ArticleBMC bioinformatics2018
Grid-based prediction of torsion angle probabilities of protein backbone and its application to discrimination of protein intrinsic disorder regions and selection of model structures.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Parameterized hypercomplex convolutional network for accurate protein backbone torsion angle prediction.Scientific reports · 2024Article
- Article
- AI applications in functional genomics.Computational and structural biotechnology journal · 2021Review
- PupStruct: Prediction of Pupylated Lysine Residues Using Structural Properties of Amino Acids.Genes · 2020Article
- A simple neural network implementation of generalized solvation free energy for assessment of protein structural models.RSC advances · 2019Article
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Authors and funding
3 authors.
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Abstract
backgroundProtein structure can be described by backbone torsion angles: rotational angles about the N-Cα bond (φ) and the Cα-C bond (ψ) or the angle between Cα
resultsHere, we propose to predict angles in fine grids of 5° by using deep learning neural networks. We found that this grid-based technique can yield 2-6% higher accuracy in predicting angles in the same 5° bin than existing prediction techniques compared. We further demonstrate the usefulness of predicted probabilities at given angle bins in discrimination of intrinsically disorder regions and in selection of protein models.
conclusionsThe proposed method may be useful for characterizing protein structure and disorder. The method is available at http://sparks-lab.org/server/SPIDER2/ as a part of SPIDER2 package.
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