Volume 16, Issue 3s
Protein Structure Prediction: From Homologue Identification to Accurate 3d Modelling Using an Ultra-Fast Search Engine
Rohit Mishra 1*, Manoj Kumar Pal 1
1*Department, Computer Science and Engineering, United University, Rawatpur, Jhalwa, Prayagra, 211012, Uttar Pradesh, India
1Department, Computer Science and Engineering, United University, Rawatpur, Jhalwa, Prayagra, 211012, Uttar Pradesh, India
Author information
1*
ORCID ID: 0009-0005-3913-4959
ABSTRACT
The Protein Data Bank (PDB) currently has three times as many proteins as it did 10 years ago. That is a significant increase. There are other datasets, such as SCOP and CATH, that exhibit a trend that is comparable to this one. Despite this, the categorisation of protein structures is still a procedure that is laborious, costly, and time-consuming. Because the amount of data is increasing at an exponential pace, the techniques of manually classifying proteins are becoming outdated. The use of precise computational and machine learning methods is a viable option that has the potential to provide a substantial boost in order to handle the growing amount of data. The introduction of lightning-fast search engines, on the other hand, has significantly sped up the process of protein structure prediction. This has made it possible to find homology, design models, and optimise them more quickly. These lightning-fast search engines represent a significant advancement in the field of modern computational biology. The declared objective of these search engines is to simplify and make the process of protein structure prediction more straightforward.
Keywords: Protein Structure Prediction, 3d modelling, ultra-fast search engine, Homologue.
How to cite this article: Mishra R, Pal MK., Protein Structure Prediction: From Homologue Identification to Accurate 3d Modelling Using an Ultra-Fast Search Engine, In Vitro, and Ex Vivo Experimental Validation Supporting Adjunct Antifungal Therapy. Int J Drug Deliv Technol. 2026;16(3s): 533-543; DOI: 10.25258/ijddt.16.3s.68