1
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Andhika NS, Biswas S, Hardcastle C, Green DJ, Ramsden SC, Birney E, Black GC, Sergouniotis PI. Using computational approaches to enhance the interpretation of missense variants in the PAX6 gene. Eur J Hum Genet 2024; 32:1005-1013. [PMID: 38849599 PMCID: PMC11292026 DOI: 10.1038/s41431-024-01638-3] [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: 01/11/2024] [Revised: 04/12/2024] [Accepted: 05/14/2024] [Indexed: 06/09/2024] Open
Abstract
The PAX6 gene encodes a highly-conserved transcription factor involved in eye development. Heterozygous loss-of-function variants in PAX6 can cause a range of ophthalmic disorders including aniridia. A key molecular diagnostic challenge is that many PAX6 missense changes are presently classified as variants of uncertain significance. While computational tools can be used to assess the effect of genetic alterations, the accuracy of their predictions varies. Here, we evaluated and optimised the performance of computational prediction tools in relation to PAX6 missense variants. Through inspection of publicly available resources (including HGMD, ClinVar, LOVD and gnomAD), we identified 241 PAX6 missense variants that were used for model training and evaluation. The performance of ten commonly used computational tools was assessed and a threshold optimization approach was utilized to determine optimal cut-off values. Validation studies were subsequently undertaken using PAX6 variants from a local database. AlphaMissense, SIFT4G and REVEL emerged as the best-performing predictors; the optimized thresholds of these tools were 0.967, 0.025, and 0.772, respectively. Combining the prediction from these top-three tools resulted in lower performance compared to using AlphaMissense alone. Tailoring the use of computational tools by employing optimized thresholds specific to PAX6 can enhance algorithmic performance. Our findings have implications for PAX6 variant interpretation in clinical settings.
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Affiliation(s)
- Nadya S Andhika
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Susmito Biswas
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Manchester Royal Eye Hospital, Manchester University NHS Foundation Trust, Manchester, UK
| | - Claire Hardcastle
- Manchester Centre for Genomic Medicine, Saint Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK
| | - David J Green
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Simon C Ramsden
- Manchester Centre for Genomic Medicine, Saint Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK
| | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, UK
| | - Graeme C Black
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Manchester Centre for Genomic Medicine, Saint Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK
| | - Panagiotis I Sergouniotis
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
- Manchester Royal Eye Hospital, Manchester University NHS Foundation Trust, Manchester, UK.
- Manchester Centre for Genomic Medicine, Saint Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK.
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, UK.
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2
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Hramyka D, Sczakiel HL, Zhao MX, Stolpe O, Nieminen M, Adam R, Danyel M, Einicke L, Hägerling R, Knaus A, Mundlos S, Schwartzmann S, Seelow D, Ehmke N, Mensah M, Boschann F, Beule D, Holtgrewe M. REEV: review, evaluate and explain variants. Nucleic Acids Res 2024; 52:W148-W158. [PMID: 38769069 PMCID: PMC11223839 DOI: 10.1093/nar/gkae366] [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: 02/13/2024] [Revised: 04/07/2024] [Accepted: 05/03/2024] [Indexed: 05/22/2024] Open
Abstract
In the era of high throughput sequencing, special software is required for the clinical evaluation of genetic variants. We developed REEV (Review, Evaluate and Explain Variants), a user-friendly platform for clinicians and researchers in the field of rare disease genetics. Supporting data was aggregated from public data sources. We compared REEV with seven other tools for clinical variant evaluation. REEV (semi-)automatically fills individual ACMG criteria facilitating variant interpretation. REEV can store disease and phenotype data related to a case to use these for phenotype similarity measures. Users can create public permanent links for individual variants that can be saved as browser bookmarks and shared. REEV may help in the fast diagnostic assessment of genetic variants in a clinical as well as in a research context. REEV (https://reev.bihealth.org/) is free and open to all users and there is no login requirement.
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Affiliation(s)
- Dzmitry Hramyka
- Berlin Institute of Health, Core Unit Bioinformatics, Berlin, Germany
| | - Henrike Lisa Sczakiel
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- BIH Biomedical Innovation Academy, Clinician Scientist Program, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- RG Development & Disease, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Max Xiaohang Zhao
- Berlin Institute of Health, Core Unit Bioinformatics, Berlin, Germany
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Oliver Stolpe
- Berlin Institute of Health, Core Unit Bioinformatics, Berlin, Germany
| | - Mikko Nieminen
- Berlin Institute of Health, Core Unit Bioinformatics, Berlin, Germany
| | - Ronja Adam
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Magdalena Danyel
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- BIH Biomedical Innovation Academy, Clinician Scientist Program, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Lara Einicke
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - René Hägerling
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- BIH Biomedical Innovation Academy, Clinician Scientist Program, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- RG Development & Disease, Max Planck Institute for Molecular Genetics, Berlin, Germany
- Berlin Institute of Health, BIH Center for Regenerative Therapies, Berlin, Germany
| | - Alexej Knaus
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Germany
| | - Stefan Mundlos
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- RG Development & Disease, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Sarina Schwartzmann
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Dominik Seelow
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Nadja Ehmke
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Martin Atta Mensah
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- BIH Biomedical Innovation Academy, Digital Clinician Scientist Program, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Felix Boschann
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- BIH Biomedical Innovation Academy, Clinician Scientist Program, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Dieter Beule
- Berlin Institute of Health, Core Unit Bioinformatics, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Manuel Holtgrewe
- Berlin Institute of Health, Core Unit Bioinformatics, Berlin, Germany
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3
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Hauser BM, Luo Y, Nathan A, Al-Moujahed A, Vavvas DG, Comander J, Pierce EA, Place EM, Bujakowska KM, Gaiha GD, Rossin EJ. Structure-based network analysis predicts pathogenic variants in human proteins associated with inherited retinal disease. NPJ Genom Med 2024; 9:31. [PMID: 38802398 PMCID: PMC11130145 DOI: 10.1038/s41525-024-00416-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 05/02/2024] [Indexed: 05/29/2024] Open
Abstract
Advances in gene sequencing technologies have accelerated the identification of genetic variants, but better tools are needed to understand which are causal of disease. This would be particularly useful in fields where gene therapy is a potential therapeutic modality for a disease-causing variant such as inherited retinal disease (IRD). Here, we apply structure-based network analysis (SBNA), which has been successfully utilized to identify variant-constrained amino acid residues in viral proteins, to identify residues that may cause IRD if subject to missense mutation. SBNA is based entirely on structural first principles and is not fit to specific outcome data, which makes it distinct from other contemporary missense prediction tools. In 4 well-studied human disease-associated proteins (BRCA1, HRAS, PTEN, and ERK2) with high-quality structural data, we find that SBNA scores correlate strongly with deep mutagenesis data. When applied to 47 IRD genes with available high-quality crystal structure data, SBNA scores reliably identified disease-causing variants according to phenotype definitions from the ClinVar database. Finally, we applied this approach to 63 patients at Massachusetts Eye and Ear (MEE) with IRD but for whom no genetic cause had been identified. Untrained models built using SBNA scores and BLOSUM62 scores for IRD-associated genes successfully predicted the pathogenicity of novel variants (AUC = 0.851), allowing us to identify likely causative disease variants in 40 IRD patients. Model performance was further augmented by incorporating orthogonal data from EVE scores (AUC = 0.927), which are based on evolutionary multiple sequence alignments. In conclusion, SBNA can used to successfully identify variants as causal of disease in human proteins and may help predict variants causative of IRD in an unbiased fashion.
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Affiliation(s)
| | - Yuyang Luo
- Harvard Medical School, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
| | - Anusha Nathan
- Ragon Institute of Mass General, MIT, and Harvard, Cambridge, MA, USA
| | - Ahmad Al-Moujahed
- Harvard Medical School, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
| | - Demetrios G Vavvas
- Harvard Medical School, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
| | - Jason Comander
- Harvard Medical School, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
| | - Eric A Pierce
- Harvard Medical School, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
| | - Emily M Place
- Harvard Medical School, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
| | - Kinga M Bujakowska
- Harvard Medical School, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
| | - Gaurav D Gaiha
- Ragon Institute of Mass General, MIT, and Harvard, Cambridge, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA
| | - Elizabeth J Rossin
- Harvard Medical School, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA.
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4
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Schubach M, Maass T, Nazaretyan L, Röner S, Kircher M. CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions. Nucleic Acids Res 2024; 52:D1143-D1154. [PMID: 38183205 PMCID: PMC10767851 DOI: 10.1093/nar/gkad989] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/14/2023] [Accepted: 10/17/2023] [Indexed: 01/07/2024] Open
Abstract
Machine Learning-based scoring and classification of genetic variants aids the assessment of clinical findings and is employed to prioritize variants in diverse genetic studies and analyses. Combined Annotation-Dependent Depletion (CADD) is one of the first methods for the genome-wide prioritization of variants across different molecular functions and has been continuously developed and improved since its original publication. Here, we present our most recent release, CADD v1.7. We explored and integrated new annotation features, among them state-of-the-art protein language model scores (Meta ESM-1v), regulatory variant effect predictions (from sequence-based convolutional neural networks) and sequence conservation scores (Zoonomia). We evaluated the new version on data sets derived from ClinVar, ExAC/gnomAD and 1000 Genomes variants. For coding effects, we tested CADD on 31 Deep Mutational Scanning (DMS) data sets from ProteinGym and, for regulatory effect prediction, we used saturation mutagenesis reporter assay data of promoter and enhancer sequences. The inclusion of new features further improved the overall performance of CADD. As with previous releases, all data sets, genome-wide CADD v1.7 scores, scripts for on-site scoring and an easy-to-use webserver are readily provided via https://cadd.bihealth.org/ or https://cadd.gs.washington.edu/ to the community.
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Affiliation(s)
- Max Schubach
- Exploratory Diagnostic Sciences, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Thorben Maass
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck, Lübeck, Germany
| | - Lusiné Nazaretyan
- Exploratory Diagnostic Sciences, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Sebastian Röner
- Exploratory Diagnostic Sciences, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Martin Kircher
- Exploratory Diagnostic Sciences, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck, Lübeck, Germany
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5
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Raimondi D, Chizari H, Verplaetse N, Löscher BS, Franke A, Moreau Y. Genome interpretation in a federated learning context allows the multi-center exome-based risk prediction of Crohn's disease patients. Sci Rep 2023; 13:19449. [PMID: 37945674 PMCID: PMC10636050 DOI: 10.1038/s41598-023-46887-2] [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: 06/12/2023] [Accepted: 11/06/2023] [Indexed: 11/12/2023] Open
Abstract
High-throughput sequencing allowed the discovery of many disease variants, but nowadays it is becoming clear that the abundance of genomics data mostly just moved the bottleneck in Genetics and Precision Medicine from a data availability issue to a data interpretation issue. To solve this empasse it would be beneficial to apply the latest Deep Learning (DL) methods to the Genome Interpretation (GI) problem, similarly to what AlphaFold did for Structural Biology. Unfortunately DL requires large datasets to be viable, and aggregating genomics datasets poses several legal, ethical and infrastructural complications. Federated Learning (FL) is a Machine Learning (ML) paradigm designed to tackle these issues. It allows ML methods to be collaboratively trained and tested on collections of physically separate datasets, without requiring the actual centralization of sensitive data. FL could thus be key to enable DL applications to GI on sufficiently large genomics data. We propose FedCrohn, a FL GI Neural Network model for the exome-based Crohn's Disease risk prediction, providing a proof-of-concept that FL is a viable paradigm to build novel ML GI approaches. We benchmark it in several realistic scenarios, showing that FL can indeed provide performances similar to conventional ML on centralized data, and that collaborating in FL initiatives is likely beneficial for most of the medical centers participating in them.
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Affiliation(s)
| | | | | | - Britt-Sabina Löscher
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
- University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
- University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Yves Moreau
- ESAT-STADIUS, KU Leuven, 3001, Leuven, Belgium
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6
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Shirvanizadeh N, Vihinen M. VariBench, new variation benchmark categories and data sets. FRONTIERS IN BIOINFORMATICS 2023; 3:1248732. [PMID: 37795169 PMCID: PMC10546188 DOI: 10.3389/fbinf.2023.1248732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/08/2023] [Indexed: 10/06/2023] Open
Affiliation(s)
| | - Mauno Vihinen
- Department of Experimental Medical Science, Lund University, Lund, Sweden
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7
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Hauser BM, Luo Y, Nathan A, Gaiha GD, Vavvas D, Comander J, Pierce EA, Place EM, Bujakowska KM, Rossin EJ. Structure-based network analysis predicts mutations associated with inherited retinal disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.05.23292247. [PMID: 37461650 PMCID: PMC10350150 DOI: 10.1101/2023.07.05.23292247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
With continued advances in gene sequencing technologies comes the need to develop better tools to understand which mutations cause disease. Here we validate structure-based network analysis (SBNA)1,2 in well-studied human proteins and report results of using SBNA to identify critical amino acids that may cause retinal disease if subject to missense mutation. We computed SBNA scores for genes with high-quality structural data, starting with validating the method using 4 well-studied human disease-associated proteins. We then analyzed 47 inherited retinal disease (IRD) genes. We compared SBNA scores to phenotype data from the ClinVar database and found a significant difference between benign and pathogenic mutations with respect to network score. Finally, we applied this approach to 65 patients at Massachusetts Eye and Ear (MEE) who were diagnosed with IRD but for whom no genetic cause was found. Multivariable logistic regression models built using SBNA scores for IRD-associated genes successfully predicted pathogenicity of novel mutations, allowing us to identify likely causative disease variants in 37 patients with IRD from our clinic. In conclusion, SBNA can be meaningfully applied to human proteins and may help predict mutations causative of IRD.
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Affiliation(s)
| | - Yuyang Luo
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA
| | - Anusha Nathan
- Ragon Institute of Mass General, MIT, and Harvard, Cambridge, MA
| | - Gaurav D. Gaiha
- Ragon Institute of Mass General, MIT, and Harvard, Cambridge, MA
| | - Demetrios Vavvas
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA
| | - Jason Comander
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA
| | - Eric A. Pierce
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA
| | - Emily M. Place
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA
| | - Kinga M. Bujakowska
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA
| | - Elizabeth J. Rossin
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA
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8
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Carpentier M, Chomilier J. Analyses of Mutation Displacements from Homology Models. Methods Mol Biol 2023; 2627:195-210. [PMID: 36959449 DOI: 10.1007/978-1-0716-2974-1_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/25/2023]
Abstract
Evaluation of the structural perturbations introduced by a single amino acid mutation is the main issue for protein structural biology. We propose here to present some recent advances in methods, allowing the splitting of distortion between the actual substitution effect and the contribution of the local flexibility of the position where the mutation occurs. Its main drawback is the need of many structures with a single mutation in each of them. To bypass this difficulty, we propose to use molecular modeling tools, with several software enabling us to build a model from a template, given the sequence. As a proof of concept, we rely on a gold standard, the human lysozyme. Both wild-type and three mutant structures are available in the PDB. Two of these mutations result in amyloid fibril formation, and the last one is neutral. As a conclusion, irrespective of the algorithm used for modeling, side chain conformations at the site of mutation are reliable, although long-range effects are out of reach of these tools.
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Affiliation(s)
- Mathilde Carpentier
- Institut Systématique Evolution Biodiversité (ISYEB), Sorbonne Université, MNHN, CNRS, EPHE, Paris, France.
| | - Jacques Chomilier
- Sorbonne Université, BiBiP, IMPMC, UMR 7590, CNRS, MNHN, Paris, France
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9
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Raimondi D, Orlando G, Verplaetse N, Fariselli P, Moreau Y. Editorial: Towards genome interpretation: Computational methods to model the genotype-phenotype relationship. FRONTIERS IN BIOINFORMATICS 2022; 2:1098941. [PMID: 36530385 PMCID: PMC9749061 DOI: 10.3389/fbinf.2022.1098941] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 11/17/2022] [Indexed: 11/12/2023] Open
Affiliation(s)
| | | | | | - Piero Fariselli
- Department of Medical Sciences, University of Torino, Torino, Italy
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10
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Yang Y, Zhao J, Zeng L, Vihinen M. ProTstab2 for Prediction of Protein Thermal Stabilities. Int J Mol Sci 2022; 23:ijms231810798. [PMID: 36142711 PMCID: PMC9505338 DOI: 10.3390/ijms231810798] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/12/2022] [Accepted: 09/13/2022] [Indexed: 11/16/2022] Open
Abstract
The stability of proteins is an essential property that has several biological implications. Knowledge about protein stability is important in many ways, ranging from protein purification and structure determination to stability in cells and biotechnological applications. Experimental determination of thermal stabilities has been tedious and available data have been limited. The introduction of limited proteolysis and mass spectrometry approaches has facilitated more extensive cellular protein stability data production. We collected melting temperature information for 34,913 proteins and developed a machine learning predictor, ProTstab2, by utilizing a gradient boosting algorithm after testing seven algorithms. The method performance was assessed on a blind test data set and showed a Pearson correlation coefficient of 0.753 and root mean square error of 7.005. Comparison to previous methods indicated that ProTstab2 had superior performance. The method is fast, so it was applied to predict and compare the stabilities of all proteins in human, mouse, and zebrafish proteomes for which experimental data were not determined. The tool is freely available.
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Affiliation(s)
- Yang Yang
- School of Computer Science and Technology, Soochow University, Suzhou 215006, China
- Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210000, China
| | - Jianjun Zhao
- School of Computer Science and Technology, Soochow University, Suzhou 215006, China
| | - Lianjie Zeng
- School of Computer Science and Technology, Soochow University, Suzhou 215006, China
| | - Mauno Vihinen
- Department of Experimental Medical Science, BMC B13, Lund University, SE-22184 Lund, Sweden
- Correspondence:
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11
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Behrendt A, Golchin P, König F, Mulnaes D, Stalke A, Dröge C, Keitel V, Gohlke H. Vasor: Accurate prediction of variant effects for amino acid substitutions in multidrug resistance protein 3. Hepatol Commun 2022; 6:3098-3111. [PMID: 36111625 PMCID: PMC9592774 DOI: 10.1002/hep4.2088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 07/26/2022] [Accepted: 08/16/2022] [Indexed: 12/14/2022] Open
Abstract
The phosphatidylcholine floppase multidrug resistance protein 3 (MDR3) is an essential hepatobiliary transport protein. MDR3 dysfunction is associated with various liver diseases, ranging from severe progressive familial intrahepatic cholestasis to transient forms of intrahepatic cholestasis of pregnancy and familial gallstone disease. Single amino acid substitutions are often found as causative of dysfunction, but identifying the substitution effect in in vitro studies is time and cost intensive. We developed variant assessor of MDR3 (Vasor), a machine learning-based model to classify novel MDR3 missense variants into the categories benign or pathogenic. Vasor was trained on the largest data set to date that is specific for benign and pathogenic variants of MDR3 and uses general predictors, namely Evolutionary Models of Variant Effects (EVE), EVmutation, PolyPhen-2, I-Mutant2.0, MUpro, MAESTRO, and PON-P2 along with other variant properties, such as half-sphere exposure and posttranslational modification site, as input. Vasor consistently outperformed the integrated general predictors and the external prediction tool MutPred2, leading to the current best prediction performance for MDR3 single-site missense variants (on an external test set: F1-score, 0.90; Matthew's correlation coefficient, 0.80). Furthermore, Vasor predictions cover the entire sequence space of MDR3. Vasor is accessible as a webserver at https://cpclab.uni-duesseldorf.de/mdr3_predictor/ for users to rapidly obtain prediction results and a visualization of the substitution site within the MDR3 structure. The MDR3-specific prediction tool Vasor can provide reliable predictions of single-site amino acid substitutions, giving users a fast way to initially assess whether a variant is benign or pathogenic.
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Affiliation(s)
- Annika Behrendt
- Institute for Pharmaceutical and Medicinal ChemistryHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Pegah Golchin
- Department of Electrical Engineering and Information TechnologyTechnische Universität DarmstadtDarmstadtGermany
| | - Filip König
- Institute for Pharmaceutical and Medicinal ChemistryHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Daniel Mulnaes
- Institute for Pharmaceutical and Medicinal ChemistryHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Amelie Stalke
- Department of Human GeneticsHannover Medical SchoolHannoverGermany,Division of Kidney, Department of Pediatric Gastroenterology and Hepatology, Liver, and Metabolic DiseasesHannover Medical SchoolHannoverGermany
| | - Carola Dröge
- Department for Gastroenterology, Hepatology, and Infectious Diseases, Medical FacultyOtto von Guericke UniversityMagdeburgGermany,Department for Gastroenterology, Hepatology, and Infectious DiseasesUniversity Hospital, Medical FacultyHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Verena Keitel
- Department for Gastroenterology, Hepatology, and Infectious Diseases, Medical FacultyOtto von Guericke UniversityMagdeburgGermany,Department for Gastroenterology, Hepatology, and Infectious DiseasesUniversity Hospital, Medical FacultyHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Holger Gohlke
- Institute for Pharmaceutical and Medicinal ChemistryHeinrich Heine University DüsseldorfDüsseldorfGermany,John‐von‐Neumann‐Institute for Computing, Jülich Supercomputing Center, Institute of Biological Information Processing (IBI‐7: Structural Biochemistry), and Institute of Bio‐ and Geosciences (IBG‐4: Bioinformatics)Forschungszentrum Jülich GmbHJülichGermany
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12
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Vihinen M. Individual Genetic Heterogeneity. Genes (Basel) 2022; 13:1626. [PMID: 36140794 PMCID: PMC9498725 DOI: 10.3390/genes13091626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 08/25/2022] [Accepted: 09/08/2022] [Indexed: 11/28/2022] Open
Abstract
Genetic variation has been widely covered in literature, however, not from the perspective of an individual in any species. Here, a synthesis of genetic concepts and variations relevant for individual genetic constitution is provided. All the different levels of genetic information and variation are covered, ranging from whether an organism is unmixed or hybrid, has variations in genome, chromosomes, and more locally in DNA regions, to epigenetic variants or alterations in selfish genetic elements. Genetic constitution and heterogeneity of microbiota are highly relevant for health and wellbeing of an individual. Mutation rates vary widely for variation types, e.g., due to the sequence context. Genetic information guides numerous aspects in organisms. Types of inheritance, whether Mendelian or non-Mendelian, zygosity, sexual reproduction, and sex determination are covered. Functions of DNA and functional effects of variations are introduced, along with mechanism that reduce and modulate functional effects, including TARAR countermeasures and intraindividual genetic conflict. TARAR countermeasures for tolerance, avoidance, repair, attenuation, and resistance are essential for life, integrity of genetic information, and gene expression. The genetic composition, effects of variations, and their expression are considered also in diseases and personalized medicine. The text synthesizes knowledge and insight on individual genetic heterogeneity and organizes and systematizes the central concepts.
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Affiliation(s)
- Mauno Vihinen
- Department of Experimental Medical Science, BMC B13, Lund University, SE-22184 Lund, Sweden
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13
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A Comprehensive Evaluation of the Performance of Prediction Algorithms on Clinically Relevant Missense Variants. Int J Mol Sci 2022; 23:ijms23147946. [PMID: 35887294 PMCID: PMC9322961 DOI: 10.3390/ijms23147946] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/09/2022] [Accepted: 07/15/2022] [Indexed: 12/12/2022] Open
Abstract
The rapid integration of genomic technologies in clinical diagnostics has resulted in the detection of a multitude of missense variants whose clinical significance is often unknown. As a result, a plethora of computational tools have been developed to facilitate variant interpretation. However, choosing an appropriate software from such a broad range of tools can be challenging; therefore, systematic benchmarking with high-quality, independent datasets is critical. Using three independent benchmarking datasets compiled from the ClinVar database, we evaluated the performance of ten widely used prediction algorithms with missense variants from 21 clinically relevant genes, including BRCA1 and BRCA2. A fourth dataset consisting of 1053 missense variants was also used to investigate the impact of type 1 circularity on their performance. The performance of the prediction algorithms varied widely across datasets. Based on Matthews Correlation Coefficient and Area Under the Curve, SNPs&GO and PMut consistently displayed an overall above-average performance across the datasets. Most of the tools demonstrated greater sensitivity and negative predictive values at the expense of lower specificity and positive predictive values. We also demonstrated that type 1 circularity significantly impacts the performance of these tools and, if not accounted for, may confound the selection of the best performing algorithms.
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14
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Anderson CL, Munawar S, Reilly L, Kamp TJ, January CT, Delisle BP, Eckhardt LL. How Functional Genomics Can Keep Pace With VUS Identification. Front Cardiovasc Med 2022; 9:900431. [PMID: 35859585 PMCID: PMC9291992 DOI: 10.3389/fcvm.2022.900431] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 06/09/2022] [Indexed: 01/03/2023] Open
Abstract
Over the last two decades, an exponentially expanding number of genetic variants have been identified associated with inherited cardiac conditions. These tremendous gains also present challenges in deciphering the clinical relevance of unclassified variants or variants of uncertain significance (VUS). This review provides an overview of the advancements (and challenges) in functional and computational approaches to characterize variants and help keep pace with VUS identification related to inherited heart diseases.
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Affiliation(s)
- Corey L. Anderson
- Cellular and Molecular Arrythmias Program, Division of Cardiovascular Medicine, Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States
| | - Saba Munawar
- Cellular and Molecular Arrythmias Program, Division of Cardiovascular Medicine, Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States
| | - Louise Reilly
- Cellular and Molecular Arrythmias Program, Division of Cardiovascular Medicine, Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States
| | - Timothy J. Kamp
- Cellular and Molecular Arrythmias Program, Division of Cardiovascular Medicine, Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States
| | - Craig T. January
- Cellular and Molecular Arrythmias Program, Division of Cardiovascular Medicine, Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States
| | - Brian P. Delisle
- Department of Physiology, University of Kentucky College of Medicine, Lexington, KY, United States
| | - Lee L. Eckhardt
- Cellular and Molecular Arrythmias Program, Division of Cardiovascular Medicine, Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States
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15
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Yang Y, Shao A, Vihinen M. PON-All: Amino Acid Substitution Tolerance Predictor for All Organisms. Front Mol Biosci 2022; 9:867572. [PMID: 35782867 PMCID: PMC9245922 DOI: 10.3389/fmolb.2022.867572] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 05/02/2022] [Indexed: 01/08/2023] Open
Abstract
Genetic variations are investigated in human and many other organisms for many purposes (e.g., to aid in clinical diagnosis). Interpretation of the identified variations can be challenging. Although some dedicated prediction methods have been developed and some tools for human variants can also be used for other organisms, the performance and species range have been limited. We developed a novel variant pathogenicity/tolerance predictor for amino acid substitutions in any organism. The method, PON-All, is a machine learning tool trained on human, animal, and plant variants. Two versions are provided, one with Gene Ontology (GO) annotations and another without these details. GO annotations are not available or are partial for many organisms of interest. The methods provide predictions for three classes: pathogenic, benign, and variants of unknown significance. On the blind test, when using GO annotations, accuracy was 0.913 and MCC 0.827. When GO features were not used, accuracy was 0.856 and MCC 0.712. The performance is the best for human and plant variants and somewhat lower for animal variants because the number of known disease-causing variants in animals is rather small. The method was compared to several other tools and was found to have superior performance. PON-All is freely available at http://structure.bmc.lu.se/PON-All and http://8.133.174.28:8999/.
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Affiliation(s)
- Yang Yang
- School of Computer Science and Technology, Soochow University, Suzhou, China
- Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China
| | - Aibin Shao
- School of Computer Science and Technology, Soochow University, Suzhou, China
| | - Mauno Vihinen
- Department of Experimental Medical Science, Lund University, Lund, Sweden
- *Correspondence: Mauno Vihinen,
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16
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Li B, Jin B, Capra JA, Bush WS. Integration of Protein Structure and Population-Scale DNA Sequence Data for Disease Gene Discovery and Variant Interpretation. Annu Rev Biomed Data Sci 2022; 5:141-161. [PMID: 35508071 DOI: 10.1146/annurev-biodatasci-122220-112147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The experimental and computational techniques for capturing information about protein structures and genetic variation within the human genome have advanced dramatically in the past 20 years, generating extensive new data resources. In this review, we discuss these advances, along with new approaches for determining the impact a genetic variant has on protein function. We focus on the potential of new methods that integrate human genetic variation into protein structures to discover relationships to disease, including the discovery of mutational hotspots in cancer-related proteins, the localization of protein-altering variants within protein regions for common complex diseases, and the assessment of variants of unknown significance for Mendelian traits. We expect that approaches that integrate these data sources will play increasingly important roles in disease gene discovery and variant interpretation. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Bian Li
- Department of Biological Sciences and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, USA
| | - Bowen Jin
- Graduate Program in Systems Biology and Bioinformatics, Department of Nutrition, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
| | - John A Capra
- Bakar Computational Health Sciences Institute and Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA;
| | - William S Bush
- Cleveland Institute for Computational Biology, Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA;
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17
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Capriotti E, Fariselli P. Evaluating the relevance of sequence conservation in the prediction of pathogenic missense variants. Hum Genet 2022; 141:1649-1658. [DOI: 10.1007/s00439-021-02419-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 12/12/2021] [Indexed: 12/28/2022]
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18
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Baek KT, Kepp KP. Data set and fitting dependencies when estimating protein mutant stability: Toward simple, balanced, and interpretable models. J Comput Chem 2022; 43:504-518. [PMID: 35040492 DOI: 10.1002/jcc.26810] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 12/13/2021] [Accepted: 01/03/2022] [Indexed: 12/27/2022]
Abstract
Accurate prediction of protein stability changes upon mutation (ΔΔG) is increasingly important to evolution studies, protein engineering, and screening of disease-causing gene variants but is challenged by biases in training data. We investigated 45 linear regression models trained on data sets that account systematically for destabilization bias and mutation-type bias BM . The models were externally validated on three test data sets probing different pathologies and for internal consistency (symmetry and neutrality). Model structure and performance substantially depended on training data and even fitting method. We developed two final models: SimBa-IB for typical natural mutations and SimBa-SYM for situations where stabilizing and destabilizing mutations occur to a similar extent. SimBa-SYM, despite is simplicity, is essentially non-biased (vs. the Ssym data set) while still performing well for all data sets (R ~ 0.46-0.54, MAE = 1.16-1.24 kcal/mol). The simple models provide advantage in terms of interpretability, use and future improvement, and are freely available on GitHub.
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Affiliation(s)
| | - Kasper P Kepp
- DTU Chemistry, Technical University of Denmark, Lyngby, Denmark
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19
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Rajput N, Gahlay GK. Identification and in silico Characterization of Deleterious Single Nucleotide Variations in Human ZP2 Gene. Front Cell Dev Biol 2021; 9:763166. [PMID: 34869353 PMCID: PMC8635754 DOI: 10.3389/fcell.2021.763166] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 10/25/2021] [Indexed: 12/15/2022] Open
Abstract
ZP2, an important component of the zona matrix, surrounds mammalian oocytes and facilitates fertilization. Recently, some studies have documented the association of mutations in genes encoding the zona matrix with the infertile status of human females. Single nucleotide polymorphisms are the most common type of genetic variations observed in a population and as per the dbSNP database, around 5,152 SNPs are reported to exist in the human ZP2 (hZP2) gene. Although a wide range of computational tools are publicly available, yet no computational studies have been done to date to identify and analyze structural and functional effects of deleterious SNPs on hZP2. In this study, we conducted a comprehensive in silico analysis of all the SNPs found in hZP2. Six different computational tools including SIFT and PolyPhen-2 predicted 18 common nsSNPs as deleterious of which 12 were predicted to most likely affect the structure/functional properties. These were either present in the N-term region crucial for sperm-zona interaction or in the zona domain. 31 additional SNPs in both coding and non-coding regions were also identified. Interestingly, some of these SNPs have been found to be present in infertile females in some recent studies.
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Affiliation(s)
- Neha Rajput
- Department of Molecular Biology and Biochemistry, Guru Nanak Dev University, Amritsar, INDIA
| | - Gagandeep Kaur Gahlay
- Department of Molecular Biology and Biochemistry, Guru Nanak Dev University, Amritsar, INDIA
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20
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Raimondi D, Corso M, Fariselli P, Moreau Y. From genotype to phenotype in Arabidopsis thaliana: in-silico genome interpretation predicts 288 phenotypes from sequencing data. Nucleic Acids Res 2021; 50:e16. [PMID: 34792168 PMCID: PMC8860592 DOI: 10.1093/nar/gkab1099] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 10/06/2021] [Accepted: 10/22/2021] [Indexed: 01/09/2023] Open
Abstract
In many cases, the unprecedented availability of data provided by high-throughput sequencing has shifted the bottleneck from a data availability issue to a data interpretation issue, thus delaying the promised breakthroughs in genetics and precision medicine, for what concerns Human genetics, and phenotype prediction to improve plant adaptation to climate change and resistance to bioagressors, for what concerns plant sciences. In this paper, we propose a novel Genome Interpretation paradigm, which aims at directly modeling the genotype-to-phenotype relationship, and we focus on A. thaliana since it is the best studied model organism in plant genetics. Our model, called Galiana, is the first end-to-end Neural Network (NN) approach following the genomes in/phenotypes out paradigm and it is trained to predict 288 real-valued Arabidopsis thaliana phenotypes from Whole Genome sequencing data. We show that 75 of these phenotypes are predicted with a Pearson correlation ≥0.4, and are mostly related to flowering traits. We show that our end-to-end NN approach achieves better performances and larger phenotype coverage than models predicting single phenotypes from the GWAS-derived known associated genes. Galiana is also fully interpretable, thanks to the Saliency Maps gradient-based approaches. We followed this interpretation approach to identify 36 novel genes that are likely to be associated with flowering traits, finding evidence for 6 of them in the existing literature.
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Affiliation(s)
| | - Massimiliano Corso
- Institut Jean-Pierre Bourgin, Université Paris-Saclay, INRAE, AgroParisTech, 78000 Versailles, France
| | - Piero Fariselli
- Department of Medical Sciences, University of Torino, 10123 Torino, Italy
| | - Yves Moreau
- ESAT-STADIUS, KU Leuven, 3001 Leuven, Belgium
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21
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Collaco JM, Raraigh KS, Betz J, Aksit MA, Blau N, Brown J, Dietz HC, MacCarrick G, Nogee LM, Sheridan MB, Vernon HJ, Beaty TH, Louis TA, Cutting GR. Accurate assignment of disease liability to genetic variants using only population data. Genet Med 2021; 24:87-99. [PMID: 34906463 DOI: 10.1016/j.gim.2021.08.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 07/23/2021] [Accepted: 08/17/2021] [Indexed: 10/19/2022] Open
Abstract
PURPOSE The growing size of public variant repositories prompted us to test the accuracy of pathogenicity prediction of DNA variants using population data alone. METHODS Under the a priori assumption that the ratio of the prevalence of variants in healthy population vs that in affected populations form 2 distinct distributions (pathogenic and benign), we used a Bayesian method to assign probability to a variant belonging to either distribution. RESULTS The approach, termed Bayesian prevalence ratio (BayPR), accurately parsed 300 of 313 expertly curated CFTR variants: 284 of 296 pathogenic/likely pathogenic variants in 1 distribution and 16 of 17 benign/likely benign variants in another. BayPR produced an area under the receiver operating characteristic curve of 0.99 for 103 functionally confirmed missense CFTR variants, which is equal to or exceeds 10 commonly used algorithms (area under the receiver operating characteristic curve range = 0.54-0.99). Application of BayPR to expertly curated variants in 8 genes associated with 7 Mendelian conditions led to the assignment of a disease-causing probability of ≥80% to 1350 of 1374 (98.3%) pathogenic/likely pathogenic variants and of ≤20% to 22 of 23 (95.7%) benign/likely benign variants. CONCLUSION Irrespective of the variant type or functional effect, the BayPR approach provides probabilities of pathogenicity for DNA variants responsible for Mendelian disorders using only the variant counts in affected and unaffected population samples.
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Affiliation(s)
- Joseph M Collaco
- Eudowood Division of Pediatric Respiratory Sciences, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Karen S Raraigh
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Joshua Betz
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD
| | - Melis Atalar Aksit
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Nenad Blau
- Division of Metabolism, University Children's Hospital Zürich, Zürich, Switzerland
| | - Jordan Brown
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Harry C Dietz
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD; Howard Hughes Medical Institute, Chevy Chase, MD
| | - Gretchen MacCarrick
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Lawrence M Nogee
- Eudowood Neonatal Pulmonary Division, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Molly B Sheridan
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Hilary J Vernon
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Terri H Beaty
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD
| | - Thomas A Louis
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD
| | - Garry R Cutting
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD.
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22
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L-Type Calcium Channel: Predicting Pathogenic/Likely Pathogenic Status for Variants of Uncertain Clinical Significance. MEMBRANES 2021; 11:membranes11080599. [PMID: 34436362 PMCID: PMC8399957 DOI: 10.3390/membranes11080599] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/01/2021] [Accepted: 08/04/2021] [Indexed: 11/25/2022]
Abstract
(1) Background: Defects in gene CACNA1C, which encodes the pore-forming subunit of the human Cav1.2 channel (hCav1.2), are associated with cardiac disorders such as atrial fibrillation, long QT syndrome, conduction disorders, cardiomyopathies, and congenital heart defects. Clinical manifestations are known only for 12% of CACNA1C missense variants, which are listed in public databases. Bioinformatics approaches can be used to predict the pathogenic/likely pathogenic status for variants of uncertain clinical significance. Choosing a bioinformatics tool and pathogenicity threshold that are optimal for specific protein families increases the reliability of such predictions. (2) Methods and Results: We used databases ClinVar, Humsavar, gnomAD, and Ensembl to compose a dataset of pathogenic/likely pathogenic and benign variants of hCav1.2 and its 20 paralogues: voltage-gated sodium and calcium channels. We further tested the performance of sixteen in silico tools in predicting pathogenic variants. ClinPred demonstrated the best performance, followed by REVEL and MCap. In the subset of 309 uncharacterized variants of hCav1.2, ClinPred predicted the pathogenicity for 188 variants. Among these, 36 variants were also categorized as pathogenic/likely pathogenic in at least one paralogue of hCav1.2. (3) Conclusions: The bioinformatics tool ClinPred and the paralogue annotation method consensually predicted the pathogenic/likely pathogenic status for 36 uncharacterized variants of hCav1.2. An analogous approach can be used to classify missense variants of other calcium channels and novel variants of hCav1.2.
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23
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Petrosino M, Novak L, Pasquo A, Chiaraluce R, Turina P, Capriotti E, Consalvi V. Analysis and Interpretation of the Impact of Missense Variants in Cancer. Int J Mol Sci 2021; 22:ijms22115416. [PMID: 34063805 PMCID: PMC8196604 DOI: 10.3390/ijms22115416] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 05/03/2021] [Accepted: 05/17/2021] [Indexed: 01/10/2023] Open
Abstract
Large scale genome sequencing allowed the identification of a massive number of genetic variations, whose impact on human health is still unknown. In this review we analyze, by an in silico-based strategy, the impact of missense variants on cancer-related genes, whose effect on protein stability and function was experimentally determined. We collected a set of 164 variants from 11 proteins to analyze the impact of missense mutations at structural and functional levels, and to assess the performance of state-of-the-art methods (FoldX and Meta-SNP) for predicting protein stability change and pathogenicity. The result of our analysis shows that a combination of experimental data on protein stability and in silico pathogenicity predictions allowed the identification of a subset of variants with a high probability of having a deleterious phenotypic effect, as confirmed by the significant enrichment of the subset in variants annotated in the COSMIC database as putative cancer-driving variants. Our analysis suggests that the integration of experimental and computational approaches may contribute to evaluate the risk for complex disorders and develop more effective treatment strategies.
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Affiliation(s)
- Maria Petrosino
- Dipartimento Scienze Biochimiche “A. Rossi Fanelli”, Sapienza University of Rome, 00185 Roma, Italy; (M.P.); (L.N.); (R.C.)
| | - Leonore Novak
- Dipartimento Scienze Biochimiche “A. Rossi Fanelli”, Sapienza University of Rome, 00185 Roma, Italy; (M.P.); (L.N.); (R.C.)
| | - Alessandra Pasquo
- ENEA CR Frascati, Diagnostics and Metrology Laboratory FSN-TECFIS-DIM, 00044 Frascati, Italy;
| | - Roberta Chiaraluce
- Dipartimento Scienze Biochimiche “A. Rossi Fanelli”, Sapienza University of Rome, 00185 Roma, Italy; (M.P.); (L.N.); (R.C.)
| | - Paola Turina
- Dipartimento di Farmacia e Biotecnologie (FaBiT), University of Bologna, 40126 Bologna, Italy;
| | - Emidio Capriotti
- Dipartimento di Farmacia e Biotecnologie (FaBiT), University of Bologna, 40126 Bologna, Italy;
- Correspondence: (E.C.); (V.C.)
| | - Valerio Consalvi
- Dipartimento Scienze Biochimiche “A. Rossi Fanelli”, Sapienza University of Rome, 00185 Roma, Italy; (M.P.); (L.N.); (R.C.)
- Correspondence: (E.C.); (V.C.)
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24
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Wilson CJ, Chang M, Karttunen M, Choy WY. KEAP1 Cancer Mutants: A Large-Scale Molecular Dynamics Study of Protein Stability. Int J Mol Sci 2021; 22:5408. [PMID: 34065616 PMCID: PMC8161161 DOI: 10.3390/ijms22105408] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 05/11/2021] [Accepted: 05/13/2021] [Indexed: 12/30/2022] Open
Abstract
We have performed 280 μs of unbiased molecular dynamics (MD) simulations to investigate the effects of 12 different cancer mutations on Kelch-like ECH-associated protein 1 (KEAP1) (G333C, G350S, G364C, G379D, R413L, R415G, A427V, G430C, R470C, R470H, R470S and G476R), one of the frequently mutated proteins in lung cancer. The aim was to provide structural insight into the effects of these mutants, including a new class of ANCHOR (additionally NRF2-complexed hypomorph) mutant variants. Our work provides additional insight into the structural dynamics of mutants that could not be analyzed experimentally, painting a more complete picture of their mutagenic effects. Notably, blade-wise analysis of the Kelch domain points to stability as a possible target of cancer in KEAP1. Interestingly, structural analysis of the R470C ANCHOR mutant, the most prevalent missense mutation in KEAP1, revealed no significant change in structural stability or NRF2 binding site dynamics, possibly indicating an covalent modification as this mutant's mode of action.
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Affiliation(s)
- Carter J. Wilson
- Department of Biochemistry, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5C1, Canada; (C.J.W.); (M.C.)
- Department of Applied Mathematics, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
| | - Megan Chang
- Department of Biochemistry, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5C1, Canada; (C.J.W.); (M.C.)
| | - Mikko Karttunen
- Department of Applied Mathematics, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
- Department of Chemistry, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 3K7, Canada
- Centre for Advanced Materials and Biomaterials Research, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
| | - Wing-Yiu Choy
- Department of Biochemistry, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5C1, Canada; (C.J.W.); (M.C.)
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25
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Savojardo C, Babbi G, Martelli PL, Casadio R. Mapping OMIM Disease-Related Variations on Protein Domains Reveals an Association Among Variation Type, Pfam Models, and Disease Classes. Front Mol Biosci 2021; 8:617016. [PMID: 34026820 PMCID: PMC8138129 DOI: 10.3389/fmolb.2021.617016] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 04/09/2021] [Indexed: 12/23/2022] Open
Abstract
Human genome resequencing projects provide an unprecedented amount of data about single-nucleotide variations occurring in protein-coding regions and often leading to observable changes in the covalent structure of gene products. For many of these variations, links to Online Mendelian Inheritance in Man (OMIM) genetic diseases are available and are reported in many databases that are collecting human variation data such as Humsavar. However, the current knowledge on the molecular mechanisms that are leading to diseases is, in many cases, still limited. For understanding the complex mechanisms behind disease insurgence, the identification of putative models, when considering the protein structure and chemico-physical features of the variations, can be useful in many contexts, including early diagnosis and prognosis. In this study, we investigate the occurrence and distribution of human disease–related variations in the context of Pfam domains. The aim of this study is the identification and characterization of Pfam domains that are statistically more likely to be associated with disease-related variations. The study takes into consideration 2,513 human protein sequences with 22,763 disease-related variations. We describe patterns of disease-related variation types in biunivocal relation with Pfam domains, which are likely to be possible markers for linking Pfam domains to OMIM diseases. Furthermore, we take advantage of the specific association between disease-related variation types and Pfam domains for clustering diseases according to the Human Disease Ontology, and we establish a relation among variation types, Pfam domains, and disease classes. We find that Pfam models are specific markers of patterns of variation types and that they can serve to bridge genes, diseases, and disease classes. Data are available as Supplementary Material for 1,670 Pfam models, including 22,763 disease-related variations associated to 3,257 OMIM diseases.
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Affiliation(s)
- Castrense Savojardo
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Giulia Babbi
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Pier Luigi Martelli
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Rita Casadio
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.,Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council, Bari, Italy
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26
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Zhou JB, Xiong Y, An K, Ye ZQ, Wu YD. IDRMutPred: predicting disease-associated germline nonsynonymous single nucleotide variants (nsSNVs) in intrinsically disordered regions. Bioinformatics 2021; 36:4977-4983. [PMID: 32756939 PMCID: PMC7755418 DOI: 10.1093/bioinformatics/btaa618] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 06/28/2020] [Accepted: 07/01/2020] [Indexed: 01/09/2023] Open
Abstract
Motivation Despite of the lack of folded structure, intrinsically disordered regions (IDRs) of proteins play versatile roles in various biological processes, and many nonsynonymous single nucleotide variants (nsSNVs) in IDRs are associated with human diseases. The continuous accumulation of nsSNVs resulted from the wide application of NGS has driven the development of disease-association prediction methods for decades. However, their performance on nsSNVs in IDRs remains inferior, possibly due to the domination of nsSNVs from structured regions in training data. Therefore, it is highly demanding to build a disease-association predictor specifically for nsSNVs in IDRs with better performance. Results We present IDRMutPred, a machine learning-based tool specifically for predicting disease-associated germline nsSNVs in IDRs. Based on 17 selected optimal features that are extracted from sequence alignments, protein annotations, hydrophobicity indices and disorder scores, IDRMutPred was trained using three ensemble learning algorithms on the training dataset containing only IDR nsSNVs. The evaluation on the two testing datasets shows that all the three prediction models outperform 17 other popular general predictors significantly, achieving the ACC between 0.856 and 0.868 and MCC between 0.713 and 0.737. IDRMutPred will prioritize disease-associated IDR germline nsSNVs more reliably than general predictors. Availability and implementation The software is freely available at http://www.wdspdb.com/IDRMutPred. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jing-Bo Zhou
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Yao Xiong
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Ke An
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Zhi-Qiang Ye
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China.,Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Yun-Dong Wu
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China.,Shenzhen Bay Laboratory, Shenzhen 518055, China.,College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
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27
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Pei J, Grishin NV. The DBSAV Database: Predicting Deleteriousness of Single Amino Acid Variations in the Human Proteome. J Mol Biol 2021; 433:166915. [PMID: 33676930 DOI: 10.1016/j.jmb.2021.166915] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 02/28/2021] [Accepted: 03/01/2021] [Indexed: 12/22/2022]
Abstract
Deleterious single amino acid variation (SAV) is one of the leading causes of human diseases. Evaluating the functional impact of SAVs is crucial for diagnosis of genetic disorders. We previously developed a deep convolutional neural network predictor, DeepSAV, to evaluate the deleterious effects of SAVs on protein function based on various sequence, structural, and functional properties. DeepSAV scores of rare SAVs observed in the human population are aggregated into a gene-level score called GTS (Gene Tolerance of rare SAVs) that reflects a gene's tolerance to deleterious missense mutations and serves as a useful tool to study gene-disease associations. In this study, we aim to enhance the performance of DeepSAV by using expanded datasets of pathogenic and benign variants, more features, and neural network optimization. We found that multiple sequence alignments built from vertebrate-level orthologs yield better prediction results compared to those built from mammalian-level orthologs. For multiple sequence alignments built from BLAST searches, optimal performance was achieved with a sequence identify cutoff of 50% to remove distant homologs. The new version of DeepSAV exhibits the best performance among standalone predictors of deleterious effects of SAVs. We developed the DBSAV database (http://prodata.swmed.edu/DBSAV) that reports GTS scores of human genes and DeepSAV scores of SAVs in the human proteome, including pathogenic and benign SAVs, population-level SAVs, and all possible SAVs by single nucleotide variations. This database serves as a useful resource for research of human SAVs and their relationships with protein functions and human diseases.
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Affiliation(s)
- Jimin Pei
- Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Nick V Grishin
- Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; Departments of Biophysics and Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
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28
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Garcia-Recio A, Gómez-Tamayo JC, Reina I, Campillo M, Cordomí A, Olivella M. TMSNP: a web server to predict pathogenesis of missense mutations in the transmembrane region of membrane proteins. NAR Genom Bioinform 2021; 3:lqab008. [PMID: 33655207 PMCID: PMC7902201 DOI: 10.1093/nargab/lqab008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 12/16/2020] [Accepted: 01/27/2021] [Indexed: 02/06/2023] Open
Abstract
The massive amount of data generated from genome sequencing brings tons of newly
identified mutations, whose pathogenic/non-pathogenic effects need to be
evaluated. This has given rise to several mutation predictor tools that, in
general, do not consider the specificities of the various protein groups. We
aimed to develop a predictor tool dedicated to membrane proteins, under the
premise that their specific structural features and environment would give
different responses to mutations compared to globular proteins. For this
purpose, we created TMSNP, a database that currently contains information from
2624 pathogenic and 196 705 non-pathogenic reported mutations located in
the transmembrane region of membrane proteins. By computing various conservation
parameters on these mutations in combination with annotations, we trained a
machine-learning model able to classify mutations as pathogenic or not. TMSNP
(freely available at http://lmc.uab.es/tmsnp/)
improves considerably the prediction power of commonly used mutation predictors
trained with globular proteins.
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Affiliation(s)
- Adrián Garcia-Recio
- Laboratori de Medicina Computacional, Facultat de Medicina, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
| | - José Carlos Gómez-Tamayo
- Pharmacoinformatics Group, Research Program on Biomedical Informatics (IMIM/UPF), 08003 Barcelona, Spain
| | - Iker Reina
- Laboratori de Medicina Computacional, Facultat de Medicina, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
| | - Mercedes Campillo
- Laboratori de Medicina Computacional, Facultat de Medicina, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
| | - Arnau Cordomí
- Laboratori de Medicina Computacional, Facultat de Medicina, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
| | - Mireia Olivella
- Bioinformatics and Medical Statistics Group, Facultat de Ciències i Tecnologia, UVIC-UCC, 08500 Vic, Barcelona, Spain
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Caldararu O, Blundell TL, Kepp KP. A base measure of precision for protein stability predictors: structural sensitivity. BMC Bioinformatics 2021; 22:88. [PMID: 33632133 PMCID: PMC7908712 DOI: 10.1186/s12859-021-04030-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 02/15/2021] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Prediction of the change in fold stability (ΔΔG) of a protein upon mutation is of major importance to protein engineering and screening of disease-causing variants. Many prediction methods can use 3D structural information to predict ΔΔG. While the performance of these methods has been extensively studied, a new problem has arisen due to the abundance of crystal structures: How precise are these methods in terms of structure input used, which structure should be used, and how much does it matter? Thus, there is a need to quantify the structural sensitivity of protein stability prediction methods. RESULTS We computed the structural sensitivity of six widely-used prediction methods by use of saturated computational mutagenesis on a diverse set of 87 structures of 25 proteins. Our results show that structural sensitivity varies massively and surprisingly falls into two very distinct groups, with methods that take detailed account of the local environment showing a sensitivity of ~ 0.6 to 0.8 kcal/mol, whereas machine-learning methods display much lower sensitivity (~ 0.1 kcal/mol). We also observe that the precision correlates with the accuracy for mutation-type-balanced data sets but not generally reported accuracy of the methods, indicating the importance of mutation-type balance in both contexts. CONCLUSIONS The structural sensitivity of stability prediction methods varies greatly and is caused mainly by the models and less by the actual protein structural differences. As a new recommended standard, we therefore suggest that ΔΔG values are evaluated on three protein structures when available and the associated standard deviation reported, to emphasize not just the accuracy but also the precision of the method in a specific study. Our observation that machine-learning methods deemphasize structure may indicate that folded wild-type structures alone, without the folded mutant and unfolded structures, only add modest value for assessing protein stability effects, and that side-chain-sensitive methods overstate the significance of the folded wild-type structure.
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Affiliation(s)
- Octav Caldararu
- DTU Chemistry, Technical University of Denmark, Building 206, 2800, Kgs. Lyngby, Denmark
| | - Tom L Blundell
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK
| | - Kasper P Kepp
- DTU Chemistry, Technical University of Denmark, Building 206, 2800, Kgs. Lyngby, Denmark.
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30
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Gemović B, Perović V, Davidović R, Drljača T, Veljkovic N. Alignment-free method for functional annotation of amino acid substitutions: Application on epigenetic factors involved in hematologic malignancies. PLoS One 2021; 16:e0244948. [PMID: 33395407 PMCID: PMC7781373 DOI: 10.1371/journal.pone.0244948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 12/21/2020] [Indexed: 11/19/2022] Open
Abstract
For the last couple of decades, there has been a significant growth in sequencing data, leading to an extraordinary increase in the number of gene variants. This places a challenge on the bioinformatics research community to develop and improve computational tools for functional annotation of new variants. Genes coding for epigenetic regulators have important roles in cancer pathogenesis and mutations in these genes show great potential as clinical biomarkers, especially in hematologic malignancies. Therefore, we developed a model that specifically focuses on these genes, with an assumption that it would outperform general models in predicting the functional effects of amino acid substitutions. EpiMut is a standalone software that implements a sequence based alignment-free method. We applied a two-step approach for generating sequence based features, relying on the biophysical and biochemical indices of amino acids and the Fourier Transform as a sequence transformation method. For each gene in the dataset, the machine learning algorithm-Naïve Bayes was used for building a model for prediction of the neutral or disease-related status of variants. EpiMut outperformed state-of-the-art tools used for comparison, PolyPhen-2, SIFT and SNAP2. Additionally, EpiMut showed the highest performance on the subset of variants positioned outside conserved functional domains of analysed proteins, which represents an important group of cancer-related variants. These results imply that EpiMut can be applied as a first choice tool in research of the impact of gene variants in epigenetic regulators, especially in the light of the biomarker role in hematologic malignancies. EpiMut is freely available at https://www.vin.bg.ac.rs/180/tools/epimut.php.
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Affiliation(s)
- Branislava Gemović
- Laboratory for Bioinformatics and Computational Chemistry, Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, Belgrade, Serbia
- * E-mail:
| | - Vladimir Perović
- Laboratory for Bioinformatics and Computational Chemistry, Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, Belgrade, Serbia
| | - Radoslav Davidović
- Laboratory for Bioinformatics and Computational Chemistry, Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, Belgrade, Serbia
| | - Tamara Drljača
- Laboratory for Bioinformatics and Computational Chemistry, Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, Belgrade, Serbia
| | - Nevena Veljkovic
- Laboratory for Bioinformatics and Computational Chemistry, Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, Belgrade, Serbia
- Heliant d.o.o., Belgrade, Serbia
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31
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Dong Y, Wang J, Zhu S, Zheng H, Wang C, Zhao P. Clinical profiles and diagnostic challenges in 1158 children with rare hepatobiliary disorders. Pediatr Res 2021; 89:238-245. [PMID: 32289814 DOI: 10.1038/s41390-020-0888-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 03/19/2020] [Indexed: 11/09/2022]
Abstract
BACKGROUND Diagnosis of rare diseases possesses a great challenge in pediatric hepatology because expert knowledge in the field is extremely insufficient. The study aims to explore new findings and collect diagnostic experience from pediatric rare liver diseases. METHODS The large-sample case analysis study included pediatric patients who had liver-involved rare diseases. All cases underwent liver biopsy and/or gene sequencing. RESULTS A total of 1158 pediatric patients were identified. Liver-based genetic diseases were most frequent (737 cases), followed by liver damages involved in extrahepatic or systemic disorders (151 cases) and cryptogenic hepatobilliary abnormalities (123 cases). Of note, diagnoses of 16 patients were re-evaluated according to genetic results combined with clinical pointers. In addition, 101 patients who underwent gene sequencing remained undiagnosed. Of them, 55 had negative genetic findings, 30 harbored mutations that failed to meet their typically pathogenic condition, and 16 had detected variants that were inconsistent with clinical pointers. CONCLUSIONS As a study involving known largest number of children with rare hepatobiliary disorders, it allows us to accumulate information (especially new findings) on the etiology and diagnosis of these disorders. The results can help to improve the diagnostic quality in the population. IMPACT Liver-based genetic diseases were most frequent in clinical profiles of pediatric rare liver diseases. Some novel variants in cases with genetic diseases (for example, two variants of c.3638G>T and c.1435G>C in a patient with progressive familial intrahepatic cholestasis type 2) were identified. As a study involving known largest number of pediatric cases with rare hepatobiliary disorders, it allows us to accumulate information on the etiology and diagnosis of these disorders. The study can help to optimize the diagnostic process and significantly improve the diagnostic quality in the field of pediatric hepatology. Given that clinical variability often exists within rare genetic disease entities and not all rare disorders are genetic, clinicians should not over-depend on the genetic results in the diagnosis.
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Affiliation(s)
- Yi Dong
- The Fifth Medical Center (formerly Beijing 302 Hospital), Chinese PLA General Hospital, 100039, Beijing, China
| | - Jian Wang
- Beijing Institute of Genomics, Chinese Academy of Sciences, 100101, Beijing, China
| | - Shishu Zhu
- The Fifth Medical Center (formerly Beijing 302 Hospital), Chinese PLA General Hospital, 100039, Beijing, China
| | - Huanwei Zheng
- The Fifth Hospital of Shijiazhuang, 050021, Shijiazhuang, Hebei Province, China
| | - Chunya Wang
- Beijing Anzhen Hospital, Capital Medical University, 100029, Beijing, China
| | - Pan Zhao
- The Fifth Medical Center (formerly Beijing 302 Hospital), Chinese PLA General Hospital, 100039, Beijing, China.
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32
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Pejaver V, Urresti J, Lugo-Martinez J, Pagel KA, Lin GN, Nam HJ, Mort M, Cooper DN, Sebat J, Iakoucheva LM, Mooney SD, Radivojac P. Inferring the molecular and phenotypic impact of amino acid variants with MutPred2. Nat Commun 2020; 11:5918. [PMID: 33219223 PMCID: PMC7680112 DOI: 10.1038/s41467-020-19669-x] [Citation(s) in RCA: 280] [Impact Index Per Article: 70.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Accepted: 10/23/2020] [Indexed: 01/02/2023] Open
Abstract
Identifying pathogenic variants and underlying functional alterations is challenging. To this end, we introduce MutPred2, a tool that improves the prioritization of pathogenic amino acid substitutions over existing methods, generates molecular mechanisms potentially causative of disease, and returns interpretable pathogenicity score distributions on individual genomes. Whilst its prioritization performance is state-of-the-art, a distinguishing feature of MutPred2 is the probabilistic modeling of variant impact on specific aspects of protein structure and function that can serve to guide experimental studies of phenotype-altering variants. We demonstrate the utility of MutPred2 in the identification of the structural and functional mutational signatures relevant to Mendelian disorders and the prioritization of de novo mutations associated with complex neurodevelopmental disorders. We then experimentally validate the functional impact of several variants identified in patients with such disorders. We argue that mechanism-driven studies of human inherited disease have the potential to significantly accelerate the discovery of clinically actionable variants.
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Affiliation(s)
- Vikas Pejaver
- Department of Computer Science, Indiana University, Bloomington, IN, USA
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Jorge Urresti
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Jose Lugo-Martinez
- Department of Computer Science, Indiana University, Bloomington, IN, USA
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA
| | - Kymberleigh A Pagel
- Department of Computer Science, Indiana University, Bloomington, IN, USA
- Institute for Computational Medicine, Whiting School of Engineering, Johns Hopkins University, 220 Hackerman Hall, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Guan Ning Lin
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, People's Republic of China
| | - Hyun-Jun Nam
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Matthew Mort
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK
| | - David N Cooper
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK
| | - Jonathan Sebat
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Beyster Center for Genomics of Psychiatric Diseases, University of California San Diego, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Lilia M Iakoucheva
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
| | - Sean D Mooney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.
| | - Predrag Radivojac
- Department of Computer Science, Indiana University, Bloomington, IN, USA.
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA.
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Sarkar A, Yang Y, Vihinen M. Variation benchmark datasets: update, criteria, quality and applications. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2020:5710862. [PMID: 32016318 PMCID: PMC6997940 DOI: 10.1093/database/baz117] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 06/03/2019] [Accepted: 07/01/2019] [Indexed: 02/07/2023]
Abstract
Development of new computational methods and testing their performance has to be carried out using experimental data. Only in comparison to existing knowledge can method performance be assessed. For that purpose, benchmark datasets with known and verified outcome are needed. High-quality benchmark datasets are valuable and may be difficult, laborious and time consuming to generate. VariBench and VariSNP are the two existing databases for sharing variation benchmark datasets used mainly for variation interpretation. They have been used for training and benchmarking predictors for various types of variations and their effects. VariBench was updated with 419 new datasets from 109 papers containing altogether 329 014 152 variants; however, there is plenty of redundancy between the datasets. VariBench is freely available at http://structure.bmc.lu.se/VariBench/. The contents of the datasets vary depending on information in the original source. The available datasets have been categorized into 20 groups and subgroups. There are datasets for insertions and deletions, substitutions in coding and non-coding region, structure mapped, synonymous and benign variants. Effect-specific datasets include DNA regulatory elements, RNA splicing, and protein property for aggregation, binding free energy, disorder and stability. Then there are several datasets for molecule-specific and disease-specific applications, as well as one dataset for variation phenotype effects. Variants are often described at three molecular levels (DNA, RNA and protein) and sometimes also at the protein structural level including relevant cross references and variant descriptions. The updated VariBench facilitates development and testing of new methods and comparison of obtained performances to previously published methods. We compared the performance of the pathogenicity/tolerance predictor PON-P2 to several benchmark studies, and show that such comparisons are feasible and useful, however, there may be limitations due to lack of provided details and shared data. Database URL: http://structure.bmc.lu.se/VariBench
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Affiliation(s)
- Anasua Sarkar
- Department of Experimental Medical Science, BMC B13, Lund University, SE-22 184 Lund, Sweden
| | - Yang Yang
- School of Computer Science and Technology, Soochow University, No1. Shizi Street, Suzhou, 215006 Jiangsu, China.,Provincial Key Laboratory for Computer Information Processing Technology, No1. Shizi Street, Soochow University, Suzhou, 215006 Jiangsu, China
| | - Mauno Vihinen
- Department of Experimental Medical Science, BMC B13, Lund University, SE-22 184 Lund, Sweden
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34
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Vihinen M. Functional effects of protein variants. Biochimie 2020; 180:104-120. [PMID: 33164889 DOI: 10.1016/j.biochi.2020.10.009] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 10/15/2020] [Accepted: 10/19/2020] [Indexed: 12/11/2022]
Abstract
Genetic and other variations frequently affect protein functions. Scientific articles can contain confusing descriptions about which function or property is affected, and in many cases the statements are pure speculation without any experimental evidence. To clarify functional effects of protein variations of genetic or non-genetic origin, a systematic conceptualisation and framework are introduced. This framework describes protein functional effects on abundance, activity, specificity and affinity, along with countermeasures, which allow cells, tissues and organisms to tolerate, avoid, repair, attenuate or resist (TARAR) the effects. Effects on abundance discussed include gene dosage, restricted expression, mis-localisation and degradation. Enzymopathies, effects on kinetics, allostery and regulation of protein activity are subtopics for the effects of variants on activity. Variation outcomes on specificity and affinity comprise promiscuity, specificity, affinity and moonlighting. TARAR mechanisms redress variations with active and passive processes including chaperones, redundancy, robustness, canalisation and metabolic and signalling rewiring. A framework for pragmatic protein function analysis and presentation is introduced. All of the mechanisms and effects are described along with representative examples, most often in relation to diseases. In addition, protein function is discussed from evolutionary point of view. Application of the presented framework facilitates unambiguous, detailed and specific description of functional effects and their systematic study.
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Affiliation(s)
- Mauno Vihinen
- Department of Experimental Medical Science, BMC B13, Lund University, SE-22 184, Lund, Sweden.
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35
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Gerasimavicius L, Liu X, Marsh JA. Identification of pathogenic missense mutations using protein stability predictors. Sci Rep 2020; 10:15387. [PMID: 32958805 PMCID: PMC7506547 DOI: 10.1038/s41598-020-72404-w] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 08/31/2020] [Indexed: 12/17/2022] Open
Abstract
Attempts at using protein structures to identify disease-causing mutations have been dominated by the idea that most pathogenic mutations are disruptive at a structural level. Therefore, computational stability predictors, which assess whether a mutation is likely to be stabilising or destabilising to protein structure, have been commonly used when evaluating new candidate disease variants, despite not having been developed specifically for this purpose. We therefore tested 13 different stability predictors for their ability to discriminate between pathogenic and putatively benign missense variants. We find that one method, FoldX, significantly outperforms all other predictors in the identification of disease variants. Moreover, we demonstrate that employing predicted absolute energy change scores improves performance of nearly all predictors in distinguishing pathogenic from benign variants. Importantly, however, we observe that the utility of computational stability predictors is highly heterogeneous across different proteins, and that they are all inferior to the best performing variant effect predictors for identifying pathogenic mutations. We suggest that this is largely due to alternate molecular mechanisms other than protein destabilisation underlying many pathogenic mutations. Thus, better ways of incorporating protein structural information and molecular mechanisms into computational variant effect predictors will be required for improved disease variant prioritisation.
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Affiliation(s)
- Lukas Gerasimavicius
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Xin Liu
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Joseph A Marsh
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.
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36
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Andersson NG, Labarque V, Letelier A, Mancuso ME, Bührlen M, Fischer K, Kartal-Kaess M, Koskenvuo M, Mikkelsen T, Ljung R. Novel F8 and F9 gene variants from the PedNet hemophilia registry classified according to ACMG/AMP guidelines. Hum Mutat 2020; 41:2058-2072. [PMID: 32935414 PMCID: PMC7756260 DOI: 10.1002/humu.24117] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 08/21/2020] [Accepted: 09/07/2020] [Indexed: 12/21/2022]
Abstract
In hemophilia A and B, analysis of the F8 and F9 gene variants enables carrier and prenatal diagnosis and prediction of risk for the development of inhibitors. The PedNet Registry collects clinical, genetic, and phenotypic data prospectively on more than 2000 children with hemophilia. The genetic reports of F8/F9 gene variants were classified uniformly to Human Genome Variation Society nomenclature and reevaluated using international population‐ and disease‐specific databases, literature survey and, where applicable, computational predictive programs. We report 88 novel variants in the F8 and F9 genes, 80 fulfilling criteria for Class 5 (pathogenic), six for Class 4 (likely pathogenic) and two fulfilling criteria for Class 3 (variant of unknown significance) of the American College of Medical Genetics and Genomics/Association for Molecular Pathologyguidelines together with information on the respective phenotype and inhibitor formation. The study highlights the need to reevaluate and update earlier genetic reports in hemophilia both locally but also in variant databases in light of changed nomenclature and new guidelines.
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Affiliation(s)
- Nadine G Andersson
- Department of Clinical Sciences and Paediatrics, Lund University, Lund, Sweden.,Centre for Thrombosis and Haemostasis, Skåne University Hospital, Lund, Sweden.,Department for Paediatric Haematology and Oncology, Skåne University Hospital, Lund, Sweden
| | - Veerle Labarque
- Department of Pediatrics, Pediatric Hemato-Oncology, University Hospitals Leuven, Leuven, Belgium
| | - Anna Letelier
- Department of Clinical Sciences and Paediatrics, Lund University, Lund, Sweden
| | | | - Martina Bührlen
- Klinikum Bremen-Mitte, Professor-Hess Children Hospital, Bremen, Germany
| | - Kathelijn Fischer
- Van Creveld Kliniek, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Mutlu Kartal-Kaess
- Division of Pediatric Hematology and Oncology, Department of Pediatrics, Inselspital, University Hospital, University of Bern, Bern, Switzerland
| | - Minna Koskenvuo
- Division of Pediatric Hematology, Oncology, and Stem Cell Transplantation, New Children's Hospital, Helsinki University Hospital, Helsinki, Finland
| | - Torben Mikkelsen
- Department of Pediatric Oncology and Hematology, University Hospital, Aarhus, Denmark
| | - Rolf Ljung
- Department of Clinical Sciences and Paediatrics, Lund University, Lund, Sweden
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Pei J, Kinch LN, Otwinowski Z, Grishin NV. Mutation severity spectrum of rare alleles in the human genome is predictive of disease type. PLoS Comput Biol 2020; 16:e1007775. [PMID: 32413045 PMCID: PMC7255613 DOI: 10.1371/journal.pcbi.1007775] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 05/28/2020] [Accepted: 03/06/2020] [Indexed: 12/19/2022] Open
Abstract
The human genome harbors a variety of genetic variations. Single-nucleotide changes that alter amino acids in protein-coding regions are one of the major causes of human phenotypic variation and diseases. These single-amino acid variations (SAVs) are routinely found in whole genome and exome sequencing. Evaluating the functional impact of such genomic alterations is crucial for diagnosis of genetic disorders. We developed DeepSAV, a deep-learning convolutional neural network to differentiate disease-causing and benign SAVs based on a variety of protein sequence, structural and functional properties. Our method outperforms most stand-alone programs, and the version incorporating population and gene-level information (DeepSAV+PG) has similar predictive power as some of the best available. We transformed DeepSAV scores of rare SAVs in the human population into a quantity termed "mutation severity measure" for each human protein-coding gene. It reflects a gene's tolerance to deleterious missense mutations and serves as a useful tool to study gene-disease associations. Genes implicated in cancer, autism, and viral interaction are found by this measure as intolerant to mutations, while genes associated with a number of other diseases are scored as tolerant. Among known disease-associated genes, those that are mutation-intolerant are likely to function in development and signal transduction pathways, while those that are mutation-tolerant tend to encode metabolic and mitochondrial proteins.
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Affiliation(s)
- Jimin Pei
- Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Lisa N. Kinch
- Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Zbyszek Otwinowski
- Departments of Biophysics and Biochemistry, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Nick V. Grishin
- Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
- Departments of Biophysics and Biochemistry, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
- * E-mail:
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Capriotti E, Montanucci L, Profiti G, Rossi I, Giannuzzi D, Aresu L, Fariselli P. Fido-SNP: the first webserver for scoring the impact of single nucleotide variants in the dog genome. Nucleic Acids Res 2020; 47:W136-W141. [PMID: 31114899 PMCID: PMC6602425 DOI: 10.1093/nar/gkz420] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Revised: 04/19/2019] [Accepted: 05/06/2019] [Indexed: 12/22/2022] Open
Abstract
As the amount of genomic variation data increases, tools that are able to score the functional impact of single nucleotide variants become more and more necessary. While there are several prediction servers available for interpreting the effects of variants in the human genome, only few have been developed for other species, and none were specifically designed for species of veterinary interest such as the dog. Here, we present Fido-SNP the first predictor able to discriminate between Pathogenic and Benign single-nucleotide variants in the dog genome. Fido-SNP is a binary classifier based on the Gradient Boosting algorithm. It is able to classify and score the impact of variants in both coding and non-coding regions based on sequence features within seconds. When validated on a previously unseen set of annotated variants from the OMIA database, Fido-SNP reaches 88% overall accuracy, 0.77 Matthews correlation coefficient and 0.91 Area Under the ROC Curve.
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Affiliation(s)
- Emidio Capriotti
- BioFolD Unit, Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Via F. Selmi 3, 40126 Bologna, Italy
| | - Ludovica Montanucci
- Department of Comparative Biomedicine and Food Science. University of Padova, Viale dell'Università, 16, 35020 Legnaro (Padova), Italy
| | - Giuseppe Profiti
- BioDec srl. Via Calzavecchio 20, 40033 Casalecchio di Reno (Bologna), Italy
| | - Ivan Rossi
- BioDec srl. Via Calzavecchio 20, 40033 Casalecchio di Reno (Bologna), Italy
| | - Diana Giannuzzi
- Department of Comparative Biomedicine and Food Science. University of Padova, Viale dell'Università, 16, 35020 Legnaro (Padova), Italy
| | - Luca Aresu
- Department of Veterinary Sciences, University of Torino, Largo P. Braccini 2, 10095 Grugliasco, (Torino), Italy
| | - Piero Fariselli
- Department of Comparative Biomedicine and Food Science. University of Padova, Viale dell'Università, 16, 35020 Legnaro (Padova), Italy.,Department of Medical Sciences, University of Torino, Via Santena 19, 10126 Torino, Italy
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Pahari S, Li G, Murthy AK, Liang S, Fragoza R, Yu H, Alexov E. SAAMBE-3D: Predicting Effect of Mutations on Protein-Protein Interactions. Int J Mol Sci 2020; 21:E2563. [PMID: 32272725 PMCID: PMC7177817 DOI: 10.3390/ijms21072563] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 04/04/2020] [Accepted: 04/05/2020] [Indexed: 12/26/2022] Open
Abstract
Maintaining wild type protein-protein interactions is essential for the normal function of cell and any mutation that alter their characteristics can cause disease. Therefore, the ability to correctly and quickly predict the effect of amino acid mutations is crucial for understanding disease effects and to be able to carry out genome-wide studies. Here, we report a new development of the SAAMBE method, SAAMBE-3D, which is a machine learning-based approach, resulting in accurate predictions and is extremely fast. It achieves the Pearson correlation coefficient ranging from 0.78 to 0.82 depending on the training protocol in benchmarking five-fold validation test against the SKEMPI v2.0 database and outperforms currently existing algorithms on various blind-tests. Furthermore, optimized and tested via five-fold cross-validation on the Cornell University dataset, the SAAMBE-3D achieves AUC of 1.0 and 0.96 on a homo and hereto-dimer test datasets. Another important feature of SAAMBE-3D is that it is very fast, it takes less than a fraction of a second to complete a prediction. SAAMBE-3D is available as a web server and as well as a stand-alone code, the last one being another important feature allowing other researchers to directly download the code and run it on their local computer. Combined all together, SAAMBE-3D is an accurate and fast software applicable for genome-wide studies to assess the effect of amino acid mutations on protein-protein interactions. The webserver and the stand-alone codes (SAAMBE-3D for predicting the change of binding free energy and SAAMBE-3D-DN for predicting if the mutation is disruptive or non-disruptive) are available.
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Affiliation(s)
- Swagata Pahari
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA; (S.P.); (G.L.); (A.K.M.)
| | - Gen Li
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA; (S.P.); (G.L.); (A.K.M.)
| | - Adithya Krishna Murthy
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA; (S.P.); (G.L.); (A.K.M.)
| | - Siqi Liang
- Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA; (S.L.); (R.F.); (H.Y.)
| | - Robert Fragoza
- Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA; (S.L.); (R.F.); (H.Y.)
| | - Haiyuan Yu
- Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA; (S.L.); (R.F.); (H.Y.)
| | - Emil Alexov
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA; (S.P.); (G.L.); (A.K.M.)
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40
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Vihinen M. Problems in variation interpretation guidelines and in their implementation in computational tools. Mol Genet Genomic Med 2020; 8:e1206. [PMID: 32160417 PMCID: PMC7507483 DOI: 10.1002/mgg3.1206] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 02/23/2020] [Indexed: 12/12/2022] Open
Abstract
Background ACMG/AMP and AMP/ASCO/CAP have released guidelines for variation interpretation, and ESHG for diagnostic sequencing. These guidelines contain recommendations including the use of computational prediction methods. The guidelines per se and the way they are implemented cause some problems. Methods Logical reasoning based on domain knowledge. Results According to the guidelines, several methods have to be used and they have to agree. This means that the methods with the poorest performance overrule the better ones. The choice of the prediction method(s) should be made by experts based on systematic benchmarking studies reporting all the relevant performance measures. Currently variation interpretation methods have been applied mainly to amino acid substitutions and splice site variants; however, predictors for some other types of variations are available and there will be tools for new application areas in the near future. Common problems in prediction method usage are discussed. The number of features used for method training or the number of variation types predicted by a tool are not indicators of method performance. Many published gene, protein or disease‐specific benchmark studies suffer from too small dataset rendering the results useless. In the case of binary predictors, equal number of positive and negative cases is beneficial for training, the imbalance has to be corrected for performance assessment. Predictors cannot be better than the data they are based on and used for training and testing. Minor allele frequency (MAF) can help to detect likely benign cases, but the recommended MAF threshold is apparently too high. The fact that many rare variants are disease‐causing or ‐related does not mean that rare variants in general would be harmful. How large a portion of the tested variants a tool can predict (coverage) is not a quality measure. Conclusion Methods used for variation interpretation have to be carefully selected. It should be possible to use only one predictor, with proven good performance or a limited number of complementary predictors with state‐of‐the‐art performance. Bear in mind that diseases and pathogenicity have a continuum and variants are not dichotomic i.e. either pathogenic or benign, either.
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Affiliation(s)
- Mauno Vihinen
- Department of Experimental Medical Science, Lund University, Lund, Sweden
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41
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Pandurangan AP, Blundell TL. Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning. Protein Sci 2020; 29:247-257. [PMID: 31693276 PMCID: PMC6933854 DOI: 10.1002/pro.3774] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 10/31/2019] [Accepted: 10/31/2019] [Indexed: 02/02/2023]
Abstract
Next-generation sequencing methods have not only allowed an understanding of genome sequence variation during the evolution of organisms but have also provided invaluable information about genetic variants in inherited disease and the emergence of resistance to drugs in cancers and infectious disease. A challenge is to distinguish mutations that are drivers of disease or drug resistance, from passengers that are neutral or even selectively advantageous to the organism. This requires an understanding of impacts of missense mutations in gene expression and regulation, and on the disruption of protein function by modulating protein stability or disturbing interactions with proteins, nucleic acids, small molecule ligands, and other biological molecules. Experimental approaches to understanding differences between wild-type and mutant proteins are most accurate but are also time-consuming and costly. Computational tools used to predict the impacts of mutations can provide useful information more quickly. Here, we focus on two widely used structure-based approaches, originally developed in the Blundell lab: site-directed mutator (SDM), a statistical approach to analyze amino acid substitutions, and mutation cutoff scanning matrix (mCSM), which uses graph-based signatures to represent the wild-type structural environment and machine learning to predict the effect of mutations on protein stability. Here, we describe DUET that uses machine learning to combine the two approaches. We discuss briefly the development of mCSM for understanding the impacts of mutations on interfaces with other proteins, nucleic acids, and ligands, and we exemplify the wide application of these approaches to understand human genetic disorders and drug resistance mutations relevant to cancer and mycobacterial infections. STATEMENT FOR A BROADER AUDIENCE: Genetic or somatic changes in genes can lead to mutations in human proteins, which give rise to genetic disorders or cancer, or to genes of pathogens leading to drug resistance. Computer software described here, using statistical approaches or machine learning, uses the information from genome sequencing of humans and pathogens, together with experimental or modeled 3D structures of gene products, the proteins, to predict impacts of mutations in genetic disease, cancer and drug resistance.
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Affiliation(s)
- Arun Prasad Pandurangan
- Department of BiochemistryUniversity of CambridgeCambridgeUK
- MRC Laboratory of Molecular BiologyCambridgeUK
| | - Tom L. Blundell
- Department of BiochemistryUniversity of CambridgeCambridgeUK
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Affiliation(s)
- Stanislav Mazurenko
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic
| | - Zbynek Prokop
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic
- International Centre for Clinical Research, St. Ann’s Hospital, 602 00 Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic
- International Centre for Clinical Research, St. Ann’s Hospital, 602 00 Brno, Czech Republic
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43
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Tang N, Sandahl TD, Ott P, Kepp KP. Computing the Pathogenicity of Wilson's Disease ATP7B Mutations: Implications for Disease Prevalence. J Chem Inf Model 2019; 59:5230-5243. [PMID: 31751128 DOI: 10.1021/acs.jcim.9b00852] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Genetic variations in the gene encoding the copper-transport protein ATP7B are the primary cause of Wilson's disease. Controversially, clinical prevalence seems much smaller than the prevalence estimated by genetic screening tools, causing fear that many people are undiagnosed, although early diagnosis and treatment is essential. To address this issue, we benchmarked 16 state-of-the-art computational disease-prediction methods against established data of missense ATP7B mutations. Our results show that the quality of the methods varies widely. We show the importance of optimizing the threshold of the methods used to distinguish pathogenic from nonpathogenic mutations against data of clinically confirmed pathogenic and nonpathogenic mutations. We find that most methods use thresholds that predict too many ATP7B mutations to be pathogenic. Thus, our findings explain the current controversy on Wilson's disease prevalence because meta-analysis and text search methods include many computational estimates that lead to higher disease prevalence than clinically observed. As proteins and diseases differ widely, a one-size-fits-all threshold cannot distinguish pathogenic and nonpathogenic mutations efficiently, as shown here. We also show that amino acid changes with small evolutionary substitution probability, mainly due to amino acid volume, are more associated with the disease, implying a pathological effect on the conformational state of the protein, which could affect copper transport or adenosine triphosphate recognition and hydrolysis. These findings may be a first step toward a more quantitative genotype-phenotype relationship of Wilson's disease.
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Affiliation(s)
- Ning Tang
- DTU Chemistry , Technical University of Denmark , Kemitorvet 206 , 2800 Kongens Lyngby , Denmark
| | - Thomas D Sandahl
- Department of Hepatology and Gastroenterology , Aarhus University Hospital , 8200 Aarhus , Denmark
| | - Peter Ott
- Department of Hepatology and Gastroenterology , Aarhus University Hospital , 8200 Aarhus , Denmark
| | - Kasper P Kepp
- DTU Chemistry , Technical University of Denmark , Kemitorvet 206 , 2800 Kongens Lyngby , Denmark
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44
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Zeng Z, Bromberg Y. Predicting Functional Effects of Synonymous Variants: A Systematic Review and Perspectives. Front Genet 2019; 10:914. [PMID: 31649718 PMCID: PMC6791167 DOI: 10.3389/fgene.2019.00914] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 08/29/2019] [Indexed: 12/13/2022] Open
Abstract
Recent advances in high-throughput experimentation have put the exploration of genome sequences at the forefront of precision medicine. In an effort to interpret the sequencing data, numerous computational methods have been developed for evaluating the effects of genome variants. Interestingly, despite the fact that every person has as many synonymous (sSNV) as non-synonymous single nucleotide variants, our ability to predict their effects is limited. The paucity of experimentally tested sSNV effects appears to be the limiting factor in development of such methods. Here, we summarize the details and evaluate the performance of nine existing computational methods capable of predicting sSNV effects. We used a set of observed and artificially generated variants to approximate large scale performance expectations of these tools. We note that the distribution of these variants across amino acid and codon types suggests purifying evolutionary selection retaining generated variants out of the observed set; i.e., we expect the generated set to be enriched for deleterious variants. Closer inspection of the relationship between the observed variant frequencies and the associated prediction scores identifies predictor-specific scoring thresholds of reliable effect predictions. Notably, across all predictors, the variants scoring above these thresholds were significantly more often generated than observed. which confirms our assumption that the generated set is enriched for deleterious variants. Finally, we find that while the methods differ in their ability to identify severe sSNV effects, no predictor appears capable of definitively recognizing subtle effects of such variants on a large scale.
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Affiliation(s)
- Zishuo Zeng
- Institute for Quantitative Biomedicine, Rutgers University, Piscataway, NJ, United States
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, United States
| | - Yana Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, United States
- Department of Genetics, Rutgers University, Human Genetics Institute, Piscataway, NJ, United States
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45
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Elbracht M, Meyer R, Eggermann T, Kurth I. [Rational use of genetic tests in internal medicine : Possibilities and limitations of next generation sequencing diagnostics]. Internist (Berl) 2019; 59:756-765. [PMID: 29946883 DOI: 10.1007/s00108-018-0457-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND New methods of molecular genetic diagnostics enable a more comprehensive genetic analysis of patients. OBJECTIVES Rational use and benefits of molecular genetic testing in patients with various internal diseases. METHOD Evaluation of topic-related literature, discussion of own experiences, as well as consideration of current guidelines. RESULTS New genetic tests, such as next generation sequencing (NGS), improve the diagnosis of hereditary diseases; however, the use of this technology also leads to additional findings, which must be carefully considered. CONCLUSION The rational use of genetic tests is a benefit for patients and can significantly influence the prevention and treatment of a disease. The increasing complexity of genetic findings requires interdisciplinary approaches involving human genetics, internal medicine, and other disciplines.
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Affiliation(s)
- M Elbracht
- Institut für Humangenetik, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Deutschland
| | - R Meyer
- Institut für Humangenetik, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Deutschland
| | - T Eggermann
- Institut für Humangenetik, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Deutschland
| | - I Kurth
- Institut für Humangenetik, Uniklinik RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Deutschland.
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46
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Garg A, Pal D. Exploring the use of molecular dynamics in assessing protein variants for phenotypic alterations. Hum Mutat 2019; 40:1424-1435. [PMID: 31106920 PMCID: PMC7318789 DOI: 10.1002/humu.23800] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 04/09/2019] [Accepted: 05/12/2019] [Indexed: 12/19/2022]
Abstract
With the advent of rapid sequencing technologies, making sense of all the genomic variations that we see among us has been a major challenge. A plethora of algorithms and methods exist that try to address genome interpretation through genotype-phenotype linkage analysis or evaluating the loss of function/stability mutations in protein. Critical Assessment of Genome Interpretation (CAGI) offers an exceptional platform to blind-test all such algorithms and methods to assess their true ability. We take advantage of this opportunity to explore the use of molecular dynamics simulation as a tool to assess alteration of phenotype, loss of protein function, interaction, and stability. The results show that coarse-grained dynamics based protein flexibility analysis on 34 CHEK2 and 1719 CALM1 single mutants perform reasonably well for class-based predictions for phenotype alteration and two-thirds of the predicted scores return a correlation coefficient of 0.6 or more. When all-atom dynamics is used to predict altered stability due to mutations for Frataxin protein (8 cases), the predictions are comparable to the state-of-the-art methods. The competitive performance of our straightforward approach to phenotype interpretation contrasts with heavily trained machine learning approaches, and open new avenues to rationally improve genome interpretation.
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Affiliation(s)
- Aditi Garg
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, India
| | - Debnath Pal
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, India
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47
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Monzon AM, Carraro M, Chiricosta L, Reggiani F, Han J, Ozturk K, Wang Y, Miller M, Bromberg Y, Capriotti E, Savojardo C, Babbi G, Martelli PL, Casadio R, Katsonis P, Lichtarge O, Carter H, Kousi M, Katsanis N, Andreoletti G, Moult J, Brenner SE, Ferrari C, Leonardi E, Tosatto SCE. Performance of computational methods for the evaluation of pericentriolar material 1 missense variants in CAGI-5. Hum Mutat 2019; 40:1474-1485. [PMID: 31260570 PMCID: PMC7354699 DOI: 10.1002/humu.23856] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 05/30/2019] [Accepted: 06/23/2019] [Indexed: 12/11/2022]
Abstract
The CAGI-5 pericentriolar material 1 (PCM1) challenge aimed to predict the effect of 38 transgenic human missense mutations in the PCM1 protein implicated in schizophrenia. Participants were provided with 16 benign variants (negative controls), 10 hypomorphic, and 12 loss of function variants. Six groups participated and were asked to predict the probability of effect and standard deviation associated to each mutation. Here, we present the challenge assessment. Prediction performance was evaluated using different measures to conclude in a final ranking which highlights the strengths and weaknesses of each group. The results show a great variety of predictions where some methods performed significantly better than others. Benign variants played an important role as negative controls, highlighting predictors biased to identify disease phenotypes. The best predictor, Bromberg lab, used a neural-network-based method able to discriminate between neutral and non-neutral single nucleotide polymorphisms. The CAGI-5 PCM1 challenge allowed us to evaluate the state of the art techniques for interpreting the effect of novel variants for a difficult target protein.
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Affiliation(s)
| | - Marco Carraro
- Department of Biomedical Sciences, University of Padua, Padua, Italy
| | - Luigi Chiricosta
- Department of Biomedical Sciences, University of Padua, Padua, Italy
| | - Francesco Reggiani
- Department of Biomedical Sciences, University of Padua, Padua, Italy
- Department of Information Engineering, University of Padua, Padua, Italy
| | - James Han
- Department of Medicine, University of California San Diego, La Jolla, California
| | - Kivilcim Ozturk
- Department of Medicine, University of California San Diego, La Jolla, California
| | - Yanran Wang
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey
| | - Maximilian Miller
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey
| | - Yana Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey
- Institute for Advanced Study, Technical University of Munich (TUM), Munich, Germany
| | - Emidio Capriotti
- Department of Pharmacy and Biotechnology, BioFolD Unit, University of Bologna, Bologna, Italy
| | - Castrense Savojardo
- Department of Pharmacy and Biotechnology, Biocomputing Group, University of Bologna, Bologna, Italy
| | - Giulia Babbi
- Department of Pharmacy and Biotechnology, Biocomputing Group, University of Bologna, Bologna, Italy
| | - Pier L Martelli
- Department of Pharmacy and Biotechnology, Biocomputing Group, University of Bologna, Bologna, Italy
| | - Rita Casadio
- Department of Pharmacy and Biotechnology, Biocomputing Group, University of Bologna, Bologna, Italy
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
| | - Hannah Carter
- Department of Medicine, University of California San Diego, La Jolla, California
| | - Maria Kousi
- MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Nicholas Katsanis
- Center for Human Disease Modeling, Duke University Medical Center, Durham, North Carolina
| | - Gaia Andreoletti
- Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA
| | - John Moult
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
| | - Steven E Brenner
- Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA
| | - Carlo Ferrari
- Department of Information Engineering, University of Padua, Padua, Italy
| | | | - Silvio C E Tosatto
- Department of Biomedical Sciences, University of Padua, Padua, Italy
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California
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48
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Orioli T, Vihinen M. Benchmarking subcellular localization and variant tolerance predictors on membrane proteins. BMC Genomics 2019; 20:547. [PMID: 31307390 PMCID: PMC6631444 DOI: 10.1186/s12864-019-5865-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Background Membrane proteins constitute up to 30% of the human proteome. These proteins have special properties because the transmembrane segments are embedded into lipid bilayer while extramembranous parts are in different environments. Membrane proteins have several functions and are involved in numerous diseases. A large number of prediction methods have been introduced to predict protein subcellular localization as well as the tolerance or pathogenicity of amino acid substitutions. Results We tested the performance of 22 tolerance predictors by collecting information on membrane proteins and variants in them. The analysis indicated that the best tools had similar prediction performance on transmembrane, inside and outside regions of transmembrane proteins and comparable to overall prediction performances for all types of proteins. PON-P2 had the highest performance followed by REVEL, MetaSVM and VEST3. Further, we tested with the high quality dataset also the performance of seven subcellular localization predictors on membrane proteins. We assessed separately the performance for single pass and multi pass membrane proteins. Predictions for multi pass proteins were more reliable than those for single pass proteins. Conclusions The predictors for variant effects had better performance than subcellular localization tools. The best tolerance predictors are highly reliable. As there are large differences in the performances of tools, end-users have to be cautious in method selection. Electronic supplementary material The online version of this article (10.1186/s12864-019-5865-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Tommaso Orioli
- International Master in Bioinformatics, School of Science, University of Bologna, Bologna, Italy.,Department of Experimental Medical Science, BMC B13, Lund University, SE-22184, Lund, Sweden
| | - Mauno Vihinen
- Department of Experimental Medical Science, BMC B13, Lund University, SE-22184, Lund, Sweden.
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49
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Prediction of disease-associated mutations in the transmembrane regions of proteins with known 3D structure. PLoS One 2019; 14:e0219452. [PMID: 31291347 PMCID: PMC6620012 DOI: 10.1371/journal.pone.0219452] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Accepted: 06/24/2019] [Indexed: 01/03/2023] Open
Abstract
Being able to assess the phenotypic effects of mutations is a much required capability in precision medicine. However, most of the currently available structure-based methods actually predict stability changes caused by mutations rather than their pathogenic potential. There are also no dedicated methods to predict damaging mutations specifically in transmembrane proteins. In this study we developed and applied a machine-learning approach to discriminate between disease-associated and benign point mutations in the transmembrane regions of proteins with known 3D structure. The method, called BorodaTM (BOosted RegressiOn trees for Disease-Associated mutations in TransMembrane proteins), was trained on sequence-, structure-, and energy-derived descriptors. When compared with the state-of-the-art methods, BorodaTM is superior in classifying point mutations in transmembrane regions. Using BorodaTM we have conducted a large-scale mutation analysis in the transmembrane regions of human proteins with known 3D structures. For each protein we generated structural models for all point mutations by replacing each residue to 19 possible residue types. We classified ~1.8 millions point mutations as benign or diseased-associated and made all predictions available as a Web-server at https://www.iitm.ac.in/bioinfo/MutHTP/boroda.php.
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Padilla N, Moles-Fernández A, Riera C, Montalban G, Özkan S, Ootes L, Bonache S, Díez O, Gutiérrez-Enríquez S, de la Cruz X. BRCA1- and BRCA2-specific in silico tools for variant interpretation in the CAGI 5 ENIGMA challenge. Hum Mutat 2019; 40:1593-1611. [PMID: 31112341 DOI: 10.1002/humu.23802] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Revised: 05/15/2019] [Accepted: 05/17/2019] [Indexed: 11/09/2022]
Abstract
BRCA1 and BRCA2 (BRCA1/2) germline variants disrupting the DNA protective role of these genes increase the risk of hereditary breast and ovarian cancers. Correct identification of these variants then becomes clinically relevant, because it may increase the survival rates of the carriers. Unfortunately, we are still unable to systematically predict the impact of BRCA1/2 variants. In this article, we present a family of in silico predictors that address this problem, using a gene-specific approach. For each protein, we have developed two tools, aimed at predicting the impact of a variant at two different levels: Functional and clinical. Testing their performance in different datasets shows that specific information compensates the small number of predictive features and the reduced training sets employed to develop our models. When applied to the variants of the BRCA1/2 (ENIGMA) challenge in the fifth Critical Assessment of Genome Interpretation (CAGI 5) we find that these methods, particularly those predicting the functional impact of variants, have a good performance, identifying the large compositional bias towards neutral variants in the CAGI sample. This performance is further improved when incorporating to our prediction protocol estimates of the impact on splicing of the target variant.
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Affiliation(s)
- Natàlia Padilla
- Research Unit in Clinical and Translational Bioinformatics, Vall d'Hebron Institute of Research (VHIR). Universitat Autònoma de Barcelona, Barcelona, Spain
| | | | - Casandra Riera
- Research Unit in Clinical and Translational Bioinformatics, Vall d'Hebron Institute of Research (VHIR). Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Gemma Montalban
- Oncogenetics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Selen Özkan
- Research Unit in Clinical and Translational Bioinformatics, Vall d'Hebron Institute of Research (VHIR). Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Lars Ootes
- Research Unit in Clinical and Translational Bioinformatics, Vall d'Hebron Institute of Research (VHIR). Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Sandra Bonache
- Oncogenetics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Orland Díez
- Oncogenetics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain.,Area of Clinical and Molecular Genetics, University Hospital of Vall d'Hebron, Barcelona, Spain
| | | | - Xavier de la Cruz
- Research Unit in Clinical and Translational Bioinformatics, Vall d'Hebron Institute of Research (VHIR). Universitat Autònoma de Barcelona, Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
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