# Improved protein structure prediction using predicted interresidue orientations

> The prediction of interresidue contacts and distances from coevolutionary data using deep learning has considerably advanced protein structure prediction. Here, we build on these advances by developing a deep residual network for predicting interresidue orientations, in addition to distances, and a ...

## Metadata
- Authors: Jianyi Yang, Ivan Anishchenko, Hahnbeom Park, Zhenling Peng, Sergey Ovchinnikov, David Baker
- Journal: Proceedings of the National Academy of Sciences
- Published: 2020-01-02
- DOI: https://doi.org/10.1073/pnas.1914677117
- Citations: 1,568
- Source: OpenAlex
- Access: Open Access

## Technology Hub
- Hub: Protein Structure Prediction
- Discipline: Biochemistry / AI
- Hub URL: https://science-database.com/technology/protein-structure
- Hub llms.txt: https://science-database.com/technology/protein-structure/llms.txt

## Abstract
The prediction of interresidue contacts and distances from coevolutionary data using deep learning has considerably advanced protein structure prediction. Here, we build on these advances by developing a deep residual network for predicting interresidue orientations, in addition to distances, and a Rosetta-constrained energy-minimization protocol for rapidly and accurately generating structure models guided by these restraints. In benchmark tests on 13th Community-Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP13)- and Continuous Automated Model Evaluation (CAMEO)-derived sets, the method outperforms all previously described structure-prediction methods. Although trained entirely on native proteins, the network consistently assigns higher probability to de novo-designed proteins, identifying the key fold-determining residues and providing an independent quantitative measure of the "ideality" of a protein structure. The method promises to be useful for a broad range of protein structure prediction and design problems.

## Links
- DOI: https://doi.org/10.1073/pnas.1914677117
- OpenAlex: https://openalex.org/W2997234557
- PDF: https://www.pnas.org/doi/pdf/10.1073/pnas.1914677117
- JSON API: https://science-database.com/api/v1/technology/protein-structure

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Paper ID: oa-W2997234557 | Hub: protein-structure