Using deep-learning predictions of inter-residue distances for model validation.

ABSTRACT: Determination of protein structures typically entails building a model that satisfies the collected experimental observations and its deposition in the Protein Data Bank. Experimental limitations can lead to unavoidable uncertainties during the process of model building, which result in the introduction of errors into the deposited model. Many metrics are available for model validation, but most are limited to consideration of the physico-chemical aspects of the model or its match to the experimental data. The latest advances in the field of deep learning have enabled the increasingly accurate prediction of inter-residue distances, an advance which has played a pivotal role in the recent improvements observed in the field of protein ab initio modelling. Here, new validation methods are pre

SUBMITTER: Sanchez Rodriguez F 

PROVIDER: S-EPMC9716559 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature