ArticleBioinformatics advances2023
SAINT-Angle: self-attention augmented inception-inside-inception network and transfer learning improve protein backbone torsion angle prediction.
Article in Bioinformatics advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Parameterized hypercomplex convolutional network for accurate protein backbone torsion angle prediction.Scientific reports · 2024Article
- Pair-EGRET: enhancing the prediction of protein-protein interaction sites through graph attention networks and protein language models.Bioinformatics (Oxford, England) · 2024Article
- DeepTM: A deep learning algorithm for prediction of melting temperature of thermophilic proteins directly from sequences.Computational and structural biotechnology journal · 2023Article
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Authors and funding
5 authors.
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Abstract
Motivation: Protein structure provides insight into how proteins interact with one another as well as their functions in living organisms. Protein backbone torsion angles ( Results: We present SAINT-Angle, a highly accurate method for predicting protein backbone torsion angles using a self-attention-based deep learning network called SAINT, which was previously developed for the protein secondary structure prediction. We extended and improved the existing SAINT architecture as well as used transfer learning to predict backbone angles. We compared the performance of SAINT-Angle with the state-of-the-art methods through an extensive evaluation study on a collection of benchmark datasets, namely, TEST2016, TEST2018, TEST2020-HQ, CAMEO and CASP. The experimental results suggest that our proposed self-attention-based network, together with transfer learning, has achieved notable improvements over the best alternate methods. Availability and implementation: SAINT-Angle is freely available as an open-source project at https://github.com/bayzidlab/SAINT-Angle. Supplementary information: Supplementary data are available at
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