ArticleActa crystallographica. Section D, Structural biology2022
Using deep-learning predictions of inter-residue distances for model validation.
Article in Acta crystallographica. Section D, Structural biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- CASP16 Protein Monomer Structure Prediction Assessment.Proteins · 2026Article
- Emerging frontiers in protein structure prediction following the AlphaFold revolution.Journal of the Royal Society, Interface · 2025Review
- Using deep-learning predictions reveals a large number of register errors in PDB depositions.IUCrJ · 2024Article
- AlphaFold two years on: Validation and impact.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Predicted models and CCP4.Acta crystallographica. Section D, Structural biology · 2023Article
- DoubleHelix: nucleic acid sequence identification, assignment and validation tool for cryo-EM and crystal structure models.Nucleic acids research · 2023Article
- Sequence-assignment validation in protein crystal structure models with checkMySequence.Acta crystallographica. Section D, Structural biology · 2023Article
- The CCP4 suite: integrative software for macromolecular crystallography.Acta crystallographica. Section D, Structural biology · 2023Article
- Automated identification of chalcogen bonds in AlphaFold protein structure database files: is it possible?Frontiers in molecular biosciences · 2023Article
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Authors and funding
4 authors.
Funding
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).
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