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TMKit: a Python interface for computational analysis of transmembrane proteins
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516361/ https://www.ncbi.nlm.nih.gov/pubmed/37594311 http://dx.doi.org/10.1093/bib/bbad288 |
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author | Sun, Jianfeng Kulandaisamy, Arulsamy Ru, Jinlong Gromiha, M Michael Cribbs, Adam P |
author_facet | Sun, Jianfeng Kulandaisamy, Arulsamy Ru, Jinlong Gromiha, M Michael Cribbs, Adam P |
author_sort | Sun, Jianfeng |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10516361 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-105163612023-09-23 TMKit: a Python interface for computational analysis of transmembrane proteins Sun, Jianfeng Kulandaisamy, Arulsamy Ru, Jinlong Gromiha, M Michael Cribbs, Adam P Brief Bioinform Problem Solving Protocol 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. Oxford University Press 2023-08-17 /pmc/articles/PMC10516361/ /pubmed/37594311 http://dx.doi.org/10.1093/bib/bbad288 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Problem Solving Protocol Sun, Jianfeng Kulandaisamy, Arulsamy Ru, Jinlong Gromiha, M Michael Cribbs, Adam P TMKit: a Python interface for computational analysis of transmembrane proteins |
title | TMKit: a Python interface for computational analysis of transmembrane proteins |
title_full | TMKit: a Python interface for computational analysis of transmembrane proteins |
title_fullStr | TMKit: a Python interface for computational analysis of transmembrane proteins |
title_full_unstemmed | TMKit: a Python interface for computational analysis of transmembrane proteins |
title_short | TMKit: a Python interface for computational analysis of transmembrane proteins |
title_sort | tmkit: a python interface for computational analysis of transmembrane proteins |
topic | Problem Solving Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516361/ https://www.ncbi.nlm.nih.gov/pubmed/37594311 http://dx.doi.org/10.1093/bib/bbad288 |
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