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Namme JN, Reza HM, Bepari AK. Computational analysis and molecular dynamics simulation of high-risk single nucleotide polymorphisms of the ADAM10 gene. J Biomol Struct Dyn 2024; 42:412-424. [PMID: 36995110 DOI: 10.1080/07391102.2023.2192890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 03/13/2023] [Indexed: 03/31/2023]
Abstract
Polymorphisms of the disintegrin and metalloproteinase domain-containing protein 10 (ADAM10) are linked to pathophysiological changes in lung inflammation, cancer, Alzheimer's disease (AD), encephalopathy, liver fibrosis, and cardiovascular diseases. In this study, we predicted the pathogenicity of ADAM10 non-synonymous single nucleotide polymorphisms (nsSNPs) in a wide array of mutation analyzing bioinformatics tools. We retrieved 423 nsSNPs from dbSNP-NCBI for the analysis, and 13 were predicted deleterious by each of the ten tools: SIFT, PROVEAN, CONDEL, PANTHER-PSEP, SNAP2, SuSPect, PolyPhen-2, Meta-SNP, Mutation Assessor and Predict-SNP. Further assessment of amino acid sequences, homology models, conservation profiles, and inter-atomic interactions identified C222G, G361E and C639Y as the most pathogenic mutations. We validated this prediction through structural stability analysis using DUET, I-Mutant Suite, SNPeffect and Dynamut. Molecular dynamics simulations and principal component analysis also indicated considerable instability of the C222G, G361E and C639Y variants. Therefore, these ADAM10 nsSNPs could be candidates for diagnostic genetic screening and therapeutic molecular targeting.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Jannatun Nayem Namme
- Department of Pharmaceutical Sciences, North South University, Dhaka, Bangladesh
| | - Hasan Mahmud Reza
- Department of Pharmaceutical Sciences, North South University, Dhaka, Bangladesh
| | - Asim Kumar Bepari
- Department of Pharmaceutical Sciences, North South University, Dhaka, Bangladesh
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2
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Kafita D, Nkhoma P, Dzobo K, Sinkala M. Shedding light on the dark genome: Insights into the genetic, CRISPR-based, and pharmacological dependencies of human cancers and disease aggressiveness. PLoS One 2023; 18:e0296029. [PMID: 38117798 PMCID: PMC10732413 DOI: 10.1371/journal.pone.0296029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/05/2023] [Indexed: 12/22/2023] Open
Abstract
Investigating the human genome is vital for identifying risk factors and devising effective therapies to combat genetic disorders and cancer. Despite the extensive knowledge of the "light genome", the poorly understood "dark genome" remains understudied. In this study, we integrated data from 20,412 protein-coding genes in Pharos and 8,395 patient-derived tumours from The Cancer Genome Atlas (TCGA) to examine the genetic and pharmacological dependencies in human cancers and their treatment implications. We discovered that dark genes exhibited high mutation rates in certain cancers, similar to light genes. By combining the drug response profiles of cancer cells with cell fitness post-CRISPR-mediated gene knockout, we identified the crucial vulnerabilities associated with both dark and light genes. Our analysis also revealed that tumours harbouring dark gene mutations displayed worse overall and disease-free survival rates than those without such mutations. Furthermore, dark gene expression levels significantly influenced patient survival outcomes. Our findings demonstrated a similar distribution of genetic and pharmacological dependencies across the light and dark genomes, suggesting that targeting the dark genome holds promise for cancer treatment. This study underscores the need for ongoing research on the dark genome to better comprehend the underlying mechanisms of cancer and develop more effective therapies.
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Affiliation(s)
- Doris Kafita
- Department of Biomedical Sciences, University of Zambia, School of Health Sciences, Lusaka, Zambia
| | - Panji Nkhoma
- Department of Biomedical Sciences, University of Zambia, School of Health Sciences, Lusaka, Zambia
| | - Kevin Dzobo
- Department of Medicine, Division of Dermatology, Hair and Skin Research Laboratory, Wound and Keloid Scarring Research Unit, The South African Medical Research Council, University of Cape Town, Cape Town, South Africa
| | - Musalula Sinkala
- Department of Biomedical Sciences, University of Zambia, School of Health Sciences, Lusaka, Zambia
- Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine and Department of Integrative Biomedical Sciences, University of Cape Town, Computational Biology Division, Cape Town, South Africa
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3
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McBride JM, Polev K, Abdirasulov A, Reinharz V, Grzybowski BA, Tlusty T. AlphaFold2 Can Predict Single-Mutation Effects. PHYSICAL REVIEW LETTERS 2023; 131:218401. [PMID: 38072605 DOI: 10.1103/physrevlett.131.218401] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 09/26/2023] [Indexed: 12/18/2023]
Abstract
AlphaFold2 (AF) is a promising tool, but is it accurate enough to predict single mutation effects? Here, we report that the localized structural deformation between protein pairs differing by only 1-3 mutations-as measured by the effective strain-is correlated across 3901 experimental and AF-predicted structures. Furthermore, analysis of ∼11 000 proteins shows that the local structural change correlates with various phenotypic changes. These findings suggest that AF can predict the range and magnitude of single-mutation effects on average, and we propose a method to improve precision of AF predictions and to indicate when predictions are unreliable.
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Affiliation(s)
- John M McBride
- Center for Soft and Living Matter, Institute for Basic Science, Ulsan 44919, South Korea
| | - Konstantin Polev
- Center for Soft and Living Matter, Institute for Basic Science, Ulsan 44919, South Korea
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, South Korea
| | - Amirbek Abdirasulov
- Department of Computer Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, South Korea
| | | | - Bartosz A Grzybowski
- Center for Soft and Living Matter, Institute for Basic Science, Ulsan 44919, South Korea
- Departments of Physics and Chemistry, Ulsan National Institute of Science and Technology, Ulsan 44919, South Korea
| | - Tsvi Tlusty
- Center for Soft and Living Matter, Institute for Basic Science, Ulsan 44919, South Korea
- Departments of Physics and Chemistry, Ulsan National Institute of Science and Technology, Ulsan 44919, South Korea
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4
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Chakraborty S, Bhattacharya I, Mitra RK. Solvation Plays a Key Role in Antioxidant-Mediated Attenuation of Elevated Creatinine Level: An In Vitro Spectroscopic Investigation. J Phys Chem B 2023; 127:8576-8585. [PMID: 37769128 DOI: 10.1021/acs.jpcb.3c05334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
An elevated level of creatinine (CRN) is a mark of kidney ailment, and prolonged retention of such condition could lead to renal failure, associated with severe ischemia. Antioxidants are clinically known to excrete CRN from the body through urine, thereby reducing its level in blood. The molecular mechanism of such an exclusion process is still illusive. As the excretion channel is urine, solvation of the solute is expected to play a pivotal role. Here, we report a detailed time-domain and frequency-domain terahertz (THz) spectroscopic investigation to understand the solvation of CRN in the presence of two model antioxidants, mostly used to treat elevated CRN level: N-Acetyl-l-cysteine (NAC) and ascorbic acid (ASC). FTIR spectroscopy in the mid-infrared region and UV absorption spectroscopy measurements coupled with quantum chemical calculations [at the B3LYP/6-311G++(d,p) level] reveal that both NAC and ASC form HBonded complexes with CRN and rapidly undergo a barrier-less proton transfer process to form creatinium ions. THz measurements provide explicit evidence of the formation of highly solvated complexes compared with bare CRN, which eventually enables its excretion through urine. These observations could provide a foundation for designing more beneficial drugs to resolve kidney diseases..
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Affiliation(s)
- Subhadip Chakraborty
- Department of Chemical and Biological Sciences, S.N. Bose National Centre for Basic Sciences; Block-JD; Sector-III; Salt Lake, Kolkata 700106, India
| | - Indrani Bhattacharya
- Department of Chemical and Biological Sciences, S.N. Bose National Centre for Basic Sciences; Block-JD; Sector-III; Salt Lake, Kolkata 700106, India
| | - Rajib Kumar Mitra
- Department of Chemical and Biological Sciences, S.N. Bose National Centre for Basic Sciences; Block-JD; Sector-III; Salt Lake, Kolkata 700106, India
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5
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Akter S, Oliveira JIN, Barton C, Sarkar MH, Shahab M, Banu TA, Goswami B, Osman E, Uzzaman MS, Nafisa T, Molla MA, Yeasmin M, Farzana M, Habib A, Shaikh AA, Khan S. Spike protein mutations and structural insights of pangolin lineage B.1.1.25 with implications for viral pathogenicity and ACE2 binding affinity. Sci Rep 2023; 13:13146. [PMID: 37573409 PMCID: PMC10423208 DOI: 10.1038/s41598-023-40005-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 08/03/2023] [Indexed: 08/14/2023] Open
Abstract
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the causative agent of COVID -19, is constantly evolving, requiring continuous genomic surveillance. In this study, we used whole-genome sequencing to investigate the genetic epidemiology of SARS-CoV-2 in Bangladesh, with particular emphasis on identifying dominant variants and associated mutations. We used high-throughput next-generation sequencing (NGS) to obtain DNA sequences from COVID-19 patient samples and compared these sequences to the Wuhan SARS-CoV-2 reference genome using the Global Initiative for Sharing All Influenza Data (GISAID). Our phylogenetic and mutational analyzes revealed that the majority (88%) of the samples belonged to the pangolin lineage B.1.1.25, whereas the remaining 11% were assigned to the parental lineage B.1.1. Two main mutations, D614G and P681R, were identified in the spike protein sequences of the samples. The D614G mutation, which is the most common, decreases S1 domain flexibility, whereas the P681R mutation may increase the severity of viral infections by increasing the binding affinity between the spike protein and the ACE2 receptor. We employed molecular modeling techniques, including protein modeling, molecular docking, and quantum mechanics/molecular mechanics (QM/MM) geometry optimization, to build and validate three-dimensional models of the S_D614G-ACE2 and S_P681R-ACE2 complexes from the predominant strains. The description of the binding mode and intermolecular contacts of the referenced systems suggests that the P681R mutation may be associated with increased viral pathogenicity in Bangladeshi patients due to enhanced electrostatic interactions between the mutant spike protein and the human ACE2 receptor, underscoring the importance of continuous genomic surveillance in the fight against COVID -19. Finally, the binding profile of the S_D614G-ACE2 and S_P681R-ACE2 complexes offer valuable insights to deeply understand the binding site characteristics that could help to develop antiviral therapeutics that inhibit protein-protein interactions between SARS-CoV-2 spike protein and human ACE2 receptor.
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Affiliation(s)
- Shahina Akter
- Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka, Bangladesh.
| | - Jonas Ivan Nobre Oliveira
- Department of Biophysics and Pharmacology, Bioscience Center, Federal University of Rio Grande do Norte, Natal, RN, 59078-900, Brazil
| | - Carl Barton
- Birkbeck, University of London, Malet St, Bloomsbury, London, WC1E 7HX, UK
| | - Murshed Hasan Sarkar
- Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka, Bangladesh
| | - Muhammad Shahab
- State Key Laboratories of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Tanjina Akhtar Banu
- Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka, Bangladesh
| | - Barna Goswami
- Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka, Bangladesh
| | - Eshrar Osman
- SciTech Consulting and Solutions, Dhaka, Bangladesh
| | | | - Tasnim Nafisa
- National Institute of Laboratory Medicine and Referral Center, Dhaka, Bangladesh
| | - Maruf Ahmed Molla
- National Institute of Laboratory Medicine and Referral Center, Dhaka, Bangladesh
- SUNY Upstate Medical University, Syracuse, NY, 13207, USA
| | - Mahmuda Yeasmin
- National Institute of Laboratory Medicine and Referral Center, Dhaka, Bangladesh
| | - Maisha Farzana
- Department of Chemistry, University of New Brunswick, Fredericton, NB, E3B 5A3, Canada
| | - Ahashan Habib
- Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka, Bangladesh
| | - Aftab Ali Shaikh
- Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka, Bangladesh
| | - Salim Khan
- Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka, Bangladesh
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6
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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] [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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7
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Durojaye OA, Sedzro DM, Idris MO, Yekeen AA, Fadahunsi AA, Alakanse OS. Identification of a Potential mRNA-based Vaccine Candidate against the SARS-CoV-2 Spike Glycoprotein: A Reverse Vaccinology Approach. ChemistrySelect 2022; 7:e202103903. [PMID: 35601809 PMCID: PMC9111088 DOI: 10.1002/slct.202103903] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 02/04/2022] [Indexed: 12/11/2022]
Abstract
The emergence of the novel coronavirus (SARS-CoV-2) in December 2019 has generated a devastating global consequence which makes the development of a rapidly deployable, effective and safe vaccine candidate an imminent global health priority. The design of most vaccine candidates has been directed at the induction of antibody responses against the trimeric spike glycoprotein of SARS-CoV-2, a class I fusion protein that aids ACE2 (angiotensin-converting enzyme 2) receptor binding. A variety of formulations and vaccinology approaches are being pursued for targeting the spike glycoprotein, including simian and human replication-defective adenoviral vaccines, subunit protein vaccines, nucleic acid vaccines and whole-inactivated SARS-CoV-2. Here, we directed a reverse vaccinology approach towards the design of a nucleic acid (mRNA-based) vaccine candidate. The "YLQPRTFLL" peptide sequence (position 269-277) which was predicted to be a B cell epitope and likewise a strong binder of the HLA*A-0201 was selected for the design of the vaccine candidate, having satisfied series of antigenicity assessments. Through the codon optimization protocol, the nucleotide sequence for the vaccine candidate design was generated and targeted at the human toll-like receptor 7 (TLR7). Bioinformatics analyses showed that the sequence "UACCUGCAGCCGCGUACCUUCCUGCUG" exhibited a strong affinity and likewise was bound to a stable cavity in the TLR7 pocket. This study is therefore expected to contribute to the research efforts directed at securing definitive preventive measures against the SARS-CoV-2 infection.
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Affiliation(s)
- Olanrewaju Ayodeji Durojaye
- MOE Key Laboratory of Membraneless Organelle and Cellular DynamicsHefei National Laboratory for Physical Sciences at the MicroscaleUniversity of Science and Technology of ChinaHefeiAnhui230027China
- School of Life SciencesUniversity of Science and Technology of ChinaHefeiAnhui230027China
- Department of Chemical SciencesCoal City University, EmeneEnugu StateNigeria
| | - Divine Mensah Sedzro
- MOE Key Laboratory of Membraneless Organelle and Cellular DynamicsHefei National Laboratory for Physical Sciences at the MicroscaleUniversity of Science and Technology of ChinaHefeiAnhui230027China
- School of Life SciencesUniversity of Science and Technology of ChinaHefeiAnhui230027China
| | | | - Abeeb Abiodun Yekeen
- School of Life SciencesUniversity of Science and Technology of ChinaHefeiAnhui230027China
| | - Adeola Abraham Fadahunsi
- Department of Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiAnhui230027China
| | - Oluwaseun Suleiman Alakanse
- School of Life SciencesUniversity of Science and Technology of ChinaHefeiAnhui230027China
- Department of BiochemistryFaculty of Life SciencesUniversity of IlorinIlorinKwara StateNigeria
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8
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Portelli S, Barr L, de Sá AG, Pires DE, Ascher DB. Distinguishing between PTEN clinical phenotypes through mutation analysis. Comput Struct Biotechnol J 2021; 19:3097-3109. [PMID: 34141133 PMCID: PMC8180946 DOI: 10.1016/j.csbj.2021.05.028] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 04/29/2021] [Accepted: 05/19/2021] [Indexed: 12/28/2022] Open
Abstract
Phosphate and tensin homolog on chromosome ten (PTEN) germline mutations are associated with an overarching condition known as PTEN hamartoma tumor syndrome. Clinical phenotypes associated with this syndrome range from macrocephaly and autism spectrum disorder to Cowden syndrome, which manifests as multiple noncancerous tumor-like growths (hamartomas), and an increased predisposition to certain cancers. It is unclear, however, the basis by which mutations might lead to these very diverse phenotypic outcomes. Here we show that, by considering the molecular consequences of mutations in PTEN on protein structure and function, we can accurately distinguish PTEN mutations exhibiting different phenotypes. Changes in phosphatase activity, protein stability, and intramolecular interactions appeared to be major drivers of clinical phenotype, with cancer-associated variants leading to the most drastic changes, while ASD and non-pathogenic variants associated with more mild and neutral changes, respectively. Importantly, we show via saturation mutagenesis that more than half of variants of unknown significance could be associated with disease phenotypes, while over half of Cowden syndrome mutations likely lead to cancer. These insights can assist in exploring potentially important clinical outcomes delineated by PTEN variation.
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Affiliation(s)
- Stephanie Portelli
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Melbourne, Victoria, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Lucy Barr
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Melbourne, Victoria, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Alex G.C. de Sá
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Melbourne, Victoria, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Baker Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Melbourne, Victoria, Australia
| | - Douglas E.V. Pires
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Melbourne, Victoria, Australia
- Systems and Computational Biology, 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, University of Melbourne, Melbourne, Victoria, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Baker Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Melbourne, Victoria, Australia
- Department of Biochemistry, University of Cambridge, 80 Tennis Ct Rd, Cambridge CB2 1GA, United States
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Disciglio V, Forte G, Fasano C, Sanese P, Lepore Signorile M, De Marco K, Grossi V, Cariola F, Simone C. APC Splicing Mutations Leading to In-Frame Exon 12 or Exon 13 Skipping Are Rare Events in FAP Pathogenesis and Define the Clinical Outcome. Genes (Basel) 2021; 12:genes12030353. [PMID: 33670833 PMCID: PMC7997234 DOI: 10.3390/genes12030353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/22/2021] [Accepted: 02/22/2021] [Indexed: 11/16/2022] Open
Abstract
Familial adenomatous polyposis (FAP) is caused by germline mutations in the tumor suppressor gene APC. To date, nearly 2000 APC mutations have been described in FAP, most of which are predicted to result in truncated protein products. Mutations leading to aberrant APC splicing have rarely been reported. Here, we characterized a novel germline heterozygous splice donor site mutation in APC exon 12 (NM_000038.5: c.1621_1626+7del) leading to exon 12 skipping in an Italian family with the attenuated FAP (AFAP) phenotype. Moreover, we performed a literature meta-analysis of APC splicing mutations. We found that 119 unique APC splicing mutations, including the one described here, have been reported in FAP patients, 69 of which have been characterized at the mRNA level. Among these, only a small proportion (9/69) results in an in-frame protein, with four mutations causing skipping of exon 12 or 13 with loss of armadillo repeat 2 (ARM2) and 3 (ARM3), and five mutations leading to skipping of exon 5, 7, 8, or (partially) 9 with loss of regions not encompassing known functional domains. The APC splicing mutations causing skipping of exon 12 or 13 considered in this study cluster with the AFAP phenotype and reveal a potential molecular mechanism of pathogenesis in FAP disease.
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Affiliation(s)
- Vittoria Disciglio
- Medical Genetics, National Institute of Gastroenterology “S. de Bellis” Research Hospital, Castellana Grotte, 70013 Bari, Italy; (G.F.); (C.F.); (P.S.); (M.L.S.); (K.D.M.); (V.G.); (F.C.)
- Correspondence: (V.D.); (C.S.)
| | - Giovanna Forte
- Medical Genetics, National Institute of Gastroenterology “S. de Bellis” Research Hospital, Castellana Grotte, 70013 Bari, Italy; (G.F.); (C.F.); (P.S.); (M.L.S.); (K.D.M.); (V.G.); (F.C.)
| | - Candida Fasano
- Medical Genetics, National Institute of Gastroenterology “S. de Bellis” Research Hospital, Castellana Grotte, 70013 Bari, Italy; (G.F.); (C.F.); (P.S.); (M.L.S.); (K.D.M.); (V.G.); (F.C.)
| | - Paola Sanese
- Medical Genetics, National Institute of Gastroenterology “S. de Bellis” Research Hospital, Castellana Grotte, 70013 Bari, Italy; (G.F.); (C.F.); (P.S.); (M.L.S.); (K.D.M.); (V.G.); (F.C.)
| | - Martina Lepore Signorile
- Medical Genetics, National Institute of Gastroenterology “S. de Bellis” Research Hospital, Castellana Grotte, 70013 Bari, Italy; (G.F.); (C.F.); (P.S.); (M.L.S.); (K.D.M.); (V.G.); (F.C.)
| | - Katia De Marco
- Medical Genetics, National Institute of Gastroenterology “S. de Bellis” Research Hospital, Castellana Grotte, 70013 Bari, Italy; (G.F.); (C.F.); (P.S.); (M.L.S.); (K.D.M.); (V.G.); (F.C.)
| | - Valentina Grossi
- Medical Genetics, National Institute of Gastroenterology “S. de Bellis” Research Hospital, Castellana Grotte, 70013 Bari, Italy; (G.F.); (C.F.); (P.S.); (M.L.S.); (K.D.M.); (V.G.); (F.C.)
| | - Filomena Cariola
- Medical Genetics, National Institute of Gastroenterology “S. de Bellis” Research Hospital, Castellana Grotte, 70013 Bari, Italy; (G.F.); (C.F.); (P.S.); (M.L.S.); (K.D.M.); (V.G.); (F.C.)
| | - Cristiano Simone
- Medical Genetics, National Institute of Gastroenterology “S. de Bellis” Research Hospital, Castellana Grotte, 70013 Bari, Italy; (G.F.); (C.F.); (P.S.); (M.L.S.); (K.D.M.); (V.G.); (F.C.)
- Department of Biomedical Sciences and Human Oncology (DIMO), Medical Genetics, University of Bari Aldo Moro, 70124 Bari, Italy
- Correspondence: (V.D.); (C.S.)
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10
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Tunstall T, Portelli S, Phelan J, Clark TG, Ascher DB, Furnham N. Combining structure and genomics to understand antimicrobial resistance. Comput Struct Biotechnol J 2020; 18:3377-3394. [PMID: 33294134 PMCID: PMC7683289 DOI: 10.1016/j.csbj.2020.10.017] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 10/15/2020] [Accepted: 10/17/2020] [Indexed: 02/07/2023] Open
Abstract
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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Affiliation(s)
- Tanushree Tunstall
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Stephanie Portelli
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Australia
- Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Australia
| | - Jody Phelan
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Taane G. Clark
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - David B. Ascher
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Australia
- Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Australia
| | - Nicholas Furnham
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
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11
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Pires DEV, Rodrigues CHM, Ascher DB. mCSM-membrane: predicting the effects of mutations on transmembrane proteins. Nucleic Acids Res 2020; 48:W147-W153. [PMID: 32469063 PMCID: PMC7319563 DOI: 10.1093/nar/gkaa416] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 05/04/2020] [Accepted: 05/28/2020] [Indexed: 12/17/2022] Open
Abstract
Significant efforts have been invested into understanding and predicting the molecular consequences of mutations in protein coding regions, however nearly all approaches have been developed using globular, soluble proteins. These methods have been shown to poorly translate to studying the effects of mutations in membrane proteins. To fill this gap, here we report, mCSM-membrane, a user-friendly web server that can be used to analyse the impacts of mutations on membrane protein stability and the likelihood of them being disease associated. mCSM-membrane derives from our well-established mutation modelling approach that uses graph-based signatures to model protein geometry and physicochemical properties for supervised learning. Our stability predictor achieved correlations of up to 0.72 and 0.67 (on cross validation and blind tests, respectively), while our pathogenicity predictor achieved a Matthew's Correlation Coefficient (MCC) of up to 0.77 and 0.73, outperforming previously described methods in both predicting changes in stability and in identifying pathogenic variants. mCSM-membrane will be an invaluable and dedicated resource for investigating the effects of single-point mutations on membrane proteins through a freely available, user friendly web server at http://biosig.unimelb.edu.au/mcsm_membrane.
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Affiliation(s)
- Douglas E V Pires
- Computational Biology and Clinical Informatics, Baker Institute, Melbourne, Victoria 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
| | - Carlos H M Rodrigues
- Computational Biology and Clinical Informatics, Baker Institute, Melbourne, Victoria 3004, Australia.,Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC, 3052, Australia
| | - David B Ascher
- Computational Biology and Clinical Informatics, Baker Institute, Melbourne, Victoria 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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12
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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] [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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13
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Abstract
Mutations in protein-coding regions can lead to large biological changes and are associated with genetic conditions, including cancers and Mendelian diseases, as well as drug resistance. Although whole genome and exome sequencing help to elucidate potential genotype-phenotype correlations, there is a large gap between the identification of new variants and deciphering their molecular consequences. A comprehensive understanding of these mechanistic consequences is crucial to better understand and treat diseases in a more personalized and effective way. This is particularly relevant considering estimates that over 80% of mutations associated with a disease are incorrectly assumed to be causative. A thorough analysis of potential effects of mutations is required to correctly identify the molecular mechanisms of disease and enable the distinction between disease-causing and non-disease-causing variation within a gene. Here we present an overview of our integrative mutation analysis platform, which focuses on refining the current genotype-phenotype correlation methods by using the wealth of protein structural information.
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14
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Karmakar M, Rodrigues CHM, Horan K, Denholm JT, Ascher DB. Structure guided prediction of Pyrazinamide resistance mutations in pncA. Sci Rep 2020; 10:1875. [PMID: 32024884 PMCID: PMC7002382 DOI: 10.1038/s41598-020-58635-x] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 11/28/2019] [Indexed: 11/29/2022] Open
Abstract
Pyrazinamide plays an important role in tuberculosis treatment; however, its use is complicated by side-effects and challenges with reliable drug susceptibility testing. Resistance to pyrazinamide is largely driven by mutations in pyrazinamidase (pncA), responsible for drug activation, but genetic heterogeneity has hindered development of a molecular diagnostic test. We proposed to use information on how variants were likely to affect the 3D structure of pncA to identify variants likely to lead to pyrazinamide resistance. We curated 610 pncA mutations with high confidence experimental and clinical information on pyrazinamide susceptibility. The molecular consequences of each mutation on protein stability, conformation, and interactions were computationally assessed using our comprehensive suite of graph-based signature methods, mCSM. The molecular consequences of the variants were used to train a classifier with an accuracy of 80%. Our model was tested against internationally curated clinical datasets, achieving up to 85% accuracy. Screening of 600 Victorian clinical isolates identified a set of previously unreported variants, which our model had a 71% agreement with drug susceptibility testing. Here, we have shown the 3D structure of pncA can be used to accurately identify pyrazinamide resistance mutations. SUSPECT-PZA is freely available at: http://biosig.unimelb.edu.au/suspect_pza/.
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Affiliation(s)
- Malancha Karmakar
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Victorian Tuberculosis Program, Melbourne Health and Department of Microbiology and Immunology, University of Melbourne, Melbourne, Victoria, Australia
| | - Carlos H M Rodrigues
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Kristy Horan
- Microbiological Diagnostic Unit Public Health Laboratory, University of Melbourne at The Peter Doherty Institute for Infection &Immunity, Melbourne, Victoria, Australia
| | - Justin T Denholm
- Victorian Tuberculosis Program, Melbourne Health and Department of Microbiology and Immunology, University of Melbourne, Melbourne, Victoria, Australia
| | - David B Ascher
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK.
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15
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A Comprehensive Computational Platform to Guide Drug Development Using Graph-Based Signature Methods. Methods Mol Biol 2020. [PMID: 32006280 DOI: 10.1007/978-1-0716-0270-6_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
High-throughput computational techniques have become invaluable tools to help increase the overall success, process efficiency, and associated costs of drug development. By designing ligands tailored to specific protein structures in a disease of interest, an understanding of molecular interactions and ways to optimize them can be achieved prior to chemical synthesis. This understanding can help direct crucial chemical and biological experiments by maximizing available resources on higher quality leads. Moreover, predicting molecular binding affinity within specific biological contexts, as well as ligand pharmacokinetics and toxicities, can aid in filtering out redundant leads early on within the process. We describe a set of computational tools which can aid in drug discovery at different stages, from hit identification (EasyVS) to lead optimization and candidate selection (CSM-lig, mCSM-lig, Arpeggio, pkCSM). Incorporating these tools along the drug development process can help ensure that candidate leads are chemically and biologically feasible to become successful and tractable drugs.
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16
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Strokach A, Corbi-Verge C, Kim PM. Predicting changes in protein stability caused by mutation using sequence-and structure-based methods in a CAGI5 blind challenge. Hum Mutat 2019; 40:1414-1423. [PMID: 31243847 PMCID: PMC6744338 DOI: 10.1002/humu.23852] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 05/16/2019] [Accepted: 06/24/2019] [Indexed: 12/26/2022]
Abstract
Predicting the impact of mutations on proteins remains an important problem. As part of the CAGI5 frataxin challenge, we evaluate the accuracy with which Provean, FoldX, and ELASPIC can predict changes in the Gibbs free energy of a protein using a limited data set of eight mutations. We find that different methods have distinct strengths and limitations, with no method being strictly superior to other methods on all metrics. ELASPIC achieves the highest accuracy while also providing a web interface which simplifies the evaluation and analysis of mutations. FoldX is slightly less accurate than ELASPIC but is easier to run locally, as it does not depend on external tools or datasets. Provean achieves reasonable results while being computational less expensive than the other methods and not requiring a structure of the protein. In addition to methods submitted to the CAGI5 community experiment, and with the aim to inform about other methods with high accuracy, we also evaluate predictions made by Rosetta's ddg_monomer protocol, Rosetta's cartesian_ddg protocol, and thermodynamic integration calculations using Amber package. ELASPIC still achieves the highest accuracy, while Rosetta's catesian_ddg protocol appears to perform best in capturing the overall trend in the data.
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Affiliation(s)
- Alexey Strokach
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Carles Corbi-Verge
- Donnelly Centre for Cellular and Biomolecular Research, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Philip M Kim
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Donnelly Centre for Cellular and Biomolecular Research, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
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17
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Rodrigues CH, Pires DE, Ascher DB. DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability. Nucleic Acids Res 2019; 46:W350-W355. [PMID: 29718330 PMCID: PMC6031064 DOI: 10.1093/nar/gky300] [Citation(s) in RCA: 624] [Impact Index Per Article: 124.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 04/16/2018] [Indexed: 12/31/2022] Open
Abstract
Proteins are highly dynamic molecules, whose function is intrinsically linked to their molecular motions. Despite the pivotal role of protein dynamics, their computational simulation cost has led to most structure-based approaches for assessing the impact of mutations on protein structure and function relying upon static structures. Here we present DynaMut, a web server implementing two distinct, well established normal mode approaches, which can be used to analyze and visualize protein dynamics by sampling conformations and assess the impact of mutations on protein dynamics and stability resulting from vibrational entropy changes. DynaMut integrates our graph-based signatures along with normal mode dynamics to generate a consensus prediction of the impact of a mutation on protein stability. We demonstrate our approach outperforms alternative approaches to predict the effects of mutations on protein stability and flexibility (P-value < 0.001), achieving a correlation of up to 0.70 on blind tests. DynaMut also provides a comprehensive suite for protein motion and flexibility analysis and visualization via a freely available, user friendly web server at http://biosig.unimelb.edu.au/dynamut/.
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Affiliation(s)
- Carlos Hm Rodrigues
- Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Australia
| | | | - David B Ascher
- Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Australia.,Instituto René Rachou, Fundação Oswaldo Cruz, Brazil.,Department of Biochemistry, University of Cambridge, UK
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18
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Empirical ways to identify novel Bedaquiline resistance mutations in AtpE. PLoS One 2019; 14:e0217169. [PMID: 31141524 PMCID: PMC6541270 DOI: 10.1371/journal.pone.0217169] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 05/01/2019] [Indexed: 12/28/2022] Open
Abstract
Clinical resistance against Bedaquiline, the first new anti-tuberculosis compound with a novel mechanism of action in over 40 years, has already been detected in Mycobacterium tuberculosis. As a new drug, however, there is currently insufficient clinical data to facilitate reliable and timely identification of genomic determinants of resistance. Here we investigate the structural basis for M. tuberculosis associated bedaquiline resistance in the drug target, AtpE. Together with the 9 previously identified resistance-associated variants in AtpE, 54 non-resistance-associated mutations were identified through comparisons of bedaquiline susceptibility across 23 different mycobacterial species. Computational analysis of the structural and functional consequences of these variants revealed that resistance associated variants were mainly localized at the drug binding site, disrupting key interactions with bedaquiline leading to reduced binding affinity. This was used to train a supervised predictive algorithm, which accurately identified likely resistance mutations (93.3% accuracy). Application of this model to circulating variants present in the Asia-Pacific region suggests that current circulating variants are likely to be susceptible to bedaquiline. We have made this model freely available through a user-friendly web interface called SUSPECT-BDQ, StrUctural Susceptibility PrEdiCTion for bedaquiline (http://biosig.unimelb.edu.au/suspect_bdq/). This tool could be useful for the rapid characterization of novel clinical variants, to help guide the effective use of bedaquiline, and to minimize the spread of clinical resistance.
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19
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Li Y, Netherland MD, Zhang C, Hong H, Gong P. In silico identification of genetic mutations conferring resistance to acetohydroxyacid synthase inhibitors: A case study of Kochia scoparia. PLoS One 2019; 14:e0216116. [PMID: 31063467 PMCID: PMC6504096 DOI: 10.1371/journal.pone.0216116] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Accepted: 04/14/2019] [Indexed: 12/17/2022] Open
Abstract
Mutations that confer herbicide resistance are a primary concern for herbicide-based chemical control of invasive plants and are often under-characterized structurally and functionally. As the outcome of selection pressure, resistance mutations usually result from repeated long-term applications of herbicides with the same mode of action and are discovered through extensive field trials. Here we used acetohydroxyacid synthase (AHAS) of Kochia scoparia (KsAHAS) as an example to demonstrate that, given the sequence of a target protein, the impact of genetic mutations on ligand binding could be evaluated and resistance mutations could be identified using a biophysics-based computational approach. Briefly, the 3D structures of wild-type (WT) and mutated KsAHAS-herbicide complexes were constructed by homology modeling, docking and molecular dynamics simulation. The resistance profile of two AHAS-inhibiting herbicides, tribenuron methyl and thifensulfuron methyl, was obtained by estimating their binding affinity with 29 KsAHAS (1 WT and 28 mutated) using 6 molecular mechanical (MM) and 18 hybrid quantum mechanical/molecular mechanical (QM/MM) methods in combination with three structure sampling strategies. By comparing predicted resistance with experimentally determined resistance in the 29 biotypes of K. scoparia field populations, we identified the best method (i.e., MM-PBSA with single structure) out of all tested methods for the herbicide-KsAHAS system, which exhibited the highest accuracy (up to 100%) in discerning mutations conferring resistance or susceptibility to the two AHAS inhibitors. Our results suggest that the in silico approach has the potential to be widely adopted for assessing mutation-endowed herbicide resistance on a case-by-case basis.
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Affiliation(s)
- Yan Li
- Bennett Aerospace, Inc., Cary, North Carolina, United States of America
| | - Michael D. Netherland
- Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, Mississippi, United States of America
| | - Chaoyang Zhang
- School of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, Mississippi, United States of America
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas, United States of America
| | - Ping Gong
- Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, Mississippi, United States of America
- * E-mail:
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20
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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] [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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21
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Strokach A, Corbi-Verge C, Teyra J, Kim PM. Predicting the Effect of Mutations on Protein Folding and Protein-Protein Interactions. Methods Mol Biol 2019; 1851:1-17. [PMID: 30298389 DOI: 10.1007/978-1-4939-8736-8_1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The function of a protein is largely determined by its three-dimensional structure and its interactions with other proteins. Changes to a protein's amino acid sequence can alter its function by perturbing the energy landscapes of protein folding and binding. Many tools have been developed to predict the energetic effect of amino acid changes, utilizing features describing the sequence of a protein, the structure of a protein, or both. Those tools can have many applications, such as distinguishing between deleterious and benign mutations and designing proteins and peptides with attractive properties. In this chapter, we describe how to use one of such tools, ELASPIC, to predict the effect of mutations on the stability of proteins and the affinity between proteins, in the context of a human protein-protein interaction network. ELASPIC uses a wide range of sequential and structural features to predict the change in the Gibbs free energy for protein folding and protein-protein interactions. It can be used both through a web server and as a stand-alone application. Since ELASPIC was trained using homology models and not crystal structures, it can be applied to a much broader range of proteins than traditional methods. It can leverage precalculated sequence alignments, homology models, and other features, in order to drastically lower the amount of time required to evaluate individual mutations and make tractable the analysis of millions of mutations affecting the majority of proteins in a genome.
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Affiliation(s)
- Alexey Strokach
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Carles Corbi-Verge
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Joan Teyra
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Philip M Kim
- Department of Computer Science, University of Toronto, Toronto, ON, Canada.
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
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22
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Rodrigues CHM, Ascher DB, Pires DEV. Kinact: a computational approach for predicting activating missense mutations in protein kinases. Nucleic Acids Res 2018; 46:W127-W132. [PMID: 29788456 PMCID: PMC6031004 DOI: 10.1093/nar/gky375] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Revised: 04/15/2018] [Accepted: 04/28/2018] [Indexed: 12/31/2022] Open
Abstract
Protein phosphorylation is tightly regulated due to its vital role in many cellular processes. While gain of function mutations leading to constitutive activation of protein kinases are known to be driver events of many cancers, the identification of these mutations has proven challenging. Here we present Kinact, a novel machine learning approach for predicting kinase activating missense mutations using information from sequence and structure. By adapting our graph-based signatures, Kinact represents both structural and sequence information, which are used as evidence to train predictive models. We show the combination of structural and sequence features significantly improved the overall accuracy compared to considering either primary or tertiary structure alone, highlighting their complementarity. Kinact achieved a precision of 87% and 94% and Area Under ROC Curve of 0.89 and 0.92 on 10-fold cross-validation, and on blind tests, respectively, outperforming well established tools (P < 0.01). We further show that Kinact performs equally well on homology models built using templates with sequence identity as low as 33%. Kinact is freely available as a user-friendly web server at http://biosig.unimelb.edu.au/kinact/.
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Affiliation(s)
- Carlos HM Rodrigues
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne
| | - David B Ascher
- Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne
- Department of Biochemistry, University of Cambridge
- Instituto René Rachou, Fundação Oswaldo Cruz
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23
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Holt KE, McAdam P, Thai PVK, Thuong NTT, Ha DTM, Lan NN, Lan NH, Nhu NTQ, Hai HT, Ha VTN, Thwaites G, Edwards DJ, Nath AP, Pham K, Ascher DB, Farrar J, Khor CC, Teo YY, Inouye M, Caws M, Dunstan SJ. Frequent transmission of the Mycobacterium tuberculosis Beijing lineage and positive selection for the EsxW Beijing variant in Vietnam. Nat Genet 2018; 50:849-856. [PMID: 29785015 PMCID: PMC6143168 DOI: 10.1038/s41588-018-0117-9] [Citation(s) in RCA: 126] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Accepted: 03/22/2018] [Indexed: 12/19/2022]
Abstract
To examine the transmission dynamics of Mycobacterium tuberculosis (Mtb) isolated from tuberculosis patients in Ho Chi Minh City, Vietnam, we sequenced the whole genomes of 1,635 isolates and compared these with 3,144 isolates from elsewhere. The data identify an underlying burden of disease caused by the endemic Mtb lineage 1 associated with the activation of long-term latent infection, and a threefold higher burden associated with the more recently introduced Beijing lineage and lineage 4 Mtb strains. We find that Beijing lineage Mtb is frequently transferred between Vietnam and other countries, and detect higher levels of transmission of Beijing lineage strains within this host population than the endemic lineage 1 Mtb. Screening for parallel evolution of Beijing lineage-associated SNPs in other Mtb lineages as a signal of positive selection, we identify an alteration in the ESX-5 type VII-secreted protein EsxW, which could potentially contribute to the enhanced transmission of Beijing lineage Mtb in Vietnamese and other host populations.
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Affiliation(s)
- Kathryn E Holt
- Department of Biochemistry and Molecular Biology, Bio 21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, Victoria, Australia.
| | - Paul McAdam
- Department of Biochemistry and Molecular Biology, Bio 21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, Victoria, Australia
| | - Phan Vuong Khac Thai
- Pham Ngoc Thach Hospital for Tuberculosis and Lung Disease, Ho Chi Minh City, Vietnam
| | | | - Dang Thi Minh Ha
- Pham Ngoc Thach Hospital for Tuberculosis and Lung Disease, Ho Chi Minh City, Vietnam
| | - Nguyen Ngoc Lan
- Pham Ngoc Thach Hospital for Tuberculosis and Lung Disease, Ho Chi Minh City, Vietnam
| | - Nguyen Huu Lan
- Pham Ngoc Thach Hospital for Tuberculosis and Lung Disease, Ho Chi Minh City, Vietnam
| | | | - Hoang Thanh Hai
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Vu Thi Ngoc Ha
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Guy Thwaites
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine, Nuffield Department of Clinical Medicine, Oxford University, Oxford, UK
| | - David J Edwards
- Department of Biochemistry and Molecular Biology, Bio 21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, Victoria, Australia
| | - Artika P Nath
- Department of Microbiology and Immunology, University of Melbourne, Parkville, Victoria, Australia
- Systems Genomics Lab, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Kym Pham
- Department of Clinical Pathology, University of Melbourne, Parkville, Victoria, Australia
| | - David B Ascher
- Department of Biochemistry and Molecular Biology, Bio 21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, Victoria, Australia
| | - Jeremy Farrar
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine, Nuffield Department of Clinical Medicine, Oxford University, Oxford, UK
| | - Chiea Chuen Khor
- Genome Institute of Singapore, Singapore, Singapore
- Singapore Eye Research Institute, Singapore, Singapore
| | - Yik Ying Teo
- Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Michael Inouye
- Systems Genomics Lab, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Department of Clinical Pathology, University of Melbourne, Parkville, Victoria, Australia
- Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratories, Cambridge, UK
| | - Maxine Caws
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
- Birat-Nepal Medical Trust, Kathmandu, Nepal
| | - Sarah J Dunstan
- Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, Victoria, Australia.
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Frequent transmission of the Mycobacterium tuberculosis Beijing lineage and positive selection for the EsxW Beijing variant in Vietnam. Nat Genet 2018. [PMID: 29785015 DOI: 10.1038/s41588-018-0117-9.] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
To examine the transmission dynamics of Mycobacterium tuberculosis (Mtb) isolated from tuberculosis patients in Ho Chi Minh City, Vietnam, we sequenced the whole genomes of 1,635 isolates and compared these with 3,144 isolates from elsewhere. The data identify an underlying burden of disease caused by the endemic Mtb lineage 1 associated with the activation of long-term latent infection, and a threefold higher burden associated with the more recently introduced Beijing lineage and lineage 4 Mtb strains. We find that Beijing lineage Mtb is frequently transferred between Vietnam and other countries, and detect higher levels of transmission of Beijing lineage strains within this host population than the endemic lineage 1 Mtb. Screening for parallel evolution of Beijing lineage-associated SNPs in other Mtb lineages as a signal of positive selection, we identify an alteration in the ESX-5 type VII-secreted protein EsxW, which could potentially contribute to the enhanced transmission of Beijing lineage Mtb in Vietnamese and other host populations.
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