Advancements in Protein Structure Prediction: Novel Bioinformatics Algorithms and Applications
Yixin Wang · Theoretical and Natural Science · 2024
This paper conducts an in-depth analysis of the progression and current state of protein structure prediction methods, tracing the evolution from traditional techniques like homology modeling and threading to cutting-edge machine learning approaches such as AlphaFold and RoseTTAFold. A special focus is placed on recent developments like ESMFold, which significantly enhances computational efficiency. The review delves into the capabilities and limitations of these models, particularly in their handling of novel proteins and complex structures, and examines their implications for fields such as drug discovery and functional genomics. A comparative analysis across various methods highlights their operational frameworks, accuracy in prediction, and applicational relevance. This exploration not only provides a comprehensive overview of the state of the art but also offers insights into potential future directions for research and development in the domain of protein structure prediction, suggesting areas where further advancements are needed to improve prediction accuracy and expand the scope of applicability.