Future of protein structure modeling

Understanding a protein’s 3D structure, which is linked to its function, is essential for biological processes, identifying disease mechanisms, and drug development. Traditional experimental techniques like X-ray crystallography, nuclear magnetic resonance (NMR), and cryogenic electron microscopy (Cryo-EM) have been used to determine precise protein structures. However, these methods are resource-intensive, resulting in only a few thousand structures being deciphered every year. This is in stark contrast to the extensive number of sequences in the UniProt database. To this end, numerous studies have investigated the possibility of integrating template-based and template-free structure prediction approaches. The most notable advancement was through the use of a hybrid approach in 2021 when DeepMind’s AlphaFold2 (AF2) introduced a protein structure prediction model with unprecedented accuracy using an end-to-end deep learning algorithm. Despite its widespread impact, AF2 continues to face architectural challenges and struggles with limited available data, preventing it from generating structures consistent with those that result from experimental methods. Recently, several techniques, such as smart sampling methods, and hybrid experimental approaches that enhance Cryo-EM maps with AF2 structures have been developed to address these limitations, surpassing AF2 and traditional methods in many instances. As developments of novel core algorithms allow AF2 to model a wider range of proteins with higher accuracy and hybrid AF2-experimental approaches expedite experimental data collection for AF2, a pure computation based solution to protein structure prediction seems increasingly likely. This review will provide an overview of AF2 and identify promising advancements in protein structure prediction since its introduction.