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Rehman G, Kashyap J, Srivastav AK, Rizvi S, Kumar U, Tyagi RK. Truncated variants of thyroid hormone receptor beta display disease-inflicting malfunctioning at cellular level. Exp Cell Res 2024; 437:114017. [PMID: 38555013 DOI: 10.1016/j.yexcr.2024.114017] [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: 01/06/2024] [Revised: 03/22/2024] [Accepted: 03/24/2024] [Indexed: 04/02/2024]
Abstract
Thyroid hormone receptor β (THRβ) is a member of the nuclear receptor superfamily of ligand-modulated transcription factors. Upon ligand binding, THRβ sequentially recruits the components of transcriptional machinery to modulate target gene expression. In addition to regulating diverse physiological processes, THRβ plays a crucial role in hypothalamus-pituitary-thyroid axis feedback regulation. Anomalies in THRβ gene/protein structure are associated with onset of diverse disease states. In this study, we investigated disease-inflicting truncated variants of THRβ using in-silico analysis and cell-based assays. We examined the THRβ truncated variants on multiple test parameters, including subcellular localization, ligand-receptor interactions, transcriptional functions, interaction with heterodimeric partner RXR, and receptor-chromatin interactions. Moreover, molecular dynamic simulation approaches predicted that shortened THRβ-LBD due to point mutations contributes proportionally to the loss of structural integrity and receptor stability. Deviant subcellular localization and compromised transcriptional function were apparent with these truncated variants. Present study shows that 'mitotic bookmarking' property of some THRβ variants is also affected. The study highlights that structural and conformational attributes of THRβ are necessary for normal receptor functioning, and any deviations may contribute to the underlying cause of the inflicted diseases. We anticipate that insights derived herein may contribute to improved mechanistic understanding to assess disease predisposition.
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Affiliation(s)
- Ghausiya Rehman
- Special Centre for Molecular Medicine, Jawaharlal Nehru University, New Delhi, 110067, India
| | - Jyoti Kashyap
- Special Centre for Molecular Medicine, Jawaharlal Nehru University, New Delhi, 110067, India
| | - Amit Kumar Srivastav
- School of Nano Sciences, Central University of Gujarat, Gandhinagar, Gujarat, 382030, India
| | - Sheeba Rizvi
- Special Centre for Molecular Medicine, Jawaharlal Nehru University, New Delhi, 110067, India
| | - Umesh Kumar
- School of Nano Sciences, Central University of Gujarat, Gandhinagar, Gujarat, 382030, India; Nutrition Biology Department, School of Interdisciplinary and Applied Sciences, Central University of Haryana, Mahendergarh, Haryana, 123031, India
| | - Rakesh K Tyagi
- Special Centre for Molecular Medicine, Jawaharlal Nehru University, New Delhi, 110067, India.
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Cevik S, Wangtiraumnuay N, Van Schelvergem K, Tsukikawa M, Capasso J, Biswas SB, Bodt B, Levin AV, Biswas-Fiss E. Protein modeling and in silico analysis to assess pathogenicity of ABCA4 variants in patients with inherited retinal disease. Mol Vis 2023; 29:217-233. [PMID: 38222458 PMCID: PMC10784225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 10/23/2023] [Indexed: 01/16/2024] Open
Abstract
Purpose The retina-specific ABCA transporter, ABCA4, plays an essential role in translocating retinoids required by the visual cycle. ABCA4 genetic variants are known to cause a wide range of inherited retinal disorders, including Stargardt disease and cone-rod dystrophy. More than 1,400 ABCA4 missense variants have been identified; however, more than half of these remain variants of uncertain significance (VUS). The purpose of this study was to employ a predictive strategy to assess the pathogenicity of ABCA4 variants in inherited retinal diseases using protein modeling and computational approaches. Methods We studied 13 clinically well-defined patients with ABCA4 retinopathies and identified the presence of 10 missense variants, including one novel variant in the ABCA4 gene, by next-generation sequencing (NGS). All variants were structurally analyzed using AlphaFold2 models and existing experimental structures of human ABCA4 protein. The results of these analyses were compared with patient clinical presentations to test the effectiveness of the methods employed in predicting variant pathogenicity. Results We conducted a phenotype-genotype comparison of 13 genetically and phenotypically well-defined retinal disease patients. The in silico protein structure analyses we employed successfully detected the deleterious effect of missense variants found in this affected patient cohort. Our study provides American College of Medical Genetics and Genomics (ACMG)-defined supporting evidence of the pathogenicity of nine missense ABCA4 variants, aligning with the observed clinical phenotypes in this cohort. Conclusions In this report, we describe a systematic approach to predicting the pathogenicity of ABCA4 variants by means of three-dimensional (3D) protein modeling and in silico structure analysis. Our results demonstrate concordance between disease severity and structural changes in protein models induced by genetic variations. Furthermore, the present study suggests that in silico protein structure analysis can be used as a predictor of pathogenicity and may facilitate the assessment of genetic VUS.
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Affiliation(s)
- Senem Cevik
- Department of Medical and Molecular Sciences, University of Delaware College of Health Sciences, Newark, DE
| | - Nutsuchar Wangtiraumnuay
- Department of Ophthalmology, Queen Sirikit National Institute of Child Health, Bangkok, Thailand
| | | | - Mai Tsukikawa
- Department of Ophthalmology, Duke University, Durham, NC
| | - Jenina Capasso
- Departments of Ophthalmology and Pediatrics, Flaum Eye Institute and Golisano Children's Hospital, University of Rochester, Rochester, NY
| | - Subhasis B Biswas
- Department of Medical and Molecular Sciences, University of Delaware College of Health Sciences, Newark, DE
| | - Barry Bodt
- College of Health Sciences Biostatistics Core Facility, University of Delaware, Newark, DE
| | - Alex V Levin
- Departments of Ophthalmology and Pediatrics, Flaum Eye Institute and Golisano Children's Hospital, University of Rochester, Rochester, NY
| | - Esther Biswas-Fiss
- Department of Medical and Molecular Sciences, University of Delaware College of Health Sciences, Newark, DE
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Vivekanandam V, Ellmers R, Jayaseelan D, Houlden H, Männikkö R, Hanna MG. In silico versus functional characterization of genetic variants: lessons from muscle channelopathies. Brain 2023; 146:1316-1321. [PMID: 36382348 DOI: 10.1093/brain/awac431] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 10/04/2022] [Accepted: 11/06/2022] [Indexed: 11/17/2022] Open
Abstract
Accurate determination of the pathogenicity of missense genetic variants of uncertain significance is a huge challenge for implementing genetic data in clinical practice. In silico predictive tools are used to score variants' pathogenicity. However, their value in clinical settings is often unclear, as they have not usually been validated against robust functional assays. We compared nine widely used in silico predictive tools, including more recently developed tools (EVE and REVEL) with detailed cell-based electrophysiology, for 126 CLCN1 variants discovered in patients with the skeletal muscle channelopathy myotonia congenita. We found poor accuracy for most tools. The highest accuracy was obtained with MutationTaster (84.58%) and REVEL (82.54%). Both of these scores showed poor specificity, although specificity was better using EVE. Combining methods based on concordance improved performance overall but still lacked specificity. Our calculated statistics for the predictive tools were different to reported values for other genes in the literature, suggesting that the utility of the tools varies between genes. Overall, current predictive tools for this chloride channel are not reliable for clinical use, and tools with better specificity are urgently required. Improving the accuracy of predictive tools is a wider issue and a huge challenge for effective clinical implementation of genetic data.
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Affiliation(s)
- Vinojini Vivekanandam
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Rebecca Ellmers
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Dipa Jayaseelan
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Henry Houlden
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Roope Männikkö
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Michael G Hanna
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
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Sadeh TT, Baines RA, Black GC, Manson F. Ca v1.4 congenital stationary night blindness is associated with an increased rate of proteasomal degradation. Front Cell Dev Biol 2023; 11:1161548. [PMID: 37206923 PMCID: PMC10188973 DOI: 10.3389/fcell.2023.1161548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 04/17/2023] [Indexed: 05/21/2023] Open
Abstract
Pathogenic, generally loss-of-function, variants in CACNA1F, encoding the Cav1.4α1 calcium channel, underlie congenital stationary night blindness type 2 (CSNB2), a rare inherited retinal disorder associated with visual disability. To establish the underlying pathomechanism, we investigated 10 clinically derived CACNA1F missense variants located across pore-forming domains, connecting loops, and the carboxy-tail domain of the Cav1.4α subunit. Homology modeling showed that all variants cause steric clashes; informatics analysis correctly predicted pathogenicity for 7/10 variants. In vitro analyses demonstrated that all variants cause a decrease in current, global expression, and protein stability and act through a loss-of-function mechanism and suggested that the mutant Cav1.4α proteins were degraded by the proteasome. We showed that the reduced current for these variants could be significantly increased through treatment with clinical proteasome inhibitors. In addition to facilitating clinical interpretation, these studies suggest that proteasomal inhibition represents an avenue of potential therapeutic intervention for CSNB2.
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Affiliation(s)
- Tal T. Sadeh
- Division of Evolution, Infection and Genomics, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Richard A. Baines
- Division of Neuroscience, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Graeme C. Black
- Division of Evolution, Infection and Genomics, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
- Manchester Centre for Genomic Medicine, Manchester Academic Health Sciences Centre, Manchester University NHS Foundation Trust, St Mary’s Hospital, Manchester, United Kingdom
- *Correspondence: Graeme C. Black,
| | - Forbes Manson
- Division of Evolution, Infection and Genomics, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
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Sallah SR, Sergouniotis PI, Hardcastle C, Ramsden S, Lotery AJ, Lench N, Lovell SC, Black GCM. Assessing the Pathogenicity of In-Frame CACNA1F Indel Variants Using Structural Modeling. J Mol Diagn 2022; 24:1232-1239. [PMID: 36191840 DOI: 10.1016/j.jmoldx.2022.09.005] [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: 09/09/2021] [Revised: 08/20/2022] [Accepted: 09/09/2022] [Indexed: 01/13/2023] Open
Abstract
Small in-frame insertion-deletion (indel) variants are a common form of genomic variation whose impact on rare disease phenotypes has been understudied. The prediction of the pathogenicity of such variants remains challenging. X-linked incomplete congenital stationary night blindness type 2 (CSNB2) is a nonprogressive, inherited retinal disorder caused by variants in CACNA1F, encoding the Cav1.4α1 channel protein. Here, structural analysis was used through homology modeling to interpret 10 disease-correlated and 10 putatively benign CACNA1F in-frame indel variants. CSNB2-correlated changes were found to be more highly conserved compared with putative benign variants. Notably, all 10 disease-correlated variants but none of the benign changes were within modeled regions of the protein. Structural analysis revealed that disease-correlated variants are predicted to destabilize the structure and function of the Cav1.4α1 channel protein. Overall, the use of structural information to interpret the consequences of in-frame indel variants provides an important adjunct that can improve the diagnosis for individuals with CSNB2.
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Affiliation(s)
- Shalaw R Sallah
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicines and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom; Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, St. Mary's Hospital, Manchester, United Kingdom.
| | - Panagiotis I Sergouniotis
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicines and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom; Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, St. Mary's Hospital, Manchester, United Kingdom
| | - Claire Hardcastle
- Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, St. Mary's Hospital, Manchester, United Kingdom
| | - Simon Ramsden
- Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, St. Mary's Hospital, Manchester, United Kingdom
| | - Andrew J Lotery
- Faculty of Medicine, University of Southampton, Southampton, United Kingdom
| | - Nick Lench
- Congenica Ltd., BioData Innovation Centre, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Simon C Lovell
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicines and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Graeme C M Black
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicines and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom; Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, St. Mary's Hospital, Manchester, United Kingdom.
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Liu Y, Yeung WSB, Chiu PCN, Cao D. Computational approaches for predicting variant impact: An overview from resources, principles to applications. Front Genet 2022; 13:981005. [PMID: 36246661 PMCID: PMC9559863 DOI: 10.3389/fgene.2022.981005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
One objective of human genetics is to unveil the variants that contribute to human diseases. With the rapid development and wide use of next-generation sequencing (NGS), massive genomic sequence data have been created, making personal genetic information available. Conventional experimental evidence is critical in establishing the relationship between sequence variants and phenotype but with low efficiency. Due to the lack of comprehensive databases and resources which present clinical and experimental evidence on genotype-phenotype relationship, as well as accumulating variants found from NGS, different computational tools that can predict the impact of the variants on phenotype have been greatly developed to bridge the gap. In this review, we present a brief introduction and discussion about the computational approaches for variant impact prediction. Following an innovative manner, we mainly focus on approaches for non-synonymous variants (nsSNVs) impact prediction and categorize them into six classes. Their underlying rationale and constraints, together with the concerns and remedies raised from comparative studies are discussed. We also present how the predictive approaches employed in different research. Although diverse constraints exist, the computational predictive approaches are indispensable in exploring genotype-phenotype relationship.
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Affiliation(s)
- Ye Liu
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - William S. B. Yeung
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Department of Obstetrics and Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Philip C. N. Chiu
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Department of Obstetrics and Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- *Correspondence: Philip C. N. Chiu, ; Dandan Cao,
| | - Dandan Cao
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- *Correspondence: Philip C. N. Chiu, ; Dandan Cao,
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Aloraini T, Aljouie A, Alniwaider R, Alharbi W, Alsubaie L, AlTuraif W, Qureshi W, Alswaid A, Eyiad W, Al Mutairi F, Ababneh F, Alfadhel M, Alfares A. The variant artificial intelligence easy scoring (VARIES) system. Comput Biol Med 2022; 145:105492. [PMID: 35585733 DOI: 10.1016/j.compbiomed.2022.105492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 03/30/2022] [Accepted: 04/02/2022] [Indexed: 11/03/2022]
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Sallah SR, Ellingford JM, Sergouniotis PI, Ramsden SC, Lench N, Lovell SC, Black GC. Improving the clinical interpretation of missense variants in X linked genes using structural analysis. J Med Genet 2021; 59:385-392. [PMID: 33766936 PMCID: PMC8961765 DOI: 10.1136/jmedgenet-2020-107404] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 01/18/2021] [Accepted: 01/21/2021] [Indexed: 11/04/2022]
Abstract
BACKGROUND Improving the clinical interpretation of missense variants can increase the diagnostic yield of genomic testing and lead to personalised management strategies. Currently, due to the imprecision of bioinformatic tools that aim to predict variant pathogenicity, their role in clinical guidelines remains limited. There is a clear need for more accurate prediction algorithms and this study aims to improve performance by harnessing structural biology insights. The focus of this work is missense variants in a subset of genes associated with X linked disorders. METHODS We have developed a protein-specific variant interpreter (ProSper) that combines genetic and protein structural data. This algorithm predicts missense variant pathogenicity by applying machine learning approaches to the sequence and structural characteristics of variants. RESULTS ProSper outperformed seven previously described tools, including meta-predictors, in correctly evaluating whether or not variants are pathogenic; this was the case for 11 of the 21 genes associated with X linked disorders that met the inclusion criteria for this study. We also determined gene-specific pathogenicity thresholds that improved the performance of VEST4, REVEL and ClinPred, the three best-performing tools out of the seven that were evaluated; this was the case in 11, 11 and 12 different genes, respectively. CONCLUSION ProSper can form the basis of a molecule-specific prediction tool that can be implemented into diagnostic strategies. It can allow the accurate prioritisation of missense variants associated with X linked disorders, aiding precise and timely diagnosis. In addition, we demonstrate that gene-specific pathogenicity thresholds for a range of missense prioritisation tools can lead to an increase in prediction accuracy.
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Affiliation(s)
- Shalaw Rassul Sallah
- Division of Evolution and Genomic Sciences, The University of Manchester Faculty of Biology, Medicine and Health, Manchester, UK.,Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester Academic Health Sciences Centre, Manchester, UK
| | - Jamie M Ellingford
- Division of Evolution and Genomic Sciences, The University of Manchester Faculty of Biology, Medicine and Health, Manchester, UK.,Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester Academic Health Sciences Centre, Manchester, UK
| | - Panagiotis I Sergouniotis
- Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester Academic Health Sciences Centre, Manchester, UK
| | - Simon C Ramsden
- Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester Academic Health Sciences Centre, Manchester, UK
| | - Nicholas Lench
- Congenica Ltd, Biodata Innovation Centre, Wellcome Genome Campus, Hinxton, London, UK
| | - Simon C Lovell
- Division of Evolution and Genomic Sciences, The University of Manchester Faculty of Biology, Medicine and Health, Manchester, UK
| | - Graeme C Black
- Division of Evolution and Genomic Sciences, The University of Manchester Faculty of Biology, Medicine and Health, Manchester, UK .,Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester Academic Health Sciences Centre, Manchester, UK
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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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