Archives of Industrial Biotechnology

All submissions of the EM system will be redirected to Online Manuscript Submission System. Authors are requested to submit articles directly to Online Manuscript Submission System of respective journal.
Reach Us +1 (629)348-3199

Opinion Article - Archives of Industrial Biotechnology (2024) Volume 8, Issue 3

Computational Approaches in Protein Structure Prediction: Methods and Applications

Rafael Chiarello*

Department of Chemical Engineering, National Cheng Kung University, Tainan, Taiwan

*Corresponding Author:
Rafael Chiarello
Department of Chemical Engineering
National Cheng Kung University
Tainan, Taiwan

Received: 13-May-2024, Manuscript No. AAAIB-24-139054; Editor assigned: 15-May-2024, PreQC No. AAAIB-24-139054 (PQ); Reviewed: 31-May-2024, QC No. AAAIB-24-139054; Revised: 12-Jun-2024, Manuscript No. AAAIB-24-139054 (R); Published: 17-Jun-2024, DOI: 10.35841/aaaib- 8.3.206

Citation: Chiarello R. Computational approaches in protein structure prediction: Methods and applications. Arch Ind Biot. 2024; 8(3):206

Visit for more related articles at Archives of Industrial Biotechnology

Proteins, the workhorses of the cell, are fundamental to nearly all biological processes. Their function is intricately linked to their three-dimensional structures, making protein structure prediction crucial for understanding biological mechanisms and developing therapeutic interventions. While experimental techniques like X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy have provided detailed insights into protein structures, they are time-consuming and costly. Computational approaches have emerged as powerful tools to predict protein structures, offering a faster and often more accessible alternative. Homology modeling, or comparative modeling, is based on the observation that protein structures are more conserved than sequences. When a protein's sequence shares significant similarity with a known structure (the template), the structure of the unknown protein (the target) can be modeled. The process involves aligning the target sequence with the template structure, building the backbone, and refining the model to ensure accuracy. Tools like SWISS-MODEL and MODELLER are widely used for homology modelling [1], [2]

Threading involves fitting a protein sequence onto a structural template, even if the sequence identity is low. This method relies on the principle that structural folds are conserved and can accommodate sequences with little similarity. Threading algorithms evaluate various templates to find the best fit based on sequence-structure Ab initio methods predict protein structures from scratch, based solely on the sequence information, without relying on known templates. These methods use physical principles and statistical potentials to search for the most stable conformation. Due to the immense conformational space, ab initio predictions are computationally intensive. Advances in this field have been driven by techniques like molecular dynamics simulations and fragment-based assembly, with Rosetta and AlphaFold being notable examples [3].

Hybrid approaches combine elements of homology modeling, threading, and ab initio methods to improve prediction accuracy. These methods leverage the strengths of each technique, using homology for regions with known structures, threading for distant homologs, and ab initio for novel regions. This integrative approach is exemplified by tools like I-TASSER and AlphaFold. Predicting protein structures is crucial in identifying potential drug targets and designing small molecules or biologics that can interact with these targets. Structure-based drug design relies on detailed knowledge of the target protein's binding sites to develop inhibitors or activators with high specificity and efficacy. Computational predictions have accelerated the development of drugs for diseases such as cancer, Alzheimer's, and COVID-19. By providing insights into the three-dimensional shape of proteins, structure prediction aids in elucidating their functional mechanisms. This understanding is vital for interpreting how proteins interact with other biomolecules, how mutations affect function, and how proteins are regulated. Such knowledge is foundational for both basic biology and applied biomedical research [4], [5]

Protein engineering involves designing proteins with new or enhanced functions. Computational structure prediction allows researchers to model the effects of mutations and design proteins with improved stability, binding affinity, or catalytic activity. Applications include developing industrial enzymes, therapeutic proteins, and biosensors. Protein structures are often more conserved than their sequences, providing a deeper understanding of evolutionary relationships. Structural predictions help identify distant homologs and infer the evolutionary history of protein families. This structural perspective complements sequence-based phylogenetic analyses [6], [7]

With the rapid increase in genomic data, many predicted proteins lack functional annotation. Computational structure prediction can help assign functions to these proteins by identifying structural similarities to known proteins, thus aiding in the functional annotation of genomes and expanding our understanding of biological diversity. Despite significant advancements, protein structure prediction faces several challenges. Accurately modeling large, multi-domain proteins, membrane proteins, and intrinsically disordered regions remains difficult. Additionally, refining predicted structures to atomic-level accuracy is an ongoing challenge [8], [9]

Future directions in protein structure prediction will likely involve integrating more diverse data types, such as cryo-EM density maps and experimental constraints, to improve accuracy. Advances in machine learning, exemplified by AlphaFold, promise to further revolutionize the field, making high-accuracy predictions more accessible and expanding the scope of computational protein biology. Computational approaches to protein structure prediction have transformed our ability to understand and manipulate biological systems. By leveraging methods such as homology modeling, threading, and ab initio predictions, researchers can now access detailed structural information that was once only obtainable through experimental methods. These predictions are driving advancements in drug discovery, protein engineering, and our overall understanding of protein function and evolution. As computational techniques continue to evolve, their impact on biology and medicine will undoubtedly grow, paving the way for new scientific breakthroughs and therapeutic innovations [10].


  1. Agapito-Tenfen SZ, Okoli AS, Bernstein MJ, et al. Revisiting risk governance of GM plants: the need to consider new and emerging gene-editing techniques. Front Plant Sci. 2018;9:1874.
  2. Indexed atGoogle ScholarCross Ref

  3. Alagoz Y, Gurkok T, Zhang B, et al. Manipulating the biosynthesis of bioactive compound alkaloids for next-generation metabolic engineering in opium poppy using CRISPR-Cas 9 genome editing technology. Sci Rep. 2016;6(1):30910.
  4. Indexed atGoogle ScholarCross Ref

  5. Bortesi L, Fischer R. The CRISPR/Cas9 system for plant genome editing and beyond. Biotechnology advances. 2015;33(1):41-52.
  6.  Indexed atGoogle ScholarCross Ref

  7. Cravens A, Payne J, Smolke CD. Synthetic biology strategies for microbial biosynthesis of plant natural products. Nat Commun. 2019;10(1):2142.
  8. Indexed atGoogle ScholarCross Ref

  9. Dong OX, Yu S, Jain R, et al. Marker-free carotenoid-enriched rice generated through targeted gene insertion using CRISPR-Cas9. Nat Commun.2020;11(1):1178.
  10. Indexed atGoogle ScholarCross Ref

  11. Luo D, Saltzman WM. Synthetic DNA delivery systems. Nat Biotechnol. 2000;18(1):33-7.
  12.  Indexed atGoogle ScholarCross Ref

  13. Kanaras AG, Kamounah FS, Schaumburg K, et al. Thioalkylated tetraethylene glycol: a new ligand for water soluble monolayer protected gold clusters. Chem Commun (Camb)2002;(20):2294-5.
  14.  Indexed atGoogle ScholarCross Ref

  15. Izumi CM, Andrade GF, Temperini ML. Surface-enhanced resonance Raman scattering of polyaniline on silver and gold colloids.J Phys Chem B.2008;112(51):16334-40
  16. Indexed atGoogle ScholarCross Ref

  17. Canter PH, Thomas H, Ernst E. Bringing medicinal plants into cultivation: opportunities and challenges for biotechnology. Trends Biotechnol. 2005;23(4):180-5.
  18. Indexed atGoogle ScholarCross Ref

  19. Zhang H, Cao Y, Xu D, et al. Gold-nanocluster-mediated delivery of siRNA to intact plant cells for efficient gene knockdown. Nano letters. 2021 Jun 21;21(13):5859-66.
  20. Indexed atGoogle ScholarCross Ref

Get the App