Abstract
The modular organization of a cell which can be determined by its interaction network allows us to understand a mesh of cooperation among the functional modules. Therefore, cellular-level identification of functional modules aids in understanding the functional and structural characteristics of the biological network of a cell and also assists in determining or comprehending the evolutionary signal. We develop ProFuMCell that performs real-time Web scraping for generating clusters of the cellular level functional units of an organism. ProFuMCell constructs the Protein Locality Graphs and clusters the cellular level functional units of an organism by utilizing experimentally verified data from various online sources. Also, we develop ProModb, a database service that houses precomputed whole-cell protein-protein interaction network-based functional modules of an organism using ProFuMCell. Our Web service is entirely synchronized with the KEGG pathway database and allows users to generate spatially localized protein modules for any organism belonging to the KEGG genome using its real-time Web scraping characteristics. Hence, the server will host as many organisms as is maintained by the KEGG database. Our Web services provide the users a comprehensive and integrated tool for an efficient browsing and extraction of the spatial locality-based protein locality graph and the functional modules constructed by gathering experimental data from several interaction databases and pathway maps. We believe that our Web services will be beneficial in pharmacological research, where a novel research domain called modular pharmacology has initiated the study on the diagnosis, prevention, and treatment of deadly diseases using functional modules.
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Data availability
Publicly available ProFuMCell (https://cosmos.iitkgp.ac.in/ProFuMCell) and ProModb (https://cosmos.iitkgp.ac.in/ProModb) are free for all the users, and there is no login requirement.
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Change history
19 April 2023
A Correction to this paper has been published: https://doi.org/10.1007/s00894-023-05554-z
References
Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM, Bolival B Jr, Assad-Garcia N, Glass JI, Covert MW (2012) A whole-cell computational model predicts phenotype from genotype. Cell 150:389–401
Yu Y, Liu J, Feng N, Song B, Zheng Z (2017) Combining sequence and Gene Ontology for protein module detection in the Weighted Network. J Theor Biol 412:107–112
Wang Y, Qian X (2014) Functional module identification in protein interaction networks by interaction patterns. Bioinformatics 30:81–93
Barabási A-L, Gulbahce N, Loscalzo J (2011) Network medicine: a network-based approach to human disease. Nat Rev Genet. 12:56–68
Segal E, Friedman N, Koller D, Regev A (2004) A module map showing conditional activity of expression modules in cancer. Nat Genet 36:1090–1098
Thiagalingam S (2006) A cascade of modules of a network defines cancer progression. Cancer Res 66:7379–7385
Wang Z, Liu J, Yu Y, Chen Y, Wang Y (2012) Modular pharmacology: the next paradigm in drug discovery. Expert Opin Drug Discovery 7:667–677
Das B, Patil AR, Mitra P (2019) A network-based zoning for parallel whole-cell simulation. Bioinformatics 35:88–94
Das B, Mitra P (2021) High-performance whole-cell simulation exploiting modular cell biology principles. Journal of Chemical Information and Modeling 61:1481–1492
Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K (2016) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45:D353–D361
Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D (2004) The database of interacting proteins: 2004 update. Nucleic Acids Res 32:D449–D451
Orchard S, Ammari M, Aranda B, Breuza L, Briganti L, Broackes-Carter F, Campbell NH, Chavali G, Chen C, Del-Toro N et al (2014) The MIntAct project-IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res 42:D358–D363
Licata L, Briganti L, Peluso D, Perfetto L, Iannuccelli M, Galeota E, Sacco F, Palma A, Nardozza AP, Santonico E et al (2012) MINT, the molecular interaction database: 2012 update. Nucleic Acids Res 40:D857–D861
Oughtred R, Stark C, Breitkreutz B-J, Rust J, Boucher L, Chang C, Kolas N, O’Donnell L, Leung G, McAdam R et al (2019) The BioGRID interaction database: 2019 update. Nucleic Acids Res 47:D529–D541
Consortium GO (2019) The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res 47:D330–D338
Consortium U (2019) UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res 47:D506–D515
Otasek D, Morris JH, Bouças J, Pico AR, Demchak B (2019) Cytoscape automation: empowering workflow-based network analysis. Genome Biol 20:1–15
Singh R, Park D, Xu J, Hosur R, Berger B (2010) Struct2Net: a web service to predict protein-protein interactions using a structure-based approach. Nucleic Acids Res 38:W508–W515
Ali W, Rito T, Reinert G, Sun F, Deane CM (2014) Alignment-free protein interaction network comparison. Bioinformatics 30:i430–i437
Schoenrock A, Burnside D, Moteshareie H, Pitre S, Hooshyar M, Green JR, Golshani A, Dehne F, Wong A (2017) Evolution of protein-protein interaction networks in yeast. PLoS ONE 12:e0171920
Roguev A, Bandyopadhyay S, Zofall M, Zhang K, Fischer T, Collins SR, Qu H, Shales M, Park H-O, Hayles J et al (2008) Conservation and rewiring of functional modules revealed by anepistasis map in fission yeast. Science 322:405–410
Alon U (2019) An introduction to systems biology: design principles of biological circuits. CRC Press
Titz B, Schlesner M, Uetz P (2004) What do we learn from high-throughput protein interaction data? Expert Rev Proteomics 1:111–121
Funding
This work was supported by the Open Competitive Grand Challenge Seed Grants (SGIGC) of Indian Institute of Technology Kharagpur (SRIC project code: WBC). B. D. is supported by an INSPIRE Fellowship (INSPIRE Code-IF150632) sponsored by the Department of Science and Technology, Government of India.
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B.D. and P.M. conceived the problem; B.D. designed and implemented the servers; B.D. analyzed the data; B.D. and P.M. wrote the paper.
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The original online version of this article was revised due to changes of method term in article title and body text.
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Das, B., Mitra, P. ProFuMCell and ProModb: Web services for analyzing interaction-based functionally localized protein modules in a cell. J Mol Model 28, 167 (2022). https://doi.org/10.1007/s00894-022-05133-8
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DOI: https://doi.org/10.1007/s00894-022-05133-8