Human domainome 1: A valuable resource for biomedical research and therapeutic development – a systematic review

Sudhir Singh; Jigar Manilal  Haria; Reena Rani; Shilpa Patrick

doi:10.5564/mjc.v27i55.4175

Authors

Sudhir Singh Department of Microbiology, Teerthanker Mahaveer Medical College & Research Center, Moradabad, Uttar Paradesh 244001, India https://orcid.org/0009-0009-0120-1262
Jigar Manilal Haria Department of General Medicine, Teerthanker Mahaveer Medical College & Research Center, Moradabad, Uttar Paradesh 244001, India https://orcid.org/0009-0003-2532-7202
Reena Rani Department of Biochemistry, Teerthanker Mahaveer Medical College & Research Center, Moradabad, Uttar Paradesh 244001, India https://orcid.org/0009-0007-7550-486X
Shilpa Patrick Department of Pharmacology Teerthanker Mahaveer Medical College & Research Center, Moradabad, Uttar Paradesh 244001, India https://orcid.org/0009-0009-2763-3346

DOI:

https://doi.org/10.5564/mjc.v27i55.4175

Keywords:

Human Domainome 1, protein domains, proteomics, biomedical research, therapeutic development, drug discovery, bioinformatics

Abstract

Recent advances in large-scale proteomics and computational biology have enabled systematic mapping of protein domains across the human proteome, giving rise to integrative resources known as domainomes. Human Domainome 1 represents a comprehensive framework for annotating protein domains, domain–domain interactions, and associated biological functions. By capturing the modular organization of proteins, this resource provides critical insights into molecular mechanisms underlying health and disease, and offers a powerful platform for rational drug discovery and precision medicine.

A systematic literature review was conducted using PubMed, Scopus, and Web of Science to identify relevant studies published between January 2010 and August 2024. Search terms included “Human Domainome 1”, “protein domains”, “domainome”, “biomedical research”, “therapeutic development”, and “drug target discovery”. Two independent reviewers screened titles, abstracts, and full texts according to predefined eligibility criteria. Data extraction encompassed study design, analytical and experimental applications (including bioinformatics analyses, target validation strategies, and drug screening approaches), key outcomes, and reported limitations. Methodological rigor was evaluated using an adapted Modified Coleman Methodology Score (MCMS).

Following full-text assessment, 35 studies met the inclusion criteria. Human Domainome 1 was applied across a wide range of biomedical contexts, including protein-protein interaction mapping, pathway reconstruction, and identification of disease-associated domains in oncology, infectious diseases, and neurological disorders. Quantitative synthesis (Table 3) demonstrated a strong association between domain architecture-based analyses and successful identification of novel biomarkers. Furthermore, multiple studies (Table 4) reported that integration of Domainome data into drug discovery workflows significantly enhanced in silico screening efficiency and structure-guided drug design, resulting in higher hit rates and improved target prioritization.

Human Domainome 1 emerges as a robust and versatile resource that substantially advances the understanding of protein function, regulation, and disease relevance. Its integration into contemporary biomedical research pipelines accelerates target identification and therapeutic development, supporting more precise and mechanism-driven drug discovery. Future efforts should prioritize the standardization of domain annotation methodologies and the expansion of functional validation across diverse biological systems to fully realize the translational potential of the Domainome.

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References

1. Beltran A., Jiang X., Shen Y., Lehner B. Site-saturation mutagenesis of 500 human protein domains. (2025) Nature, 637, 885-894. https://doi.org/10.1038/s41586-024-08370-4

2. Reimand J., Hui S., Jain S., Law B., Bader G.D. (2012) Domain-mediated protein interaction prediction: From genome to network. FEBS Lett., 586(17), 2751-2763. https://doi.org/10.1016/j.febslet.2012.04.027

3. Yellaboina S., Tasneem A., Zaykin D.V., Raghavachari B., Jothi R. (2011) DOMINE: A comprehensive collection of known and predicted domain-domain interactions. Nucleic Acids Res., 39 (Database issue), D730-D735. https://doi.org/10.1093/nar/gkq1229

4. Das J., Yu H. (2012) HINT: High-quality protein interactomes and their applications in understanding human disease. BMC Syst. Biol., 6, 92. https://doi.org/10.1186/1752-0509-6-92

5. Yu X., Wallqvist A., Reifman J. (2012) Inferring high-confidence human protein-protein interactions. BMC Bioinformatics, 13, 79. https://doi.org/10.1186/1471-2105-13-79

6. Wang X., Wei X., Thijssen B., Das J., Lipkin S.M., Yu H. (2012) Three-dimensional reconstruction of protein networks provides insight into human genetic disease. Nat, Biotechnol., 30, 159-164. https://doi.org/10.1038/nbt.2106

7. Guruharsha K.G., Rual J.F., Zhai B., Mintseris J., Vaidya P., et al. (2011) A protein complex network of Drosophila melanogaster. Cell, 147(3), 690-703. https://doi.org/10.1016/j.cell.2011.08.047

8. Malovannaya A., Lanz R.B., Jung S.Y., Bulynko Y, Le N.T., et al. (2011) Analysis of the human endogenous coregulator complexome. Cell, 145(5), 787-799. https://doi.org/10.1016/j.cell.2011.05.006

9. Dixon J.R., Selvaraj S., Yue F., Kim A., Li Y., et al. (2012) Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature, 485, 376-380. https://doi.org/10.1038/nature11082

10. Vidal M., Cusick M.E., Barabási A.L. (2011) Interactome networks and human disease. Cell, 144(6), 986-998. https://doi.org/10.1016/j.cell.2011.02.016

11. Rolland T., Tasan M., Charloteaux B., Pevzner S.J., Zhong Q. et al. (2014) A proteome-scale map of the human interactome network. Cell, 159(5), 1212-1226. https://doi.org/10.1016/j.cell.2014.10.050

12. Luck K., Kim D.K., Lambourne L., Spirohn K., Begg B.E., et al. (2020) A reference map of the human binary protein interactome. Nature, 580, 402-408. https://doi.org/10.1038/s41586-020-2188-x

13. Sahni N., Yi S., Taipale M., Fuxman Bass J.I., Coulombe-Huntington J., et al. (2015) Widespread macromolecular interaction perturbations in human genetic disorders. Cell, 161(3), 647-660. https://doi.org/10.1016/j.cell.2015.04.013

14. Iyer L.M., Aravind L. (2012) The evolutionary history of protein domains and their impact on biological systems. Curr. Opin. Struct. Biol., 22(3), 409-417. https://doi.org/10.1016/j.sbi.2012.03.012

15. Punta M., Coggill P.C., Eberhardt R.Y., Mistry J., Tate J., et al. (2012) The Pfam protein families database. Nucleic Acids Res, 40, D290-D301. https://doi.org/10.1093/nar/gkr1065

16. Finn R.D., Bateman A., Clements J., Coggill P., Eberhardt R.Y., et al. (2014) Pfam: The protein families database. Nucleic Acids Res., 42, D222-D230. https://doi.org/10.1093/nar/gkt1223

17. Marchler-Bauer A., Bo Y., Han L., He J., Lanczycki C.J., et al. (2017) CDD/SPARCLE: Functional classification of proteins via conserved domain architectures. Nucleic Acids Res., 45, D200-D203. https://doi.org/10.1093/nar/gkw1129

18. Finn R.D., Attwood T.K., Babbitt P.C., Bateman A., Bork P., et al. (2017) InterPro in 2017-Beyond protein family and domain annotations. Nucleic Acids Res., 45, D190-D199. https://doi.org/10.1093/nar/gkw1107

19. Mitchell A.L., Attwood T.K., Babbitt P.C., Blum M., Bork P., et al. (2019) InterPro in 2019: Improving coverage and classification of protein sequences. Nucleic Acids Res., 47, D351-D360. https://doi.org/10.1093/nar/gky1100

20. Oughtred R., Stark C., Breitkreutz B.J., Rust J., Boucher L., et al. (2019) The BioGRID interaction database. Nucleic Acids Res., 47, D529-D541. https://doi.org/10.1093/nar/gky1079

21. Orchard S., Ammari M., Aranda B., Breuza L., Briganti L., et al. (2012) The MIntAct project-IntAct as a common curation platform. Nucleic Acids Res., 42, D358-D363. https://doi.org/10.1093/nar/gkt1115

22. Szklarczyk D., Gable A.L., Nastou K.C., Lyon D., Kirsch R., et al. (2021) The STRING database in 2021: Customizable protein-protein networks. Nucleic Acids Res., 49, D605-D612. https://doi.org/10.1093/nar/gkaa1074

23. Mosca R., Ceol A., Aloy P. (2013) Adding structural details to protein networks. Nat. Methods, 10, 47-53. https://doi.org/10.1038/nmeth.2289

24. Meyer M.J., Das J., Wang X., Yu H. (2013) INstruct: A database of high-quality 3D structurally resolved protein interactome networks. Bioinformatics, 29(12), 1577-1579. https://doi.org/10.1093/bioinformatics/btt181

25. Finn R.D., Mistry J., Tate J., Coggill P., Heger A., et al. (2016) The Pfam protein families database: Towards a more sustainable future. Nucleic Acids Res., 44, D279-D285. https://doi.org/10.1093/nar/gkv1344

26. Illergard K., Ardell D.H., Elofsson A. (2009) Structure is three to ten times more conserved than sequence. J. Mol. Biol., 388(3), 604-612. https://doi.org/10.1016/j.jmb.2009.03.035

27. Chothia C., Gough J., Vogel C., Teichmann S.A. (2003) Evolution of the protein repertoire. Science, 300, 1701-1703. https://doi.org/10.1126/science.1085371

28. Vogel C., Bashton M., Kerrison N.D., Chothia C., Teichmann S.A. (2004) Structure, function and evolution of multidomain proteins. Curr. Opin. Struct. Biol.,14, 208-216. https://doi.org/10.1016/j.sbi.2004.03.011

29. Tokuriki N., Tawfik D.S. (2009) Protein dynamism and evolvability. Science, 324, 203-207. https://doi.org/10.1126/science.1169375

30. Fowler D.M., Fields S. (2014) Deep mutational scanning: A new style of protein science. Nat. Methods, 11, 801-807. https://doi.org/10.1038/nmeth.3027