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Gu X, Kovacs AS, Myung Y, Ascher DB. Mutations in Glycosyltransferases and Glycosidases: Implications for Associated Diseases. Biomolecules 2024; 14:497. [PMID: 38672513 PMCID: PMC11048727 DOI: 10.3390/biom14040497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 03/20/2024] [Accepted: 03/22/2024] [Indexed: 04/28/2024] Open
Abstract
Glycosylation, a crucial and the most common post-translational modification, coordinates a multitude of biological functions through the attachment of glycans to proteins and lipids. This process, predominantly governed by glycosyltransferases (GTs) and glycoside hydrolases (GHs), decides not only biomolecular functionality but also protein stability and solubility. Mutations in these enzymes have been implicated in a spectrum of diseases, prompting critical research into the structural and functional consequences of such genetic variations. This study compiles an extensive dataset from ClinVar and UniProt, providing a nuanced analysis of 2603 variants within 343 GT and GH genes. We conduct thorough MTR score analyses for the proteins with the most documented variants using MTR3D-AF2 via AlphaFold2 (AlphaFold v2.2.4) predicted protein structure, with the analyses indicating that pathogenic mutations frequently correlate with Beta Bridge secondary structures. Further, the calculation of the solvent accessibility score and variant visualisation show that pathogenic mutations exhibit reduced solvent accessibility, suggesting the mutated residues are likely buried and their localisation is within protein cores. We also find that pathogenic variants are often found proximal to active and binding sites, which may interfere with substrate interactions. We also incorporate computational predictions to assess the impact of these mutations on protein function, utilising tools such as mCSM to predict the destabilisation effect of variants. By identifying these critical regions that are prone to disease-associated mutations, our study opens avenues for designing small molecules or biologics that can modulate enzyme function or compensate for the loss of stability due to these mutations.
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Affiliation(s)
- Xiaotong Gu
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD 4000, Australia; (X.G.); (A.S.K.); (Y.M.)
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
| | - Aaron S. Kovacs
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD 4000, Australia; (X.G.); (A.S.K.); (Y.M.)
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
| | - Yoochan Myung
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD 4000, Australia; (X.G.); (A.S.K.); (Y.M.)
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
| | - David B. Ascher
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD 4000, Australia; (X.G.); (A.S.K.); (Y.M.)
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
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Myung Y, de Sá AGC, Ascher DB. Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction. Nucleic Acids Res 2024:gkae254. [PMID: 38634808 DOI: 10.1093/nar/gkae254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 03/20/2024] [Accepted: 04/10/2024] [Indexed: 04/19/2024] Open
Abstract
Evaluating pharmacokinetic properties of small molecules is considered a key feature in most drug development and high-throughput screening processes. Generally, pharmacokinetics, which represent the fate of drugs in the human body, are described from four perspectives: absorption, distribution, metabolism and excretion-all of which are closely related to a fifth perspective, toxicity (ADMET). Since obtaining ADMET data from in vitro, in vivo or pre-clinical stages is time consuming and expensive, many efforts have been made to predict ADMET properties via computational approaches. However, the majority of available methods are limited in their ability to provide pharmacokinetics and toxicity for diverse targets, ensure good overall accuracy, and offer ease of use, interpretability and extensibility for further optimizations. Here, we introduce Deep-PK, a deep learning-based pharmacokinetic and toxicity prediction, analysis and optimization platform. We applied graph neural networks and graph-based signatures as a graph-level feature to yield the best predictive performance across 73 endpoints, including 64 ADMET and 9 general properties. With these powerful models, Deep-PK supports molecular optimization and interpretation, aiding users in optimizing and understanding pharmacokinetics and toxicity for given input molecules. The Deep-PK is freely available at https://biosig.lab.uq.edu.au/deeppk/.
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Affiliation(s)
- Yoochan Myung
- School of Chemistry and Molecular Biosciences, The Australian Centre for Ecogenomics, The University of Queensland, Brisbane, Queensland 4072, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia
| | - Alex G C de Sá
- School of Chemistry and Molecular Biosciences, The Australian Centre for Ecogenomics, The University of Queensland, Brisbane, Queensland 4072, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia
- Baker Department of Cardiometabolic Health, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - David B Ascher
- School of Chemistry and Molecular Biosciences, The Australian Centre for Ecogenomics, The University of Queensland, Brisbane, Queensland 4072, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia
- Baker Department of Cardiometabolic Health, The University of Melbourne, Parkville, Victoria 3010, Australia
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Park JB, Jang BS, Chang JH, Kim JH, Hong KY, Jin US, Chang H, Myung Y, Jeong JH, Heo CY, Kim IA, Shin KH. Impact of the New ESTRO-ACROP Target Volume Delineation Guideline on Breast-Related Complications after Implant-Based Reconstruction and Postmastectomy Radiotherapy. Int J Radiat Oncol Biol Phys 2023; 117:e198. [PMID: 37784842 DOI: 10.1016/j.ijrobp.2023.06.1070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) The European Society for Radiotherapy and Oncology Advisory Committee in Radiation Oncology Practice (ESTRO-ACROP) recently updated a new target volume delineation guideline for postmastectomy radiotherapy (PMRT) after implant-based reconstruction. This study aimed whether this change has impact on breast-related complications. MATERIALS/METHODS We retrospectively reviewed patients who underwent PMRT after mastectomy with tissue expander or permanent implant insertion from 2016 to 2021. In total, 412 patients were included; 277 received RT by the new ESTRO-ACROP target delineation (ESTRO-T), and 135 received RT by conventional target delineation (CONV-T). The primary endpoint was comparison between the target groups of major breast-related complication, including infection, capsular contracture, deformity and necrosis requiring re-operation or re-hospitalization during follow-up after RT or delayed implant replacement. Complications were evaluated according to the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0., and capsular contracture was graded by the Baker Classification. RESULTS The median follow-up was 29.5 months (range, 0.3-76.8). The 1-, 2-, and 3-year incidence rates of major breast-related complication were 5.7%, 10.0%, and 11.6% in the ESTRO-T group, and 8.2%, 13.8%, and 14.7% in the CONV-T groups; it did not show a difference between the groups (P = 0.55). In multivariate analyses, target delineation is not significantly associated with the major complications (hazard ratio [HR] = 0.93; P = 0.83, Table 1). There was no significant difference between the ESTRO-T and CONV-T groups in the incidence of any breast-related complications (3-year cumulative incidence, 37.3% vs. 29.4%, respectively; P = 0.28). Symptomatic RT-induced pneumonitis rates were 2.7% in the ESTRO-T group (7 patients) and 2.2% in the CONV-T group (3 patients). Only one local recurrence event occurred in the ESTRO-T group, which was within the ESTRO-target volume. CONCLUSION Target volume delineation according to the new ESTRO-ACROP guideline did not reduce the risk of major or any breast-related complications. As the dosimetric benefits of heart and lung have been reported, further analyses with long-term follow-up are necessary to evaluate whether it could be connected to better clinical outcomes.
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Affiliation(s)
- J B Park
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Korea, Republic of (South) Korea
| | - B S Jang
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Korea, Republic of (South) Korea
| | - J H Chang
- University of California San Francisco, Department of Radiation Oncology, San Francisco, CA
| | - J H Kim
- Department of Radiation Oncology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea, Republic of (South) Korea
| | - K Y Hong
- Department of Plastic and Reconstructive Surgery, Seoul National University Hospital, Seoul, Korea, Republic of (South) Korea
| | - U S Jin
- Department of Plastic and Reconstructive Surgery, Seoul National University College of Medicine, Seoul, Korea, Republic of (South) Korea
| | - H Chang
- 2nd Department of Plastic and Reconstructive Surgery, Seoul National University Hospital, Seoul, Korea, Republic of (South) Korea
| | - Y Myung
- Department of Plastic and Reconstructive Surgery, Seoul National University Bundang Hospital, Seongnam, Korea, Republic of (South) Korea
| | - J H Jeong
- Department of Plastic and Reconstructive Surgery, Seoul National University Bundang Hospital, Seongnam, Korea, Republic of (South) Korea
| | - C Y Heo
- Department of Plastic and Reconstructive Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea, Republic of (South) Korea
| | - I A Kim
- Department of Radiation Oncology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea, Republic of (South) Korea
| | - K H Shin
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Korea, Republic of (South) Korea
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Al-Jarf R, Karmakar M, Myung Y, Ascher DB. Uncovering the Molecular Drivers of NHEJ DNA Repair-Implicated Missense Variants and Their Functional Consequences. Genes (Basel) 2023; 14:1890. [PMID: 37895239 PMCID: PMC10606680 DOI: 10.3390/genes14101890] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 09/24/2023] [Accepted: 09/27/2023] [Indexed: 10/29/2023] Open
Abstract
Variants in non-homologous end joining (NHEJ) DNA repair genes are associated with various human syndromes, including microcephaly, growth delay, Fanconi anemia, and different hereditary cancers. However, very little has been done previously to systematically record the underlying molecular consequences of NHEJ variants and their link to phenotypic outcomes. In this study, a list of over 2983 missense variants of the principal components of the NHEJ system, including DNA Ligase IV, DNA-PKcs, Ku70/80 and XRCC4, reported in the clinical literature, was initially collected. The molecular consequences of variants were evaluated using in silico biophysical tools to quantitatively assess their impact on protein folding, dynamics, stability, and interactions. Cancer-causing and population variants within these NHEJ factors were statistically analyzed to identify molecular drivers. A comprehensive catalog of NHEJ variants from genes known to be mutated in cancer was curated, providing a resource for better understanding their role and molecular mechanisms in diseases. The variant analysis highlighted different molecular drivers among the distinct proteins, where cancer-driving variants in anchor proteins, such as Ku70/80, were more likely to affect key protein-protein interactions, whilst those in the enzymatic components, such as DNA-PKcs, were likely to be found in intolerant regions undergoing purifying selection. We believe that the information acquired in our database will be a powerful resource to better understand the role of non-homologous end-joining DNA repair in genetic disorders, and will serve as a source to inspire other investigations to understand the disease further, vital for the development of improved therapeutic strategies.
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Affiliation(s)
- Raghad Al-Jarf
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Parkville, VIC 3052, Australia (M.K.)
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, VIC 3052, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
| | - Malancha Karmakar
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Parkville, VIC 3052, Australia (M.K.)
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, VIC 3052, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
| | - Yoochan Myung
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Parkville, VIC 3052, Australia (M.K.)
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, VIC 3052, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, St. Lucia, QLD 4072, Australia
| | - David B. Ascher
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Parkville, VIC 3052, Australia (M.K.)
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, VIC 3052, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, St. Lucia, QLD 4072, Australia
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Myung Y, Pires DEV, Ascher DB. Understanding the complementarity and plasticity of antibody-antigen interfaces. Bioinformatics 2023:btad392. [PMID: 37382557 DOI: 10.1093/bioinformatics/btad392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 01/24/2023] [Accepted: 06/27/2023] [Indexed: 06/30/2023]
Abstract
MOTIVATION While antibodies have been ground-breaking therapeutic agents, the structural determinants for antibody binding specificity remain to be fully elucidated, which is compounded by the virtually unlimited repertoire of antigens they can recognise. Here, we have explored the structural landscapes of antibody-antigen interfaces to identify the structural determinants driving target recognition by assessing concavity and interatomic interactions. RESULTS We found that complementarity-determining regions utilised deeper concavity with their longer H3 loops, especially H3 loops of nanobody showing the deepest use of concavity. Of all amino acid residues found in complementarity-determining regions, tryptophan used deeper concavity, especially in nanobodies, making it suitable for leveraging concave antigen surfaces. Similarly, antigens utilised arginine to bind to deeper pockets of the antibody surface. Our findings fill a gap in knowledge about the antibody specificity, binding affinity, and the nature of antibody-antigen interface features, which will lead to a better understanding of how antibodies can be more effective to target druggable sites on antigen surfaces. AVAILABILITY The data and scripts are available at: https://github.com/YoochanMyung/scripts. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yoochan Myung
- Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, VIC Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD Australia
| | - Douglas E V Pires
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC Australia
- School of Computing and Information Systems, University of Melbourne, Melbourne, VIC Australia
| | - David B Ascher
- Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, VIC Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD Australia
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Ascher DB, Kaminskas LM, Myung Y, Pires DEV. Using Graph-Based Signatures to Guide Rational Antibody Engineering. Methods Mol Biol 2023; 2552:375-397. [PMID: 36346604 DOI: 10.1007/978-1-0716-2609-2_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Antibodies are essential experimental and diagnostic tools and as biotherapeutics have significantly advanced our ability to treat a range of diseases. With recent innovations in computational tools to guide protein engineering, we can now rationally design better antibodies with improved efficacy, stability, and pharmacokinetics. Here, we describe the use of the mCSM web-based in silico suite, which uses graph-based signatures to rapidly identify the structural and functional consequences of mutations, to guide rational antibody engineering to improve stability, affinity, and specificity.
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Affiliation(s)
- David B Ascher
- Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Department of Biochemistry, Cambridge University, Cambridge, UK
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, Queensland, Australia
| | - Lisa M Kaminskas
- School of Biological Sciences, University of Queensland, St Lucia, QLD, Australia
| | - Yoochan Myung
- Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, Queensland, Australia
| | - Douglas E V Pires
- Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC, Australia.
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
- School of Computing and Information Systems, University of Melbourne, Parkville, VIC, Australia.
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Tichkule S, Myung Y, Naung MT, Ansell BRE, Guy AJ, Srivastava N, Mehra S, Cacciò SM, Mueller I, Barry AE, van Oosterhout C, Pope B, Ascher DB, Jex AR. VIVID: a web application for variant interpretation and visualisation in multidimensional analyses. Mol Biol Evol 2022; 39:6697981. [PMID: 36103257 PMCID: PMC9514033 DOI: 10.1093/molbev/msac196] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Large-scale comparative genomics- and population genetic studies generate enormous amounts of polymorphism data in the form of DNA variants. Ultimately, the goal of many of these studies is to associate genetic variants to phenotypes or fitness. We introduce VIVID, an interactive, user-friendly web application that integrates a wide range of approaches for encoding genotypic to phenotypic information in any organism or disease, from an individual or population, in three-dimensional (3D) space. It allows mutation mapping and annotation, calculation of interactions and conservation scores, prediction of harmful effects, analysis of diversity and selection, and 3D visualization of genotypic information encoded in Variant Call Format on AlphaFold2 protein models. VIVID enables the rapid assessment of genes of interest in the study of adaptive evolution and the genetic load, and it helps prioritizing targets for experimental validation. We demonstrate the utility of VIVID by exploring the evolutionary genetics of the parasitic protist Plasmodium falciparum, revealing geographic variation in the signature of balancing selection in potential targets of functional antibodies.
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Affiliation(s)
- Swapnil Tichkule
- Population Health and Immunity, Walter and Eliza Hall Institute of Medical Research , Melbourne , Australia
- Department of Medical Biology, University of Melbourne , Melbourne , Australia
| | - Yoochan Myung
- Systems and Computational Biology, Bio21 Institute, University of Melbourne , Melbourne , Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes , Melbourne , Australia
| | - Myo T Naung
- Population Health and Immunity, Walter and Eliza Hall Institute of Medical Research , Melbourne , Australia
- Department of Medical Biology, University of Melbourne , Melbourne , Australia
| | - Brendan R E Ansell
- Population Health and Immunity, Walter and Eliza Hall Institute of Medical Research , Melbourne , Australia
| | - Andrew J Guy
- School of Science, RMIT University , Melbourne , Australia
| | - Namrata Srivastava
- Department of Data Science and AI, Monash University , Melbourne , Australia
| | - Somya Mehra
- Life Sciences Discipline, Burnet Institute , Melbourne , Australia
| | - Simone M Cacciò
- Department of Infectious Disease, Istituto Superiore di Sanità , Rome , Italy
| | - Ivo Mueller
- Population Health and Immunity, Walter and Eliza Hall Institute of Medical Research , Melbourne , Australia
| | - Alyssa E Barry
- Life Sciences Discipline, Burnet Institute , Melbourne , Australia
- Institute of Mental and Physical Health and Clinical Translation (IMPACT) and School of Medicine, Deakin University , Geelong , Australia
| | - Cock van Oosterhout
- School of Environmental Sciences, University of East Anglia, Norwich Research Park , Norwich , UK
| | - Bernard Pope
- Melbourne Bioinformatics, University of Melbourne , Melbourne , Australia
- Australian BioCommons , Sydney , Australia
- Department of Clinical Pathology, University of Melbourne , Melbourne , Australia
- Department of Surgery (Royal Melbourne Hospital), University of Melbourne , Melbourne , Australia
| | - David B Ascher
- Systems and Computational Biology, Bio21 Institute, University of Melbourne , Melbourne , Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes , Melbourne , Australia
| | - Aaron R Jex
- Population Health and Immunity, Walter and Eliza Hall Institute of Medical Research , Melbourne , Australia
- Faculty of Veterinary and Agricultural Sciences, University of Melbourne , Melbourne , Australia
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Ruff KM, Choi YH, Cox D, Ormsby AR, Myung Y, Ascher DB, Radford SE, Pappu RV, Hatters DM. Sequence grammar underlying the unfolding and phase separation of globular proteins. Mol Cell 2022; 82:3193-3208.e8. [PMID: 35853451 PMCID: PMC10846692 DOI: 10.1016/j.molcel.2022.06.024] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 05/05/2022] [Accepted: 06/15/2022] [Indexed: 12/23/2022]
Abstract
Aberrant phase separation of globular proteins is associated with many diseases. Here, we use a model protein system to understand how the unfolded states of globular proteins drive phase separation and the formation of unfolded protein deposits (UPODs). We find that for UPODs to form, the concentrations of unfolded molecules must be above a threshold value. Additionally, unfolded molecules must possess appropriate sequence grammars to drive phase separation. While UPODs recruit molecular chaperones, their compositional profiles are also influenced by synergistic physicochemical interactions governed by the sequence grammars of unfolded proteins and cellular proteins. Overall, the driving forces for phase separation and the compositional profiles of UPODs are governed by the sequence grammars of unfolded proteins. Our studies highlight the need for uncovering the sequence grammars of unfolded proteins that drive UPOD formation and cause gain-of-function interactions whereby proteins are aberrantly recruited into UPODs.
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Affiliation(s)
- Kiersten M Ruff
- Department of Biomedical Engineering, Center for Science & Engineering of Living Systems, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Yoon Hee Choi
- Department of Biochemistry and Pharmacology and Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Dezerae Cox
- Department of Biochemistry and Pharmacology and Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Angelique R Ormsby
- Department of Biochemistry and Pharmacology and Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Yoochan Myung
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia; Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, The University of Melbourne, Melbourne, VIC 3010, Australia; Systems and Computational Biology, Bio21 Institute, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - David B Ascher
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia; Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, The University of Melbourne, Melbourne, VIC 3010, Australia; Systems and Computational Biology, Bio21 Institute, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Sheena E Radford
- Astbury Centre for Structural and Molecular Biology, School of Molecular and Cellular Biology, University of Leeds, Leeds LS2 9JT, UK
| | - Rohit V Pappu
- Department of Biomedical Engineering, Center for Science & Engineering of Living Systems, Washington University in St. Louis, St. Louis, MO 63130, USA.
| | - Danny M Hatters
- Department of Biochemistry and Pharmacology and Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Melbourne, VIC 3010, Australia.
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Kim D, Lee S, Kim E, Kang E, Myung Y, Heo C, Kim I, Jang B. PO-1215 Feasibility of anomaly score detected with deep learning in irradiated breast with reconstruction. Radiother Oncol 2022. [DOI: 10.1016/s0167-8140(22)03179-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Myung Y, Pires DEV, Ascher DB. CSM-AB: graph-based antibody-antigen binding affinity prediction and docking scoring function. Bioinformatics 2022; 38:1141-1143. [PMID: 34734992 DOI: 10.1093/bioinformatics/btab762] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 10/18/2021] [Accepted: 11/01/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Understanding antibody-antigen interactions is key to improving their binding affinities and specificities. While experimental approaches are fundamental for developing new therapeutics, computational methods can provide quick assessment of binding landscapes, guiding experimental design. Despite this, little effort has been devoted to accurately predicting the binding affinity between antibodies and antigens and to develop tailored docking scoring functions for this type of interaction. Here, we developed CSM-AB, a machine learning method capable of predicting antibody-antigen binding affinity by modelling interaction interfaces as graph-based signatures. RESULTS CSM-AB outperformed alternative methods achieving a Pearson's correlation of up to 0.64 on blind tests. We also show CSM-AB can accurately rank near-native poses, working effectively as a docking scoring function. We believe CSM-AB will be an invaluable tool to assist in the development of new immunotherapies. AVAILABILITY AND IMPLEMENTATION CSM-AB is freely available as a user-friendly web interface and API at http://biosig.unimelb.edu.au/csm_ab/datasets. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yoochan Myung
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia
| | - Douglas E V Pires
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia.,School of Computing and Information Systems, University of Melbourne, Melbourne, VIC, Australia.,School of Chemistry and Molecular Biosciences, University Of Queensland, St Lucia, QLD, Australia
| | - David B Ascher
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia.,School of Chemistry and Molecular Biosciences, University Of Queensland, St Lucia, QLD, Australia
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Nguyen TB, Myung Y, de Sá AGC, Pires DEV, Ascher DB. mmCSM-NA: accurately predicting effects of single and multiple mutations on protein-nucleic acid binding affinity. NAR Genom Bioinform 2021; 3:lqab109. [PMID: 34805992 PMCID: PMC8600011 DOI: 10.1093/nargab/lqab109] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 09/20/2021] [Accepted: 10/27/2021] [Indexed: 02/02/2023] Open
Abstract
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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Affiliation(s)
- Thanh Binh Nguyen
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia,School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Yoochan Myung
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Alex G C de Sá
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia,School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
| | | | - David B Ascher
- To whom correspondence should be addressed. Tel: +61 90354794;
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12
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Lin H, Ascher DB, Myung Y, Lamborg CH, Hallam SJ, Gionfriddo CM, Holt KE, Moreau JW. Mercury methylation by metabolically versatile and cosmopolitan marine bacteria. ISME J 2021; 15:1810-1825. [PMID: 33504941 DOI: 10.1101/2020.06.03.132969] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 12/17/2020] [Indexed: 05/21/2023]
Abstract
Microbes transform aqueous mercury (Hg) into methylmercury (MeHg), a potent neurotoxin that accumulates in terrestrial and marine food webs, with potential impacts on human health. This process requires the gene pair hgcAB, which encodes for proteins that actuate Hg methylation, and has been well described for anoxic environments. However, recent studies report potential MeHg formation in suboxic seawater, although the microorganisms involved remain poorly understood. In this study, we conducted large-scale multi-omic analyses to search for putative microbial Hg methylators along defined redox gradients in Saanich Inlet, British Columbia, a model natural ecosystem with previously measured Hg and MeHg concentration profiles. Analysis of gene expression profiles along the redoxcline identified several putative Hg methylating microbial groups, including Calditrichaeota, SAR324 and Marinimicrobia, with the last the most active based on hgc transcription levels. Marinimicrobia hgc genes were identified from multiple publicly available marine metagenomes, consistent with a potential key role in marine Hg methylation. Computational homology modelling predicts that Marinimicrobia HgcAB proteins contain the highly conserved amino acid sites and folding structures required for functional Hg methylation. Furthermore, a number of terminal oxidases from aerobic respiratory chains were associated with several putative novel Hg methylators. Our findings thus reveal potential novel marine Hg-methylating microorganisms with a greater oxygen tolerance and broader habitat range than previously recognized.
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Affiliation(s)
- Heyu Lin
- School of Earth Sciences, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - David B Ascher
- Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, VIC, 3010, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, PO Box 6492, Melbourne, VIC, 3004, Australia
| | - Yoochan Myung
- Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, VIC, 3010, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, PO Box 6492, Melbourne, VIC, 3004, Australia
| | - Carl H Lamborg
- Department of Ocean Sciences, University of California, Santa Cruz, CA, 95064, USA
| | - Steven J Hallam
- Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC, V6T 1Z1, Canada
- Genome Science and Technology Program, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Caitlin M Gionfriddo
- Biosciences Division, Oak Ridge National Laboratory, PO Box 2008, Oak Ridge, TN, 37831, USA
- Smithsonian Environmental Research Center, Edgewater, MD, 21037, USA
| | - Kathryn E Holt
- Department of Infectious Diseases, Central Clinical School, Monash University, Monash, VIC, 3800, Australia
- Department of Infection Biology, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - John W Moreau
- School of Earth Sciences, The University of Melbourne, Parkville, VIC, 3010, Australia.
- Currently at School of Geographical & Earth Sciences, University of Glasgow, Glasgow, G12 8QQ, UK.
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13
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Lin H, Ascher DB, Myung Y, Lamborg CH, Hallam SJ, Gionfriddo CM, Holt KE, Moreau JW. Mercury methylation by metabolically versatile and cosmopolitan marine bacteria. ISME J 2021; 15:1810-1825. [PMID: 33504941 PMCID: PMC8163782 DOI: 10.1038/s41396-020-00889-4] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 12/17/2020] [Indexed: 01/30/2023]
Abstract
Microbes transform aqueous mercury (Hg) into methylmercury (MeHg), a potent neurotoxin that accumulates in terrestrial and marine food webs, with potential impacts on human health. This process requires the gene pair hgcAB, which encodes for proteins that actuate Hg methylation, and has been well described for anoxic environments. However, recent studies report potential MeHg formation in suboxic seawater, although the microorganisms involved remain poorly understood. In this study, we conducted large-scale multi-omic analyses to search for putative microbial Hg methylators along defined redox gradients in Saanich Inlet, British Columbia, a model natural ecosystem with previously measured Hg and MeHg concentration profiles. Analysis of gene expression profiles along the redoxcline identified several putative Hg methylating microbial groups, including Calditrichaeota, SAR324 and Marinimicrobia, with the last the most active based on hgc transcription levels. Marinimicrobia hgc genes were identified from multiple publicly available marine metagenomes, consistent with a potential key role in marine Hg methylation. Computational homology modelling predicts that Marinimicrobia HgcAB proteins contain the highly conserved amino acid sites and folding structures required for functional Hg methylation. Furthermore, a number of terminal oxidases from aerobic respiratory chains were associated with several putative novel Hg methylators. Our findings thus reveal potential novel marine Hg-methylating microorganisms with a greater oxygen tolerance and broader habitat range than previously recognized.
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Affiliation(s)
- Heyu Lin
- grid.1008.90000 0001 2179 088XSchool of Earth Sciences, The University of Melbourne, Parkville, VIC 3010 Australia
| | - David B. Ascher
- grid.1008.90000 0001 2179 088XStructural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, VIC 3010 Australia ,grid.1051.50000 0000 9760 5620Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, PO Box 6492, Melbourne, VIC 3004 Australia
| | - Yoochan Myung
- grid.1008.90000 0001 2179 088XStructural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, VIC 3010 Australia ,grid.1051.50000 0000 9760 5620Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, PO Box 6492, Melbourne, VIC 3004 Australia
| | - Carl H. Lamborg
- grid.205975.c0000 0001 0740 6917Department of Ocean Sciences, University of California, Santa Cruz, CA 95064 USA
| | - Steven J. Hallam
- grid.17091.3e0000 0001 2288 9830Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC V6T 1Z1 Canada ,grid.17091.3e0000 0001 2288 9830Genome Science and Technology Program, University of British Columbia, Vancouver, BC V6T 1Z4 Canada
| | - Caitlin M. Gionfriddo
- grid.135519.a0000 0004 0446 2659Biosciences Division, Oak Ridge National Laboratory, PO Box 2008, Oak Ridge, TN 37831 USA ,grid.419533.90000 0000 8612 0361Present Address: Smithsonian Environmental Research Center, Edgewater, MD 21037 USA
| | - Kathryn E. Holt
- grid.1002.30000 0004 1936 7857Department of Infectious Diseases, Central Clinical School, Monash University, Monash, VIC 3800 Australia ,grid.8991.90000 0004 0425 469XDepartment of Infection Biology, London School of Hygiene & Tropical Medicine, London, WC1E 7HT UK
| | - John W. Moreau
- grid.1008.90000 0001 2179 088XSchool of Earth Sciences, The University of Melbourne, Parkville, VIC 3010 Australia ,grid.8756.c0000 0001 2193 314XPresent Address: Currently at School of Geographical & Earth Sciences, University of Glasgow, Glasgow, G12 8QQ UK
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14
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Portelli S, Olshansky M, Rodrigues CHM, D'Souza EN, Myung Y, Silk M, Alavi A, Pires DEV, Ascher DB. Author Correction: Exploring the structural distribution of genetic variation in SARS-CoV-2 with the COVID-3D online resource. Nat Genet 2021; 53:254. [PMID: 33398199 PMCID: PMC7781176 DOI: 10.1038/s41588-020-00775-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Stephanie Portelli
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Moshe Olshansky
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Carlos H M Rodrigues
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Elston N D'Souza
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Yoochan Myung
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Michael Silk
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Azadeh Alavi
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Douglas E V Pires
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.,School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - David B Ascher
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia. .,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia. .,Department of Biochemistry, University of Cambridge, Cambridge, UK.
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15
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Portelli S, Myung Y, Furnham N, Vedithi SC, Pires DEV, Ascher DB. Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches. Sci Rep 2020; 10:18120. [PMID: 33093532 PMCID: PMC7581776 DOI: 10.1038/s41598-020-74648-y] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Accepted: 09/21/2020] [Indexed: 01/23/2023] Open
Abstract
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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Affiliation(s)
- Stephanie Portelli
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Victoria, 3010, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, 3004, VIC, Australia
| | - Yoochan Myung
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Victoria, 3010, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, 3004, VIC, Australia
| | - Nicholas Furnham
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | | | - Douglas E V Pires
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, 3004, VIC, Australia
- School of Computing and Information Systems, University of Melbourne, Victoria, 3010, Australia
| | - David B Ascher
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Victoria, 3010, Australia.
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, 3004, VIC, Australia.
- Department of Biochemistry, University of Cambridge, Cambridge, UK.
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16
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Portelli S, Olshansky M, Rodrigues CHM, D'Souza EN, Myung Y, Silk M, Alavi A, Pires DEV, Ascher DB. Exploring the structural distribution of genetic variation in SARS-CoV-2 with the COVID-3D online resource. Nat Genet 2020; 52:999-1001. [PMID: 32908256 DOI: 10.1038/s41588-020-0693-3] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Stephanie Portelli
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Moshe Olshansky
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Carlos H M Rodrigues
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Elston N D'Souza
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Yoochan Myung
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Michael Silk
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Azadeh Alavi
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Douglas E V Pires
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.,School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - David B Ascher
- Structural Biology and Bioinformatics, Department of Biochemistry, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia. .,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia. .,Department of Biochemistry, University of Cambridge, Cambridge, UK.
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17
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Myung Y, Rodrigues CHM, Ascher DB, Pires DEV. mCSM-AB2: guiding rational antibody design using graph-based signatures. Bioinformatics 2020; 36:1453-1459. [PMID: 31665262 DOI: 10.1093/bioinformatics/btz779] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 10/07/2019] [Accepted: 10/23/2019] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION 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. RESULTS 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. AVAILABILITY AND IMPLEMENTATION mCSM-AB2 is available as a user-friendly and freely accessible web server providing rapid analysis of both individual mutations or the entire binding interface to guide rational antibody affinity maturation at http://biosig.unimelb.edu.au/mcsm_ab2. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yoochan Myung
- Department of Biochemistry and Molecular Biology.,ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, VIC 3010, Australia.,Structural Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
| | - Carlos H M Rodrigues
- Department of Biochemistry and Molecular Biology.,ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, VIC 3010, Australia.,Structural Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
| | - David B Ascher
- Department of Biochemistry and Molecular Biology.,ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, VIC 3010, Australia.,Structural Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia.,Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK
| | - Douglas E V Pires
- Department of Biochemistry and Molecular Biology.,ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, VIC 3010, Australia.,Structural Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia.,School of Computing and Information Systems, University of Melbourne, Melbourne, VIC 3010, Australia
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18
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Myung Y, Pires DEV, Ascher DB. mmCSM-AB: guiding rational antibody engineering through multiple point mutations. Nucleic Acids Res 2020; 48:W125-W131. [PMID: 32432715 PMCID: PMC7319589 DOI: 10.1093/nar/gkaa389] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 04/18/2020] [Accepted: 05/16/2020] [Indexed: 12/15/2022] Open
Abstract
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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Affiliation(s)
- Yoochan Myung
- Computational Biology and Clinical Informatics, Baker Institute, Melbourne, VIC 3004, Australia
- Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC 3052, Australia
| | - Douglas E V Pires
- Computational Biology and Clinical Informatics, Baker Institute, Melbourne, VIC 3004, Australia
- Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC 3052, Australia
- School of Computing and Information Systems, University of Melbourne, Parkville, VIC 3052, Australia
| | - David B Ascher
- Computational Biology and Clinical Informatics, Baker Institute, Melbourne, VIC 3004, Australia
- Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC 3052, Australia
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK
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Seo M, Shin HK, Myung Y, Hwang S, No KT. Development of Natural Compound Molecular Fingerprint (NC-MFP) with the Dictionary of Natural Products (DNP) for natural product-based drug development. J Cheminform 2020; 12:6. [PMID: 33431009 PMCID: PMC6977316 DOI: 10.1186/s13321-020-0410-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 01/11/2020] [Indexed: 12/21/2022] Open
Abstract
Computer-aided research on the relationship between molecular structures of natural compounds (NC) and their biological activities have been carried out extensively because the molecular structures of new drug candidates are usually analogous to or derived from the molecular structures of NC. In order to express the relationship physically realistically using a computer, it is essential to have a molecular descriptor set that can adequately represent the characteristics of the molecular structures belonging to the NC’s chemical space. Although several topological descriptors have been developed to describe the physical, chemical, and biological properties of organic molecules, especially synthetic compounds, and have been widely used for drug discovery researches, these descriptors have limitations in expressing NC-specific molecular structures. To overcome this, we developed a novel molecular fingerprint, called Natural Compound Molecular Fingerprints (NC-MFP), for explaining NC structures related to biological activities and for applying the same for the natural product (NP)-based drug development. NC-MFP was developed to reflect the structural characteristics of NCs and the commonly used NP classification system. NC-MFP is a scaffold-based molecular fingerprint method comprising scaffolds, scaffold-fragment connection points (SFCP), and fragments. The scaffolds of the NC-MFP have a hierarchical structure. In this study, we introduce 16 structural classes of NPs in the Dictionary of Natural Product database (DNP), and the hierarchical scaffolds of each class were calculated using the Bemis and Murko (BM) method. The scaffold library in NC-MFP comprises 676 scaffolds. To compare how well the NC-MFP represents the structural features of NCs compared to the molecular fingerprints that have been widely used for organic molecular representation, two kinds of binary classification tasks were performed. Task I is a binary classification of the NCs in commercially available library DB into a NC or synthetic compound. Task II is classifying whether NCs with inhibitory activity in seven biological target proteins are active or inactive. Two tasks were developed with some molecular fingerprints, including NC-MFP, using the 1-nearest neighbor (1-NN) method. The performance of task I showed that NC-MFP is a practical molecular fingerprint to classify NC structures from the data set compared with other molecular fingerprints. Performance of task II with NC-MFP outperformed compared with other molecular fingerprints, suggesting that the NC-MFP is useful to explain NC structures related to biological activities. In conclusion, NC-MFP is a robust molecular fingerprint in classifying NC structures and explaining the biological activities of NC structures. Therefore, we suggest NC-MFP as a potent molecular descriptor of the virtual screening of NC for natural product-based drug development.![]()
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Affiliation(s)
- Myungwon Seo
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, Republic of Korea
| | - Hyun Kil Shin
- Department of Predictive Toxicology, Korea Institute of Toxicology, Daejeon, Republic of Korea
| | - Yoochan Myung
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Sungbo Hwang
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, Republic of Korea.,Bioinformatics and Molecular Design Research Center, Yonsei Engineering Research Park, Seoul, Republic of Korea
| | - Kyoung Tai No
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, Republic of Korea. .,Bioinformatics and Molecular Design Research Center, Yonsei Engineering Research Park, Seoul, Republic of Korea.
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20
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Rodrigues CHM, Myung Y, Pires DEV, Ascher DB. mCSM-PPI2: predicting the effects of mutations on protein-protein interactions. Nucleic Acids Res 2019; 47:W338-W344. [PMID: 31114883 PMCID: PMC6602427 DOI: 10.1093/nar/gkz383] [Citation(s) in RCA: 192] [Impact Index Per Article: 38.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 04/30/2019] [Accepted: 05/20/2019] [Indexed: 12/13/2022] Open
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 http://biosig.unimelb.edu.au/mcsm_ppi2/.
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Affiliation(s)
- Carlos H M Rodrigues
- Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Australia
- ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, Australia
- Structural Biology and Bioinformatics, Baker Heart and Diabetes Institute, Melbourne, Australia
| | - Yoochan Myung
- Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Australia
- ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, Australia
- Structural Biology and Bioinformatics, Baker Heart and Diabetes Institute, Melbourne, Australia
| | - Douglas E V Pires
- Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Australia
- ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, Australia
- Structural Biology and Bioinformatics, Baker Heart and Diabetes Institute, Melbourne, Australia
| | - David B Ascher
- Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Australia
- ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, Australia
- Structural Biology and Bioinformatics, Baker Heart and Diabetes Institute, Melbourne, Australia
- Department of Biochemistry, University of Cambridge, Cambridge, UK
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Pires DEV, Rodrigues CHM, Albanaz ATS, Karmakar M, Myung Y, Xavier J, Michanetzi EM, Portelli S, Ascher DB. Exploring Protein Supersecondary Structure Through Changes in Protein Folding, Stability, and Flexibility. Methods Mol Biol 2019; 1958:173-185. [PMID: 30945219 DOI: 10.1007/978-1-4939-9161-7_9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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.
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Affiliation(s)
- Douglas E V Pires
- Instituto René Rachou, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil. .,Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia.
| | - Carlos H M Rodrigues
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia
| | | | - Malancha Karmakar
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia
| | - Yoochan Myung
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia
| | - Joicymara Xavier
- Instituto René Rachou, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Eleni-Maria Michanetzi
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia
| | - Stephanie Portelli
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia
| | - David B Ascher
- Instituto René Rachou, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.,Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia.,Department of Biochemistry, University of Cambridge, Cambridge, UK
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Baek RM, Myung Y. Intraoperative, surgeon-view, high-definition video broadcasting in intraoral surgery. Br J Oral Maxillofac Surg 2017; 55:561-562. [DOI: 10.1016/j.bjoms.2016.11.314] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 11/14/2016] [Indexed: 11/27/2022]
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Affiliation(s)
- Yoochan Myung
- Department of Biochemistry, Kangwon National University, Chunchon, Republic of Korea
| | - Seongyeol Yeom
- Department of Biochemistry, Kangwon National University, Chunchon, Republic of Korea
| | - Sanghwa Han
- Department of Biochemistry, Kangwon National University, Chunchon, Republic of Korea
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