A Perspective on the Prospective Use of AI in Protein Structure Prediction

• DOI: 10.31219/osf.io/75kfs
• Publication Date: 2023-08-26T05:01:53Z
• Authors: Raphaelle Versini; Sujith Sritharan; Burcu Aykaç Fas; Thibault TUBIANA; Sana Zineb Aimeur; Julien HENRI; Marie Erard; Oliver Nüsse; ANDREANI Jessica; Marc Baaden; Patrick Fuchs; Tatiana Galochkina; Alexios Chatzigoulas; Zoe Cournia; Hubert Santuz; Sophie Sacquin-Mora; Antoine Taly
• Keywords: Structural bioinformatics

AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable effective methods for predicting protein structures. This article explores their impact limitations, focusing on integration into experimental pipelines application in diverse classes, including membrane proteins, intrinsically disordered proteins (IDPs), oligomers.In pipelines, AF2 models aid X-ray crystallography resolving the phase problem, while complementarity with Mass Spectrometry NMR data enhances structure determination flexibility prediction. Predicting of remains challenging both RF due to difficulties capturing conformational ensembles interactions membrane. Improvements incorporating membrane-specific features effect mutations are crucial. For Intrinsically Disordered Proteins, AF2's confidence score (pLDDT) serves a competitive disorder predictor, but integrative approaches molecular dynamics simulations or hydrophobic cluster analyses advocated accurate representation. show promising results oligomeric models, outperforming traditional docking methods, AlphaFold-Multimer showing improved performance, however, somes caveats remain particular proteins. Real-life examples demonstrate predictive capabilities unknown structures, should be evaluated agreement data. Furthermore, combining can used complementarily. In this perspective we propose "wish list" improving deep learning-based folding prediction using constraints modifying binding partners post-translational modifications. Additionally, meta-tool ranking suggesting composite is suggested, driving future advancements rapidly evolving field.