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Lin YJ, Menon AS, Hu Z, Brenner SE. Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.25.600283. [PMID: 38979289 PMCID: PMC11230257 DOI: 10.1101/2024.06.25.600283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb). Results The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods. Conclusions VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. Availability VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.
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Affiliation(s)
- Yu-Jen Lin
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- Center for Computational Biology, University of California, Berkeley, California 94720, USA
| | - Arul S. Menon
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, USA
| | - Zhiqiang Hu
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
- Currently at: Illumina, Foster City, California 94404, USA
| | - Steven E. Brenner
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- Center for Computational Biology, University of California, Berkeley, California 94720, USA
- College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, USA
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
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2
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Banerjee A, Mathew S, Naqvi MM, Yilmaz SZ, Zacharopoulou M, Doruker P, Kumita JR, Yang SH, Gur M, Itzhaki LS, Gordon R, Bahar I. Influence of point mutations on PR65 conformational adaptability: Insights from molecular simulations and nanoaperture optical tweezers. SCIENCE ADVANCES 2024; 10:eadn2208. [PMID: 38820156 PMCID: PMC11141623 DOI: 10.1126/sciadv.adn2208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 04/29/2024] [Indexed: 06/02/2024]
Abstract
PR65 is the HEAT repeat scaffold subunit of the heterotrimeric protein phosphatase 2A (PP2A) and an archetypal tandem repeat protein. Its conformational mechanics plays a crucial role in PP2A function by opening/closing substrate binding/catalysis interface. Using in silico saturation mutagenesis, we identified PR65 "hinge" residues whose substitutions could alter its conformational adaptability and thereby PP2A function, and selected six mutations that were verified to be expressed and soluble. Molecular simulations and nanoaperture optical tweezers revealed consistent results on the specific effects of the mutations on the structure and dynamics of PR65. Two mutants observed in simulations to stabilize extended/open conformations exhibited higher corner frequencies and lower translational scattering in experiments, indicating a shift toward extended conformations, whereas another displayed the opposite features, confirmed by both simulations and experiments. The study highlights the power of single-molecule nanoaperture-based tweezers integrated with in silico approaches for exploring the effect of mutations on protein structure and dynamics.
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Affiliation(s)
- Anupam Banerjee
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Samuel Mathew
- Department of Electrical and Computer Engineering, University of Victoria, Victoria V8P 5C2, Canada
| | - Mohsin M. Naqvi
- Department of Pharmacology, University of Cambridge, Tennis Court Road, Cambridge CB2 1PD, UK
| | - Sema Z. Yilmaz
- Department of Mechanical Engineering, Istanbul Technical University, 34437 Istanbul, Turkey
| | - Maria Zacharopoulou
- Department of Pharmacology, University of Cambridge, Tennis Court Road, Cambridge CB2 1PD, UK
| | - Pemra Doruker
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Janet R. Kumita
- Department of Pharmacology, University of Cambridge, Tennis Court Road, Cambridge CB2 1PD, UK
| | - Shang-Hua Yang
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Mert Gur
- Department of Mechanical Engineering, Istanbul Technical University, 34437 Istanbul, Turkey
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Laura S. Itzhaki
- Department of Pharmacology, University of Cambridge, Tennis Court Road, Cambridge CB2 1PD, UK
| | - Reuven Gordon
- Department of Electrical and Computer Engineering, University of Victoria, Victoria V8P 5C2, Canada
| | - Ivet Bahar
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
- Department of Biochemistry and Cell Biology, School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
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Sun J, Qu J, Zhao C, Zhang X, Liu X, Wang J, Wei C, Liu X, Wang M, Zeng P, Tang X, Ling X, Qing L, Jiang S, Chen J, Chen TSR, Kuang Y, Gao J, Zeng X, Huang D, Yuan Y, Fan L, Yu H, Ding J. Precise prediction of phase-separation key residues by machine learning. Nat Commun 2024; 15:2662. [PMID: 38531854 DOI: 10.1038/s41467-024-46901-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 03/13/2024] [Indexed: 03/28/2024] Open
Abstract
Understanding intracellular phase separation is crucial for deciphering transcriptional control, cell fate transitions, and disease mechanisms. However, the key residues, which impact phase separation the most for protein phase separation function have remained elusive. We develop PSPHunter, which can precisely predict these key residues based on machine learning scheme. In vivo and in vitro validations demonstrate that truncating just 6 key residues in GATA3 disrupts phase separation, enhancing tumor cell migration and inhibiting growth. Glycine and its motifs are enriched in spacer and key residues, as revealed by our comprehensive analysis. PSPHunter identifies nearly 80% of disease-associated phase-separating proteins, with frequent mutated pathological residues like glycine and proline often residing in these key residues. PSPHunter thus emerges as a crucial tool to uncover key residues, facilitating insights into phase separation mechanisms governing transcriptional control, cell fate transitions, and disease development.
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Affiliation(s)
- Jun Sun
- Department of Thoracic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jiale Qu
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Cai Zhao
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xinyao Zhang
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xinyu Liu
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jia Wang
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, 511436, China
| | - Chao Wei
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xinyi Liu
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Mulan Wang
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Pengguihang Zeng
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xiuxiao Tang
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xiaoru Ling
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Li Qing
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Shaoshuai Jiang
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jiahao Chen
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Tara S R Chen
- Department of Rehabilitation Medicine, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, Guangdong, 518107, China
| | - Yalan Kuang
- Department of Thoracic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China
| | - Jinhang Gao
- Department of Thoracic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China
| | - Xiaoxi Zeng
- Department of Thoracic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China
| | - Dongfeng Huang
- Department of Rehabilitation Medicine, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, Guangdong, 518107, China
| | - Yong Yuan
- Department of Thoracic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China.
| | - Lili Fan
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, School of Traditional Chinese Medicine, Jinan University, Guangzhou, Guangdong, China.
| | - Haopeng Yu
- Department of Thoracic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China.
| | - Junjun Ding
- Department of Thoracic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China.
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.
- Department of Rehabilitation Medicine, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, Guangdong, 518107, China.
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4
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Wei X, Li H, Zhu T, Sun Z, Sui R. Genotype-Phenotype Associations in an X-Linked Retinoschisis Patient Cohort: The Molecular Dynamic Insight and a Promising SD-OCT Indicator. Invest Ophthalmol Vis Sci 2024; 65:17. [PMID: 38324300 PMCID: PMC10854265 DOI: 10.1167/iovs.65.2.17] [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: 07/31/2023] [Accepted: 01/23/2024] [Indexed: 02/08/2024] Open
Abstract
Purpose This study investigated a three-dimensional indicator in spectral-domain optical coherence tomography (SD-OCT) and established phenotype-genotype correlation in X-linked retinoschisis (XLRS). Methods Thirty-seven patients with XLRS underwent comprehensive ophthalmic examinations, including visual acuity (VA), fundus examination, electroretinogram (ERG), and SD-OCT. SD-OCT parameters of central foveal thickness (CFT), cyst cavity volume (CCV), and photoreceptor outer segment length were assessed. CCV was defined as the sum of the areas of cyst cavities in uential B-scans, measured automatically by self-developed software (OCT-CCSEG). Structural changes of the protein associated with missense variants were quantified by molecular dynamics (MD). The correlation between genotype and phenotype was analyzed. Results Twenty-seven different RS1 variants were identified, including a novel variant c.336_337insT(p.L113Sfs*8). The average age of onset was 14.76 ± 15.75 years, and the mean VA was 0.84 ± 0.43 logMAR. The mean CCV was 1.69 ± 1.87 mm3, correlating significantly with CFT (R = 0.66; P < 0.01). In the genotype-phenotype analysis of missense variants, CCV significantly correlated with the structural effect on the protein of mutational changes referred to as wild type, including root-mean-square deviation (R = 0.34; P = 0.04), solvent accessible surface area (R = 0.38; P = 0.02), and surface hydrophobic area (R = 0.37; P = 0.03). The amplitude of scotopic 3.0 ERG a-waves and b-waves significantly correlated with the percentage change of the β-strand in the secondary structure (a-wave: R = -0.58, P < 0.01; b-wave: R = -0.53, P < 0.01). Conclusions CCV is a promising indicator to quantify the structural disorganization of XLRS retina. The OCT-CCSEG software calculated CCV automatically, potentially facilitating prognosis assessment and development of personalized treatment. Moreover, MD-involved genotype-phenotype analysis suggests an association between protein structural alterations and XLRS severity measured by CCV and ERG.
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Affiliation(s)
- Xing Wei
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Hui Li
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Tian Zhu
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Zixi Sun
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Ruifang Sui
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
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Shojaei M, Mohammadvand N, Doğan T, Alkan C, Çetin Atalay R, Acar AC. An integrative framework for clinical diagnosis and knowledge discovery from exome sequencing data. Comput Biol Med 2024; 169:107810. [PMID: 38134749 DOI: 10.1016/j.compbiomed.2023.107810] [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: 03/28/2023] [Revised: 11/06/2023] [Accepted: 12/03/2023] [Indexed: 12/24/2023]
Abstract
Non-silent single nucleotide genetic variants, like nonsense changes and insertion-deletion variants, that affect protein function and length substantially are prevalent and are frequently misclassified. The low sensitivity and specificity of existing variant effect predictors for nonsense and indel variations restrict their use in clinical applications. We propose the Pathogenic Mutation Prediction (PMPred) method to predict the pathogenicity of single nucleotide variations, which impair protein function by prematurely terminating a protein's elongation during its synthesis. The prediction starts by monitoring functional effects (Gene Ontology annotation changes) of the change in sequence, using an existing ensemble machine learning model (UniGOPred). This, in turn, reveals the mutations that significantly deviate functionally from the wild-type sequence. We have identified novel harmful mutations in patient data and present them as motivating case studies. We also show that our method has increased sensitivity and specificity compared to state-of-the-art, especially in single nucleotide variations that produce large functional changes in the final protein. As further validation, we have done a comparative docking study on such a variation that is misclassified by existing methods and, using the altered binding affinities, show how PMPred can correctly predict the pathogenicity when other tools miss it. PMPred is freely accessible as a web service at https://pmpred.kansil.org/, and the related code is available at https://github.com/kansil/PMPred.
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Affiliation(s)
- Mona Shojaei
- Cancer Systems Biology Laboratory, Graduate School of Informatics, Middle East Technical University, Ankara 06800 Turkey
| | - Navid Mohammadvand
- Biological Data Science Lab, Dept. of Computer Engineering, Hacettepe University, Ankara 06800 Turkey
| | - Tunca Doğan
- Biological Data Science Lab, Dept. of Computer Engineering, Hacettepe University, Ankara 06800 Turkey; Dept. of Bioinformatics, Graduate School of Health Sciences, Hacettepe University, Ankara 06800 Turkey
| | - Can Alkan
- Department of Computer Engineering, Bilkent University, Ankara 06800 Turkey
| | - Rengül Çetin Atalay
- Department of Medicine, University of Chicago, Chicago, IL, USA; Section of Pulmonary and Critical Care Medicine, University of Chicago, 5841 S. Maryland Avenue, MC6026, Chicago, IL, 60637, USA
| | - Aybar C Acar
- Cancer Systems Biology Laboratory, Graduate School of Informatics, Middle East Technical University, Ankara 06800 Turkey.
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Hassi NK, Weston T, Rinaldi G, Ng JC, Smahi A, Twelves S, Davan-Wetton C, Fakhreddine D, Fraternali F, Capon F. In Silico and In Vitro Analysis of IL36RN Alterations Reveals Critical Residues for the Function of the Interleukin-36 Receptor Complex. J Invest Dermatol 2023; 143:2468-2475.e6. [PMID: 37414245 PMCID: PMC10824670 DOI: 10.1016/j.jid.2023.06.191] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 05/18/2023] [Accepted: 06/08/2023] [Indexed: 07/08/2023]
Abstract
Generalized pustular psoriasis is a potentially life-threatening skin disease, associated with IL36RN disease alleles. IL36RN encodes the IL-36 receptor antagonist (IL-36Ra), a protein that downregulates the activity of IL-36 cytokines by blocking their receptor (IL-36R). Although generalized pustular psoriasis can be treated with IL-36R inhibitors, the structural underpinnings of the IL-36Ra/IL-36R interaction remain poorly understood. In this study, we sought to address this question by systematically investigating the effects of IL36RN sequence changes. We experimentally characterized the effects of 30 IL36RN variants on protein stability. In parallel, we used a machinelearning tool (Rhapsody) to analyze the IL-36Ra three-dimensional structure and predict the impact of all possible amino acid substitutions. This integrated approach identified 21 amino acids that are essential for IL-36Ra stability. We next investigated the effects of IL36RN changes on IL-36Ra/IL-36R binding and IL-36R signaling. Combining invitro assays and machine learning with a second program (mCSM), we identified 13 amino acids that are critical for IL-36Ra/IL36R engagement. Finally, we experimentally validated three representative predictions, further confirming the reliability of Rhapsody and mCSM. These findings shed light on the structural determinants of IL-36Ra activity, with potential to facilitate the design of new IL-36 inhibitors and aid the interpretation of IL36RN variants in diagnostic settings.
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Affiliation(s)
- Niina K Hassi
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, United Kingdom
| | - Timir Weston
- Randall Centre for Cell & Molecular Biophysics, School of Basic and Medical Biosciences, King's College London, London, United Kingdom
| | - Giulia Rinaldi
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, United Kingdom
| | - Joseph C Ng
- Randall Centre for Cell & Molecular Biophysics, School of Basic and Medical Biosciences, King's College London, London, United Kingdom; Institute of Structural and Molecular Biology, University College London, London, United Kingdom
| | - Asma Smahi
- IMAGINE Institute INSERM UMR 1163, Paris, France
| | - Sophie Twelves
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, United Kingdom
| | - Camilla Davan-Wetton
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, United Kingdom
| | - Dana Fakhreddine
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, United Kingdom
| | - Franca Fraternali
- Randall Centre for Cell & Molecular Biophysics, School of Basic and Medical Biosciences, King's College London, London, United Kingdom; Institute of Structural and Molecular Biology, University College London, London, United Kingdom
| | - Francesca Capon
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, United Kingdom.
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7
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Wang YJ, Vu GH, Mu TW. Pathogenicity Prediction of GABA A Receptor Missense Variants. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.14.567135. [PMID: 38014242 PMCID: PMC10680766 DOI: 10.1101/2023.11.14.567135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Variants in the genes encoding the subunits of gamma-aminobutyric acid type A (GABA A ) receptors are associated with epilepsy. To date, over 1000 clinical variants have been identified in these genes. However, the majority of these variants lack functional studies and their clinical significance is uncertain although accumulating evidence indicates that proteostasis deficiency is the major disease-causing mechanism for GABA A receptor variants. Here, we apply two state-of-the-art modeling tools, namely AlphaMissense, which uses an artificial intelligence-based approach based on AlphaFold structures, and Rhapsody, which integrates sequence evolution and known structure-based data, to predict the pathogenicity of saturating missense variants in genes that encode the major subunits of GABA A receptors in the central nervous system, including GABRA1 , GABRB2 , GABRB3 , and GABRG2 . Our results demonstrate that the predicted pathogenicity correlates well between AlphaMissense and Rhapsody although AlphaMissense tends to generate higher pathogenic probability. Furthermore, almost all annotated pathogenic variants in the ClinVar clinical database are successfully identified from the prediction, whereas uncertain variants from ClinVar partially due to the lack of experimental data are differentiated into different pathogenicity groups. The pathogenicity prediction of GABA A receptor missense variants provides a resource to the community as well as guidance for future experimental and clinical investigations.
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Bahar I, Banerjee A, Mathew S, Naqvi M, Yilmaz S, Zachoropoulou M, Doruker P, Kumita J, Yang SH, Gur M, Itzhaki L, Gordon R. Influence of Point Mutations on PR65 Conformational Adaptability: Insights from Nanoaperture Optical Tweezer Experiments and Molecular Simulations. RESEARCH SQUARE 2023:rs.3.rs-3599809. [PMID: 38014259 PMCID: PMC10680943 DOI: 10.21203/rs.3.rs-3599809/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
PR65 is the HEAT-repeat scaffold subunit of the heterotrimeric protein phosphatase 2A (PP2A) and an archetypal tandem-repeat protein, forming a spring-like architecture. PR65 conformational mechanics play a crucial role in PP2A function by opening/closing the substrate-binding/catalysis interface. Using in-silico saturation mutagenesis we identified "hinge" residues of PR65, whose substitutions are predicted to restrict its conformational adaptability and thereby disrupt PP2A function. Molecular simulations revealed that a subset of hinge mutations stabilized the extended/open conformation, whereas another had the opposite effect. By trapping in nanoaperture optical tweezer, we characterized PR65 motion and showed that the former mutants exhibited higher corner frequencies and lower translational scattering, indicating a shift towards extended conformations, whereas the latter showed the opposite behavior. Thus, experiments confirm the conformations predicted computationally. The study highlights the utility of nanoaperture-based tweezers for exploring structure and dynamics, and the power of integrating this single-molecule method with in silico approaches.
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9
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Chi YI, Jorge SD, Jensen DR, Smith BC, Volkman BF, Mathison AJ, Lomberk G, Zimmermann MT, Urrutia R. A multi-layered computational structural genomics approach enhances domain-specific interpretation of Kleefstra syndrome variants in EHMT1. Comput Struct Biotechnol J 2023; 21:5249-5258. [PMID: 37954151 PMCID: PMC10632586 DOI: 10.1016/j.csbj.2023.10.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 10/06/2023] [Accepted: 10/12/2023] [Indexed: 11/14/2023] Open
Abstract
This study investigates the functional significance of assorted variants of uncertain significance (VUS) in euchromatic histone lysine methyltransferase 1 (EHMT1), which is critical for early development and normal physiology. EHMT1 mutations cause Kleefstra syndrome and are linked to various human cancers. However, accurate functional interpretations of these variants are yet to be made, limiting diagnoses and future research. To overcome this, we integrate conventional tools for variant calling with computational biophysics and biochemistry to conduct multi-layered mechanistic analyses of the SET catalytic domain of EHMT1, which is critical for this protein function. We use molecular mechanics and molecular dynamics (MD)-based metrics to analyze the SET domain structure and functional motions resulting from 97 Kleefstra syndrome missense variants within the domain. Our approach allows us to classify the variants in a mechanistic manner into SV (Structural Variant), DV (Dynamic Variant), SDV (Structural and Dynamic Variant), and VUS (Variant of Uncertain Significance). Our findings reveal that the damaging variants are mostly mapped around the active site, substrate binding site, and pre-SET regions. Overall, we report an improvement for this method over conventional tools for variant interpretation and simultaneously provide a molecular mechanism for variant dysfunction.
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Affiliation(s)
- Young-In Chi
- Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
- Division of Research, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Salomão D. Jorge
- Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Davin R. Jensen
- Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Brian C. Smith
- Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Brian F. Volkman
- Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Angela J. Mathison
- Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
- Division of Research, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Gwen Lomberk
- Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
- Division of Research, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, USA
- Department of Pharmacology and Toxicology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Michael T. Zimmermann
- Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA
- Clinical and Translational Sciences Institute, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Raul Urrutia
- Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
- Division of Research, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, USA
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA
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10
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Chi YI, Jorge SD, Jensen DR, Smith BC, Volkman BF, Mathison AJ, Lomberk G, Zimmermann MT, Urrutia R. A Multi-Layered Computational Structural Genomics Approach Enhances Domain-Specific Interpretation of Kleefstra Syndrome Variants in EHMT1. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.06.556558. [PMID: 37786696 PMCID: PMC10541560 DOI: 10.1101/2023.09.06.556558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
This study investigates the functional significance of assorted variants of uncertain significance (VUS) in euchromatic histone lysine methyltransferase 1 (EHMT1), which is critical for early development and normal physiology. EHMT1 mutations cause Kleefstra syndrome and are linked to various human cancers. However, accurate functional interpretation of these variants are yet to be made, limiting diagnoses and future research. To overcome this, we integrate conventional tools for variant calling with computational biophysics and biochemistry to conduct multi-layered mechanistic analyses of the SET catalytic domain of EHMT1, which is critical for this protein function. We use molecular mechanics and molecular dynamics (MD)-based metrics to analyze the SET domain structure and functional motions resulting from 97 Kleefstra syndrome missense variants within this domain. Our approach allows us to classify the variants in a mechanistic manner into SV (Structural Variant), DV (Dynamic Variant), SDV (Structural and Dynamic Variant), and VUS (Variant of Uncertain Significance). Our findings reveal that the damaging variants are mostly mapped around the active site, substrate binding site, and pre-SET regions. Overall, we report an improvement for this method over conventional tools for variant interpretation and simultaneously provide a molecular mechanism of variant dysfunction.
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11
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Wang B, Lei X, Tian W, Perez-Rathke A, Tseng YY, Liang J. Structure-based pathogenicity relationship identifier for predicting effects of single missense variants and discovery of higher-order cancer susceptibility clusters of mutations. Brief Bioinform 2023; 24:bbad206. [PMID: 37332013 PMCID: PMC10359089 DOI: 10.1093/bib/bbad206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 04/19/2023] [Accepted: 05/13/2023] [Indexed: 06/20/2023] Open
Abstract
We report the structure-based pathogenicity relationship identifier (SPRI), a novel computational tool for accurate evaluation of pathological effects of missense single mutations and prediction of higher-order spatially organized units of mutational clusters. SPRI can effectively extract properties determining pathogenicity encoded in protein structures, and can identify deleterious missense mutations of germ line origin associated with Mendelian diseases, as well as mutations of somatic origin associated with cancer drivers. It compares favorably to other methods in predicting deleterious mutations. Furthermore, SPRI can discover spatially organized pathogenic higher-order spatial clusters (patHOS) of deleterious mutations, including those of low recurrence, and can be used for discovery of candidate cancer driver genes and driver mutations. We further demonstrate that SPRI can take advantage of AlphaFold2 predicted structures and can be deployed for saturation mutation analysis of the whole human proteome.
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Affiliation(s)
- Boshen Wang
- Center for Bioinformatics and Quantitative Biology, Richard and Loan Hill, Department of Biomedical Engineering, University of Illinois at Chicago, W103 Suite, 820 S Wood St, 60612 IL, USA
| | - Xue Lei
- Center for Bioinformatics and Quantitative Biology, Richard and Loan Hill, Department of Biomedical Engineering, University of Illinois at Chicago, W103 Suite, 820 S Wood St, 60612 IL, USA
| | - Wei Tian
- Center for Bioinformatics and Quantitative Biology, Richard and Loan Hill, Department of Biomedical Engineering, University of Illinois at Chicago, W103 Suite, 820 S Wood St, 60612 IL, USA
| | - Alan Perez-Rathke
- Center for Bioinformatics and Quantitative Biology, Richard and Loan Hill, Department of Biomedical Engineering, University of Illinois at Chicago, W103 Suite, 820 S Wood St, 60612 IL, USA
| | - Yan-Yuan Tseng
- Center for Molecular Medicine and Genetics, Biochemistry and Molecular Biology Department, School of Medicine, Wayne State University, 540 E. Canfield Avenue, 48201MI, USA
| | - Jie Liang
- Center for Bioinformatics and Quantitative Biology, Richard and Loan Hill, Department of Biomedical Engineering, University of Illinois at Chicago, W103 Suite, 820 S Wood St, 60612 IL, USA
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12
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Ramakrishnan G, Baakman C, Heijl S, Vroling B, van Horck R, Hiraki J, Xue LC, Huynen MA. Understanding structure-guided variant effect predictions using 3D convolutional neural networks. Front Mol Biosci 2023; 10:1204157. [PMID: 37475887 PMCID: PMC10354367 DOI: 10.3389/fmolb.2023.1204157] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 06/22/2023] [Indexed: 07/22/2023] Open
Abstract
Predicting pathogenicity of missense variants in molecular diagnostics remains a challenge despite the available wealth of data, such as evolutionary information, and the wealth of tools to integrate that data. We describe DeepRank-Mut, a configurable framework designed to extract and learn from physicochemically relevant features of amino acids surrounding missense variants in 3D space. For each variant, various atomic and residue-level features are extracted from its structural environment, including sequence conservation scores of the surrounding amino acids, and stored in multi-channel 3D voxel grids which are then used to train a 3D convolutional neural network (3D-CNN). The resultant model gives a probabilistic estimate of whether a given input variant is disease-causing or benign. We find that the performance of our 3D-CNN model, on independent test datasets, is comparable to other widely used resources which also combine sequence and structural features. Based on the 10-fold cross-validation experiments, we achieve an average accuracy of 0.77 on the independent test datasets. We discuss the contribution of the variant neighborhood in the model's predictive power, in addition to the impact of individual features on the model's performance. Two key features: evolutionary information of residues in the variant neighborhood and their solvent accessibilities were observed to influence the predictions. We also highlight how predictions are impacted by the underlying disease mechanisms of missense mutations and offer insights into understanding these to improve pathogenicity predictions. Our study presents aspects to take into consideration when adopting deep learning approaches for protein structure-guided pathogenicity predictions.
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Affiliation(s)
- Gayatri Ramakrishnan
- Department of Medical Biosciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Coos Baakman
- Department of Medical Biosciences, Radboud University Medical Center, Nijmegen, Netherlands
| | | | | | | | | | - Li C. Xue
- Department of Medical Biosciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Martijn A. Huynen
- Department of Medical Biosciences, Radboud University Medical Center, Nijmegen, Netherlands
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13
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Konkankit CC, Rackovsky S. Global Survey of Protein Dynamic Properties. J Phys Chem B 2023. [PMID: 37368985 DOI: 10.1021/acs.jpcb.3c02609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
Using tools developed to study the dynamic bioinformatics of proteins, we are able to study the dynamic characteristics of very large numbers of protein sequences simultaneously. We study herein the distribution of protein sequences in a space determined by sequence mobility. It is shown that there are statistically significant differences in mobility distribution between folded sequences of different structural classes and between those and sequences of intrinsically disordered proteins. It is also shown that the several regions of mobility space differ significantly with respect to structural makeup. Helical proteins are shown to have distinctive dynamic characteristics at both extremes of the mobility spectrum.
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Affiliation(s)
- Chilaluck C Konkankit
- Department of Chemistry and Chemical Biology, Baker Laboratory, Cornell University, Ithaca, New York 14853, United States
| | - S Rackovsky
- Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, New York 14642, United States
- Department of Chemistry and Chemical Biology, Baker Laboratory, Cornell University, Ithaca, New York 14853, United States
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14
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Hopkins CE, McCormick K, Brock T, Wood M, Ruggiero S, Mcbride K, Kim C, Lawson JA, Helbig I, Bainbridge MN. Clinical variants in Caenorhabditis elegans expressing human STXBP1 reveal a novel class of pathogenic variants and classify variants of uncertain significance. GENETICS IN MEDICINE OPEN 2023; 1:100823. [PMID: 38827422 PMCID: PMC11141691 DOI: 10.1016/j.gimo.2023.100823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Purpose Modeling disease variants in animals is useful for drug discovery, understanding disease pathology, and classifying variants of uncertain significance (VUS) as pathogenic or benign. Methods Using Clustered Regularly Interspaced Short Palindromic Repeats, we performed a Whole-gene Humanized Animal Model procedure to replace the coding sequence of the animal model's unc-18 ortholog with the coding sequence for the human STXBP1 gene. Next, we used Clustered Regularly Interspaced Short Palindromic Repeats to introduce precise point variants in the Whole-gene Humanized Animal Model-humanized STXBP1 locus from 3 clinical categories (benign, pathogenic, and VUS). Twenty-six phenotypic features extracted from video recordings were used to train machine learning classifiers on 25 pathogenic and 32 benign variants. Results Using multiple models, we were able to obtain a diagnostic sensitivity near 0.9. Twenty-three VUS were also interrogated and 8 of 23 (34.8%) were observed to be functionally abnormal. Interestingly, unsupervised clustering identified 2 distinct subsets of known pathogenic variants with distinct phenotypic features; both p.Tyr75Cys and p.Arg406Cys cluster away from other variants and show an increase in swim speed compared with hSTXBP1 worms. This leads to the hypothesis that the mechanism of disease for these 2 variants may differ from most STXBP1-mutated patients and may account for some of the clinical heterogeneity observed in the patient population. Conclusion We have demonstrated that automated analysis of a small animal system is an effective, scalable, and fast way to understand functional consequences of variants in STXBP1 and identify variant-specific intensities of aberrant activity suggesting a genotype-to-phenotype correlation is likely to occur in human clinical variations of STXBP1.
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Affiliation(s)
| | | | | | | | - Sarah Ruggiero
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA
- Department of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA
- University of Pennsylvania, Neuroscience Program, Philadelphia, PA
| | | | | | | | - Ingo Helbig
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA
- Department of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA
- University of Pennsylvania, Neuroscience Program, Philadelphia, PA
| | - Matthew N. Bainbridge
- Codified Genomics, LLC, Houston, TX
- Rady Children’s Institute for Genomic Medicine, San Diego, CA
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15
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Banerjee A, Bahar I. Structural Dynamics Predominantly Determine the Adaptability of Proteins to Amino Acid Deletions. Int J Mol Sci 2023; 24:8450. [PMID: 37176156 PMCID: PMC10179678 DOI: 10.3390/ijms24098450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/01/2023] [Accepted: 05/06/2023] [Indexed: 05/15/2023] Open
Abstract
The insertion or deletion (indel) of amino acids has a variety of effects on protein function, ranging from disease-forming changes to gaining new functions. Despite their importance, indels have not been systematically characterized towards protein engineering or modification goals. In the present work, we focus on deletions composed of multiple contiguous amino acids (mAA-dels) and their effects on the protein (mutant) folding ability. Our analysis reveals that the mutant retains the native fold when the mAA-del obeys well-defined structural dynamics properties: localization in intrinsically flexible regions, showing low resistance to mechanical stress, and separation from allosteric signaling paths. Motivated by the possibility of distinguishing the features that underlie the adaptability of proteins to mAA-dels, and by the rapid evaluation of these features using elastic network models, we developed a positive-unlabeled learning-based classifier that can be adopted for protein design purposes. Trained on a consolidated set of features, including those reflecting the intrinsic dynamics of the regions where the mAA-dels occur, the new classifier yields a high recall of 84.3% for identifying mAA-dels that are stably tolerated by the protein. The comparative examination of the relative contribution of different features to the prediction reveals the dominant role of structural dynamics in enabling the adaptation of the mutant to mAA-del without disrupting the native fold.
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Affiliation(s)
- Anupam Banerjee
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Ivet Bahar
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
- Department of Biochemistry and Cell Biology, Stony Brook University, Stony Brook, NY 11794, USA
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16
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Banerjee A, Saha S, Tvedt NC, Yang LW, Bahar I. Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods. Curr Opin Struct Biol 2023; 78:102517. [PMID: 36587424 PMCID: PMC10038760 DOI: 10.1016/j.sbi.2022.102517] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 11/19/2022] [Accepted: 11/22/2022] [Indexed: 12/31/2022]
Abstract
Proteins sample an ensemble of conformers under physiological conditions, having access to a spectrum of modes of motions, also called intrinsic dynamics. These motions ensure the adaptation to various interactions in the cell, and largely assist in, if not determine, viable mechanisms of biological function. In recent years, machine learning frameworks have proven uniquely useful in structural biology, and recent studies further provide evidence to the utility and/or necessity of considering intrinsic dynamics for increasing their predictive ability. Efficient quantification of dynamics-based attributes by recently developed physics-based theories and models such as elastic network models provides a unique opportunity to generate data on dynamics for training ML models towards inferring mechanisms of protein function, assessing pathogenicity, or estimating binding affinities.
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Affiliation(s)
- Anupam Banerjee
- Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh PA 15261, USA
| | - Satyaki Saha
- Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh PA 15261, USA
| | - Nathan C Tvedt
- Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh PA 15261, USA; Computational and Applied Mathematics and Statistics, The College of William and Mary, Williamsburg, VA 23185, USA
| | - Lee-Wei Yang
- Institute of Bioinformatics and Structural Biology, and PhD Program in Biomedical Artificial Intelligence, National Tsing Hua University, Hsinchu 300044, Taiwan; Physics Division, National Center for Theoretical Sciences, Taipei 106319, Taiwan
| | - Ivet Bahar
- Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh PA 15261, USA.
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17
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Das L, Shekhar S, Chandrani P, Varma AK. In silico structural analysis of secretory clusterin to assess pathogenicity of mutations identified in the evolutionarily conserved regions. J Biomol Struct Dyn 2023; 41:469-478. [PMID: 34821197 DOI: 10.1080/07391102.2021.2007791] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Clusterin (CLU) is a secreted glycoprotein, heterodimeric in nature, and is expressed in a wide variety of tissues and body fluids such as serum and plasma. CLU has also been known to be a promising biomarker for cell death, malignancy, cancer progression, and resistance development. However, the lack of a CLU crystal structure obstructs understanding the possible role of reported mutations on the structure, and the subsequent effects on downstream signaling pathways and cancer progression. Considering the importance of crystal structure, a model structure of the pre-secretory isoform of CLU was built to predict the effect of mutations at the molecular level. Ab initio model was built using RaptorX, and loop refinement and energy minimization were carried out with ModLoop, ModRefiner, and GalaxyWeb servers. The cancer associated mutational spectra of CLU was retrieved from the cBioPortal server and 117 unique missense mutations were identified. Evolutionarily conserved regions and pathogenicity of mutations identified in CLU were analyzed using ConSurf and Rhapsody, respectively. Furthermore, sequence and structure-based mutational analysis were carried out with iSTABLE, DynaMut and PremPS servers. Molecular dynamics simulations were carried out with GROMACS for 50 ns to determine the stability of the wild type and mutant protein structures. A dynamically stable model structure of pre-secretory CLU (psCLU) which has high concurrence with the sequence based secondary structure predictions has been explored. Changes in the intra-atomic interactions and folding pattern between wild type and mutant structures were observed. To our conclusion, eleven mutations with the highest structural and functional significance have been predicted to have pathogenic and deleterious effects.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Lipi Das
- Advanced Centre for Treatment, Research and Education in Cancer, Navi Mumbai, India.,Homi Bhabha National Institute, Training School Complex, Mumbai, India
| | - Shashank Shekhar
- Supercomputing Facility for Bioinformatics & Computational Biology, Indian Institute of Technology, New Delhi, India
| | - Pratik Chandrani
- Advanced Centre for Treatment, Research and Education in Cancer, Navi Mumbai, India.,Homi Bhabha National Institute, Training School Complex, Mumbai, India.,Medical Oncology Molecular Lab, Tata Memorial Hospital, Mumbai, India
| | - Ashok K Varma
- Advanced Centre for Treatment, Research and Education in Cancer, Navi Mumbai, India.,Homi Bhabha National Institute, Training School Complex, Mumbai, India
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18
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Wu TH, Lin PC, Chou HH, Shen MR, Hsieh SY. Pathogenicity Prediction of Single Amino Acid Variants With Machine Learning Model Based on Protein Structural Energies. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:606-615. [PMID: 34962874 DOI: 10.1109/tcbb.2021.3139048] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The most popular tools for predicting pathogenicity of single amino acid variants (SAVs) were developed based on sequence-based techniques. SAVs may change protein structure and function. In the context of van der Waals force and disulfide bridge calculations, no method directly predicts the impact of mutations on the energies of the protein structure. Here, we combined machine learning methods and energy scores of protein structures calculated by Rosetta Energy Function 2015 to predict SAV pathogenicity. The accuracy level of our model (0.76) is higher than that of six prediction tools. Further analyses revealed that the differential reference energies, attractive energies, and solvation of polar atoms between wildtype and mutant side-chains played essential roles in distinguishing benign from pathogenic variants. These features indicated the physicochemical properties of amino acids, which were observed in 3D structures instead of sequences. We added 16 features to Rhapsody (the prediction tool we used for our data set) and consequently improved its performance. The results indicated that these energy scores were more appropriate and more detailed representations of the pathogenicity of SAVs.
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19
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A comprehensive in silico analysis of multiple sclerosis related non-synonymous SNPs and their potential effects on protein structure and function. Mult Scler Relat Disord 2022; 68:104253. [PMID: 36544314 DOI: 10.1016/j.msard.2022.104253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 10/11/2022] [Accepted: 10/16/2022] [Indexed: 11/21/2022]
Abstract
BACKGROUND Multiple Sclerosis (MS) is an autoimmune and central nervous system disease characterized by an inflammatory demyelinating process in the brain. Although the exact cause of MS is still unclear, environmental, and genetic factors are known to play a role in the development of disease. New molecular markers must be identified to understand the mechanism of disease formation and progression. We investigated the effects of MS-related non-synonymous single-nucleotide polymorphisms (nsSNPs) on the structure and function of identified proteins in this study. METHODS Missense variations associated with MS were extracted from the NHGRI-EBI GWAS database. Functional and structural analysis of nsSNPs on mapped genes was performed using g:Profiler, Wikipathway, KEGG, Reactome and Gene ontology programs (p < 0.05 was accepted statistically significant). Amino acid sequence-based analysis was performed to identify deleterious variants by using PROVEAN and PredictSNP tools. Finally, protein structure analyzes were performed on deleterious protein variants by DynaMut, Mutabind2 and Missense3D servers to identify changes in protein stability and flexibility. RESULTS 10 target nsSNPs were identified. Among these rs34536443, rs10936599, rs2293152, rs11808092, rs1129183 were found deleterious according to amino acid sequence-based analysis. Furthermore, structure-based analyses show that TYK2 (P1104A), MYNN (H6Q), EVI5 (Q612H), and LZTFL1 (D246N) substitutions increase protein stability and decrease structure flexibility, whereas STAT3 (R426G) substitution decreases protein stability and increases structure flexibility. CONCLUSION We revealed that identified nsSNPs have potential effects on stability and flexibility of the target proteins. The prominent target genes are thought to have significant impacts on the pathogenesis of MS. Further in vitro and in vivo studies are required to validate our in silico results.
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20
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Koleske ML, McInnes G, Brown JEH, Thomas N, Hutchinson K, Chin MY, Koehl A, Arkin MR, Schlessinger A, Gallagher RC, Song YS, Altman RB, Giacomini KM. Functional genomics of OCTN2 variants informs protein-specific variant effect predictor for Carnitine Transporter Deficiency. Proc Natl Acad Sci U S A 2022; 119:e2210247119. [PMID: 36343260 PMCID: PMC9674959 DOI: 10.1073/pnas.2210247119] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 09/16/2022] [Indexed: 11/09/2022] Open
Abstract
Genetic variants in SLC22A5, encoding the membrane carnitine transporter OCTN2, cause the rare metabolic disorder Carnitine Transporter Deficiency (CTD). CTD is potentially lethal but actionable if detected early, with confirmatory diagnosis involving sequencing of SLC22A5. Interpretation of missense variants of uncertain significance (VUSs) is a major challenge. In this study, we sought to characterize the largest set to date (n = 150) of OCTN2 variants identified in diverse ancestral populations, with the goals of furthering our understanding of the mechanisms leading to OCTN2 loss-of-function (LOF) and creating a protein-specific variant effect prediction model for OCTN2 function. Uptake assays with 14C-carnitine revealed that 105 variants (70%) significantly reduced transport of carnitine compared to wild-type OCTN2, and 37 variants (25%) severely reduced function to less than 20%. All ancestral populations harbored LOF variants; 62% of green fluorescent protein (GFP)-tagged variants impaired OCTN2 localization to the plasma membrane of human embryonic kidney (HEK293T) cells, and subcellular localization significantly associated with function, revealing a major LOF mechanism of interest for CTD. With these data, we trained a model to classify variants as functional (>20% function) or LOF (<20% function). Our model outperformed existing state-of-the-art methods as evaluated by multiple performance metrics, with mean area under the receiver operating characteristic curve (AUROC) of 0.895 ± 0.025. In summary, in this study we generated a rich dataset of OCTN2 variant function and localization, revealed important disease-causing mechanisms, and improved upon machine learning-based prediction of OCTN2 variant function to aid in variant interpretation in the diagnosis and treatment of CTD.
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Affiliation(s)
- Megan L. Koleske
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94143
| | - Gregory McInnes
- Biomedical Informatics Training Program, Stanford University, Stanford, CA 94305
- Empirico Inc., San Diego, CA 92122
| | - Julia E. H. Brown
- Program in Bioethics, University of California, San Francisco, CA 94143
- Institute for Health & Aging, University of California, San Francisco, CA 94143
| | - Neil Thomas
- Computer Science Division, University of California, Berkeley, CA 94720
| | - Keino Hutchinson
- Department of Pharmacological Sciences, Icahn School of Medicine at Mt. Sinai, New York, NY 10029
| | - Marcus Y. Chin
- Small Molecule Discovery Center, Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94143
| | - Antoine Koehl
- Department of Statistics, University of California, Berkeley, CA 94720
| | - Michelle R. Arkin
- Small Molecule Discovery Center, Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94143
| | - Avner Schlessinger
- Department of Pharmacological Sciences, Icahn School of Medicine at Mt. Sinai, New York, NY 10029
| | - Renata C. Gallagher
- Institute for Human Genetics, University of California, San Francisco, CA 94143
- Department of Pediatrics, University of California, San Francisco, CA 94143
| | - Yun S. Song
- Computer Science Division, University of California, Berkeley, CA 94720
- Department of Statistics, University of California, Berkeley, CA 94720
| | - Russ B. Altman
- Department of Bioengineering, Stanford University, Stanford, CA 94305
- Department of Genetics, Stanford University, Stanford, CA 94305
| | - Kathleen M. Giacomini
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94143
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21
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Predicting Deleterious Non-Synonymous Single Nucleotide Polymorphisms (nsSNPs) of HRAS Gene and In Silico Evaluation of Their Structural and Functional Consequences towards Diagnosis and Prognosis of Cancer. BIOLOGY 2022; 11:biology11111604. [DOI: 10.3390/biology11111604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/28/2022] [Accepted: 10/09/2022] [Indexed: 11/06/2022]
Abstract
The Harvey rat sarcoma (HRAS) proto-oncogene belongs to the RAS family and is one of the pathogenic genes that cause cancer. Deleterious nsSNPs might have adverse consequences at the protein level. This study aimed to investigate deleterious nsSNPs in the HRAS gene in predicting structural alterations associated with mutants that disrupt normal protein–protein interactions. Functional and structural analysis was employed in analyzing the HRAS nsSNPs. Putative post-translational modification sites and the changes in protein–protein interactions, which included a variety of signal cascades, were also investigated. Five different bioinformatics tools predicted 33 nsSNPs as “pathogenic” or “harmful”. Stability analysis predicted rs1554885139, rs770492627, rs1589792804, rs730880460, rs104894227, rs104894227, and rs121917759 as unstable. Protein–protein interaction analysis revealed that HRAS has a hub connecting three clusters consisting of 11 proteins, and changes in HRAS might cause signal cascades to dissociate. Furthermore, Kaplan–Meier bioinformatics analyses indicated that the HRAS gene deregulation affected the overall survival rate of patients with breast cancer, leading to prognostic significance. Thus, based on these analyses, our study suggests that the reported nsSNPs of HRAS may serve as potential targets for different proteomic studies, diagnoses, and therapeutic interventions focusing on cancer.
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22
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Leung A, Sacristan-Reviriego A, Perdigão PRL, Sai H, Georgiou M, Kalitzeos A, Carr AJF, Coffey PJ, Michaelides M, Bainbridge J, Cheetham ME, van der Spuy J. Investigation of PTC124-mediated translational readthrough in a retinal organoid model of AIPL1-associated Leber congenital amaurosis. Stem Cell Reports 2022; 17:2187-2202. [PMID: 36084639 PMCID: PMC9561542 DOI: 10.1016/j.stemcr.2022.08.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 08/10/2022] [Accepted: 08/12/2022] [Indexed: 10/26/2022] Open
Abstract
Leber congenital amaurosis type 4 (LCA4), caused by AIPL1 mutations, is characterized by severe sight impairment in infancy and rapidly progressing degeneration of photoreceptor cells. We generated retinal organoids using induced pluripotent stem cells (iPSCs) from renal epithelial cells obtained from four children with AIPL1 nonsense mutations. iPSC-derived photoreceptors exhibited the molecular hallmarks of LCA4, including undetectable AIPL1 and rod cyclic guanosine monophosphate (cGMP) phosphodiesterase (PDE6) compared with control or CRISPR-corrected organoids. Increased levels of cGMP were detected. The translational readthrough-inducing drug (TRID) PTC124 was investigated as a potential therapeutic agent. LCA4 retinal organoids exhibited low levels of rescue of full-length AIPL1. However, this was insufficient to fully restore PDE6 in photoreceptors and reduce cGMP. LCA4 retinal organoids are a valuable platform for in vitro investigation of novel therapeutic agents.
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Affiliation(s)
- Amy Leung
- UCL Institute of Ophthalmology, London EC1V 9EL, UK
| | | | | | - Hali Sai
- UCL Institute of Ophthalmology, London EC1V 9EL, UK
| | - Michalis Georgiou
- UCL Institute of Ophthalmology, London EC1V 9EL, UK; Moorfields Eye Hospital NHS Foundation Trust, London EC1V 2PD, UK
| | - Angelos Kalitzeos
- UCL Institute of Ophthalmology, London EC1V 9EL, UK; Moorfields Eye Hospital NHS Foundation Trust, London EC1V 2PD, UK
| | | | | | - Michel Michaelides
- UCL Institute of Ophthalmology, London EC1V 9EL, UK; Moorfields Eye Hospital NHS Foundation Trust, London EC1V 2PD, UK
| | - James Bainbridge
- UCL Institute of Ophthalmology, London EC1V 9EL, UK; Moorfields Eye Hospital NHS Foundation Trust, London EC1V 2PD, UK
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23
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Lin PC, Tsai YS, Yeh YM, Shen MR. Cutting-Edge AI Technologies Meet Precision Medicine to Improve Cancer Care. Biomolecules 2022; 12:biom12081133. [PMID: 36009026 PMCID: PMC9405970 DOI: 10.3390/biom12081133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 08/11/2022] [Accepted: 08/15/2022] [Indexed: 11/18/2022] Open
Abstract
To provide precision medicine for better cancer care, researchers must work on clinical patient data, such as electronic medical records, physiological measurements, biochemistry, computerized tomography scans, digital pathology, and the genetic landscape of cancer tissue. To interpret big biodata in cancer genomics, an operational flow based on artificial intelligence (AI) models and medical management platforms with high-performance computing must be set up for precision cancer genomics in clinical practice. To work in the fast-evolving fields of patient care, clinical diagnostics, and therapeutic services, clinicians must understand the fundamentals of the AI tool approach. Therefore, the present article covers the following four themes: (i) computational prediction of pathogenic variants of cancer susceptibility genes; (ii) AI model for mutational analysis; (iii) single-cell genomics and computational biology; (iv) text mining for identifying gene targets in cancer; and (v) the NVIDIA graphics processing units, DRAGEN field programmable gate arrays systems and AI medical cloud platforms in clinical next-generation sequencing laboratories. Based on AI medical platforms and visualization, large amounts of clinical biodata can be rapidly copied and understood using an AI pipeline. The use of innovative AI technologies can deliver more accurate and rapid cancer therapy targets.
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Affiliation(s)
- Peng-Chan Lin
- Department of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
- Department of Genomic Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| | - Yi-Shan Tsai
- Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| | - Yu-Min Yeh
- Department of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| | - Meng-Ru Shen
- Institute of Clinical Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
- Department of Obstetrics and Gynecology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
- Department of Pharmacology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
- Correspondence: ; Tel.: +886-6-235-3535
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24
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Khan AS, Parvez N, Ahsan T, Shoily SS, Sajib AA. A comprehensive in silico exploration of the impacts of missense variants on two different conformations of human pirin protein. BULLETIN OF THE NATIONAL RESEARCH CENTRE 2022; 46:225. [PMID: 35967515 PMCID: PMC9362109 DOI: 10.1186/s42269-022-00917-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 07/21/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Pirin, a member of the cupin superfamily, is an iron-binding non-heme protein. It acts as a coregulator of several transcription factors, especially the members of NFκB transcription factor family. Based on the redox state of its iron cofactor, it can assume two different conformations and thereby act as a redox sensor inside the nucleus. Previous studies suggested that pirin may be associated with cancer, inflammatory diseases as well as COVID-19 severities. Hence, it is important to explore the pathogenicity of its missense variants. In this study, we used a number of in silico tools to investigate the effects of missense variants of pirin on its structure, stability, metal cofactor binding affinity and interactions with partner proteins. In addition, we used protein dynamics simulation to elucidate the effects of selected variants on its dynamics. Furthermore, we calculated the frequencies of haplotypes containing pirin missense variants across five major super-populations (African, Admixed American, East Asian, European and South Asian). RESULTS Among a total of 153 missense variants of pirin, 45 were uniformly predicted to be pathogenic. Of these, seven variants can be considered for further experimental studies. Variants R59P and L116P were predicted to significantly destabilize and damage pirin structure, substantially reduce its affinity to its binding partners and alter pirin residue fluctuation profile via changing the flexibility of several key residues. Additionally, variants R59Q, F78V, G98D, V151D and L220P were found to impact pirin structure and function in multiple ways. As no haplotype was identified to be harboring more than one missense variant, further interrogation of the individual effects of these seven missense variants is highly recommended. CONCLUSIONS Pirin is involved in the transcriptional regulation of several genes and can play an important role in inflammatory responses. The variants predicted to be pathogenic in this study may thus contribute to a better understanding of the underlying molecular mechanisms of various inflammatory diseases. Future studies should be focused on clarifying if any of these variants can be used as disease biomarkers. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1186/s42269-022-00917-7.
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Affiliation(s)
- Auroni Semonti Khan
- Department of Genetic Engineering and Biotechnology, Jagannath University, Dhaka, 1100 Bangladesh
| | - Nahid Parvez
- Department of Genetic Engineering and Biotechnology, Jagannath University, Dhaka, 1100 Bangladesh
| | - Tamim Ahsan
- Molecular Biotechnology Division, National Institute of Biotechnology, Savar, Dhaka, 1349 Bangladesh
| | - Sabrina Samad Shoily
- Department of Genetic Engineering and Biotechnology, University of Dhaka, Dhaka, 1000 Bangladesh
| | - Abu Ashfaqur Sajib
- Department of Genetic Engineering and Biotechnology, University of Dhaka, Dhaka, 1000 Bangladesh
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25
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Schreiber J, Nair S, Balsubramani A, Kundaje A. Accelerating in silico saturation mutagenesis using compressed sensing. Bioinformatics 2022; 38:3557-3564. [PMID: 35678521 PMCID: PMC9272795 DOI: 10.1093/bioinformatics/btac385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 05/10/2022] [Accepted: 06/06/2022] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION In silico saturation mutagenesis (ISM) is a popular approach in computational genomics for calculating feature attributions on biological sequences that proceeds by systematically perturbing each position in a sequence and recording the difference in model output. However, this method can be slow because systematically perturbing each position requires performing a number of forward passes proportional to the length of the sequence being examined. RESULTS In this work, we propose a modification of ISM that leverages the principles of compressed sensing to require only a constant number of forward passes, regardless of sequence length, when applied to models that contain operations with a limited receptive field, such as convolutions. Our method, named Yuzu, can reduce the time that ISM spends in convolution operations by several orders of magnitude and, consequently, Yuzu can speed up ISM on several commonly used architectures in genomics by over an order of magnitude. Notably, we found that Yuzu provides speedups that increase with the complexity of the convolution operation and the length of the sequence being analyzed, suggesting that Yuzu provides large benefits in realistic settings. AVAILABILITY AND IMPLEMENTATION We have made this tool available at https://github.com/kundajelab/yuzu.
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Affiliation(s)
- Jacob Schreiber
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Surag Nair
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Akshay Balsubramani
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
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26
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Katsonis P, Wilhelm K, Williams A, Lichtarge O. Genome interpretation using in silico predictors of variant impact. Hum Genet 2022; 141:1549-1577. [PMID: 35488922 PMCID: PMC9055222 DOI: 10.1007/s00439-022-02457-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 04/17/2022] [Indexed: 02/06/2023]
Abstract
Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.
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Affiliation(s)
- Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
| | - Kevin Wilhelm
- Graduate School of Biomedical Sciences, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Amanda Williams
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA. .,Department of Biochemistry, Human Genetics and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA. .,Department of Pharmacology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA. .,Computational and Integrative Biomedical Research Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
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27
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Ose NJ, Butler BM, Kumar A, Kazan IC, Sanderford M, Kumar S, Ozkan SB. Dynamic coupling of residues within proteins as a mechanistic foundation of many enigmatic pathogenic missense variants. PLoS Comput Biol 2022; 18:e1010006. [PMID: 35389981 PMCID: PMC9017885 DOI: 10.1371/journal.pcbi.1010006] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 04/19/2022] [Accepted: 03/09/2022] [Indexed: 01/07/2023] Open
Abstract
Many pathogenic missense mutations are found in protein positions that are neither well-conserved nor fall in any known functional domains. Consequently, we lack any mechanistic underpinning of dysfunction caused by such mutations. We explored the disruption of allosteric dynamic coupling between these positions and the known functional sites as a possible mechanism for pathogenesis. In this study, we present an analysis of 591 pathogenic missense variants in 144 human enzymes that suggests that allosteric dynamic coupling of mutated positions with known active sites is a plausible biophysical mechanism and evidence of their functional importance. We illustrate this mechanism in a case study of β-Glucocerebrosidase (GCase) in which a vast majority of 94 sites harboring Gaucher disease-associated missense variants are located some distance away from the active site. An analysis of the conformational dynamics of GCase suggests that mutations on these distal sites cause changes in the flexibility of active site residues despite their distance, indicating a dynamic communication network throughout the protein. The disruption of the long-distance dynamic coupling caused by missense mutations may provide a plausible general mechanistic explanation for biological dysfunction and disease.
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Affiliation(s)
- Nicholas J. Ose
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona, United States of America
| | - Brandon M. Butler
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona, United States of America
| | - Avishek Kumar
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona, United States of America
| | - I. Can Kazan
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona, United States of America
| | - Maxwell Sanderford
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, Pennsylvania, United States of America
- Department of Biology, Temple University, Philadelphia, Pennsylvania, United States of America
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, Pennsylvania, United States of America
- Department of Biology, Temple University, Philadelphia, Pennsylvania, United States of America
- Center for Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia
| | - S. Banu Ozkan
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona, United States of America
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28
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Structural Bioinformatics Enhances the Interpretation of Somatic Mutations in KDM6A Found in Human Cancers. Comput Struct Biotechnol J 2022; 20:2200-2211. [PMID: 35615018 PMCID: PMC9111933 DOI: 10.1016/j.csbj.2022.04.028] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/18/2022] [Accepted: 04/18/2022] [Indexed: 11/24/2022] Open
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29
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Analysis of missense variants in the human genome reveals widespread gene-specific clustering and improves prediction of pathogenicity. Am J Hum Genet 2022; 109:457-470. [PMID: 35120630 PMCID: PMC8948164 DOI: 10.1016/j.ajhg.2022.01.006] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 01/11/2022] [Indexed: 12/11/2022] Open
Abstract
We used a machine learning approach to analyze the within-gene distribution of missense variants observed in hereditary conditions and cancer. When applied to 840 genes from the ClinVar database, this approach detected a significant non-random distribution of pathogenic and benign variants in 387 (46%) and 172 (20%) genes, respectively, revealing that variant clustering is widespread across the human exome. This clustering likely occurs as a consequence of mechanisms shaping pathogenicity at the protein level, as illustrated by the overlap of some clusters with known functional domains. We then took advantage of these findings to develop a pathogenicity predictor, MutScore, that integrates qualitative features of DNA substitutions with the new additional information derived from this positional clustering. Using a random forest approach, MutScore was able to identify pathogenic missense mutations with very high accuracy, outperforming existing predictive tools, especially for variants associated with autosomal-dominant disease and cancer. Thus, the within-gene clustering of pathogenic and benign DNA changes is an important and previously underappreciated feature of the human exome, which can be harnessed to improve the prediction of pathogenicity and disambiguation of DNA variants of uncertain significance.
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30
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Høie MH, Cagiada M, Beck Frederiksen AH, Stein A, Lindorff-Larsen K. Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation. Cell Rep 2022; 38:110207. [PMID: 35021073 DOI: 10.1016/j.celrep.2021.110207] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 10/01/2021] [Accepted: 12/13/2021] [Indexed: 01/23/2023] Open
Abstract
Understanding and predicting the functional consequences of single amino acid changes is central in many areas of protein science. Here, we collect and analyze experimental measurements of effects of >150,000 variants in 29 proteins. We use biophysical calculations to predict changes in stability for each variant and assess them in light of sequence conservation. We find that the sequence analyses give more accurate prediction of variant effects than predictions of stability and that about half of the variants that show loss of function do so due to stability effects. We construct a machine learning model to predict variant effects from protein structure and sequence alignments and show how the two sources of information support one another and enable mechanistic interpretations. Together, our results show how one can leverage large-scale experimental assessments of variant effects to gain deeper and general insights into the mechanisms that cause loss of function.
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Affiliation(s)
- Magnus Haraldson Høie
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark
| | - Matteo Cagiada
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark
| | - Anders Haagen Beck Frederiksen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark
| | - Amelie Stein
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark.
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark.
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31
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OUP accepted manuscript. Brief Funct Genomics 2022; 21:202-215. [DOI: 10.1093/bfgp/elac003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 01/29/2022] [Accepted: 02/15/2022] [Indexed: 11/14/2022] Open
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32
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Lai J, Yang J, Gamsiz Uzun ED, Rubenstein BM, Sarkar IN. LYRUS: a machine learning model for predicting the pathogenicity of missense variants. BIOINFORMATICS ADVANCES 2021; 2:vbab045. [PMID: 35036922 PMCID: PMC8754197 DOI: 10.1093/bioadv/vbab045] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 12/08/2021] [Accepted: 12/21/2021] [Indexed: 01/27/2023]
Abstract
SUMMARY Single amino acid variations (SAVs) are a primary contributor to variations in the human genome. Identifying pathogenic SAVs can provide insights to the genetic architecture of complex diseases. Most approaches for predicting the functional effects or pathogenicity of SAVs rely on either sequence or structural information. This study presents 〈Lai Yang Rubenstein Uzun Sarkar〉 (LYRUS), a machine learning method that uses an XGBoost classifier to predict the pathogenicity of SAVs. LYRUS incorporates five sequence-based, six structure-based and four dynamics-based features. Uniquely, LYRUS includes a newly proposed sequence co-evolution feature called the variation number. LYRUS was trained using a dataset that contains 4363 protein structures corresponding to 22 639 SAVs from the ClinVar database, and tested using the VariBench testing dataset. Performance analysis showed that LYRUS achieved comparable performance to current variant effect predictors. LYRUS's performance was also benchmarked against six Deep Mutational Scanning datasets for PTEN and TP53. AVAILABILITY AND IMPLEMENTATION LYRUS is freely available and the source code can be found at https://github.com/jiaying2508/LYRUS. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Jiaying Lai
- Center for Biomedical Informatics, Brown University, Providence, RI 02903, USA,Center for Computational Molecular Biology, Brown University, Providence, RI 02906, USA
| | - Jordan Yang
- Department of Chemistry, Brown University, Providence, RI 02906, USA
| | - Ece D Gamsiz Uzun
- Center for Computational Molecular Biology, Brown University, Providence, RI 02906, USA,Department of Pathology and Laboratory Medicine, Brown University Alpert Medical School, Providence, RI 02903, USA,Department of Pathology, Rhode Island Hospital, Providence, RI 02903, USA
| | - Brenda M Rubenstein
- Center for Computational Molecular Biology, Brown University, Providence, RI 02906, USA,Department of Chemistry, Brown University, Providence, RI 02906, USA,To whom correspondence should be addressed. and
| | - Indra Neil Sarkar
- Center for Biomedical Informatics, Brown University, Providence, RI 02903, USA,Rhode Island Quality Institute, Providence, RI 02908, USA,To whom correspondence should be addressed. and
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33
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Ozturk K, Carter H. Predicting functional consequences of mutations using molecular interaction network features. Hum Genet 2021; 141:1195-1210. [PMID: 34432150 PMCID: PMC8873243 DOI: 10.1007/s00439-021-02329-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 07/31/2021] [Indexed: 12/13/2022]
Abstract
Variant interpretation remains a central challenge for precision medicine. Missense variants are particularly difficult to understand as they change only a single amino acid in a protein sequence yet can have large and varied effects on protein activity. Numerous tools have been developed to identify missense variants with putative disease consequences from protein sequence and structure. However, biological function arises through higher order interactions among proteins and molecules within cells. We therefore sought to capture information about the potential of missense mutations to perturb protein interaction networks by integrating protein structure and interaction data. We developed 16 network-based annotations for missense mutations that provide orthogonal information to features classically used to prioritize variants. We then evaluated them in the context of a proven machine-learning framework for variant effect prediction across multiple benchmark datasets to demonstrate their potential to improve variant classification. Interestingly, network features resulted in larger performance gains for classifying somatic mutations than for germline variants, possibly due to different constraints on what mutations are tolerated at the cellular versus organismal level. Our results suggest that modeling variant potential to perturb context-specific interactome networks is a fruitful strategy to advance in silico variant effect prediction.
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Affiliation(s)
- Kivilcim Ozturk
- Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA.,Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | - Hannah Carter
- Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA. .,Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA. .,Moores Cancer Center, University of California San Diego, La Jolla, CA, USA.
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34
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Gisonno RA, Masson T, Ramella NA, Barrera EE, Romanowski V, Tricerri MA. Evolutionary and structural constraints influencing apolipoprotein A-I amyloid behavior. Proteins 2021; 90:258-269. [PMID: 34414600 DOI: 10.1002/prot.26217] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 07/27/2021] [Accepted: 08/09/2021] [Indexed: 12/13/2022]
Abstract
Apolipoprotein A-I (apoA-I) has a key function in the reverse cholesterol transport. However, aggregation of apoA-I single point mutants can lead to hereditary amyloid pathology. Although several studies have tackled the biophysical and structural consequences introduced by these mutations, there is little information addressing the relationship between the evolutionary and structural features that contribute to the amyloid behavior of apoA-I. We combined evolutionary studies, in silico mutagenesis and molecular dynamics (MD) simulations to provide a comprehensive analysis of the conservation and pathogenic role of the aggregation-prone regions (APRs) present in apoA-I. Sequence analysis demonstrated that among the four amyloidogenic regions described for human apoA-I, only two (APR1 and APR4) are evolutionary conserved across different species of Sarcopterygii. Moreover, stability analysis carried out with the FoldX engine showed that APR1 contributes to the marginal stability of apoA-I. Structural properties of full-length apoA-I models suggest that aggregation is avoided by placing APRs into highly packed and rigid portions of its native fold. Compared to silent variants extracted from the gnomAD database, the thermodynamic and pathogenic impact of amyloid mutations showed evidence of a higher destabilizing effect. MD simulations of the amyloid variant G26R evidenced the partial unfolding of the alpha-helix bundle with the concomitant exposure of APR1 to the solvent, suggesting an insight into the early steps involved in its aggregation. Our findings highlight APR1 as a relevant component for apoA-I structural integrity and emphasize a destabilizing effect of amyloid variants that leads to the exposure of this region.
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Affiliation(s)
- Romina A Gisonno
- Facultad de Ciencias Médicas, Instituto de Investigaciones Bioquímicas de La Plata (INIBIOLP, CONICET-UNLP), Universidad Nacional de La Plata, La Plata, Argentina
| | - Tomas Masson
- Facultad de Ciencias Exactas, Instituto de Biotecnología y Biología Molecular (IBBM, CONICET-UNLP), Universidad Nacional de La Plata, La Plata, Argentina
| | - Nahuel A Ramella
- Facultad de Ciencias Médicas, Instituto de Investigaciones Bioquímicas de La Plata (INIBIOLP, CONICET-UNLP), Universidad Nacional de La Plata, La Plata, Argentina
| | - Exequiel E Barrera
- Group of Biomolecular Simulations, Institut Pasteur de Montevideo, Montevideo, Uruguay
| | - Víctor Romanowski
- Facultad de Ciencias Exactas, Instituto de Biotecnología y Biología Molecular (IBBM, CONICET-UNLP), Universidad Nacional de La Plata, La Plata, Argentina
| | - M Alejandra Tricerri
- Facultad de Ciencias Médicas, Instituto de Investigaciones Bioquímicas de La Plata (INIBIOLP, CONICET-UNLP), Universidad Nacional de La Plata, La Plata, Argentina
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Mikulska-Ruminska K, Anthonymuthu TS, Levkina A, Shrivastava IH, Kapralov AA, Bayır H, Kagan VE, Bahar I. NO ● Represses the Oxygenation of Arachidonoyl PE by 15LOX/PEBP1: Mechanism and Role in Ferroptosis. Int J Mol Sci 2021; 22:ijms22105253. [PMID: 34067535 PMCID: PMC8156958 DOI: 10.3390/ijms22105253] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 05/06/2021] [Accepted: 05/12/2021] [Indexed: 12/18/2022] Open
Abstract
We recently discovered an anti-ferroptotic mechanism inherent to M1 macrophages whereby high levels of NO● suppressed ferroptosis via inhibition of hydroperoxy-eicosatetraenoyl-phosphatidylethanolamine (HpETE-PE) production by 15-lipoxygenase (15LOX) complexed with PE-binding protein 1 (PEBP1). However, the mechanism of NO● interference with 15LOX/PEBP1 activity remained unclear. Here, we use a biochemical model of recombinant 15LOX-2 complexed with PEBP1, LC-MS redox lipidomics, and structure-based modeling and simulations to uncover the mechanism through which NO● suppresses ETE-PE oxidation. Our study reveals that O2 and NO● use the same entry pores and channels connecting to 15LOX-2 catalytic site, resulting in a competition for the catalytic site. We identified residues that direct O2 and NO● to the catalytic site, as well as those stabilizing the esterified ETE-PE phospholipid tail. The functional significance of these residues is supported by in silico saturation mutagenesis. We detected nitrosylated PE species in a biochemical system consisting of 15LOX-2/PEBP1 and NO● donor and in RAW264.7 M2 macrophages treated with ferroptosis-inducer RSL3 in the presence of NO●, in further support of the ability of NO● to diffuse to, and react at, the 15LOX-2 catalytic site. The results provide first insights into the molecular mechanism of repression of the ferroptotic Hp-ETE-PE production by NO●.
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Affiliation(s)
- Karolina Mikulska-Ruminska
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA;
- Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Toruń, Grudziadzka 5, 87-100 Toruń, Poland
- Correspondence: (K.M.-R.); (V.E.K.); (I.B.)
| | - Tamil S. Anthonymuthu
- Department of Critical Care Medicine, Safar Center for Resuscitation Research, Children’s Neuroscience Institute, Children’s Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA 15260, USA; (T.S.A.); (H.B.)
| | - Anastasia Levkina
- Department of Environmental and Occupational Health and Center for Free Radical and Antioxidant Health, University of Pittsburgh, Pittsburgh, PA 15260, USA; (A.L.); (A.A.K.)
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Ostrovityanova 1, 117997 Moscow, Russia
| | - Indira H. Shrivastava
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA;
- Department of Environmental and Occupational Health and Center for Free Radical and Antioxidant Health, University of Pittsburgh, Pittsburgh, PA 15260, USA; (A.L.); (A.A.K.)
| | - Alexandr A. Kapralov
- Department of Environmental and Occupational Health and Center for Free Radical and Antioxidant Health, University of Pittsburgh, Pittsburgh, PA 15260, USA; (A.L.); (A.A.K.)
| | - Hülya Bayır
- Department of Critical Care Medicine, Safar Center for Resuscitation Research, Children’s Neuroscience Institute, Children’s Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA 15260, USA; (T.S.A.); (H.B.)
- Department of Environmental and Occupational Health and Center for Free Radical and Antioxidant Health, University of Pittsburgh, Pittsburgh, PA 15260, USA; (A.L.); (A.A.K.)
| | - Valerian E. Kagan
- Department of Environmental and Occupational Health and Center for Free Radical and Antioxidant Health, University of Pittsburgh, Pittsburgh, PA 15260, USA; (A.L.); (A.A.K.)
- Department of Radiation Oncology, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Institute of Regenerative Medicine, IM Sechenov Moscow State Medical University, 119048 Moscow, Russia
- Correspondence: (K.M.-R.); (V.E.K.); (I.B.)
| | - Ivet Bahar
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA;
- Correspondence: (K.M.-R.); (V.E.K.); (I.B.)
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36
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Photoreceptor phosphodiesterase (PDE6): activation and inactivation mechanisms during visual transduction in rods and cones. Pflugers Arch 2021; 473:1377-1391. [PMID: 33860373 DOI: 10.1007/s00424-021-02562-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 03/27/2021] [Accepted: 03/30/2021] [Indexed: 01/16/2023]
Abstract
Rod and cone photoreceptors of the vertebrate retina utilize cGMP as the primary intracellular messenger for the visual signaling pathway that converts a light stimulus into an electrical response. cGMP metabolism in the signal-transducing photoreceptor outer segment reflects the balance of cGMP synthesis (catalyzed by guanylyl cyclase) and degradation (catalyzed by the photoreceptor phosphodiesterase, PDE6). Upon light stimulation, rapid activation of PDE6 by the heterotrimeric G-protein (transducin) triggers a dramatic drop in cGMP levels that lead to cell hyperpolarization. Following cessation of the light stimulus, the lifetime of activated PDE6 is also precisely regulated by additional processes. This review summarizes recent advances in the structural characterization of the rod and cone PDE6 catalytic and regulatory subunits in the context of previous biochemical studies of the enzymological properties and allosteric regulation of PDE6. Emphasis is given to recent advances in understanding the structural and conformational changes underlying the mechanism by which the activated transducin α-subunit binds to-and relieves inhibition of-PDE6 catalysis that is controlled by its intrinsically disordered, inhibitory γ-subunit. The role of the regulator of G-protein signaling 9-1 (RGS9-1) in regulating the lifetime of the transducin-PDE6 is also briefly covered. The therapeutic potential of pharmacological compounds acting as inhibitors or activators targeting PDE6 is discussed in the context of inherited retinal diseases resulting from mutations in rod and cone PDE6 genes as well as other inherited defects that arise from excessive cGMP accumulation in retinal photoreceptor cells.
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Engvall M, Kawasaki A, Carelli V, Wibom R, Bruhn H, Lesko N, Schober FA, Wredenberg A, Wedell A, Träisk F. Case Report: A Novel Mutation in the Mitochondrial MT-ND5 Gene Is Associated With Leber Hereditary Optic Neuropathy (LHON). Front Neurol 2021; 12:652590. [PMID: 33841319 PMCID: PMC8027302 DOI: 10.3389/fneur.2021.652590] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 03/02/2021] [Indexed: 11/13/2022] Open
Abstract
Leber hereditary optic neuropathy (LHON) is a mitochondrial disease causing severe bilateral visual loss, typically in young adults. The disorder is commonly caused by one of three primary point mutations in mitochondrial DNA, but a number of other rare mutations causing or associated with the clinical syndrome of LHON have been reported. The mutations in LHON are almost exclusively located in genes encoding subunits of complex I in the mitochondrial respiratory chain. Here we report two patients, a mother and her son, with the typical LHON phenotype. Genetic investigations for the three common mutations were negative, instead we found a new and previously unreported mutation in mitochondrial DNA. This homoplasmic mutation, m.13345G>A, is located in the MT-ND5 gene, encoding a core subunit in complex I in the mitochondrial respiratory chain. Investigation of the patients mitochondrial respiratory chain in muscle found a mild defect in the combined activity of complex I+III. In the literature six other mutations in the MT-ND5 gene have been associated with LHON and by this report a new putative mutation in the MT-ND5 can be added.
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Affiliation(s)
- Martin Engvall
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Centre for Inherited Metabolic Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Aki Kawasaki
- Hopital Ophtalmique Jules Gonin, Fondation Asile des Aveugles, University of Lausanne, Lausanne, Switzerland
| | - Valerio Carelli
- Programma di Neurogenetica, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.,Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy
| | - Rolf Wibom
- Centre for Inherited Metabolic Diseases, Karolinska University Hospital, Stockholm, Sweden.,Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Helene Bruhn
- Centre for Inherited Metabolic Diseases, Karolinska University Hospital, Stockholm, Sweden.,Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Nicole Lesko
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Centre for Inherited Metabolic Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Florian A Schober
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Anna Wredenberg
- Centre for Inherited Metabolic Diseases, Karolinska University Hospital, Stockholm, Sweden.,Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Anna Wedell
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Centre for Inherited Metabolic Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Frank Träisk
- Department of Clinical Neuroscience, Division of Eye and Vision, St. Erik Eye Hospital, Karolinska Institutet, Solna, Sweden.,Department of Neuro-Ophthalmology, St.Erik Eye Hospital, Solna, Sweden
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Nikolopoulos G, Smith CEL, Poulter JA, Murillo G, Silva S, Lamb T, Berry IR, Brown CJ, Day PF, Soldani F, Al-Bahlani S, Harris SA, O'Connell MJ, Inglehearn CF, Mighell AJ. Spectrum of pathogenic variants and founder effects in amelogenesis imperfecta associated with MMP20. Hum Mutat 2021; 42:567-576. [PMID: 33600052 DOI: 10.1002/humu.24187] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 02/08/2021] [Accepted: 02/14/2021] [Indexed: 11/12/2022]
Abstract
Amelogenesis imperfecta (AI) describes a heterogeneous group of developmental enamel defects that typically have Mendelian inheritance. Exome sequencing of 10 families with recessive hypomaturation AI revealed four novel and one known variants in the matrix metallopeptidase 20 (MMP20) gene that were predicted to be pathogenic. MMP20 encodes a protease that cleaves the developing extracellular enamel matrix and is necessary for normal enamel crystal growth during amelogenesis. New homozygous missense changes were shared between four families of Pakistani heritage (c.625G>C; p.(Glu209Gln)) and two of Omani origin (c.710C>A; p.(Ser237Tyr)). In two families of UK origin and one from Costa Rica, affected individuals were homozygous for the previously reported c.954-2A>T; p.(Ile319Phefs*19) variant. For each of these variants, microsatellite haplotypes appeared to exclude a recent founder effect, but elements of haplotype were conserved, suggesting more distant founding ancestors. New compound heterozygous changes were identified in one family of the European heritage: c.809_811+12delinsCCAG; p.(?) and c.1122A>C; p.(Gln374His). This report further elucidates the mutation spectrum of MMP20 and the probable impact on protein function, confirms a consistent hypomaturation phenotype and shows that mutations in MMP20 are a common cause of autosomal recessive AI in some communities.
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Affiliation(s)
- Georgios Nikolopoulos
- Division of Molecular Medicine, Leeds Institute of Medical Research, University of Leeds, Leeds, UK.,Department of Oral Biology, School of Dentistry, St James's University Hospital, University of Leeds, Leeds, UK
| | - Claire E L Smith
- Division of Molecular Medicine, Leeds Institute of Medical Research, University of Leeds, Leeds, UK
| | - James A Poulter
- Division of Molecular Medicine, Leeds Institute of Medical Research, University of Leeds, Leeds, UK
| | - Gina Murillo
- School of Dentistry, Universidad de Costa Rica, Ciudad Universitaria Rodrigo Facio, San Pedro Montes De Oca, Costa Rica
| | - Sandra Silva
- Cellular and Molecular Biology Centre (CBCM), Universidad de Costa Rica, Ciudad Universitaria Rodrigo Facio, San Pedro Montes De Oca, Costa Rica
| | - Teresa Lamb
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Ian R Berry
- Leeds Genetics Laboratory, St James's University Hospital, Leeds, UK
| | | | - Peter F Day
- Department of Paediatric Dentistry, Leeds Dental Institute, University of Leeds, Leeds, UK.,Community Dental Service, Horton Park Health Centre, Bradford District Care NHS Foundation Trust, Bradford, UK
| | - Francesca Soldani
- Community Dental Service, Horton Park Health Centre, Bradford District Care NHS Foundation Trust, Bradford, UK
| | - Suhaila Al-Bahlani
- Dental & O.M.F.S Clinic, Al Nahdha Hospital, Ministry of Health, Muscat, Oman
| | - Sarah A Harris
- School of Physics, University of Leeds, Leeds, UK.,Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds, UK
| | - Mary J O'Connell
- School of Biology, Faculty of Biological Sciences, University of Leeds, Leeds, UK.,School of Life Sciences, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK
| | - Chris F Inglehearn
- Division of Molecular Medicine, Leeds Institute of Medical Research, University of Leeds, Leeds, UK
| | - Alan J Mighell
- Department of Oral Biology, School of Dentistry, St James's University Hospital, University of Leeds, Leeds, UK
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Sayılgan JF, Haliloğlu T, Gönen M. Protein dynamics analysis identifies candidate cancer driver genes and mutations in TCGA data. Proteins 2021; 89:721-730. [PMID: 33550612 DOI: 10.1002/prot.26054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 01/04/2021] [Accepted: 01/31/2021] [Indexed: 11/09/2022]
Abstract
Recently, it has been showed that cancer missense mutations selectively target the neighborhood of hinge residues, which are key sites in protein dynamics. Here, we show that this approach can be extended to find previously unknown candidate mutations and genes. To this aim, we developed a computational pipeline to detect significantly enriched three-dimensional (3D) clustering of missense mutations around hinge residues. The hinge residues were detected by applying a Gaussian network model. By systematically analyzing the PanCancer compendium of somatic mutations in nearly 10 000 tumors from the Cancer Genome Atlas, we identified candidate genes and mutations in addition to well known ones. For instance, we found significantly enriched 3D clustering of missense mutations in known cancer genes including CDK4, CDKN2A, TCL1A, and MAPK1. Beside these known genes, we also identified significantly enriched 3D clustering of missense mutations around hinge residues in PLA2G4A, which may lead to excessive phosphorylation of the extracellular signal-regulated kinases. Furthermore, we demonstrated that hinge-based features improves pathogenicity prediction for missense mutations. Our results show that the consideration of clustering around hinge residues can help us explain the functional role of the mutations in known cancer genes and identify candidate genes.
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Affiliation(s)
- Jan Fehmi Sayılgan
- Graduate School of Sciences and Engineering, Koç University, İstanbul, Turkey
| | - Türkan Haliloğlu
- Department of Chemical Engineering, School of Engineering, Boğaziçi University, İstanbul, Turkey.,Polymer Research Center, Boğaziçi University, İstanbul, Turkey
| | - Mehmet Gönen
- Department of Industrial Engineering, College of Engineering, Koç University, İstanbul, Turkey.,School of Medicine, Koç University, İstanbul, Turkey
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Chi YI, Stodola TJ, De Assuncao TM, Leverence EN, Tripathi S, Dsouza NR, Mathison AJ, Basel DG, Volkman BF, Smith BC, Lomberk G, Zimmermann MT, Urrutia R. Molecular mechanics and dynamic simulations of well-known Kabuki syndrome-associated KDM6A variants reveal putative mechanisms of dysfunction. Orphanet J Rare Dis 2021; 16:66. [PMID: 33546721 PMCID: PMC7866879 DOI: 10.1186/s13023-021-01692-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 01/15/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Kabuki syndrome is a genetic disorder that affects several body systems and presents with variations in symptoms and severity. The syndrome is named for a common phenotype of faces resembling stage makeup used in a Japanese traditional theatrical art named kabuki. The most frequent cause of this syndrome is mutations in the H3K4 family of histone methyltransferases while a smaller percentage results from genetic alterations affecting the histone demethylase, KDM6A. Because of the rare presentation of the latter form of the disease, little is known about how missense changes in the KDM6A protein sequence impact protein function. RESULTS In this study, we use molecular mechanic and molecular dynamic simulations to enhance the annotation and mechanistic interpretation of the potential impact of eleven KDM6A missense variants found in Kabuki syndrome patients. These variants (N910S, D980V, S1025G, C1153R, C1153Y, P1195L, L1200F, Q1212R, Q1248R, R1255W, and R1351Q) are predicted to be pathogenic, likely pathogenic or of uncertain significance by sequence-based analysis. Here, we demonstrate, for the first time, that although Kabuki syndrome missense variants are found outside the functionally critical regions, they could affect overall function by significantly disrupting global and local conformation (C1153R, C1153Y, P1195L, L1200F, Q1212R, Q1248R, R1255W and R1351Q), chemical environment (C1153R, C1153Y, P1195L, L1200F, Q1212R, Q1248R, R1255W and R1351Q), and/or molecular dynamics of the catalytic domain (all variants). In addition, our approaches predict that many mutations, in particular C1153R, could allosterically disrupt the key enzymatic interactions of KDM6A. CONCLUSIONS Our study demonstrates that the KDM6A Kabuki syndrome variants may impair histone demethylase function through various mechanisms that include altered protein integrity, local environment, molecular interactions and protein dynamics. Molecular dynamics simulations of the wild type and the variants are critical to gain a better understanding of molecular dysfunction. This type of comprehensive structure- and MD-based analyses should help develop improved impact scoring systems to interpret the damaging effects of variants in this protein and other related proteins as well as provide detailed mechanistic insight that is not currently predictable from sequence alone.
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Affiliation(s)
- Young-In Chi
- Genomic Sciences and Precision Medicine Center (GSPMC), Medical College of Wisconsin, Milwaukee, WI, USA.,Bioinformatics Research and Development Laboratory, and Precision Medicine Simulation Unit, GSPMC, Medical College of Wisconsin, Milwaukee, WI, USA.,Division of Research, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Timothy J Stodola
- Genomic Sciences and Precision Medicine Center (GSPMC), Medical College of Wisconsin, Milwaukee, WI, USA.,Bioinformatics Research and Development Laboratory, and Precision Medicine Simulation Unit, GSPMC, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Thiago M De Assuncao
- Genomic Sciences and Precision Medicine Center (GSPMC), Medical College of Wisconsin, Milwaukee, WI, USA.,Division of Research, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Elise N Leverence
- Genomic Sciences and Precision Medicine Center (GSPMC), Medical College of Wisconsin, Milwaukee, WI, USA
| | - Swarnendu Tripathi
- Genomic Sciences and Precision Medicine Center (GSPMC), Medical College of Wisconsin, Milwaukee, WI, USA.,Bioinformatics Research and Development Laboratory, and Precision Medicine Simulation Unit, GSPMC, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Nikita R Dsouza
- Genomic Sciences and Precision Medicine Center (GSPMC), Medical College of Wisconsin, Milwaukee, WI, USA.,Bioinformatics Research and Development Laboratory, and Precision Medicine Simulation Unit, GSPMC, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Angela J Mathison
- Genomic Sciences and Precision Medicine Center (GSPMC), Medical College of Wisconsin, Milwaukee, WI, USA.,Bioinformatics Research and Development Laboratory, and Precision Medicine Simulation Unit, GSPMC, Medical College of Wisconsin, Milwaukee, WI, USA.,Division of Research, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Donald G Basel
- Genomic Sciences and Precision Medicine Center (GSPMC), Medical College of Wisconsin, Milwaukee, WI, USA.,Division of Pediatric Genetics, Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Brian F Volkman
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Brian C Smith
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Gwen Lomberk
- Genomic Sciences and Precision Medicine Center (GSPMC), Medical College of Wisconsin, Milwaukee, WI, USA.,Division of Research, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, USA.,Department of Pharmacology and Toxicology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Michael T Zimmermann
- Genomic Sciences and Precision Medicine Center (GSPMC), Medical College of Wisconsin, Milwaukee, WI, USA.,Bioinformatics Research and Development Laboratory, and Precision Medicine Simulation Unit, GSPMC, Medical College of Wisconsin, Milwaukee, WI, USA.,Clinical and Translational Sciences Institute, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Raul Urrutia
- Genomic Sciences and Precision Medicine Center (GSPMC), Medical College of Wisconsin, Milwaukee, WI, USA. .,Bioinformatics Research and Development Laboratory, and Precision Medicine Simulation Unit, GSPMC, Medical College of Wisconsin, Milwaukee, WI, USA. .,Division of Research, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, USA. .,Division of Pediatric Genetics, Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, USA.
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Phospholipase iPLA 2β averts ferroptosis by eliminating a redox lipid death signal. Nat Chem Biol 2021; 17:465-476. [PMID: 33542532 PMCID: PMC8152680 DOI: 10.1038/s41589-020-00734-x] [Citation(s) in RCA: 169] [Impact Index Per Article: 56.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 12/23/2020] [Indexed: 12/17/2022]
Abstract
Ferroptosis, triggered by discoordination of iron, thiols and lipids, leads to the accumulation of 15-hydroperoxy (Hp)-arachidonoyl-phosphatidylethanolamine (15-HpETE-PE), generated by complexes of 15-lipoxygenase (15-LOX) and a scaffold protein, phosphatidylethanolamine (PE)-binding protein (PEBP)1. As the Ca2+-independent phospholipase A2β (iPLA2β, PLA2G6 or PNPLA9 gene) can preferentially hydrolyze peroxidized phospholipids, it may eliminate the ferroptotic 15-HpETE-PE death signal. Here, we demonstrate that by hydrolyzing 15-HpETE-PE, iPLA2β averts ferroptosis, whereas its genetic or pharmacological inactivation sensitizes cells to ferroptosis. Given that PLA2G6 mutations relate to neurodegeneration, we examined fibroblasts from a patient with a Parkinson's disease (PD)-associated mutation (fPDR747W) and found selectively decreased 15-HpETE-PE-hydrolyzing activity, 15-HpETE-PE accumulation and elevated sensitivity to ferroptosis. CRISPR-Cas9-engineered Pnpla9R748W/R748W mice exhibited progressive parkinsonian motor deficits and 15-HpETE-PE accumulation. Elevated 15-HpETE-PE levels were also detected in midbrains of rotenone-infused parkinsonian rats and α-synuclein-mutant SncaA53T mice, with decreased iPLA2β expression and a PD-relevant phenotype. Thus, iPLA2β is a new ferroptosis regulator, and its mutations may be implicated in PD pathogenesis.
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ADDRESS: A Database of Disease-associated Human Variants Incorporating Protein Structure and Folding Stabilities. J Mol Biol 2021; 433:166840. [PMID: 33539887 DOI: 10.1016/j.jmb.2021.166840] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 01/17/2021] [Accepted: 01/20/2021] [Indexed: 11/22/2022]
Abstract
Numerous human diseases are caused by mutations in genomic sequences. Since amino acid changes affect protein function through mechanisms often predictable from protein structure, the integration of structural and sequence data enables us to estimate with greater accuracy whether and how a given mutation will lead to disease. Publicly available annotated databases enable hypothesis assessment and benchmarking of prediction tools. However, the results are often presented as summary statistics or black box predictors, without providing full descriptive information. We developed a new semi-manually curated human variant database presenting information on the protein contact-map, sequence-to-structure mapping, amino acid identity change, and stability prediction for the popular UniProt database. We found that the profiles of pathogenic and benign missense polymorphisms can be effectively deduced using decision trees and comparative analyses based on the presented dataset. The database is made publicly available through https://zhanglab.ccmb.med.umich.edu/ADDRESS.
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43
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Yazar M, Özbek P. In Silico Tools and Approaches for the Prediction of Functional and Structural Effects of Single-Nucleotide Polymorphisms on Proteins: An Expert Review. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2020; 25:23-37. [PMID: 33058752 DOI: 10.1089/omi.2020.0141] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Single-nucleotide polymorphisms (SNPs) are single-base variants that contribute to human biological variation and pathogenesis of many human diseases. Among all SNP types, nonsynonymous single-nucleotide polymorphisms (nsSNPs) can alter many structural, biochemical, and functional features of a protein such as folding characteristics, charge distribution, stability, dynamics, and interactions with other proteins/nucleotides. These modifications in the protein structure can lead nsSNPs to be closely associated with many multifactorial diseases such as cancer, diabetes, and neurodegenerative diseases. Predicting structural and functional effects of nsSNPs with experimental approaches can be time-consuming and costly; hence, computational prediction tools and algorithms are being widely and increasingly utilized in biology and medical research. This expert review examines the in silico tools and algorithms for the prediction of functional or structural effects of SNP variants, in addition to the description of the phenotypic effects of nsSNPs on protein structure, association between pathogenicity of variants, and functional or structural features of disease-associated variants. Finally, case studies investigating the functional and structural effects of nsSNPs on selected protein structures are highlighted. We conclude that creating a consistent workflow with a combination of in silico approaches or tools should be considered to increase the performance, accuracy, and precision of the biological and clinical predictions made in silico.
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Affiliation(s)
- Metin Yazar
- Department of Bioengineering, Marmara University, Göztepe, İstanbul, Turkey.,Department of Genetics and Bioengineering, Istanbul Okan University, Tuzla, Istanbul, Turkey
| | - Pemra Özbek
- Department of Bioengineering, Marmara University, Göztepe, İstanbul, Turkey
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Structure of the human clamp loader reveals an autoinhibited conformation of a substrate-bound AAA+ switch. Proc Natl Acad Sci U S A 2020; 117:23571-23580. [PMID: 32907938 DOI: 10.1073/pnas.2007437117] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
DNA replication requires the sliding clamp, a ring-shaped protein complex that encircles DNA, where it acts as an essential cofactor for DNA polymerases and other proteins. The sliding clamp needs to be opened and installed onto DNA by a clamp loader ATPase of the AAA+ family. The human clamp loader replication factor C (RFC) and sliding clamp proliferating cell nuclear antigen (PCNA) are both essential and play critical roles in several diseases. Despite decades of study, no structure of human RFC has been resolved. Here, we report the structure of human RFC bound to PCNA by cryogenic electron microscopy to an overall resolution of ∼3.4 Å. The active sites of RFC are fully bound to adenosine 5'-triphosphate (ATP) analogs, which is expected to induce opening of the sliding clamp. However, we observe the complex in a conformation before PCNA opening, with the clamp loader ATPase modules forming an overtwisted spiral that is incapable of binding DNA or hydrolyzing ATP. The autoinhibited conformation observed here has many similarities to a previous yeast RFC:PCNA crystal structure, suggesting that eukaryotic clamp loaders adopt a similar autoinhibited state early on in clamp loading. Our results point to a "limited change/induced fit" mechanism in which the clamp first opens, followed by DNA binding, inducing opening of the loader to release autoinhibition. The proposed change from an overtwisted to an active conformation reveals an additional regulatory mechanism for AAA+ ATPases. Finally, our structural analysis of disease mutations leads to a mechanistic explanation for the role of RFC in human health.
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Humbert J, Salian S, Makrythanasis P, Lemire G, Rousseau J, Ehresmann S, Garcia T, Alasiri R, Bottani A, Hanquinet S, Beaver E, Heeley J, Smith ACM, Berger SI, Antonarakis SE, Yang XJ, Côté J, Campeau PM. De Novo KAT5 Variants Cause a Syndrome with Recognizable Facial Dysmorphisms, Cerebellar Atrophy, Sleep Disturbance, and Epilepsy. Am J Hum Genet 2020; 107:564-574. [PMID: 32822602 PMCID: PMC7477011 DOI: 10.1016/j.ajhg.2020.08.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 07/21/2020] [Indexed: 12/11/2022] Open
Abstract
KAT5 encodes an essential lysine acetyltransferase, previously called TIP60, which is involved in regulating gene expression, DNA repair, chromatin remodeling, apoptosis, and cell proliferation; but it remains unclear whether variants in this gene cause a genetic disease. Here, we study three individuals with heterozygous de novo missense variants in KAT5 that affect normally invariant residues, with one at the chromodomain (p.Arg53His) and two at or near the acetyl-CoA binding site (p.Cys369Ser and p.Ser413Ala). All three individuals have cerebral malformations, seizures, global developmental delay or intellectual disability, and severe sleep disturbance. Progressive cerebellar atrophy was also noted. Histone acetylation assays with purified variant KAT5 demonstrated that the variants decrease or abolish the ability of the resulting NuA4/TIP60 multi-subunit complexes to acetylate the histone H4 tail in chromatin. Transcriptomic analysis in affected individual fibroblasts showed deregulation of multiple genes that control development. Moreover, there was also upregulated expression of PER1 (a key gene involved in circadian control) in agreement with sleep anomalies in all of the individuals. In conclusion, dominant missense KAT5 variants cause histone acetylation deficiency with transcriptional dysregulation of multiples genes, thereby leading to a neurodevelopmental syndrome with sleep disturbance, cerebellar atrophy, and facial dysmorphisms, and suggesting a recognizable syndrome.
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Affiliation(s)
- Jonathan Humbert
- St-Patrick Research Group in Basic Oncology, Laval University Cancer Research Center, Axe Oncologie du Centre de Recherche du Centre Hospitalier Universitaire de Quebec-Université Laval, Quebec City, QC G1R 3S3, Canada
| | - Smrithi Salian
- Sainte-Justine Hospital Research Center, University of Montreal, Montreal, QC H3T 1C5, Canada
| | - Periklis Makrythanasis
- Biomedical Research Foundation of the Academy of Athens, Athens 115 27, Greece; Department of Genetic Medicine and Development, University of Geneva Medical School and Geneva University Hospitals, 1211 Geneva, Switzerland
| | - Gabrielle Lemire
- Sainte-Justine Hospital Research Center, University of Montreal, Montreal, QC H3T 1C5, Canada
| | - Justine Rousseau
- Sainte-Justine Hospital Research Center, University of Montreal, Montreal, QC H3T 1C5, Canada
| | - Sophie Ehresmann
- Sainte-Justine Hospital Research Center, University of Montreal, Montreal, QC H3T 1C5, Canada
| | - Thomas Garcia
- Sainte-Justine Hospital Research Center, University of Montreal, Montreal, QC H3T 1C5, Canada
| | - Rami Alasiri
- Rosalind and Morris Goodman Cancer Research Centre, Department of Medicine, McGill University, Montreal, QC H3A 1A3, Canada
| | - Armand Bottani
- Service of Genetic Medicine, Geneva University Hospitals, 1211 Geneva, Switzerland
| | - Sylviane Hanquinet
- Unit of Pediatric Radiology, Geneva University Hospitals, 1211 Geneva, Switzerland
| | - Erin Beaver
- Mercy Kids Genetics, St. Louis, MO 63141, USA
| | | | - Ann C M Smith
- Office of the Clinical Director, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20894, USA
| | - Seth I Berger
- Children's National Health System, Washington, DC 20010, USA
| | - Stylianos E Antonarakis
- Department of Genetic Medicine and Development, University of Geneva Medical School and Geneva University Hospitals, 1211 Geneva, Switzerland
| | - Xiang-Jiao Yang
- Rosalind and Morris Goodman Cancer Research Centre, Department of Medicine, McGill University, Montreal, QC H3A 1A3, Canada
| | - Jacques Côté
- St-Patrick Research Group in Basic Oncology, Laval University Cancer Research Center, Axe Oncologie du Centre de Recherche du Centre Hospitalier Universitaire de Quebec-Université Laval, Quebec City, QC G1R 3S3, Canada
| | - Philippe M Campeau
- Sainte-Justine Hospital Research Center, University of Montreal, Montreal, QC H3T 1C5, Canada.
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Carpentier M, Chomilier J. Analyses of displacements resulting from a point mutation in proteins. J Struct Biol 2020; 211:107543. [PMID: 32522553 DOI: 10.1016/j.jsb.2020.107543] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 04/28/2020] [Accepted: 05/31/2020] [Indexed: 11/19/2022]
Abstract
The effects of a single residue substitution on the protein backbone are frequently quite small and there are many other potential sources of structural variation for protein. We present here a methodology considering different sources of distortions in order to isolate the very effect of the mutation. To validate our methodology, we consider a well-studied family with many single mutants: the human lysozyme. Most of the perturbations are expected to be at the very localisation of the mutation, but in many cases the effects are propagated at long range. We show that the distances between the mutated residue and the 5% most disturbed residues exponentially decreases. One third of the affected residues are in direct contact with the mutated position; the remaining two thirds are potential allosteric effects. We confirm the reliability of the residues identified as significantly perturbed by comparing our results to experimental studies. We confirm with the present method all the previously identified perturbations. This study shows that mutations have long-range impact on protein backbone that can be detected, although the displacement of the affected atoms is small.
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Affiliation(s)
- Mathilde Carpentier
- Institut Systématique Evolution Biodiversité (ISYEB), Sorbonne Université, MNHN, CNRS, EPHE, 57 rue Cuvier, CP 50, 75005 Paris, France.
| | - Jacques Chomilier
- Sorbonne Université, BiBiP IMPMC UMR 7590, CNRS, MNHN, Paris, France.
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Krieger JM, Doruker P, Scott AL, Perahia D, Bahar I. Towards gaining sight of multiscale events: utilizing network models and normal modes in hybrid methods. Curr Opin Struct Biol 2020; 64:34-41. [PMID: 32622329 DOI: 10.1016/j.sbi.2020.05.013] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 05/13/2020] [Accepted: 05/20/2020] [Indexed: 11/28/2022]
Abstract
With the explosion of normal mode analyses (NMAs) based on elastic network models (ENMs) in the last decade, and the proven precision of MD simulations for visualizing interactions at atomic scale, many hybrid methods have been proposed in recent years. These aim at exploiting the best of both worlds: the atomic precision of MD that often fall short of exploring time and length scales of biological interest, and the capability of ENM-NMA to predict the cooperative and often functional rearrangements of large structures and assemblies, albeit at low resolution. We present an overview of recent progress in the field with examples of successful applications highlighting the utility of such hybrid methods and pointing to emerging future directions guided by advances in experimental characterization of biomolecular systems structure and dynamics.
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Affiliation(s)
- James M Krieger
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Suite 3064 BST3, Pittsburgh, PA 15260, USA
| | - Pemra Doruker
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Suite 3064 BST3, Pittsburgh, PA 15260, USA
| | - Ana Ligia Scott
- Laboratory of Bioinformatics and Computational Biology, Federal University of ABC, Santo André, SP, Brazil
| | - David Perahia
- Laboratoire de Biologie et de Pharmacologie Appliquée, Ecole Normale Superieure Paris-Saclay, UMR 8113, CNRS, 4 Avenue des Sciences, 91190 Gif-sur-Yvette, France
| | - Ivet Bahar
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Suite 3064 BST3, Pittsburgh, PA 15260, USA.
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Ponzoni L, Nguyen NH, Bahar I, Brodsky JL. Complementary computational and experimental evaluation of missense variants in the ROMK potassium channel. PLoS Comput Biol 2020; 16:e1007749. [PMID: 32251469 PMCID: PMC7162551 DOI: 10.1371/journal.pcbi.1007749] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Revised: 04/16/2020] [Accepted: 02/25/2020] [Indexed: 02/02/2023] Open
Abstract
The renal outer medullary potassium (ROMK) channel is essential for potassium transport in the kidney, and its dysfunction is associated with a salt-wasting disorder known as Bartter syndrome. Despite its physiological significance, we lack a mechanistic understanding of the molecular defects in ROMK underlying most Bartter syndrome-associated mutations. To this end, we employed a ROMK-dependent yeast growth assay and tested single amino acid variants selected by a series of computational tools representative of different approaches to predict each variants’ pathogenicity. In one approach, we used in silico saturation mutagenesis, i.e. the scanning of all possible single amino acid substitutions at all sequence positions to estimate their impact on function, and then employed a new machine learning classifier known as Rhapsody. We also used two additional tools, EVmutation and Polyphen-2, which permitted us to make consensus predictions on the pathogenicity of single amino acid variants in ROMK. Experimental tests performed for selected mutants in different classes validated the vast majority of our predictions and provided insights into variants implicated in ROMK dysfunction. On a broader scope, our analysis suggests that consolidation of data from complementary computational approaches provides an improved and facile method to predict the severity of an amino acid substitution and may help accelerate the identification of disease-causing mutations in any protein. As the number of sequenced human genomes rises, a major challenge is to identify which single amino acid variations in a protein affect function and predispose individuals to disease. While predictive algorithms are available for this purpose, a comparative analysis of recently developed algorithms has not been adequately performed, nor is it clear whether combining algorithms would improve predictive power. To this end, we compared the efficacy of three publicly available algorithms and applied the results to Bartter syndrome, a human disease for which numerous poorly-characterized single amino acid variants have been identified and for which there is no cure. In silico saturation mutagenesis, i.e., the computational prediction of pathogenesis for every possible amino acid substitution, allowed us to experimentally test predictions by measuring the activity of an ion channel linked to Bartter syndrome. Based on data from blinded experiments, we discovered that Rhapsody and EVmutation successfully predicted deleterious mutations. Moreover, Rhapsody—which takes into account evolutionary as well as structural and dynamic considerations—predicted that >90% of known Bartter syndrome mutations are deleterious. Overall, our data will aid investigators who wish to test single amino acid variants in any protein and aid biomedical researchers who wish to develop hypotheses on the potential severity of genetic variants uncovered from genome databases.
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Affiliation(s)
- Luca Ponzoni
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Nga H. Nguyen
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Ivet Bahar
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- * E-mail: (IB); (JLB)
| | - Jeffrey L. Brodsky
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- * E-mail: (IB); (JLB)
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Essential site scanning analysis: A new approach for detecting sites that modulate the dispersion of protein global motions. Comput Struct Biotechnol J 2020; 18:1577-1586. [PMID: 32637054 PMCID: PMC7330491 DOI: 10.1016/j.csbj.2020.06.020] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 06/08/2020] [Accepted: 06/10/2020] [Indexed: 12/14/2022] Open
Abstract
Despite the wealth of methods developed for exploring the molecular basis of allostery in biomolecular systems, there is still a need for structure-based predictive tools that can efficiently detect susceptible sites for triggering allosteric responses. Toward this goal, we introduce here an elastic network model (ENM)-based method, Essential Site Scanning Analysis (ESSA). Essential sites are here defined as residues that would significantly alter the protein's global dynamics if bound to a ligand. To mimic the crowding induced upon substrate binding, the heavy atoms of each residue are incorporated as additional network nodes into the α-carbon-based ENM, and the resulting shifts in soft mode frequencies are used as a metric for evaluating the essentiality of each residue. Results on a dataset of monomeric proteins indicate the enrichment of allosteric and orthosteric binding sites, as well as global hinge regions among essential residues, highlighting the significant role of these sites in controlling the overall structural dynamics. Further integration of ESSA with information on predicted pockets and their local hydrophobicity density enables successful predictions of allosteric pockets for both ligand-bound and -unbound structures. ESSA can be efficiently applied to large multimeric systems. Three case studies, namely (i) G-protein binding to a GPCR, (ii) heterotrimeric assembly of the Ser/Thr protein phosphatase PP2A, and (iii) allo-targeting of AMPA receptor, demonstrate the utility of ESSA for identifying essential sites and narrowing down target allosteric sites identified by druggability simulations.
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