Treffer: Binding Affinity Prediction of Membrane Protein-Protein Complexes Using MPA-Pred.
Original Publication: Clifton, N.J. : Humana Press,
Almeida JG, Preto AJ, Koukos PI, Bonvin A, Moreira IS (2017) Membrane proteins structures: a review on computational modeling tools. Biochim Biophys Acta Biomembr 1859:2021–2039. (PMID: 10.1016/j.bbamem.2017.07.00828716627)
Daley DO (2008) The assembly of membrane proteins into complexes. Curr Opin Struct Biol 18:420–424. (PMID: 10.1016/j.sbi.2008.04.00618539022)
Nikam R, Ridha F, Jemimah S, Yugandhar K, Gromiha MM (2024) Binding affinity changes upon mutation in protein–protein complexes. In: Gromiha MM (ed) Protein mutations: consequences on structure, functions, and diseases. World Scientific, Singapore, pp 105–122. (PMID: 10.1142/9789811293269_0005)
Ridha F, Kulandaisamy A, Michael Gromiha M (2023) MPAD: a database for binding affinity of membrane protein-protein complexes and their mutants. J Mol Biol 435:167870. (PMID: 10.1016/j.jmb.2022.16787036309134)
Siebenmorgen T, Zacharias M (2020) Computational prediction of protein–protein binding affinities. WIREs Comput Mol Sci 10:e1448. (PMID: 10.1002/wcms.1448)
Vangone A, Bonvin AMJJ (2017) PRODIGY: a contact-based predictor of binding affinity in protein-protein complexes. Bio Protoc 7:e2124. (PMID: 344584478376549)
Romero-Molina S, Ruiz-Blanco YB, Mieres-Perez J, Harms M, Münch J, Ehrmann M, Sanchez-Garcia E (2022) PPI-affinity: a web tool for the prediction and optimization of protein-peptide and protein-protein binding affinity. J Proteome Res 21:1829–1841. (PMID: 10.1021/acs.jproteome.2c00020356544129361347)
Yugandhar K, Gromiha MM (2014) Protein-protein binding affinity prediction from amino acid sequence. Bioinformatics 30:3583–3589. (PMID: 10.1093/bioinformatics/btu58025172924)
Abbasi WA, Yaseen A, Hassan FU, Andleeb S, Minhas FUAA (2020) Island: in-silico proteins binding affinity prediction using sequence information. Biodata Min 13:20. (PMID: 10.1186/s13040-020-00231-w332924197688004)
Nikam R, Yugandhar K, Gromiha MM (2023) Deep learning-based method for predicting and classifying the binding affinity of protein-protein complexes. Biochim Biophys Acta Proteins Proteom 1871:140948. (PMID: 10.1016/j.bbapap.2023.14094837567456)
Ridha F, Gromiha MM (2024) MPA-Pred: a machine learning approach for predicting the binding affinity of membrane protein-protein complexes. Proteins 92:499–508. (PMID: 10.1002/prot.2663337949651)
Yu L, Sun C, Song D et al (2005) Nuclear magnetic resonance structural studies of a potassium channel-charybdotoxin complex. Biochemistry 44:15834–15841. (PMID: 10.1021/bi051656d16313186)
UniProt Consortium (2021) UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res 49:D480–D489. (PMID: 10.1093/nar/gkaa1100)
Uzunçayır S, Vera-Rodriguez A, Regenthal P, Åbacka H, Emanuelsson C, Bahl CD, Lindkvist-Petersson K (2022) Analyses of the complex formation of staphylococcal enterotoxin a and the human gp130 cytokine receptor. FEBS Lett 596:910–923. (PMID: 10.1002/1873-3468.1429235060124)
Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M (2022) ColabFold: making protein folding accessible to all. Nat Methods 19:679–682. (PMID: 10.1038/s41592-022-01488-1356373079184281)
Bryant P, Pozzati G, Elofsson A (2022) Improved prediction of protein-protein interactions using AlphaFold2. Nat Commun 13:1265. (PMID: 10.1038/s41467-022-28865-w352731468913741)
Saier MH, Reddy VS, Moreno-Hagelsieb G, Hendargo KJ, Zhang Y, Iddamsetty V, Lam K, Tian N, Russum S, Wang J, Medrano-Soto A (2021) The transporter classification database (TCDB): 2021 update. Nucleic Acids Res 49:D461–D467. (PMID: 10.1093/nar/gkaa100433170213)
Chang A, Jeske L, Ulbrich S, Hofmann J, Koblitz J, Schomburg I, Neumann-Schaal M, Jahn D, Schomburg D (2021) BRENDA, the ELIXIR core data resource in 2021: new developments and updates. Nucleic Acids Res 49:D498–D508. (PMID: 10.1093/nar/gkaa102533211880)
Weitere Informationen
Membrane protein-protein interactions, essential for various cellular functions, are primarily governed by their binding affinities. Although numerous computational tools are available for predicting the binding affinity of globular protein-protein complexes, no specific tool exists for membrane protein-protein complexes. Experimental approaches to determine these affinities are costly and time-consuming, limiting large-scale applications. In this chapter, we describe MPA-Pred, a novel machine-learning-based method developed to quantitatively predict the binding affinity of membrane protein-protein complexes. We have derived both structure- and sequence-based features, as well as classified membrane proteins based on their type and function, resulting in improved performance. Extensive evaluations, including training, cross-validation, and independent test sets, demonstrate that MPA-Pred outperforms existing methods in the prediction task. The method is implemented as a user-friendly web server, freely available at https://web.iitm.ac.in/bioinfo2/MPA-Pred/ , and is built using HTML and Python, supporting recent versions of major browsers such as Chrome, Firefox, and Safari. The method serves as a valuable tool for large-scale predictions of novel membrane protein complex affinities and can aid in improving drug design strategies. Here, we overview the methodology, demonstrate the utility of our method through two case studies, and provide guidance on how to run the tool and interpret the results.
(© 2026. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)