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Türkyılmaz A, Sağer SG, Tekin E, Teralı K, Düzkalır H, Eser M, Akın Y. Expanding the clinical and genetic landscape of (developmental) epileptic encephalopathy with spike-and-wave activation in sleep: results from studies of a Turkish cohort. Neurogenetics 2024; 25:119-130. [PMID: 38388889 DOI: 10.1007/s10048-024-00751-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 02/16/2024] [Indexed: 02/24/2024]
Abstract
The terms developmental epileptic encephalopathy with spike-and-wave activation in sleep (DEE-SWAS) and epileptic encephalopathy with spike-and-wave activation in sleep (EE-SWAS) designate a spectrum of conditions that are typified by different combinations of motor, cognitive, language, and behavioral regression linked to robust spike-and-wave activity during sleep. In this study, we aimed at describing the clinical and molecular findings in "(developmental) epileptic encephalopathy with spike-and-wave activation in sleep" (D)EE-SWAS) patients as well as at contributing to the genetic etiologic spectrum of (D)EE-SWAS. Single nucleotide polymorphism (SNP) array and whole-exome sequencing (WES) techniques were used to determine the underlying genetic etiologies. Of the 24 patients included in the study, 8 (33%) were female and 16 (67%) were male. The median age at onset of the first seizure was 4 years and the median age at diagnosis of (D)EE-SWAS was 5 years. Of the 24 cases included in the study, 13 were compatible with the clinical diagnosis of DEE-SWAS and 11 were compatible with the clinical diagnosis of EE-SWAS. Abnormal perinatal history was present in four cases (17%), and two cases (8%) had a family history of epilepsy. Approximately two-thirds (63%) of all patients had abnormalities detected on brain computerized tomography/magnetic resonance (CT/MR) imaging. After SNP array and WES analysis, the genetic etiology was revealed in 7 out of 24 (29%) cases. Three of the variants detected were novel (SLC12A5, DLG4, SLC9A6). This study revealed for the first time that Smith-Magenis syndrome, SCN8A-related DEE type 13 and SLC12A5 gene variation are involved in the genetic etiology of (D)EE-SWAS. (D)EE-SWAS is a genetically diverse disorder with underlying copy number variations and single-gene abnormalities. In the current investigation, rare novel variations in genes known to be related to (D)EE-SWAS and not previously reported genes to be related to (D)EE-SWAS were discovered, adding to the molecular genetic spectrum. Molecular etiology enables the patient and family to receive thorough and accurate genetic counseling as well as a personalized medicine approach.
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Affiliation(s)
- Ayberk Türkyılmaz
- Department of Medical Genetics, Karadeniz Technical University Faculty of Medicine, Ortahisar, 61100, Trabzon, Türkiye.
| | - Safiye Güneş Sağer
- Department of Pediatric Neurology, Kartal Dr. Lütfi Kirdar City Hospital, Istanbul, Türkiye
| | - Emine Tekin
- Department of Pediatric Neurology, Giresun University Maternity and Children Hospital, Giresun, Türkiye
| | - Kerem Teralı
- Department of Medical Biochemistry, Cyprus International University Faculty of Medicine, Nicosia, Cyprus
| | - Hanife Düzkalır
- Department of Radiology, Kartal Dr. Lütfi Kirdar City Hospital, Istanbul, Türkiye
| | - Metin Eser
- Department of Medical Genetics, Ümraniye Research and Training Hospital, Istanbul, Türkiye
| | - Yasemin Akın
- Department of Pediatrics, Kartal Dr. Lütfi Kirdar City Hospital, Istanbul, Türkiye
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2
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Yan Z, Ge F, Liu Y, Zhang Y, Li F, Song J, Yu DJ. TransEFVP: A Two-Stage Approach for the Prediction of Human Pathogenic Variants Based on Protein Sequence Embedding Fusion. J Chem Inf Model 2024; 64:1407-1418. [PMID: 38334115 DOI: 10.1021/acs.jcim.3c02019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
Studying the effect of single amino acid variations (SAVs) on protein structure and function is integral to advancing our understanding of molecular processes, evolutionary biology, and disease mechanisms. Screening for deleterious variants is one of the crucial issues in precision medicine. Here, we propose a novel computational approach, TransEFVP, based on large-scale protein language model embeddings and a transformer-based neural network to predict disease-associated SAVs. The model adopts a two-stage architecture: the first stage is designed to fuse different feature embeddings through a transformer encoder. In the second stage, a support vector machine model is employed to quantify the pathogenicity of SAVs after dimensionality reduction. The prediction performance of TransEFVP on blind test data achieves a Matthews correlation coefficient of 0.751, an F1-score of 0.846, and an area under the receiver operating characteristic curve of 0.871, higher than the existing state-of-the-art methods. The benchmark results demonstrate that TransEFVP can be explored as an accurate and effective SAV pathogenicity prediction method. The data and codes for TransEFVP are available at https://github.com/yzh9607/TransEFVP/tree/master for academic use.
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Affiliation(s)
- Zihao Yan
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, PR China
| | - Fang Ge
- State Key Laboratory of Organic Electronics and lnformation Displays & lnstitute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan Road, Nanjing 210023, PR China
| | - Yan Liu
- Department of Computer Science, Yangzhou University, Yangzhou 225100, PR China
| | - Yumeng Zhang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, PR China
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Fuyi Li
- South Australian immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia 5005, Australia
- The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Victoria 3000, Australia
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, PR China
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3
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Pandey M, Shah SK, Gromiha MM. Computational approaches for identifying disease-causing mutations in proteins. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2023; 139:141-171. [PMID: 38448134 DOI: 10.1016/bs.apcsb.2023.11.007] [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: 03/08/2024]
Abstract
Advancements in genome sequencing have expanded the scope of investigating mutations in proteins across different diseases. Amino acid mutations in a protein alter its structure, stability and function and some of them lead to diseases. Identification of disease-causing mutations is a challenging task and it will be helpful for designing therapeutic strategies. Hence, mutation data available in the literature have been curated and stored in several databases, which have been effectively utilized for developing computational methods to identify deleterious mutations (drivers), using sequence and structure-based properties of proteins. In this chapter, we describe the contents of specific databases that have information on disease-causing and neutral mutations followed by sequence and structure-based properties. Further, characteristic features of disease-causing mutations will be discussed along with computational methods for identifying cancer hotspot residues and disease-causing mutations in proteins.
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Affiliation(s)
- Medha Pandey
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Suraj Kumar Shah
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India; International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama, Japan.
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4
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Ou YY, Ho QT, Chang HT. Recent advances in features generation for membrane protein sequences: From multiple sequence alignment to pre-trained language models. Proteomics 2023; 23:e2200494. [PMID: 37863817 DOI: 10.1002/pmic.202200494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 09/19/2023] [Accepted: 09/20/2023] [Indexed: 10/22/2023]
Abstract
Membrane proteins play a crucial role in various cellular processes and are essential components of cell membranes. Computational methods have emerged as a powerful tool for studying membrane proteins due to their complex structures and properties that make them difficult to analyze experimentally. Traditional features for protein sequence analysis based on amino acid types, composition, and pair composition have limitations in capturing higher-order sequence patterns. Recently, multiple sequence alignment (MSA) and pre-trained language models (PLMs) have been used to generate features from protein sequences. However, the significant computational resources required for MSA-based features generation can be a major bottleneck for many applications. Several methods and tools have been developed to accelerate the generation of MSAs and reduce their computational cost, including heuristics and approximate algorithms. Additionally, the use of PLMs such as BERT has shown great potential in generating informative embeddings for protein sequence analysis. In this review, we provide an overview of traditional and more recent methods for generating features from protein sequences, with a particular focus on MSAs and PLMs. We highlight the advantages and limitations of these approaches and discuss the methods and tools developed to address the computational challenges associated with features generation. Overall, the advancements in computational methods and tools provide a promising avenue for gaining deeper insights into the function and properties of membrane proteins, which can have significant implications in drug discovery and personalized medicine.
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Affiliation(s)
- Yu-Yen Ou
- Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan
- Graduate Program in Biomedical Informatics, Yuan Ze University, Chung-Li, Taiwan
| | - Quang-Thai Ho
- Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan
| | - Heng-Ta Chang
- Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan
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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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6
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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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7
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Elnaggar A, Heinzinger M, Dallago C, Rehawi G, Wang Y, Jones L, Gibbs T, Feher T, Angerer C, Steinegger M, Bhowmik D, Rost B. ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022; 44:7112-7127. [PMID: 34232869 DOI: 10.1109/tpami.2021.3095381] [Citation(s) in RCA: 307] [Impact Index Per Article: 153.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models (BERT, Albert, Electra, T5) on data from UniRef and BFD containing up to 393 billion amino acids. The protein LMs (pLMs) were trained on the Summit supercomputer using 5616 GPUs and TPU Pod up-to 1024 cores. Dimensionality reduction revealed that the raw pLM-embeddings from unlabeled data captured some biophysical features of protein sequences. We validated the advantage of using the embeddings as exclusive input for several subsequent tasks: (1) a per-residue (per-token) prediction of protein secondary structure (3-state accuracy Q3=81%-87%); (2) per-protein (pooling) predictions of protein sub-cellular location (ten-state accuracy: Q10=81%) and membrane versus water-soluble (2-state accuracy Q2=91%). For secondary structure, the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without multiple sequence alignments (MSAs) or evolutionary information thereby bypassing expensive database searches. Taken together, the results implied that pLMs learned some of the grammar of the language of life. All our models are available through https://github.com/agemagician/ProtTrans.
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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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Elnaggar A, Heinzinger M, Dallago C, Rehawi G, Wang Y, Jones L, Gibbs T, Feher T, Angerer C, Steinegger M, Bhowmik D, Rost B. ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022. [PMID: 34232869 DOI: 10.1101/2020.07.12.199554] [Citation(s) in RCA: 66] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models (BERT, Albert, Electra, T5) on data from UniRef and BFD containing up to 393 billion amino acids. The protein LMs (pLMs) were trained on the Summit supercomputer using 5616 GPUs and TPU Pod up-to 1024 cores. Dimensionality reduction revealed that the raw pLM-embeddings from unlabeled data captured some biophysical features of protein sequences. We validated the advantage of using the embeddings as exclusive input for several subsequent tasks: (1) a per-residue (per-token) prediction of protein secondary structure (3-state accuracy Q3=81%-87%); (2) per-protein (pooling) predictions of protein sub-cellular location (ten-state accuracy: Q10=81%) and membrane versus water-soluble (2-state accuracy Q2=91%). For secondary structure, the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without multiple sequence alignments (MSAs) or evolutionary information thereby bypassing expensive database searches. Taken together, the results implied that pLMs learned some of the grammar of the language of life. All our models are available through https://github.com/agemagician/ProtTrans.
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10
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Ge F, Zhang Y, Xu J, Muhammad A, Song J, Yu DJ. Prediction of disease-associated nsSNPs by integrating multi-scale ResNet models with deep feature fusion. Brief Bioinform 2021; 23:6483068. [PMID: 34953462 DOI: 10.1093/bib/bbab530] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 11/13/2021] [Accepted: 11/16/2021] [Indexed: 11/13/2022] Open
Abstract
More than 6000 human diseases have been recorded to be caused by non-synonymous single nucleotide polymorphisms (nsSNPs). Rapid and accurate prediction of pathogenic nsSNPs can improve our understanding of the principle and design of new drugs, which remains an unresolved challenge. In the present work, a new computational approach, termed MSRes-MutP, is proposed based on ResNet blocks with multi-scale kernel size to predict disease-associated nsSNPs. By feeding the serial concatenation of the extracted four types of features, the performance of MSRes-MutP does not obviously improve. To address this, a second model FFMSRes-MutP is developed, which utilizes deep feature fusion strategy and multi-scale 2D-ResNet and 1D-ResNet blocks to extract relevant two-dimensional features and physicochemical properties. FFMSRes-MutP with the concatenated features achieves a better performance than that with individual features. The performance of FFMSRes-MutP is benchmarked on five different datasets. It achieves the Matthew's correlation coefficient (MCC) of 0.593 and 0.618 on the PredictSNP and MMP datasets, which are 0.101 and 0.210 higher than that of the existing best method PredictSNP1. When tested on the HumDiv and HumVar datasets, it achieves MCC of 0.9605 and 0.9507, and area under curve (AUC) of 0.9796 and 0.9748, which are 0.1747 and 0.2669, 0.0853 and 0.1335, respectively, higher than the existing best methods PolyPhen-2 and FATHMM (weighted). In addition, on blind test using a third-party dataset, FFMSRes-MutP performs as the second-best predictor (with MCC and AUC of 0.5215 and 0.7633, respectively), when compared with the other four predictors. Extensive benchmarking experiments demonstrate that FFMSRes-MutP achieves effective feature fusion and can be explored as a useful approach for predicting disease-associated nsSNPs. The webserver is freely available at http://csbio.njust.edu.cn/bioinf/ffmsresmutp/ for academic use.
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Affiliation(s)
- Fang Ge
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, China
| | - Ying Zhang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, China
| | - Jian Xu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, China
| | - Arif Muhammad
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, China
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.,Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, China
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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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Narykov O, Johnson NT, Korkin D. Predicting protein interaction network perturbation by alternative splicing with semi-supervised learning. Cell Rep 2021; 37:110045. [PMID: 34818539 DOI: 10.1016/j.celrep.2021.110045] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 07/21/2021] [Accepted: 11/02/2021] [Indexed: 10/19/2022] Open
Abstract
Alternative splicing introduces an additional layer of protein diversity and complexity in regulating cellular functions that can be specific to the tissue and cell type, physiological state of a cell, or disease phenotype. Recent high-throughput experimental studies have illuminated the functional role of splicing events through rewiring protein-protein interactions; however, the extent to which the macromolecular interactions are affected by alternative splicing has yet to be fully understood. In silico methods provide a fast and cheap alternative to interrogating functional characteristics of thousands of alternatively spliced isoforms. Here, we develop an accurate feature-based machine learning approach that predicts whether a protein-protein interaction carried out by a reference isoform is perturbed by an alternatively spliced isoform. Our method, called the alternatively spliced interactions prediction (ALT-IN) tool, is compared with the state-of-the-art PPI prediction tools and shows superior performance, achieving 0.92 in precision and recall values.
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Affiliation(s)
- Oleksandr Narykov
- Department of Computer Science, and Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Nathan T Johnson
- Department of Computer Science, and Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA, USA; Harvard Program in Therapeutic Sciences, Harvard Medical School, and Breast Tumor Immunology Laboratory, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Dmitry Korkin
- Department of Computer Science, and Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA, USA.
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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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Kalman ZE, Mészáros B, Gáspári Z, Dobson L. Distribution of disease-causing germline mutations in coiled-coils implies an important role of their N-terminal region. Sci Rep 2020; 10:17333. [PMID: 33060664 PMCID: PMC7562717 DOI: 10.1038/s41598-020-74354-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 09/21/2020] [Indexed: 11/08/2022] Open
Abstract
Next-generation sequencing resulted in the identification of a huge number of naturally occurring variations in human proteins. The correct interpretation of the functional effects of these variations necessitates the understanding of how they modulate protein structure. Coiled-coils are α-helical structures responsible for a diverse range of functions, but most importantly, they facilitate the structural organization of macromolecular scaffolds via oligomerization. In this study, we analyzed a comprehensive set of disease-associated germline mutations in coiled-coil structures. Our results suggest an important role of residues near the N-terminal part of coiled-coil regions, possibly critical for superhelix assembly and folding in some cases. We also show that coiled-coils of different oligomerization states exhibit characteristically distinct patterns of disease-causing mutations. Our study provides structural and functional explanations on how disease emerges through the mutation of these structural motifs.
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Affiliation(s)
- Zsofia E Kalman
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/A, 1083, Budapest, Hungary
- 3in-PPCU Research Group, 2500, Esztergom, Hungary
| | - Bálint Mészáros
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - Zoltán Gáspári
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/A, 1083, Budapest, Hungary.
| | - Laszlo Dobson
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/A, 1083, Budapest, Hungary.
- Research Centre for Natural Sciences, Magyar Tudósok Körútja 2, 1117, Budapest, Hungary.
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15
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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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16
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Rangaswamy U, Dharshini SAP, Yesudhas D, Gromiha MM. VEPAD - Predicting the effect of variants associated with Alzheimer's disease using machine learning. Comput Biol Med 2020; 124:103933. [PMID: 32828070 DOI: 10.1016/j.compbiomed.2020.103933] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 07/25/2020] [Accepted: 07/25/2020] [Indexed: 12/26/2022]
Abstract
INTRODUCTION Alzheimer's disease (AD) is a complex and heterogeneous disease that affects neuronal cells over time and it is prevalent among all neurodegenerative diseases. Next Generation Sequencing (NGS) techniques are widely used for developing high-throughput screening methods to identify biomarkers and variants, which help early diagnosis and treatments. OBJECTIVE The primary purpose of this study is to develop a classification model using machine learning for predicting the deleterious effect of variants with respect to AD. METHODS We have constructed a set of 20,401 deleterious and 37,452 control variants from Genome-Wide Association Study (GWAS) and Genotype-Tissue Expression (GTEx) portals, respectively. Recursive feature elimination using cross-validation (RFECV) followed by a forward feature selection method was utilized to select the important features and a random forest classifier was used for distinguishing between deleterious and neutral variants. RESULTS Our method showed an accuracy of 81.21% on 10-fold cross-validation and 70.63% on a test set of 5785 variants. The same test set was used to compare the performance of CADD and FATHMM and their accuracies are in the range of 54%-62%. CONCLUSION Our model is freely available as the Variant Effect Predictor for Alzheimer's Disease (VEPAD) at http://web.iitm.ac.in/bioinfo2/vepad/. VEPAD can be used to predict the effect of new variants associated with AD.
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Affiliation(s)
- Uday Rangaswamy
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, India
| | - S Akila Parvathy Dharshini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, India
| | - Dhanusha Yesudhas
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, India
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, India; School of Computing, Tokyo Tech World Research Hub Initiative (WRHI), Institute of Innovative Research, Tokyo Institute of Technology, Midori-ku, Kanagawa, 226-8503, Yokohama, Japan.
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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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