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Membranome 3.0: Database of single-pass membrane proteins with AlphaFold models
dc.contributor.authorLomize, Andrei L.
dc.contributor.authorSchnitzer, Kevin A.
dc.contributor.authorTodd, Spencer C.
dc.contributor.authorCherepanov, Stanislav
dc.contributor.authorOuteiral, Carlos
dc.contributor.authorDeane, Charlotte M.
dc.contributor.authorPogozheva, Irina D.
dc.date.accessioned2022-05-06T17:25:10Z
dc.date.available2023-06-06 13:25:08en
dc.date.available2022-05-06T17:25:10Z
dc.date.issued2022-05
dc.identifier.citationLomize, Andrei L.; Schnitzer, Kevin A.; Todd, Spencer C.; Cherepanov, Stanislav; Outeiral, Carlos; Deane, Charlotte M.; Pogozheva, Irina D. (2022). "Membranome 3.0: Database of single-pass membrane proteins with AlphaFold models." Protein Science 31(5): n/a-n/a.
dc.identifier.issn0961-8368
dc.identifier.issn1469-896X
dc.identifier.urihttps://hdl.handle.net/2027.42/172241
dc.description.abstractThe Membranome database provides comprehensive structural information on single-pass (i.e., bitopic) membrane proteins from six evolutionarily distant organisms, including protein–protein interactions, complexes, mutations, experimental structures, and models of transmembrane α-helical dimers. We present a new version of this database, Membranome 3.0, which was significantly updated by revising the set of 5,758 bitopic proteins and incorporating models generated by AlphaFold 2 in the database. The AlphaFold models were parsed into structural domains located at the different membrane sides, modified to exclude low-confidence unstructured terminal regions and signal sequences, validated through comparison with available experimental structures, and positioned with respect to membrane boundaries. Membranome 3.0 was re-developed to facilitate visualization and comparative analysis of multiple 3D structures of proteins that belong to a specified family, complex, biological pathway, or membrane type. New tools for advanced search and analysis of proteins, their interactions, complexes, and mutations were included. The database is freely accessible at https://membranome.org.
dc.publisherJohn Wiley & Sons, Inc.
dc.subject.otherweb tool
dc.subject.othervisualization
dc.subject.othernetwork analysis
dc.subject.otherfull-length protein model
dc.titleMembranome 3.0: Database of single-pass membrane proteins with AlphaFold models
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelBiological Chemistry
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/172241/1/pro4318_am.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/172241/2/pro4318-sup-0001-SupInfo.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/172241/3/pro4318.pdf
dc.identifier.doi10.1002/pro.4318
dc.identifier.sourceProtein Science
dc.identifier.citedreferenceMeldal BHM, Bye AJH, Gajdoš L, et al. Complex portal 2018: Extended content and enhanced visualization tools for macromolecular complexes. Nucleic Acids Res. 2019; 47: D550 – D558.
dc.identifier.citedreferenceAkdel M, Pires DEV, Porta Pardo E, et al. A structural biology community assessment of AlphaFold 2 applications. bioRxiv. 2021; 2. https://doi.org/10.1101/2021.09.26.461876
dc.identifier.citedreferenceOuteiral C, Nissley DA, Deane CM. Current structure predictors are not learning the physics of protein folding. Bioinformatics. 2022; 38: 1881 – 1887.
dc.identifier.citedreferencePak MA, Markhieva KA, Novikova MS, et al. Using AlphaFold to predict the impact of single mutations on protein stability and function. bioRxiv. 2021. https://doi.org/10.1101/2021.09.19.460937
dc.identifier.citedreferenceVaradi M, Anyango S, Deshpande M, et al. AlphaFold protein structure database: Massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 2022; 50: D439 – D444.
dc.identifier.citedreferenceLaskowski RA, Thornton JM. PDBsum extras: SARS-Cov-2 and AlphaFold models. Protein Sci. 2022; 31; 283 – 289.
dc.identifier.citedreferenceDas J, Yu H. HINT: High-quality protein interactomes and their applications in understanding human disease. BMC Syst Biol. 2012; 6: 92.
dc.identifier.citedreferenceAlonso-López D, Campos-Laborie FJ, Gutiérrez MA, et al. APID database: Redefining protein-protein interaction experimental evidences and binary interactomes. Database (Oxford). 2019; 2019: baz005.
dc.identifier.citedreferenceOrchard S, Ammari M, Aranda B, et al. The MIntAct project--intact as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 2014; 42: D358 – D363.
dc.identifier.citedreferenceOughtred R, Stark C, Breitkreutz BJ, et al. The bioGRID interaction database: 2019 update. Nucleic Acids Res. 2019; 47: D529 – D541.
dc.identifier.citedreferenceSzklarczyk D, Gable AL, Lyon D, et al. STRING V11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019; 47: D607 – D613.
dc.identifier.citedreferenceJassal B, Matthews L, Viteri G, et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 2020; 48: D498 – D503.
dc.identifier.citedreferenceGiurgiu M, Reinhard J, Brauner B, et al. CORUM: The comprehensive resource of mammalian protein complexes-2019. Nucleic Acids Res. 2019; 47: D559 – D563.
dc.identifier.citedreferenceKulandaisamy A, Binny Priya S, Sakthivel R, et al. MutHTP: Mutations in human transmembrane proteins. Bioinformatics. 2018; 34: 2325 – 2326.
dc.identifier.citedreferenceKanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017; 45: D353 – D361.
dc.identifier.citedreferencePaley S, Karp PD. The BioCyc metabolic network explorer. BMC Bioinformatics. 2021; 22: 208.
dc.identifier.citedreferenceMartens M, Ammar A, Riutta A, et al. WikiPathways: Connecting communities. Nucleic Acids Res. 2021; 49: D613 – D621.
dc.identifier.citedreferenceWishart DS, Guo A, Oler E, et al. HMDB 5.0: The human metabolome database for 2022. Nucleic Acids Res. 2022; 50: D622 – D631.
dc.identifier.citedreferenceThornton JM, Laskowski RA, Borkakoti N. AlphaFold heralds a data-driven revolution in biology and medicine. Nat Med. 2021; 27: 1666 – 1669.
dc.identifier.citedreferenceZhang Y, Skolnick J. TM-align: A protein structure alignment algorithm based on the TM-score. Nucleic Acids Res. 2005; 33: 2302 – 2309.
dc.identifier.citedreferenceEvans R, O’Neill M, Pritzel A, et al. Protein complex prediction with AlphaFold-Multimer. bioRxiv. 2021. https://doi.org/10.1101/2021.10.04.463034
dc.identifier.citedreferenceWang J, Youkharibache P, Zhang D, et al. iCn3D, a web-based 3D viewer for sharing 1D/2D/3D representations of biomolecular structures. Bioinformatics. 2020; 36: 131 – 135.
dc.identifier.citedreferenceVirag I, Stoicu-Tivadar L, Crişan-Vida M. Gesture interaction browser-based 3D molecular viewer. Stud Health Technol Inform. 2016; 226: 17 – 20.
dc.identifier.citedreferenceHerráez A. Biomolecules in the computer: Jmol to the rescue. Biochem Mol Biol Educ. 2006; 34: 255 – 261.
dc.identifier.citedreferenceFranz M, Lopes CT, Huck G, Dong Y, Sumer O, Bader GD. Cytoscape.Js: A graph theory library for visualisation and analysis. Bioinformatics. 2016; 32: 309 – 311.
dc.identifier.citedreferencePogozheva ID, Lomize AL. Evolution and adaptation of single-pass transmembrane proteins. Biochim Biophys Acta Biomembr. 2018; 1860: 364 – 377.
dc.identifier.citedreferenceLomize AL, Lomize MA, Krolicki SR, Pogozheva ID. Membranome: A database for proteome-wide analysis of single-pass membrane proteins. Nucleic Acids Res. 2017; 45: D250 – D255.
dc.identifier.citedreferenceLomize AL, Hage JM, Pogozheva ID. Membranome 2.0: Database for proteome-wide profiling of bitopic proteins and their dimers. Bioinformatics. 2018; 34: 1061 – 1062.
dc.identifier.citedreferenceConsortium The UniProt. UniProt: The universal protein knowledgebase. Nucleic Acids Res. 2017; 45: D158 – D169.
dc.identifier.citedreferenceEl-Gebali S, Mistry J, Bateman A, et al. The Pfam protein families database in 2019. Nucleic Acids Res. 2019; 47: D427 – D432.
dc.identifier.citedreferenceDeLano WL. The PyMOL molecular graphics system. San Carlos, CA: Delano Scientific LLC, 2003. http://www.pymol.org/.
dc.identifier.citedreferenceBurley SK, Berman HM, Kleywegt GJ, Markley JL, Nakamura H, Velankar S. Protein Data Bank (PDB): The single global macromolecular structure archive. Methods Mol Biol. 2017; 1607: 627 – 641.
dc.identifier.citedreferenceLaskowski RA, Jabłońska J, Pravda L, Vařeková RS, Thornton JM. PDBsum: Structural summaries of PDB entries. Protein Sci. 2018; 27: 129 – 134.
dc.identifier.citedreferenceLomize MA, Pogozheva ID, Joo H, Mosberg HI, Lomize AL. OPM database and PPM web server: Resources for positioning of proteins in membranes. Nucleic Acids Res. 2012; 40: D370 – D376.
dc.identifier.citedreferenceLomize AL, Schnitzer KA, Todd SC, Pogozheva ID. Thermodynamics-based molecular modeling of α-helices in membranes and micelles. J Chem Inf Model. 2021; 61: 2884 – 2896.
dc.identifier.citedreferenceLomize AL, Pogozheva ID. TMDOCK: An energy-based method for modeling alpha-helical dimers in membranes. J Mol Biol. 2017; 429: 390 – 398.
dc.identifier.citedreferenceKryshtafovych A, Schwede T, Topf M, Fidelis K, Moult J. Critical assessment of methods of protein structure prediction (CASP)-round XIV. Proteins. 2021; 89: 1607 – 1617.
dc.identifier.citedreferenceJumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021; 596: 583 – 589.
dc.identifier.citedreferenceTunyasuvunakool K, Adler J, Wu Z, et al. Highly accurate protein structure prediction for the human proteome. Nature. 2021; 596: 590 – 596.
dc.working.doiNOen
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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