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TMKit: a Python interface for computational analysis of transmembrane proteins.
Sun, Jianfeng; Kulandaisamy, Arulsamy; Ru, Jinlong; Gromiha, M Michael; Cribbs, Adam P.
Afiliação
  • Sun J; Nuffield Department of Orthopedics, Rheumatology, and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Headington, Oxford OX3 7LD, UK.
  • Kulandaisamy A; Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India.
  • Ru J; Chair of Prevention of Microbial Diseases, School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany.
  • Gromiha MM; Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India.
  • Cribbs AP; Nuffield Department of Orthopedics, Rheumatology, and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Headington, Oxford OX3 7LD, UK.
Brief Bioinform ; 24(5)2023 09 20.
Article em En | MEDLINE | ID: mdl-37594311
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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Biologia Computacional Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Biologia Computacional Idioma: En Ano de publicação: 2023 Tipo de documento: Article