Verburgt, J.; Zhang, Z.; Kihara, D.

In the past several years significant advances have been made in the field of deep learning based computational modeling of proteins, with DeepMinds AlphaFold2 being among the most prominent. Alongside the atom coordinates, these computationally modeled protein structures typically contain self confidence metrics that can be used to gauge the relative modeling quality of individual residues, or the protein as a whole. Unfortunately, these scores are not always accurate, and may sometimes annotate poorly modeled regions of the protein as high confidence. Here, we introduce EQA-Fold to address this problem. EQA-Fold overhauls the LDDT prediction head of AlphaFold to provide more accurate self-confidence scores. We show that EQA-Fold is able to provide more accurate self confidence scores than the standard AlphaFold architectures, as well as recent Model Quality Assessment protocols.