Revolutionizing structural biology: AI-driven protein structure prediction from AlphaFold to next-generation innovations
Abstract
Protein structure modeling from the prediction algorithm has become a valuable tool in biology and medicine with computational advances. Accurate protein structure prediction is critical in druglike compound discovery, disease mechanism understanding, and protein engineering because it provides molecular level insights into protein folding and its effects on molecular and cellular function. This chapter covers the evolution of protein structure prediction, from traditional methods like homology modeling, threading, and ab initio procedures and the new emerging AlphaFold's influence. AlphaFold's highly recognized precision level and open-access data democratized structural biology research, and that lead to inspiring new prediction models like RoseTTAFold and OmegaFold tools. Alpha Folds design, methodology, and highly accurate performance are thoroughly examined, and comparisons are performed with similar tools. We also highlight limitations, such as protein complex and dynamics forecasting, post-AlphaFold developments in structural databases, computer resources, and multi-scale modeling. Protein structure modeling and predictions have a wide range of applications in biomedical research, including drug discovery, functional annotation, and synthetic biology. Future directions include the integration of protein structure prediction with systems biology and genomics, as well as the use of next-generation AI and quantum computing to boost prediction accuracy. This research…
How strong is the evidence?
Evidence Hub reads this as a review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
Read the paper
Related studies
- In silico toxicology protocols
- QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
- Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2's Chemisorption and Diffusion in Mg-MOF-74
- In-silico strategies in nano-drug design: Bridging nanomaterials and pharmacological applications
- mRNA codon optimization with quantum computers
- The computational power of the human brain
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.