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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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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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Jayaraj JM, Jothimani M, Palanisamy CP, Pentikäinen OT, Pannipara M, Al-Sehemi AG, Muthusamy K, Gopinath K. Computational Study on the Inhibitory Effect of Natural Compounds against the SARS-CoV-2 Proteins. Bioinorg Chem Appl 2022; 2022:8635054. [PMID: 35340421 PMCID: PMC8948605 DOI: 10.1155/2022/8635054] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 02/11/2022] [Indexed: 02/06/2023] Open
Abstract
COVID-19 is more virulent and challenging to human life. In India, the Ministry of AYUSH recommended some strategies through Siddha, homeopathy, and other methods to effectively manage COVID-19 (Guidelines for AYUSH Clinical Studies in COVID-19, 2020). Kabasura Kudineer and homeopathy medicines are in use for the prevention and treatment of COVID-19 infection; however, the mechanism of action is less explored. This study aims to understand the antagonist activity of natural compounds found in Kabasura Kudineer and homeopathy medicines against the SARS-CoV-2 using computational methods. Potential compounds were screened against NSP-12, NSP-13, NSP-14, NSP-15, main protease, and spike proteins. Structure-based virtual screening results shows that, out of 14,682 Kabasura Kudineer compounds, the 250395, 129677029, 44259583, 44259584, and 88583189 compounds and, out of 3,112 homeopathy compounds, the 3802778, 320361, 5315832, 14590080, and 74029795 compounds have good scoring function against the SARS-CoV-2 structural and nonstructural proteins. As a result of docking, homeopathy compounds have a docking score ranging from -5.636 to 13.631 kcal/mol, while Kabasura Kudineer compounds have a docking score varying from -8.290 to -13.759 kcal/mol. It has been found that the selected compounds bind well to the active site of SARS-CoV-2 proteins and form hydrogen bonds. The molecular dynamics simulation study shows that the selected compounds have maintained stable conformation in the simulation period and interact with the target. This study supports the antagonist activity of natural compounds from Kabasura Kudineer and homeopathy against SARS-CoV-2's structural and nonstructural proteins.
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Affiliation(s)
- John Marshal Jayaraj
- Pharmacogenomics and CADD Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Muralidharan Jothimani
- Pharmacogenomics and CADD Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Chella Perumal Palanisamy
- State Key Laboratory of Biobased Materials and Green Paper Making, School of Food Science and Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, Shandong, China
| | - Olli T. Pentikäinen
- Faculty of Medicine, Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Mehboobali Pannipara
- Research Centre for Advanced Materials Science, King Khalid University, Abha 61413, Saudi Arabia
| | | | - Karthikeyan Muthusamy
- Pharmacogenomics and CADD Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Krishnasamy Gopinath
- Faculty of Medicine, Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland
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Sánchez Rodríguez F, Simpkin AJ, Davies OR, Keegan RM, Rigden DJ. Helical ensembles outperform ideal helices in molecular replacement. ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY 2020; 76:962-970. [PMID: 33021498 PMCID: PMC7543657 DOI: 10.1107/s205979832001133x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 08/18/2020] [Indexed: 03/21/2023]
Abstract
Helical ensembles solve more structures by MR with AMPLE than do ideal helices and at no greater CPU cost. The conventional approach in molecular replacement is the use of a related structure as a search model. However, this is not always possible as the availability of such structures can be scarce for poorly characterized families of proteins. In these cases, alternative approaches can be explored, such as the use of small ideal fragments that share high, albeit local, structural similarity with the unknown protein. Earlier versions of AMPLE enabled the trialling of a library of ideal helices, which worked well for largely helical proteins at suitable resolutions. Here, the performance of libraries of helical ensembles created by clustering helical segments is explored. The impacts of different B-factor treatments and different degrees of structural heterogeneity are explored. A 30% increase in the number of solutions obtained by AMPLE was observed when using this new set of ensembles compared with the performance with ideal helices. The boost in performance was notable across three different fold classes: transmembrane, globular and coiled-coil structures. Furthermore, the increased effectiveness of these ensembles was coupled to a reduction in the time required by AMPLE to reach a solution. AMPLE users can now take full advantage of this new library of search models by activating the ‘helical ensembles’ mode.
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Affiliation(s)
- Filomeno Sánchez Rodríguez
- Institute of Structural, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Adam J Simpkin
- Institute of Structural, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Owen R Davies
- Institute for Cell and Molecular Biosciences, Newcastle University, Framlington Place, Newcastle upon Tyne NE2 4HH, United Kingdom
| | - Ronan M Keegan
- UKRI-STFC, Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
| | - Daniel J Rigden
- Institute of Structural, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
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Keegan RM, McNicholas SJ, Thomas JMH, Simpkin AJ, Simkovic F, Uski V, Ballard CC, Winn MD, Wilson KS, Rigden DJ. Recent developments in MrBUMP: better search-model preparation, graphical interaction with search models, and solution improvement and assessment. Acta Crystallogr D Struct Biol 2018; 74:167-182. [PMID: 29533225 PMCID: PMC5947758 DOI: 10.1107/s2059798318003455] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 02/27/2018] [Indexed: 01/21/2023] Open
Abstract
Increasing sophistication in molecular-replacement (MR) software and the rapid expansion of the PDB in recent years have allowed the technique to become the dominant method for determining the phases of a target structure in macromolecular X-ray crystallography. In addition, improvements in bioinformatic techniques for finding suitable homologous structures for use as MR search models, combined with developments in refinement and model-building techniques, have pushed the applicability of MR to lower sequence identities and made weak MR solutions more amenable to refinement and improvement. MrBUMP is a CCP4 pipeline which automates all stages of the MR procedure. Its scope covers everything from the sourcing and preparation of suitable search models right through to rebuilding of the positioned search model. Recent improvements to the pipeline include the adoption of more sensitive bioinformatic tools for sourcing search models, enhanced model-preparation techniques including better ensembling of homologues, and the use of phase improvement and model building on the resulting solution. The pipeline has also been deployed as an online service through CCP4 online, which allows its users to exploit large bioinformatic databases and coarse-grained parallelism to speed up the determination of a possible solution. Finally, the molecular-graphics application CCP4mg has been combined with MrBUMP to provide an interactive visual aid to the user during the process of selecting and manipulating search models for use in MR. Here, these developments in MrBUMP are described with a case study to explore how some of the enhancements to the pipeline and to CCP4mg can help to solve a difficult case.
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Affiliation(s)
- Ronan M. Keegan
- CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
- STFC, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
| | - Stuart J. McNicholas
- York Structural Biology Laboratory, Department of Chemistry, University of York, York YO10 5DD, England
| | - Jens M. H. Thomas
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Adam J. Simpkin
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
- Synchrotron SOLEIL, L’Orme des Merisiers, Saint Aubin, BP 48, 91192 Gif-sur-Yvette, France
| | - Felix Simkovic
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Ville Uski
- CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
- STFC, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
| | - Charles C. Ballard
- CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
- STFC, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
| | - Martyn D. Winn
- CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
- STFC, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
| | - Keith S. Wilson
- York Structural Biology Laboratory, Department of Chemistry, University of York, York YO10 5DD, England
| | - Daniel J. Rigden
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
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