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

Rodriguez, Filomeno Sanchez, Chojnowski, Grzegorz, Keegan, Ronan M and Rigden, Daniel J ORCID: 0000-0002-7565-8937 (2022) Using deep-learning predictions of inter-residue distances for model validation. ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY, 78 (Pt 12). pp. 1412-1427.

Access the full-text of this item by clicking on the Open Access link.

Official URL: http://dx.doi.org/10.1107/s2059798322010415

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 presented based on the use of these precise inter-residue distance predictions, which are compared with the distances observed in the protein model. Sequence-register errors are particularly clearly detected and the register shifts required for their correction can be reliably determined. The method is available in the ConKit package (https://www.conkit.org).

Item Type:	Article
Uncontrolled Keywords:	model validation, inter-residue distances, AlphaFold2, ConKit, conkit-validate
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology
Depositing User:	Symplectic Admin
Date Deposited:	07 Dec 2022 10:11
Last Modified:	18 Jan 2023 19:41
DOI:	10.1107/S2059798322010415
Open Access URL:	https://doi.org/10.1107/S2059798322010415
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3166536