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

DOI: 10.1107/S2059798322010415

Authors: Filomeno Sanchez Rodriguez (University of Liverpool; Diamond Light Source) , Grzegorz Chojnowski (European Molecular Biology Laboratory) , Ronan M. Keegan (Research Complex at Harwell) , Daniel J. Rigden (University of Liverpool)
Co-authored by industrial partner: No

Type: Journal Paper
Journal: Acta Crystallographica Section D Structural Biology , VOL 78

State: Published (Approved)
Published: December 2022

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).

Journal Keywords: model validation; inter-residue distances; AlphaFold2; ConKit; conkit-validate

Subject Areas: Technique Development, Information and Communication Technology, Biology and Bio-materials


Technical Areas:

Added On: 28/11/2022 08:40

Documents:
Acta Crystallographica Section D - 2022 - S nchez Rodr guez - Using deep%E2%80%90learning predictions of inter%E2%80%90residue distances.pdf

Discipline Tags:

Artificial Intelligence Technique Development - Life Sciences & Biotech Information & Communication Technologies Structural biology Life Sciences & Biotech

Technical Tags: