Using deep-learning predictions reveals a large number of register errors in PDB depositions.

ABSTRACT: The accuracy of the information in the Protein Data Bank (PDB) is of great importance for the myriad downstream applications that make use of protein structural information. Despite best efforts, the occasional introduction of errors is inevitable, especially where the experimental data are of limited resolution. A novel protein structure validation approach based on spotting inconsistencies between the residue contacts and distances observed in a structural model and those computationally predicted by methods such as AlphaFold2 has previously been established. It is particularly well suited to the detection of register errors. Importantly, this new approach is orthogonal to traditional methods based on stereochemistry or map-model agreement, and is resolution independent. Here, thousands

SUBMITTER: Sanchez Rodriguez F 

PROVIDER: S-EPMC11533997 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature