Abstract
Protein analysis has been completely transformed by the swift growth of bioinformatics, which has improved protein structure prediction, simulated interactions, and clarified functional interactions. To improve our knowledge of proteomics, this review carefully examines the application of diverse bioinformatics methods in protein analysis. We evaluate computational methods such as molecular dynamics simulations and machine learning algorithms critically, with an emphasis on their applicability to modeling protein-protein interactions and protein tertiary structure prediction. Our findings show that these methods are useful for predicting protein functions and interactions, which are important for drug discovery and development. We also talk about the important implications of these developments for our knowledge of complex biological systems and disease mechanisms at the molecular level. This review also provides insights into the existing and future potential of bioinformatics tools, emphasizing their vital role in revolutionizing protein analysis. We additionally offer future strategies to improve our knowledge and management of complex disorders, particularly highlighting the need for integrated, multi-scale approaches and additional research on underrepresented proteins.
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Article Type: Review Article
EUR J SUSTAIN DEV RES, Volume 9, Issue 3, 2025, Article No: em0298
https://doi.org/10.29333/ejosdr/16340
Publication date: 01 Jul 2025
Online publication date: 05 May 2025
Article Views: 92
Article Downloads: 35
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How to cite this article
APA
Ogunjobi, T. T., Okorie, I. C., Gigam-Ozuzu, C. D., Olorunleke, J. V., Ogunleye, F. I., Irimoren, E. O., Atanda, D. O., Okafor, A. M.-A., Agbo, C. E., Okunbi, F. O., Umoren, O. D., Adidu, A. D., & Ojo, E. O. (2025). Bioinformatics tools in protein analysis: Structure prediction, interaction modelling, and function relationship. European Journal of Sustainable Development Research, 9(3), em0298. https://doi.org/10.29333/ejosdr/16340
Vancouver
Ogunjobi TT, Okorie IC, Gigam-Ozuzu CD, Olorunleke JV, Ogunleye FI, Irimoren EO, et al. Bioinformatics tools in protein analysis: Structure prediction, interaction modelling, and function relationship. EUR J SUSTAIN DEV RES. 2025;9(3):em0298. https://doi.org/10.29333/ejosdr/16340
AMA
Ogunjobi TT, Okorie IC, Gigam-Ozuzu CD, et al. Bioinformatics tools in protein analysis: Structure prediction, interaction modelling, and function relationship. EUR J SUSTAIN DEV RES. 2025;9(3), em0298. https://doi.org/10.29333/ejosdr/16340
Chicago
Ogunjobi, Taiwo Temitope, Ijeoma Chineme Okorie, Chimaobi Divine Gigam-Ozuzu, Jumoke Victoria Olorunleke, Felix Iyanu Ogunleye, Emmanuella Osaruese Irimoren, Dorcas Oyedolapo Atanda, Adaobi Mary-Ann Okafor, Chinyere Eucharia Agbo, Favour Onasokhare Okunbi, Otoh Dayo Umoren, Adoyi Daniel Adidu, and Emmanuel Oluwadamilare Ojo. "Bioinformatics tools in protein analysis: Structure prediction, interaction modelling, and function relationship". European Journal of Sustainable Development Research 2025 9 no. 3 (2025): em0298. https://doi.org/10.29333/ejosdr/16340
Harvard
Ogunjobi, T. T., Okorie, I. C., Gigam-Ozuzu, C. D., Olorunleke, J. V., Ogunleye, F. I., Irimoren, E. O., . . . Ojo, E. O. (2025). Bioinformatics tools in protein analysis: Structure prediction, interaction modelling, and function relationship. European Journal of Sustainable Development Research, 9(3), em0298. https://doi.org/10.29333/ejosdr/16340
MLA
Ogunjobi, Taiwo Temitope et al. "Bioinformatics tools in protein analysis: Structure prediction, interaction modelling, and function relationship". European Journal of Sustainable Development Research, vol. 9, no. 3, 2025, em0298. https://doi.org/10.29333/ejosdr/16340