampin resistance in leprosy. AUTHOR SUMMARY: The emergence of primary and secondary drug resistance to rifampin in leprosy is a growing concern and poses a threat to the leprosy control and elimination measures globally. In the absence of an effective in-vitro system to detect and monitor phenotypic resistance to rifampin in leprosy, diagnosis mainly relies on the presence of mutations in drug resistance determining regions of the rpoB gene that encodes the β-subunit of RNAP 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 clinically detected drug resistance in leprosy. Here we propose an in-silico saturation mutagenesis approach to comprehensively elucidate the structural implications of any mutations that exist or that can arise in the β-subunit of RNAP 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 potential choice for small molecule binding and structure guided drug discovery.
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    ProCarbDB: a database of carbohydrate-binding proteins
    Copoiu, L ; Torres, PHM ; Ascher, DB ; Blundell, TL ; Malhotra, S (OXFORD UNIV PRESS, 2020-01-08)
    Carbohydrate-binding proteins play crucial roles across all organisms and viruses. The complexity of carbohydrate structures, together with inconsistencies in how their 3D structures are reported, has led to difficulties in characterizing the protein-carbohydrate interfaces. In order to better understand protein-carbohydrate interactions, we have developed an open-access database, ProCarbDB, which, unlike the Protein Data Bank (PDB), clearly distinguishes between the complete carbohydrate ligands and their monomeric units. ProCarbDB is a comprehensive database containing over 5200 3D X-ray crystal structures of protein-carbohydrate complexes. In ProCarbDB, the complete carbohydrate ligands are annotated and all their interactions are displayed. Users can also select any protein residue in the proximity of the ligand to inspect its interactions with the carbohydrate ligand and with other neighbouring protein residues. Where available, additional curated information on the binding affinity of the complex and the effects of mutations on the binding have also been provided in the database. We believe that ProCarbDB will be an invaluable resource for understanding protein-carbohydrate interfaces. The ProCarbDB web server is freely available at http://www.procarbdb.science/procarb.
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    A missense mutation in the MLKL brace region promotes lethal neonatal inflammation and hematopoietic dysfunction
    Hildebrand, JM ; Kauppi, M ; Majewski, IJ ; Liu, Z ; Cox, AJ ; Miyake, S ; Petrie, EJ ; Silk, MA ; Li, Z ; Tanzer, MC ; Brumatti, G ; Young, SN ; Hall, C ; Garnish, SE ; Corbin, J ; Stutz, MD ; Di Rago, L ; Gangatirkar, P ; Josefsson, EC ; Rigbye, K ; Anderton, H ; Rickard, JA ; Tripaydonis, A ; Sheridan, J ; Scerri, TS ; Jackson, VE ; Czabotar, PE ; Zhang, J-G ; Varghese, L ; Allison, CC ; Pellegrini, M ; Tannahill, GM ; Hatchell, EC ; Willson, TA ; Stockwell, D ; de Graaf, CA ; Collinge, J ; Hilton, A ; Silke, N ; Spall, SK ; Chau, D ; Athanasopoulos, V ; Metcalf, D ; Laxer, RM ; Bassuk, AG ; Darbro, BW ; Singh, MAF ; Vlahovich, N ; Hughes, D ; Kozlovskaia, M ; Ascher, DB ; Warnatz, K ; Venhoff, N ; Thiel, J ; Biben, C ; Blum, S ; Reveille, J ; Hildebrand, MS ; Vinuesa, CG ; McCombe, P ; Brown, MA ; Kile, BT ; McLean, C ; Bahlo, M ; Masters, SL ; Nakano, H ; Ferguson, PJ ; Murphy, JM ; Alexander, WS ; Silke, J (NATURE PORTFOLIO, 2020-06-19)
    MLKL is the essential effector of necroptosis, a form of programmed lytic cell death. We have isolated a mouse strain with a single missense mutation, MlklD139V, that alters the two-helix 'brace' that connects the killer four-helix bundle and regulatory pseudokinase domains. This confers constitutive, RIPK3 independent killing activity to MLKL. Homozygous mutant mice develop lethal postnatal inflammation of the salivary glands and mediastinum. The normal embryonic development of MlklD139V homozygotes until birth, and the absence of any overt phenotype in heterozygotes provides important in vivo precedent for the capacity of cells to clear activated MLKL. These observations offer an important insight into the potential disease-modulating roles of three common human MLKL polymorphisms that encode amino acid substitutions within or adjacent to the brace region. Compound heterozygosity of these variants is found at up to 12-fold the expected frequency in patients that suffer from a pediatric autoinflammatory disease, chronic recurrent multifocal osteomyelitis (CRMO).
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    Structure guided prediction of Pyrazinamide resistance mutations in pncA
    Karmakar, M ; Rodrigues, CHM ; Horan, K ; Denholm, JT ; Ascher, DB (NATURE PORTFOLIO, 2020-02-05)
    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: http://biosig.unimelb.edu.au/suspect_pza/.