ArticleProceedings of the National Academy of Sciences of the United States of America2022
Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- A comprehensive benchmarking of the AlphaFold3 for predicting biomacromolecules and their interactions.Briefings in bioinformatics · 2025Article
- Multimeric protein interaction and complex prediction: Structure, dynamics and function.Computational and structural biotechnology journal · 2025Review
- Bioinformatic Analysis of WNT Family Proteins.Bioinformatics and biology insights · 2025Article
- Deep learning for the PSIPRED Protein Analysis Workbench.Nucleic acids research · 2024Article
- Freeprotmap: waiting-free prediction method for protein distance map.BMC bioinformatics · 2024Article
- Guiding discovery of protein sequence-structure-function modeling.Bioinformatics (Oxford, England) · 2024Article
- Machine Learning-Guided Protein Engineering.ACS catalysis · 2023Review
- When will RNA get its AlphaFold moment?Nucleic acids research · 2023Article
- When will RNA get its AlphaFold moment?Nucleic acids research · 2023Article
- Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms.Genomics, proteomics & bioinformatics · 2023Review
- Open-Source Machine Learning in Computational Chemistry.Journal of chemical information and modeling · 2023Review
- E-SNPs&GO: embedding of protein sequence and function improves the annotation of human pathogenic variants.Bioinformatics (Oxford, England) · 2022Article
- Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks.Nature communications · 2022Article
- Single-sequence protein structure prediction using a language model and deep learning.Nature biotechnology · 2022Article
- AlphaFold2 models indicate that protein sequence determines both structure and dynamics.Scientific reports · 2022Article
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4 authors.
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Abstract
Deep learning-based prediction of protein structure usually begins by constructing a multiple sequence alignment (MSA) containing homologs of the target protein. The most successful approaches combine large feature sets derived from MSAs, and considerable computational effort is spent deriving these input features. We present a method that greatly reduces the amount of preprocessing required for a target MSA, while producing main chain coordinates as a direct output of a deep neural network. The network makes use of just three recurrent networks and a stack of residual convolutional layers, making the predictor very fast to run, and easy to install and use. Our approach constructs a directly learned representation of the sequences in an MSA, starting from a one-hot encoding of the sequences. When supplemented with an approximate precision matrix, the learned representation can be used to produce structural models of comparable or greater accuracy as compared to our original DMPfold method, while requiring less than a second to produce a typical model. This level of accuracy and speed allows very large-scale three-dimensional modeling of proteins on minimal hardware, and we demonstrate this by producing models for over 1.3 million uncharacterized regions of proteins extracted from the BFD sequence clusters. After constructing an initial set of approximate models, we select a confident subset of over 30,000 models for further refinement and analysis, revealing putative novel protein folds. We also provide updated models for over 5,000 Pfam families studied in the original DMPfold paper.
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