1
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Shanmugam NRS, Kulandaisamy A, Veluraja K, Gromiha MM. CarbDisMut: database on neutral and disease-causing mutations in human carbohydrate-binding proteins. Glycobiology 2024; 34:cwae011. [PMID: 38335248 DOI: 10.1093/glycob/cwae011] [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: 01/09/2023] [Revised: 01/03/2024] [Indexed: 02/12/2024] Open
Abstract
Protein-carbohydrate interactions are involved in several cellular and biological functions. Integrating structure and function of carbohydrate-binding proteins with disease-causing mutations help to understand the molecular basis of diseases. Although databases are available for protein-carbohydrate complexes based on structure, binding affinity and function, no specific database for mutations in human carbohydrate-binding proteins is reported in the literature. We have developed a novel database, CarbDisMut, a comprehensive integrated resource for disease-causing mutations with sequence and structural features. It has 1.17 million disease-associated mutations and 38,636 neutral mutations from 7,187 human carbohydrate-binding proteins. The database is freely available at https://web.iitm.ac.in/bioinfo2/carbdismut. The web-site is implemented using HTML, PHP and JavaScript and supports recent versions of all major browsers, such as Firefox, Chrome and Opera.
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Affiliation(s)
- N R Siva Shanmugam
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - A Kulandaisamy
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- Basic and Translational Research, Department of Cardiology, Boston Children's Hospital, Boston, MA 02115, United States
| | - K Veluraja
- PSN College of Engineering and Technology, Melathediyoor, Tirunelveli, Tamil Nadu 627451, India
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- Department of Computer Science, Tokyo Tech World Research Hub Initiative (WRHI), Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, Kanagawa 226-8501, Japan
- Department of Computer Science, National University of Singapore, 117417, Singapore
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2
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Dobson L, Gerdán C, Tusnády S, Szekeres L, Kuffa K, Langó T, Zeke A, Tusnády GE. UniTmp: unified resources for transmembrane proteins. Nucleic Acids Res 2024; 52:D572-D578. [PMID: 37870462 PMCID: PMC10767979 DOI: 10.1093/nar/gkad897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/03/2023] [Accepted: 10/04/2023] [Indexed: 10/24/2023] Open
Abstract
The UNIfied database of TransMembrane Proteins (UniTmp) is a comprehensive and freely accessible resource of transmembrane protein structural information at different levels, from localization of protein segments, through the topology of the protein to the membrane-embedded 3D structure. We not only annotated tens of thousands of new structures and experiments, but we also developed a new system that can serve these resources in parallel. UniTmp is a unified platform that merges TOPDB (Topology Data Bank of Transmembrane Proteins), TOPDOM (database of conservatively located domains and motifs in proteins), PDBTM (Protein Data Bank of Transmembrane Proteins) and HTP (Human Transmembrane Proteome) databases and provides interoperability between the incorporated resources and an easy way to keep them regularly updated. The current update contains 9235 membrane-embedded structures, 9088 sequences with 536 035 topology-annotated segments and 8692 conservatively localized protein domains or motifs as well as 5466 annotated human transmembrane proteins. The UniTmp database can be accessed at https://www.unitmp.org.
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Affiliation(s)
- László Dobson
- Protein Bioinformatics Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Magyar Tudósok körútja 2, H-1117, Hungary
- Department of Bioinformatics, Semmelweis University, Budapest, Tűzoltó u. 7, H-1094, Hungary
| | - Csongor Gerdán
- Protein Bioinformatics Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Magyar Tudósok körútja 2, H-1117, Hungary
| | - Simon Tusnády
- Department of Bioinformatics, Semmelweis University, Budapest, Tűzoltó u. 7, H-1094, Hungary
| | - Levente Szekeres
- Protein Bioinformatics Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Magyar Tudósok körútja 2, H-1117, Hungary
| | - Katalin Kuffa
- Protein Bioinformatics Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Magyar Tudósok körútja 2, H-1117, Hungary
- Doctoral School of Biology, Institute of Biology, ELTE Eötvös Loránd University, Budapest, Pázmány P. stny. 1/C, H-1117, Hungary
| | - Tamás Langó
- Protein Bioinformatics Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Magyar Tudósok körútja 2, H-1117, Hungary
| | - András Zeke
- Protein Bioinformatics Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Magyar Tudósok körútja 2, H-1117, Hungary
| | - Gábor E Tusnády
- Protein Bioinformatics Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Magyar Tudósok körútja 2, H-1117, Hungary
- Department of Bioinformatics, Semmelweis University, Budapest, Tűzoltó u. 7, H-1094, Hungary
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3
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Ramakrishna Reddy P, Kulandaisamy A, Michael Gromiha M. TMH Stab-pred: Predicting the stability of α-helical membrane proteins using sequence and structural features. Methods 2023; 218:118-124. [PMID: 37572768 DOI: 10.1016/j.ymeth.2023.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Revised: 08/02/2023] [Accepted: 08/04/2023] [Indexed: 08/14/2023] Open
Abstract
The folding and stability of transmembrane proteins (TMPs) are governed by the insertion of secondary structural elements into the cell membrane followed by their assembly. Understanding the important features that dictate the stability of TMPs is important for elucidating their functions. In this work, we related sequence and structure-based parameters with free energy (ΔG0) of α-helical membrane proteins. Our results showed that the free energy transfer of hydrophobic peptides, relative contact order, total interaction energy, number of hydrogen bonds and lipid accessibility of transmembrane regions are important for stability. Further, we have developed multiple-regression models to predict the stability of α-helical membrane proteins using these features and our method can predict the stability with a correlation and mean absolute error (MAE) of 0.89 and 1.21 kcal/mol, respectively, on jack-knife test. The method was validated with a blind test set of three recently reported experimental ΔG0, which could predict the stability within an average MAE of 0.51 kcal/mol. Further, we developed a webserver for predicting the stability and it is freely available at (https://web.iitm.ac.in/bioinfo2/TMHS/). The importance of selected parameters and limitations are discussed.
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Affiliation(s)
- P Ramakrishna Reddy
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India
| | - A Kulandaisamy
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India; Basic and Translational Research Division, Department of Cardiology, Boston Children's Hospital, Boston, MA 02115, USA
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India; Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan; Department of Computer Science, National University of Singapore, Singapore.
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4
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Sun J, Kulandaisamy A, Ru J, Gromiha MM, Cribbs AP. TMKit: a Python interface for computational analysis of transmembrane proteins. Brief Bioinform 2023; 24:bbad288. [PMID: 37594311 PMCID: PMC10516361 DOI: 10.1093/bib/bbad288] [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: 04/17/2023] [Revised: 07/07/2023] [Accepted: 07/18/2023] [Indexed: 08/19/2023] Open
Abstract
Transmembrane proteins are receptors, enzymes, transporters and ion channels that are instrumental in regulating a variety of cellular activities, such as signal transduction and cell communication. Despite tremendous progress in computational capacities to support protein research, there is still a significant gap in the availability of specialized computational analysis toolkits for transmembrane protein research. Here, we introduce TMKit, an open-source Python programming interface that is modular, scalable and specifically designed for processing transmembrane protein data. TMKit is a one-stop computational analysis tool for transmembrane proteins, enabling users to perform database wrangling, engineer features at the mutational, domain and topological levels, and visualize protein-protein interaction interfaces. In addition, TMKit includes seqNetRR, a high-performance computing library that allows customized construction of a large number of residue connections. This library is particularly well suited for assigning correlation matrix-based features at a fast speed. TMKit should serve as a useful tool for researchers in assisting the study of transmembrane protein sequences and structures. TMKit is publicly available through https://github.com/2003100127/tmkit and https://tmkit-guide.herokuapp.com/doc/overview.
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Affiliation(s)
- Jianfeng Sun
- Nuffield Department of Orthopedics, Rheumatology, and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Headington, Oxford OX3 7LD, UK
| | - Arulsamy Kulandaisamy
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India
| | - Jinlong Ru
- Chair of Prevention of Microbial Diseases, School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India
| | - Adam P Cribbs
- Nuffield Department of Orthopedics, Rheumatology, and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Headington, Oxford OX3 7LD, UK
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5
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Sun J, Kulandaisamy A, Liu J, Hu K, Gromiha MM, Zhang Y. Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications. Comput Struct Biotechnol J 2023; 21:1205-1226. [PMID: 36817959 PMCID: PMC9932300 DOI: 10.1016/j.csbj.2023.01.036] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/16/2023] [Accepted: 01/25/2023] [Indexed: 01/29/2023] Open
Abstract
Membrane proteins mediate a wide spectrum of biological processes, such as signal transduction and cell communication. Due to the arduous and costly nature inherent to the experimental process, membrane proteins have long been devoid of well-resolved atomic-level tertiary structures and, consequently, the understanding of their functional roles underlying a multitude of life activities has been hampered. Currently, computational tools dedicated to furthering the structure-function understanding are primarily focused on utilizing intelligent algorithms to address a variety of site-wise prediction problems (e.g., topology and interaction sites), but are scattered across different computing sources. Moreover, the recent advent of deep learning techniques has immensely expedited the development of computational tools for membrane protein-related prediction problems. Given the growing number of applications optimized particularly by manifold deep neural networks, we herein provide a review on the current status of computational strategies mainly in membrane protein type classification, topology identification, interaction site detection, and pathogenic effect prediction. Meanwhile, we provide an overview of how the entire prediction process proceeds, including database collection, data pre-processing, feature extraction, and method selection. This review is expected to be useful for developing more extendable computational tools specific to membrane proteins.
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Affiliation(s)
- Jianfeng Sun
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Headington, Oxford OX3 7LD, UK
| | - Arulsamy Kulandaisamy
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India
| | - Jacklyn Liu
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6BT, UK
| | - Kai Hu
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China
| | - M. Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India,Corresponding authors.
| | - Yuan Zhang
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China,Corresponding authors.
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6
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Płatek T, Sordyl M, Polus A, Olszanecka A, Kroczka S, Solnica B. Analysis of tafazzin and deoxyribonuclease 1 like 1 transcripts and X chromosome sequencing in the evaluation of the effect of mosaicism in the TAZ gene on phenotypes in a family affected by Barth syndrome. Mutat Res 2022; 826:111812. [PMID: 36628843 DOI: 10.1016/j.mrfmmm.2022.111812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 11/11/2022] [Accepted: 12/23/2022] [Indexed: 12/31/2022]
Abstract
Barth syndrome is a rare disease affecting mitochondria structure and function in males. In our previous study, we have shown a new mutation (c.83T>A, p.Val28Glu) in the TAZ gene in two affected patients with congenital cardiomyopathy. Furthermore, women in this family had no mutations in their blood cells, whereas they only had mutations in the oral epithelial cells. The objective of the project was to evaluate the effect of intertissue mosaicisms on the Barth syndrome phenotypes, searching for another disease-related loci on chromosome X and finally to assess the consequences of the mutation. We conducted the advanced genetic study including cytogenetic research (constitutional karyotyping in blood and fibroblasts), NGS sequencing (with custom chromosome X sequencing together with the evaluation of loss of heterozygosity (LOH) and aberrations (CNV) in the whole genome) in four different tissues and sequencing of tafazzin and deoxyribonuclease 1 like 1 transcripts. The presence of deletions within the 5'untranslated region of the TAZ gene and/or the noncoding regions of the DNASE1L1 gene were detected in several tissues. Whereas, there is no intertissue mosaicism regarding point mutation in TAZ gene in all investigated tissues in female carriers. Only the male patient presented biochemical markers and neurological symptoms of Barth syndrome. All the female carriers are healthy and have normal tafazzin and deoxyribonuclease 1 like 1 transcripts in 2 analyzed tissues. The conclusion of this study is that we cannot rule out or confirm mosaicism in the noncoding regions of TAZ or DNASE1L1 genes, but this is not clinically relevant in female carriers because they are healthy. Finally, it has been proven that mutation (c.83T>A, p.Val28Glu) is responsible for disease in males in this family.
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Affiliation(s)
- Teresa Płatek
- Department of Clinical Biochemistry, Jagiellonian University Medical College, Kraków, Poland.
| | - Maria Sordyl
- Department of Clinical Biochemistry, Jagiellonian University Medical College, Kraków, Poland
| | - Anna Polus
- Department of Clinical Biochemistry, Jagiellonian University Medical College, Kraków, Poland
| | - Agnieszka Olszanecka
- 1st Department of Cardiology, Interventional Electrocardiology and Hypertension, Faculty of Medicine, Jagiellonian University Medical College, Krakow, Poland
| | - Sławomir Kroczka
- Department of Child and Adolescent Neurology, Jagiellonian University Medical College, Krakow, Poland; Department of Child Neurology, University Children's Hospital, Krakow, Poland
| | - Bogdan Solnica
- Department of Clinical Biochemistry, Jagiellonian University Medical College, Kraków, Poland
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7
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Murugesan A, Nguyen P, Ramesh T, Yli-Harja O, Kandhavelu M, Saravanan KM. Molecular modeling and dynamics studies of the synthetic small molecule agonists with GPR17 and P2Y1 receptor. J Biomol Struct Dyn 2022; 40:12908-12916. [PMID: 34542380 DOI: 10.1080/07391102.2021.1977707] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The human Guanine Protein coupled membrane Receptor 17 (hGPR17), an orphan receptor that activates uracil nucleotides and cysteinyl leukotrienes is considered as a crucial target for the neurodegenerative diseases. Yet, the detailed molecular interaction of potential synthetic ligands of GPR17 needs to be characterized. Here, we have studied a comparative analysis on the interaction specificity of GPR17-ligands with hGPR17 and human purinergic G protein-coupled receptor (hP2Y1) receptors. Previously, we have simulated the interaction stability of synthetic ligands such as T0510.3657, AC1MLNKK, and MDL29951 with hGPR17 and hP2Y1 receptor in the lipid environment. In the present work, we have comparatively studied the protein-ligand interaction of hGPR17-T0510.3657 and P2Y1-MRS2500. Sequence analysis and structural superimposition of hGPR17 and hP2Y1 receptor revealed the similarities in the structural arrangement with the local backbone root mean square deviation (RMSD) value of 1.16 Å and global backbone RMSD value of 5.30 Å. The comparative receptor-ligand interaction analysis between hGPR17 and hP2Y1 receptor exposed the distinct binding sites in terms of geometrical properties. Further, the molecular docking of T0510.3657 with the hP2Y1 receptor have shown non-specific interaction. The experimental validation also revealed that Gi-coupled activation of GPR17 by specific ligands leads to the adenylyl cyclase inhibition, while there is no inhibition upon hP2Y1 activation. Overall, the above findings suggest that T0510.3657-GPR17 binding specificity could be further explored for the treatment of numerous neuronal diseases. Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Akshaya Murugesan
- Molecular Signaling Lab, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,Department of Biotechnology, Lady Doak College, Thallakulam, Madurai, India
| | - Phung Nguyen
- Molecular Signaling Lab, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Thiyagarajan Ramesh
- Department of Basic Medical Sciences, College of Medicine, Prince Sattam Bin Abdulaziz University, Al Kharj, Kingdom of Saudi Arabia
| | - Olli Yli-Harja
- Computational Systems Biology Group, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,Institute for Systems Biology, Seattle, WA, USA
| | | | - Konda Mani Saravanan
- Scigen Research and Innovation Pvt Ltd, Periyar Technology Business Incubator, Thanjavur, Tamil Nadu, India
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8
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MPAD: A Database for Binding Affinity of Membrane Protein–protein Complexes and their Mutants. J Mol Biol 2022:167870. [DOI: 10.1016/j.jmb.2022.167870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/20/2022] [Accepted: 10/20/2022] [Indexed: 11/06/2022]
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9
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Lomize AL, Schnitzer KA, Todd SC, Cherepanov S, Outeiral C, Deane CM, Pogozheva ID. Membranome 3.0: Database of single-pass membrane proteins with AlphaFold models. Protein Sci 2022; 31:e4318. [PMID: 35481632 PMCID: PMC9047035 DOI: 10.1002/pro.4318] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/09/2022] [Accepted: 04/11/2022] [Indexed: 12/14/2022]
Abstract
The Membranome database provides comprehensive structural information on single‐pass (i.e., bitopic) membrane proteins from six evolutionarily distant organisms, including protein–protein interactions, complexes, mutations, experimental structures, and models of transmembrane α‐helical dimers. We present a new version of this database, Membranome 3.0, which was significantly updated by revising the set of 5,758 bitopic proteins and incorporating models generated by AlphaFold 2 in the database. The AlphaFold models were parsed into structural domains located at the different membrane sides, modified to exclude low‐confidence unstructured terminal regions and signal sequences, validated through comparison with available experimental structures, and positioned with respect to membrane boundaries. Membranome 3.0 was re‐developed to facilitate visualization and comparative analysis of multiple 3D structures of proteins that belong to a specified family, complex, biological pathway, or membrane type. New tools for advanced search and analysis of proteins, their interactions, complexes, and mutations were included. The database is freely accessible at https://membranome.org.
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Affiliation(s)
- Andrei L Lomize
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, USA
| | - Kevin A Schnitzer
- Department of Electrical Engineering and Computer Science, College of Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Spencer C Todd
- Department of Electrical Engineering and Computer Science, College of Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | | | | | | | - Irina D Pogozheva
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, USA
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MutTMPredictor: Robust and accurate cascade XGBoost classifier for prediction of mutations in transmembrane proteins. Comput Struct Biotechnol J 2021; 19:6400-6416. [PMID: 34938415 PMCID: PMC8649221 DOI: 10.1016/j.csbj.2021.11.024] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 11/05/2021] [Accepted: 11/15/2021] [Indexed: 12/11/2022] Open
Abstract
Prediction of mutations in transmembrane proteins is of significance for diseases diagnosis. Building on the evolutionary information, proposed the Gaussian WAPSSM algorithm. Based on WAPSSM and sequence and structure-based features, proposed the cascade XGBoost algorithm. Webserver is freely at (http://csbio.njust.edu.cn/bioinf/ffmsresmutp/). Implement MutTMPredictor to predict mutations in transmembrane proteins.
Transmembrane proteins have critical biological functions and play a role in a multitude of cellular processes including cell signaling, transport of molecules and ions across membranes. Approximately 60% of transmembrane proteins are considered as drug targets. Missense mutations in such proteins can lead to many diverse diseases and disorders, such as neurodegenerative diseases and cystic fibrosis. However, there are limited studies on mutations in transmembrane proteins. In this work, we first design a new feature encoding method, termed weight attenuation position-specific scoring matrix (WAPSSM), which builds upon the protein evolutionary information. Then, we propose a new mutation prediction algorithm (cascade XGBoost) by leveraging the idea learned from consensus predictors and gcForest. Multi-level experiments illustrate the effectiveness of WAPSSM and cascade XGBoost algorithms. Finally, based on WAPSSM and other three types of features, in combination with the cascade XGBoost algorithm, we develop a new transmembrane protein mutation predictor, named MutTMPredictor. We benchmark the performance of MutTMPredictor against several existing predictors on seven datasets. On the 546 mutations dataset, MutTMPredictor achieves the accuracy (ACC) of 0.9661 and the Matthew’s Correlation Coefficient (MCC) of 0.8950. While on the 67,584 dataset, MutTMPredictor achieves an MCC of 0.7523 and area under curve (AUC) of 0.8746, which are 0.1625 and 0.0801 respectively higher than those of the existing best predictor (fathmm). Besides, MutTMPredictor also outperforms two specific predictors on the Pred-MutHTP datasets. The results suggest that MutTMPredictor can be used as an effective method for predicting and prioritizing missense mutations in transmembrane proteins. The MutTMPredictor webserver and datasets are freely accessible at http://csbio.njust.edu.cn/bioinf/muttmpredictor/ for academic use.
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Key Words
- 1000 Genomes, 1000 genomes project consortium
- APOGEE, pathogenicity prediction through the logistic model tree
- BorodaTM, boosted regression trees for disease-associated mutations in transmembrane proteins
- COSMIC, catalogue of somatic mutations in cancer
- Cascade XGBoost
- ClinVar, clinical variants
- Condel, consensus deleteriousness score of missense mutations
- Disease-associated mutations
- Entprise, entropy and predicted protein structure
- ExAC, the exome aggregation consortium
- Meta-SNP, meta single nucleotide polymorphism
- Mutation prediction
- PROVEAN, protein variation effect analyzer
- PolyPhen, polymorphism phenotyping
- PolyPhen-2, polymorphism phenotyping v2
- Pred-MutHTP, prediction of mutations in human transmembrane proteins
- PredictSNP1, predict single nucleotide polymorphism v1
- Protein evolutionary information
- REVEL, rare exome variant ensemble learner
- SDM, site-directed mutate
- SIFT, sorting intolerant from tolerant
- SNAP, screening for non-acceptable polymorphisms
- SNP&GO, single nucleotide polymorphisms and gene ontology annotations
- SwissVar, variants in UniProtKB/Swiss-Prot
- TMSNP, transmembrane single nucleotide polymorphisms
- Transmembrane protein
- WEKA, waikato environment for knowledge analysis
- fathmm, functional analysis through hidden markov models
- humsavar, human polymorphisms and disease mutations
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11
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Srinivasan K, Banerjee A, Baid P, Dhur A, Sengupta J. Ribosome-membrane crosstalk: Co-translational targeting pathways of proteins across membranes in prokaryotes and eukaryotes. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2021; 128:163-198. [PMID: 35034718 DOI: 10.1016/bs.apcsb.2021.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Ribosomes are the molecular machine of living cells designed for decoding mRNA-encoded genetic information into protein. Being sophisticated machinery, both in design and function, the ribosome not only carries out protein synthesis, but also coordinates several other ribosome-associated cellular processes. One such process is the translocation of proteins across or into the membrane depending on their secretory or membrane-associated nature. These proteins comprise a large portion of a cell's proteome and act as key factors for cellular survival as well as several crucial functional pathways. Protein transport to extra- and intra-cytosolic compartments (across the eukaryotic endoplasmic reticulum (ER) or across the prokaryotic plasma membrane) or insertion into membranes majorly occurs through an evolutionarily conserved protein-conducting channel called translocon (eukaryotic Sec61 or prokaryotic SecYEG channels). Targeting proteins to the membrane-bound translocon may occur via post-translational or co-translational modes and it is often mediated by recognition of an N-terminal signal sequence in the newly synthesizes polypeptide chain. Co-translational translocation is coupled to protein synthesis where the ribosome-nascent chain complex (RNC) itself is targeted to the translocon. Here, in the light of recent advances in structural and functional studies, we discuss our current understanding of the mechanistic models of co-translational translocation, coordinated by the actively translating ribosomes, in prokaryotes and eukaryotes.
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Affiliation(s)
- Krishnamoorthi Srinivasan
- Division of Structural Biology and Bioinformatics, CSIR-Indian Institute of Chemical Biology, Kolkata, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Aneek Banerjee
- Division of Structural Biology and Bioinformatics, CSIR-Indian Institute of Chemical Biology, Kolkata, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Priya Baid
- Division of Structural Biology and Bioinformatics, CSIR-Indian Institute of Chemical Biology, Kolkata, India
| | - Ankit Dhur
- Division of Structural Biology and Bioinformatics, CSIR-Indian Institute of Chemical Biology, Kolkata, India
| | - Jayati Sengupta
- Division of Structural Biology and Bioinformatics, CSIR-Indian Institute of Chemical Biology, Kolkata, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India.
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12
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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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13
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Kulandaisamy A, Zaucha J, Frishman D, Gromiha MM. MPTherm-pred: Analysis and Prediction of Thermal Stability Changes upon Mutations in Transmembrane Proteins. J Mol Biol 2020; 433:166646. [PMID: 32920050 DOI: 10.1016/j.jmb.2020.09.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Revised: 09/04/2020] [Accepted: 09/04/2020] [Indexed: 01/06/2023]
Abstract
The stability of membrane proteins differs from globular proteins due to the presence of nonpolar membrane-spanning regions. Using a dataset of 929 membrane protein mutations whose effects on thermal stability (ΔTm) were experimentally determined, we found that the average ΔTm due to 190 stabilizing and 232 destabilizing mutations occurring in membrane-spanning regions are 2.43(3.1) °C and -5.48(5.5) °C, respectively. The ΔTm values for mutations occurring in solvent-exposed regions are 2.56(2.82) and - 6.8(7.2) °C. We have systematically analyzed the factors influencing the stability of mutants and observed that changes in hydrophobicity, number of contacts between Cα atoms and frequency of aliphatic residues are important determinants of the stability change induced by mutations occurring in membrane-spanning regions. We have developed structure- and sequence-based machine learning predictors of ΔTm due to mutations specifically for membrane proteins. They showed a correlation and mean absolute error (MAE) of 0.72 and 2.85 °C, respectively, between experimental and predicted ΔTm for mutations in membrane-spanning regions on 10-fold group-wise cross-validation. The average correlation and MAE for mutations in aqueous regions are 0.73 and 3.7 °C, respectively. These MAE values are about 50% lower than standard deviations from the mean ΔTm values. The reliability of the method was affirmed on a test set of mutations occurring in evolutionary independent protein sequences. The developed MPTherm-pred server for predicting thermal stability changes upon mutations in membrane proteins is available at https://web.iitm.ac.in/bioinfo2/mpthermpred/. Our results provide insights into factors influencing the stability of membrane proteins and can aid in designing mutants that are more resistant to thermal stress.
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Affiliation(s)
- A Kulandaisamy
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India
| | - Jan Zaucha
- Department of Bioinformatics, Technische Universität München, Wissenschaftszentrum Weihenstephan, Freising, Germany
| | - Dmitrij Frishman
- Department of Bioinformatics, Technische Universität München, Wissenschaftszentrum Weihenstephan, Freising, Germany; Department of Bioinformatics, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India.
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14
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Zaucha J, Heinzinger M, Kulandaisamy A, Kataka E, Salvádor ÓL, Popov P, Rost B, Gromiha MM, Zhorov BS, Frishman D. Mutations in transmembrane proteins: diseases, evolutionary insights, prediction and comparison with globular proteins. Brief Bioinform 2020; 22:5872174. [PMID: 32672331 DOI: 10.1093/bib/bbaa132] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 05/26/2020] [Accepted: 05/28/2020] [Indexed: 12/18/2022] Open
Abstract
Membrane proteins are unique in that they interact with lipid bilayers, making them indispensable for transporting molecules and relaying signals between and across cells. Due to the significance of the protein's functions, mutations often have profound effects on the fitness of the host. This is apparent both from experimental studies, which implicated numerous missense variants in diseases, as well as from evolutionary signals that allow elucidating the physicochemical constraints that intermembrane and aqueous environments bring. In this review, we report on the current state of knowledge acquired on missense variants (referred to as to single amino acid variants) affecting membrane proteins as well as the insights that can be extrapolated from data already available. This includes an overview of the annotations for membrane protein variants that have been collated within databases dedicated to the topic, bioinformatics approaches that leverage evolutionary information in order to shed light on previously uncharacterized membrane protein structures or interaction interfaces, tools for predicting the effects of mutations tailored specifically towards the characteristics of membrane proteins as well as two clinically relevant case studies explaining the implications of mutated membrane proteins in cancer and cardiomyopathy.
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Affiliation(s)
- Jan Zaucha
- Department of Bioinformatics of the TUM School of Life Sciences Weihenstephan in Freising, Germany
| | - Michael Heinzinger
- Department of Informatics, Bioinformatics and Computational Biology of the TUM Faculty of Informatics in Garching, Germany
| | - A Kulandaisamy
- Department of Biotechnology of the IIT Bhupat and Jyoti Mehta School of BioSciences in Madras, India
| | - Evans Kataka
- Department of Bioinformatics of the TUM School of Life Sciences Weihenstephan in Freising, Germany
| | - Óscar Llorian Salvádor
- Department of Informatics, Bioinformatics and Computational Biology of the TUM Faculty of Informatics in Garching, Germany
| | - Petr Popov
- Center for Computational and Data-Intensive Science and Engineering of the Skolkovo Institute of Science and Technology in Moscow, Russia
| | - Burkhard Rost
- Department of Informatics, Bioinformatics and Computational Biology at the TUM Faculty of Informatics in Garching, Germany
| | | | - Boris S Zhorov
- Department of Biochemistry and Biomedical Sciences, McMaster University in Hamilton, Canada
| | - Dmitrij Frishman
- Department of Bioinformatics at the TUM School of Life Sciences Weihenstephan in Freising, Germany
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15
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Partial proteolysis improves the identification of the extracellular segments of transmembrane proteins by surface biotinylation. Sci Rep 2020; 10:8880. [PMID: 32483232 PMCID: PMC7264363 DOI: 10.1038/s41598-020-65831-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 05/08/2020] [Indexed: 01/11/2023] Open
Abstract
Transmembrane proteins (TMP) play a crucial role in several physiological processes. Despite their importance and diversity, only a few TMP structures have been determined by high-resolution protein structure characterization methods so far. Due to the low number of determined TMP structures, the parallel development of various bioinformatics and experimental methods was necessary for their topological characterization. The combination of these methods is a powerful approach in the determination of TMP topology as in the Constrained Consensus TOPology prediction. To support the prediction, we previously developed a high-throughput topology characterization method based on primary amino group-labelling that is still limited in identifying all TMPs and their extracellular segments on the surface of a particular cell type. In order to generate more topology information, a new step, a partial proteolysis of the cell surface has been introduced to our method. This step results in new primary amino groups in the proteins that can be biotinylated with a membrane-impermeable agent while the cells still remain intact. Pre-digestion also promotes the emergence of modified peptides that are more suitable for MS/MS analysis. The modified sites can be utilized as extracellular constraints in topology predictions and may contribute to the refined topology of these proteins.
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16
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Kulandaisamy A, Sakthivel R, Gromiha MM. MPTherm: database for membrane protein thermodynamics for understanding folding and stability. Brief Bioinform 2020; 22:2119-2125. [PMID: 32337573 DOI: 10.1093/bib/bbaa064] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 03/19/2020] [Indexed: 12/21/2022] Open
Abstract
The functions of membrane proteins (MPs) are attributed to their structure and stability. Factors influencing the stability of MPs differ from globular proteins due to the presence of membrane spanning regions. Thermodynamic data of MPs aid to understand the relationship among their structure, stability and function. Although a wealth of experimental data on thermodynamics of MPs are reported in the literature, there is no database available explicitly for MPs. In this work, we have developed a database for MP thermodynamics, MPTherm, which contains more than 7000 thermodynamic data from about 320 MPs. Each entry contains protein sequence and structural information, membrane topology, experimental conditions, thermodynamic parameters such as melting temperature, free energy, enthalpy etc. and literature information. MPTherm assists users to retrieve the data by using different search and display options. We have also provided the sequence and structure visualization as well as cross-links to UniProt and PDB databases. MPTherm database is freely available at http://www.iitm.ac.in/bioinfo/mptherm/. It is implemented in HTML, PHP, MySQL and JavaScript, and supports the latest versions of major browsers, such as Firefox, Chrome and Opera. MPTherm would serve as an effective resource for understanding the stability of MPs, development of prediction tools and identifying drug targets for diseases associated with MPs.
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Affiliation(s)
| | - R Sakthivel
- Medical Biochemistry from University of Madras, India
| | - M Michael Gromiha
- Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India
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17
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Kulandaisamy A, Zaucha J, Sakthivel R, Frishman D, Michael Gromiha M. Pred‐MutHTP: Prediction of disease‐causing and neutral mutations in human transmembrane proteins. Hum Mutat 2019; 41:581-590. [DOI: 10.1002/humu.23961] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 11/05/2019] [Accepted: 11/20/2019] [Indexed: 12/24/2022]
Affiliation(s)
- A. Kulandaisamy
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciencesIndian Institute of Technology MadrasChennai Tamilnadu India
| | - Jan Zaucha
- Department of Bioinformatics, Wissenschaftszentrum WeihenstephanTechnische Universität MünchenFreising Germany
| | - Ramasamy Sakthivel
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciencesIndian Institute of Technology MadrasChennai Tamilnadu India
| | - Dmitrij Frishman
- Department of Bioinformatics, Wissenschaftszentrum WeihenstephanTechnische Universität MünchenFreising Germany
- Department of BioinformaticsPeter the Great St. Petersburg Polytechnic UniversitySt. Petersburg Russian Federation
| | - M. Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciencesIndian Institute of Technology MadrasChennai Tamilnadu India
- Advanced Computational Drug Discovery Unit, Tokyo Tech World Research Hub Initiative (WRHI), Institute of Innovative ResearchTokyo Institute of TechnologyYokohama Japan
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18
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Kuligina ES, Sokolenko AP, Bizin IV, Romanko AA, Zagorodnev KA, Anisimova MO, Krylova DD, Anisimova EI, Mantseva MA, Varma AK, Hasan SK, Ni VI, Koloskov AV, Suspitsin EN, Venina AR, Aleksakhina SN, Sokolova TN, Milanović AM, Schürmann P, Prokofyeva DS, Bermisheva MA, Khusnutdinova EK, Bogdanova N, Dörk T, Imyanitov EN. Exome sequencing study of Russian breast cancer patients suggests a predisposing role for USP39. Breast Cancer Res Treat 2019; 179:731-742. [DOI: 10.1007/s10549-019-05492-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 11/07/2019] [Indexed: 12/11/2022]
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19
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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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20
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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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21
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Kulandaisamy A, Priya SB, Sakthivel R, Frishman D, Gromiha MM. Statistical analysis of disease‐causing and neutral mutations in human membrane proteins. Proteins 2019; 87:452-466. [DOI: 10.1002/prot.25667] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 01/16/2019] [Accepted: 01/31/2019] [Indexed: 11/11/2022]
Affiliation(s)
- A. Kulandaisamy
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BiosciencesIndian Institute of Technology Madras Chennai Tamil Nadu India
| | - S. Binny Priya
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BiosciencesIndian Institute of Technology Madras Chennai Tamil Nadu India
| | - R. Sakthivel
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BiosciencesIndian Institute of Technology Madras Chennai Tamil Nadu India
| | - Dmitrij Frishman
- Department of BioinformaticsPeter the Great St. Petersburg Polytechnic University St. Petersburg Russian Federation
- Department of BioinformaticsTechnische Universität München, Wissenschaftszentrum Weihenstephan Freising Germany
| | - M. Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BiosciencesIndian Institute of Technology Madras Chennai Tamil Nadu India
- Advanced Computational Drug Discovery Unit (ACDD)Institute of Innovative Research, Tokyo Institute of Technology Yokohama Kanagawa Japan
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22
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Ganesan K, Kulandaisamy A, Binny Priya S, Gromiha MM. HuVarBase: A human variant database with comprehensive information at gene and protein levels. PLoS One 2019; 14:e0210475. [PMID: 30703169 PMCID: PMC6354970 DOI: 10.1371/journal.pone.0210475] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 12/25/2018] [Indexed: 12/20/2022] Open
Abstract
Human variant databases could be better exploited if the variant data available in multiple resources is integrated in a single comprehensive resource along with sequence and structural features. Such integration would improve the analyses of variants for disease prediction, prevention or treatment. The HuVarBase (HUmanVARiantdataBASE) assimilates publicly available human variant data at protein level and gene level into a comprehensive resource. Protein level data such as amino acid sequence, secondary structure of the mutant residue, domain, function, subcellular location and post-translational modification are integrated with gene level data such as gene name, chromosome number & genome position, DNA mutation, mutation type origin and rs ID number. Disease class has been added for the disease causing variants. The database is publicly available at https://www.iitm.ac.in/bioinfo/huvarbase. A total of 774,863 variant records, integrated in the HuVarBase, can be searched with options to display, visualize and download the results.
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Affiliation(s)
- Kaliappan Ganesan
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai, Tamilnadu, India
- * E-mail: (MMG); (KG)
| | - A. Kulandaisamy
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai, Tamilnadu, India
| | - S. Binny Priya
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai, Tamilnadu, India
| | - M. Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai, Tamilnadu, India
- Advanced Computational Drug Discovery Unit (ACDD), Institute of Innovative Research, Tokyo Institute of Technology, Midori-ku, Yokohama, Kanagawa, Japan
- * E-mail: (MMG); (KG)
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