Protein-protein Interaction Prediction with Deep Learning: A Comprehensive Review

Overview

Journal Comput Struct Biotechnol J

Specialty Biotechnology

Date 2022 Oct 10

PMID 36212542

Authors

Farzan Soleymani

Eric Paquet

Herna Viktor

Wojtek Michalowski

Davide Spinello

Affiliations

Soon will be listed here.

Abstract

Most proteins perform their biological function by interacting with themselves or other molecules. Thus, one may obtain biological insights into protein functions, disease prevalence, and therapy development by identifying protein-protein interactions (PPI). However, finding the interacting and non-interacting protein pairs through experimental approaches is labour-intensive and time-consuming, owing to the variety of proteins. Hence, protein-protein interaction and protein-ligand binding problems have drawn attention in the fields of bioinformatics and computer-aided drug discovery. Deep learning methods paved the way for scientists to predict the 3-D structure of proteins from genomes, predict the functions and attributes of a protein, and modify and design new proteins to provide desired functions. This review focuses on recent deep learning methods applied to problems including predicting protein functions, protein-protein interaction and their sites, protein-ligand binding, and protein design.

Citing Articles

HSSPPI: hierarchical and spatial-sequential modeling for PPIs prediction.

Li Y, Tian Z, Nan X, Zhang S, Zhou Q, Lu S Brief Bioinform. 2025; 26(2).

PMID: 40037640 PMC: 11879409. DOI: 10.1093/bib/bbaf079.

ISLRWR: A network diffusion algorithm for drug-target interactions prediction.

Sun L, Yin Z, Lu L PLoS One. 2025; 20(1):e0302281.

PMID: 39883675 PMC: 11781719. DOI: 10.1371/journal.pone.0302281.

PRITrans: A Transformer-Based Approach for the Prediction of the Effects of Missense Mutation on Protein-RNA Interactions.

Ge F, Li C, Zhang C, Zhang M, Yu D Int J Mol Sci. 2024; 25(22).

PMID: 39596413 PMC: 11594650. DOI: 10.3390/ijms252212348.

Drug Discovery in the Age of Artificial Intelligence: Transformative Target-Based Approaches.

Patne A, Dhulipala S, Lawless W, Prakash S, Mohapatra S, Mohapatra S Int J Mol Sci. 2024; 25(22).

PMID: 39596300 PMC: 11594879. DOI: 10.3390/ijms252212233.

Artificial Intelligence-Driven Computational Approaches in the Development of Anticancer Drugs.

Garg P, Singhal G, Kulkarni P, Horne D, Salgia R, Singhal S Cancers (Basel). 2024; 16(22).

PMID: 39594838 PMC: 11593155. DOI: 10.3390/cancers16223884.

References

Fu L, Niu B, Zhu Z, Wu S, Li W . CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012; 28(23):3150-2. PMC: 3516142. DOI: 10.1093/bioinformatics/bts565. View

Manekar S, Sathe S . A benchmark study of k-mer counting methods for high-throughput sequencing. Gigascience. 2018; 7(12). PMC: 6280066. DOI: 10.1093/gigascience/giy125. View

Liang S, Zhang C, Liu S, Zhou Y . Protein binding site prediction using an empirical scoring function. Nucleic Acids Res. 2006; 34(13):3698-707. PMC: 1540721. DOI: 10.1093/nar/gkl454. View

Li W, Godzik A . Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006; 22(13):1658-9. DOI: 10.1093/bioinformatics/btl158. View

Huang P, Boyken S, Baker D . The coming of age of de novo protein design. Nature. 2016; 537(7620):320-7. DOI: 10.1038/nature19946. View

Socolich M, Lockless S, Russ W, Lee H, Gardner K, Ranganathan R . Evolutionary information for specifying a protein fold. Nature. 2005; 437(7058):512-8. DOI: 10.1038/nature03991. View

MacBeath G, Schreiber S . Printing proteins as microarrays for high-throughput function determination. Science. 2000; 289(5485):1760-3. DOI: 10.1126/science.289.5485.1760. View

Sandhya S, Mudgal R, Kumar G, Sowdhamini R, Srinivasan N . Protein sequence design and its applications. Curr Opin Struct Biol. 2016; 37:71-80. DOI: 10.1016/j.sbi.2015.12.004. View

Law K . Definitions for Hydrophilicity, Hydrophobicity, and Superhydrophobicity: Getting the Basics Right. J Phys Chem Lett. 2015; 5(4):686-8. DOI: 10.1021/jz402762h. View

10.

Wang C, Bradley P, Baker D . Protein-protein docking with backbone flexibility. J Mol Biol. 2007; 373(2):503-19. DOI: 10.1016/j.jmb.2007.07.050. View

11.

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

12.

Keegan J, Slabaugh G, Arridge S, Ye X, Guo Y, Yu S . DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction. IEEE Trans Med Imaging. 2018; 37(6):1310-1321. DOI: 10.1109/TMI.2017.2785879. View

13.

Ogmen U, Keskin O, Aytuna A, Nussinov R, Gursoy A . PRISM: protein interactions by structural matching. Nucleic Acids Res. 2005; 33(Web Server issue):W331-6. PMC: 1160261. DOI: 10.1093/nar/gki585. View

14.

Brizuela L, Braun P, LaBaer J . FLEXGene repository: from sequenced genomes to gene repositories for high-throughput functional biology and proteomics. Mol Biochem Parasitol. 2001; 118(2):155-65. DOI: 10.1016/s0166-6851(01)00366-8. View

15.

Lee S, Chan C, Tsai C, Lai J, Wang F, Kao C . Ortholog-based protein-protein interaction prediction and its application to inter-species interactions. BMC Bioinformatics. 2008; 9 Suppl 12:S11. PMC: 2638151. DOI: 10.1186/1471-2105-9-S12-S11. View

16.

Jamasb A, Day B, Cangea C, Lio P, Blundell T . Deep Learning for Protein-Protein Interaction Site Prediction. Methods Mol Biol. 2021; 2361:263-288. DOI: 10.1007/978-1-0716-1641-3_16. View

17.

Hu L, Wang X, Huang Y, Hu P, You Z . A survey on computational models for predicting protein-protein interactions. Brief Bioinform. 2021; 22(5). DOI: 10.1093/bib/bbab036. View

18.

Ding W, Nakai K, Gong H . Protein design via deep learning. Brief Bioinform. 2022; 23(3). PMC: 9116377. DOI: 10.1093/bib/bbac102. View

19.

Chen J, Zheng S, Zhao H, Yang Y . Structure-aware protein solubility prediction from sequence through graph convolutional network and predicted contact map. J Cheminform. 2021; 13(1):7. PMC: 7869490. DOI: 10.1186/s13321-021-00488-1. View

20.

Sun T, Zhou B, Lai L, Pei J . Sequence-based prediction of protein protein interaction using a deep-learning algorithm. BMC Bioinformatics. 2017; 18(1):277. PMC: 5445391. DOI: 10.1186/s12859-017-1700-2. View