SENSE-PPI Reconstructs Interactomes Within, Across, and Between Species at the Genome Scale

Overview

Journal iScience

Publisher Cell Press

Date 2024 Jul 26

PMID 39055916

Authors

Konstantin Volzhenin

Lucie Bittner

Alessandra Carbone

Affiliations

Soon will be listed here.

Abstract

computational reconstructions of protein-protein interaction (PPI) networks will provide invaluable insights into cellular systems, enabling the discovery of novel molecular interactions and elucidating biological mechanisms within and between organisms. Leveraging the latest generation protein language models and recurrent neural networks, we present SENSE-PPI, a sequence-based deep learning model that efficiently reconstructs PPIs, distinguishing partners among tens of thousands of proteins and identifying specific interactions within functionally similar proteins. SENSE-PPI demonstrates high accuracy, limited training requirements, and versatility in cross-species predictions, even with non-model organisms and human-virus interactions. Its performance decreases for phylogenetically more distant model and non-model organisms, but signal alteration is very slow. In this regard, it demonstrates the important role of parameters in protein language models. SENSE-PPI is very fast and can test 10,000 proteins against themselves in a matter of hours, enabling the reconstruction of genome-wide proteomes.

References

Oates M, Romero P, Ishida T, Ghalwash M, Mizianty M, Xue B . D²P²: database of disordered protein predictions. Nucleic Acids Res. 2012; 41(Database issue):D508-16. PMC: 3531159. DOI: 10.1093/nar/gks1226. View

Yu H, Luscombe N, Lu H, Zhu X, Xia Y, Han J . Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs. Genome Res. 2004; 14(6):1107-18. PMC: 419789. DOI: 10.1101/gr.1774904. View

Li F, Zhao X, Li M, He K, Huang C, Zhou Y . Insect genomes: progress and challenges. Insect Mol Biol. 2019; 28(6):739-758. DOI: 10.1111/imb.12599. View

Rao R, Bhattacharya N, Thomas N, Duan Y, Chen X, Canny J . Evaluating Protein Transfer Learning with TAPE. Adv Neural Inf Process Syst. 2021; 32:9689-9701. PMC: 7774645. View

Chen M, Ju C, Zhou G, Chen X, Zhang T, Chang K . Multifaceted protein-protein interaction prediction based on Siamese residual RCNN. Bioinformatics. 2019; 35(14):i305-i314. PMC: 6681469. DOI: 10.1093/bioinformatics/btz328. View

Rives A, Meier J, Sercu T, Goyal S, Lin Z, Liu J . Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc Natl Acad Sci U S A. 2021; 118(15). PMC: 8053943. DOI: 10.1073/pnas.2016239118. View

Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W . Evolutionary-scale prediction of atomic-level protein structure with a language model. Science. 2023; 379(6637):1123-1130. DOI: 10.1126/science.ade2574. View

Salwinski L, Miller C, Smith A, Pettit F, Bowie J, Eisenberg D . The Database of Interacting Proteins: 2004 update. Nucleic Acids Res. 2003; 32(Database issue):D449-51. PMC: 308820. DOI: 10.1093/nar/gkh086. View

Pentony M, Jones D . Modularity of intrinsic disorder in the human proteome. Proteins. 2009; 78(1):212-21. DOI: 10.1002/prot.22504. View

10.

Madan S, Demina V, Stapf M, Ernst O, Frohlich H . Accurate prediction of virus-host protein-protein interactions via a Siamese neural network using deep protein sequence embeddings. Patterns (N Y). 2022; 3(9):100551. PMC: 9481957. DOI: 10.1016/j.patter.2022.100551. View

11.

Mosca R, Ceol A, Aloy P . Interactome3D: adding structural details to protein networks. Nat Methods. 2013; 10(1):47-53. DOI: 10.1038/nmeth.2289. View

12.

Rolland T, Tasan M, Charloteaux B, Pevzner S, Zhong Q, Sahni N . A proteome-scale map of the human interactome network. Cell. 2014; 159(5):1212-1226. PMC: 4266588. DOI: 10.1016/j.cell.2014.10.050. View

13.

Thompson L, Sanders J, McDonald D, Amir A, Ladau J, Locey K . A communal catalogue reveals Earth's multiscale microbial diversity. Nature. 2017; 551(7681):457-463. PMC: 6192678. DOI: 10.1038/nature24621. View

14.

Garcia-Garcia J, Schleker S, Klein-Seetharaman J, Oliva B . BIPS: BIANA Interolog Prediction Server. A tool for protein-protein interaction inference. Nucleic Acids Res. 2012; 40(Web Server issue):W147-51. PMC: 3394316. DOI: 10.1093/nar/gks553. View

15.

Tsukiyama S, Hasan M, Fujii S, Kurata H . LSTM-PHV: prediction of human-virus protein-protein interactions by LSTM with word2vec. Brief Bioinform. 2021; 22(6). PMC: 8574953. DOI: 10.1093/bib/bbab228. View

16.

. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res. 2022; 51(D1):D523-D531. PMC: 9825514. DOI: 10.1093/nar/gkac1052. View

17.

Steinegger M, Soding J . MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat Biotechnol. 2017; 35(11):1026-1028. DOI: 10.1038/nbt.3988. View

18.

Laddach A, Ng J, Chung S, Fraternali F . Genetic variants and protein-protein interactions: a multidimensional network-centric view. Curr Opin Struct Biol. 2018; 50:82-90. DOI: 10.1016/j.sbi.2017.12.006. View

19.

Schaefer M, Fontaine J, Vinayagam A, Porras P, Wanker E, Andrade-Navarro M . HIPPIE: Integrating protein interaction networks with experiment based quality scores. PLoS One. 2012; 7(2):e31826. PMC: 3279424. DOI: 10.1371/journal.pone.0031826. View

20.

Sacquin-Mora S, Carbone A, Lavery R . Identification of protein interaction partners and protein-protein interaction sites. J Mol Biol. 2008; 382(5):1276-89. DOI: 10.1016/j.jmb.2008.08.002. View