AlphaFold2 in Biomedical Research: Facilitating the Development of Diagnostic Strategies for Disease

Overview

Journal Front Mol Biosci

Specialty Biology

Date 2024 Aug 14

PMID 39139810

Authors

Hong Zhang

Jiajing Lan

Huijie Wang

Ruijie Lu

Nanqi Zhang

Xiaobai He

Jun Yang

Linjie Chen

Affiliations

Soon will be listed here.

Abstract

Proteins, as the primary executors of physiological activity, serve as a key factor in disease diagnosis and treatment. Research into their structures, functions, and interactions is essential to better understand disease mechanisms and potential therapies. DeepMind's AlphaFold2, a deep-learning protein structure prediction model, has proven to be remarkably accurate, and it is widely employed in various aspects of diagnostic research, such as the study of disease biomarkers, microorganism pathogenicity, antigen-antibody structures, and missense mutations. Thus, AlphaFold2 serves as an exceptional tool to bridge fundamental protein research with breakthroughs in disease diagnosis, developments in diagnostic strategies, and the design of novel therapeutic approaches and enhancements in precision medicine. This review outlines the architecture, highlights, and limitations of AlphaFold2, placing particular emphasis on its applications within diagnostic research grounded in disciplines such as immunology, biochemistry, molecular biology, and microbiology.

References

Tunyasuvunakool K, Adler J, Wu Z, Green T, Zielinski M, Zidek A . Highly accurate protein structure prediction for the human proteome. Nature. 2021; 596(7873):590-596. PMC: 8387240. DOI: 10.1038/s41586-021-03828-1. View

Marchal I . RoseTTAFold expands to all-atom for biomolecular prediction and design. Nat Biotechnol. 2024; 42(4):571. DOI: 10.1038/s41587-024-02211-5. View

Zhang Z, Wu S, Stenoien D, Pasa-Tolic L . High-throughput proteomics. Annu Rev Anal Chem (Palo Alto Calif). 2014; 7:427-54. DOI: 10.1146/annurev-anchem-071213-020216. View

Hekkelman M, de Vries I, Joosten R, Perrakis A . AlphaFill: enriching AlphaFold models with ligands and cofactors. Nat Methods. 2022; 20(2):205-213. PMC: 9911346. DOI: 10.1038/s41592-022-01685-y. View

Freeman A, Declais A, Wilson T, Lilley D . Biochemical and mechanistic analysis of the cleavage of branched DNA by human ANKLE1. Nucleic Acids Res. 2023; 51(11):5743-5754. PMC: 10287932. DOI: 10.1093/nar/gkad416. View

Madi-Moussa D, Deracinois B, Teiar R, Li Y, Mihasan M, Flahaut C . Structure of Lacticaseicin 30 and Its Engineered Variants Revealed an Interplay between the N-Terminal and C-Terminal Regions in the Activity against Gram-Negative Bacteria. Pharmaceutics. 2022; 14(9). PMC: 9505257. DOI: 10.3390/pharmaceutics14091921. View

Ruffolo J, Chu L, Mahajan S, Gray J . Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies. Nat Commun. 2023; 14(1):2389. PMC: 10129313. DOI: 10.1038/s41467-023-38063-x. View

Suzek B, Wang Y, Huang H, McGarvey P, Wu C . UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics. 2014; 31(6):926-32. PMC: 4375400. DOI: 10.1093/bioinformatics/btu739. View

Jaomanjaka F, Claisse O, Philippe C, le Marrec C . Complete Genome Sequence of Lytic Oenococcus oeni Bacteriophage OE33PA. Microbiol Resour Announc. 2018; 7(6). PMC: 6256464. DOI: 10.1128/MRA.00818-18. View

10.

Abanades B, Wong W, Boyles F, Georges G, Bujotzek A, Deane C . ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins. Commun Biol. 2023; 6(1):575. PMC: 10227038. DOI: 10.1038/s42003-023-04927-7. View

11.

Hanash S . Disease proteomics. Nature. 2003; 422(6928):226-32. DOI: 10.1038/nature01514. View

12.

Liu J, Zhang X, Huang Y, Hao G, Yang G . Multi-level bioinformatics resources support drug target discovery of protein-protein interactions. Drug Discov Today. 2024; 29(5):103979. DOI: 10.1016/j.drudis.2024.103979. View

13.

Buel G, Walters K . Can AlphaFold2 predict the impact of missense mutations on structure?. Nat Struct Mol Biol. 2022; 29(1):1-2. PMC: 11218004. DOI: 10.1038/s41594-021-00714-2. View

14.

Paiva V, de Souza Gomes I, Monteiro C, Mendonca M, Martins P, Santana C . Protein structural bioinformatics: An overview. Comput Biol Med. 2022; 147:105695. DOI: 10.1016/j.compbiomed.2022.105695. View

15.

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

16.

Ibrahim T, Khandare V, Mirkin F, Tumtas Y, Bubeck D, Bozkurt T . AlphaFold2-multimer guided high-accuracy prediction of typical and atypical ATG8-binding motifs. PLoS Biol. 2023; 21(2):e3001962. PMC: 9907853. DOI: 10.1371/journal.pbio.3001962. View

17.

Visconti V, Brunetti G, Giordano A, Raponi G . RT-PCR for the diagnosis of infection: the final answer has yet to come. J Clin Pathol. 2017; 70(12):1090-1091. DOI: 10.1136/jclinpath-2017-204523. View

18.

Wayment-Steele H, Ojoawo A, Otten R, Apitz J, Pitsawong W, Homberger M . Predicting multiple conformations via sequence clustering and AlphaFold2. Nature. 2023; 625(7996):832-839. PMC: 10808063. DOI: 10.1038/s41586-023-06832-9. View

19.

Chang L, Huang R, Chen J, Li G, Shi G, Xu B . An alpha-helix variant p.Arg156Pro in LMNA as a cause of hereditary dilated cardiomyopathy: genetics and bioinfomatics exploration. BMC Med Genomics. 2023; 16(1):229. PMC: 10544607. DOI: 10.1186/s12920-023-01661-1. View

20.

Polonsky K, Pupko T, Freund N . Evaluation of the Ability of AlphaFold to Predict the Three-Dimensional Structures of Antibodies and Epitopes. J Immunol. 2023; 211(10):1578-1588. DOI: 10.4049/jimmunol.2300150. View