ArticleInternational journal of molecular sciences2021
Evaluation of Deep Neural Network ProSPr for Accurate Protein Distance Predictions on CASP14 Targets.
Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 12 citations in OpenAlex.
- Protein structure prediction via deep learning: an in-depth review.Frontiers in pharmacology · 2025Review
- Article
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- Same disease - different effect: maternal diabetes impact on birth weight stratified by fetal sex.Archives of gynecology and obstetrics · 2024Article
- Shared structural features of Miro binding control mitochondrial homeostasis.The EMBO journal · 2024Article
- Exploring Scoring Function Space: Developing Computational Models for Drug Discovery.Current medicinal chemistry · 2024Review
- Improved Protein Real-Valued Distance Prediction Using Deep Residual Dense Network (DRDN).The protein journal · 2022Article
- Training undergraduate research assistants with an outcome-oriented and skill-based mentoring strategy.Acta crystallographica. Section D, Structural biology · 2022Article
- Early human trophoblast development: from morphology to function.Cellular and molecular life sciences : CMLS · 2022Review
- Assortment of Frontiers in Protein Science.International journal of molecular sciences · 2022Article
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5 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
The field of protein structure prediction has recently been revolutionized through the introduction of deep learning. The current state-of-the-art tool AlphaFold2 can predict highly accurate structures; however, it has a prohibitively long inference time for applications that require the folding of hundreds of sequences. The prediction of protein structure annotations, such as amino acid distances, can be achieved at a higher speed with existing tools, such as the ProSPr network. Here, we report on important updates to the ProSPr network, its performance in the recent Critical Assessment of Techniques for Protein Structure Prediction (CASP14) competition, and an evaluation of its accuracy dependency on sequence length and multiple sequence alignment depth. We also provide a detailed description of the architecture and the training process, accompanied by reusable code. This work is anticipated to provide a solid foundation for the further development of protein distance prediction tools.
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