Advancing protein structure prediction beyond AlphaFold2
Sanggeun Park, Sojung Myung, Minkyung Baek
Abstract
Accurate prediction of protein structures is essential for understanding their biological functions. The release of AlphaFold2 in 2021 marked a significant breakthrough, delivering unprecedented accuracy. However, challenges remain, particularly for proteins with limited evolutionary data or complex molecular interactions. This review explores efforts to enhance AlphaFold2’s performance through advanced sequence search techniques and alternative approaches, including protein language models and frameworks that integrate diverse biomolecular interactions. We propose that future progress will depend on developing models grounded in fundamental physicochemical principles, offering more accurate and comprehensive predictions across a wider spectrum of biological systems. • AlphaFold2 achieves high accuracy in protien structure predictions but still has limitations. • Recent advances integrate protein language models and broader biomolecular contexts. • Emphasis on physicochemical principles may improve predictions for complex systems.