ArticleProceedings of the National Academy of Sciences of the United States of America2024
Single-sequence protein structure prediction by integrating protein language models.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Hierarchical Organization of Evolutionary Constraint in Duplicated EF-Hand Motifs.Journal of molecular evolution · 2026Article
- Combining AI Structure Prediction and Integrative Modeling for Nanobody-Antigen Complexes.Journal of chemical information and modeling · 2026Article
- On the generalization and usability of cofolding models for GPCR drug discovery.npj drug discovery · 2026Article
- Pocket restraints guided by B-cell epitope prediction improve Chai-1 antibody-antigen structure modeling.Protein science : a publication of the Protein Society · 2026Article
- Emergence of specific binding and catalysis from a designed generalist binding protein.Nature chemistry · 2026Article
- Probe-based identification of metal-binding sites using deep learning representations.Nature communications · 2026Article
- Protein Structure Prediction Methods.Advances in experimental medicine and biology · 2026Review
- The transformative impact of AI-enabled AlphaFold 3: evolution, current status, and future prospects in structural biology.Frontiers in artificial intelligence · 2026Review
- Article
- Prebiotically Plausible Peptides can Self-assemble into β-rich Nanostructures.bioRxiv : the preprint server for biology · 2025Article
- Article
- Chemosensory Receptors in Vertebrates: Structure and Computational Modeling Insights.International journal of molecular sciences · 2025Review
- Locality-aware pooling enhances protein language model performance across varied applications.Bioinformatics (Oxford, England) · 2025Article
- De novo design of porphyrin-containing proteins as efficient and stereoselective catalysts.Science (New York, N.Y.) · 2025Article
- GDFold2: A fast and parallelizable protein folding environment with freely defined objective functions.Protein science : a publication of the Protein Society · 2025Article
- Accurate prediction of nucleic acid binding proteins using protein language model.Bioinformatics advances · 2025Article
- The success rate of processed predicted models in molecular replacement: implications for experimental phasing in the AlphaFold era.Acta crystallographica. Section D, Structural biology · 2024Article
- Prediction of protein secondary structure by the improved TCN-BiLSTM-MHA model with knowledge distillation.Scientific reports · 2024Article
- Recent Progress of Protein Tertiary Structure Prediction.Molecules (Basel, Switzerland) · 2024Review
- AI Models for Protein Design are Driving Antibody Engineering.Current opinion in biomedical engineering · 2023Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Protein structure prediction has been greatly improved by deep learning in the past few years. However, the most successful methods rely on multiple sequence alignment (MSA) of the sequence homologs of the protein under prediction. In nature, a protein folds in the absence of its sequence homologs and thus, a MSA-free structure prediction method is desired. Here, we develop a single-sequence-based protein structure prediction method RaptorX-Single by integrating several protein language models and a structure generation module and then study its advantage over MSA-based methods. Our experimental results indicate that in addition to running much faster than MSA-based methods such as AlphaFold2, RaptorX-Single outperforms AlphaFold2 and other MSA-free methods in predicting the structure of antibodies (after fine-tuning on antibody data), proteins of very few sequence homologs, and single mutation effects. By comparing different protein language models, our results show that not only the scale but also the training data of protein language models will impact the performance. RaptorX-Single also compares favorably to MSA-based AlphaFold2 when the protein under prediction has a large number of sequence homologs.
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