ArticleCell systems2017
Folding Membrane Proteins by Deep Transfer Learning.
Article in Cell systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- AI in Drug Discovery: Clinical Failures, Regulatory Reality, and the Validation Crisis Behind the Hype.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Article
- Deep transfer learning for inter-chain contact predictions of transmembrane protein complexes.Nature communications · 2023Article
- Energy Profile Bayes and Thompson Optimized Convolutional Neural Network protein structure prediction.Neural computing & applications · 2023Article
- Contact-Assisted Threading in Low-Homology Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications.Computational and structural biotechnology journal · 2023Review
- Establishment of anti-DKK3 peptide for the cancer control in head and neck squamous cell carcinoma (HNSCC).Cancer cell international · 2022Article
- Prokaryotic and eukaryotic promoters identification based on residual network transfer learning.Bioprocess and biosystems engineering · 2022Article
- Membrane contact probability: An essential and predictive character for the structural and functional studies of membrane proteins.PLoS computational biology · 2022Article
- A tale of solving two computational challenges in protein science: neoantigen prediction and protein structure prediction.Briefings in bioinformatics · 2022Review
- Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14.Proteins · 2022Article
- Relevance of lipoproteins, membranes, and extracellular vesicles in understanding C-reactive protein biochemical structure and biological activities.Frontiers in cardiovascular medicine · 2022Review
- From cell lines to cancer patients: personalized drug synergy prediction.Bioinformatics (Oxford, England) · 2022Article
- A duplicated copy of id2b is an unusual sex-determining candidate gene on the Y chromosome of arapaima (Arapaima gigas).Scientific reports · 2021Article
- Improved protein structure prediction by deep learning irrespective of co-evolution information.Nature machine intelligence · 2021Article
- Deep template-based protein structure prediction.PLoS computational biology · 2021Article
- Hybridized distance- and contact-based hierarchical structure modeling for folding soluble and membrane proteins.PLoS computational biology · 2021Article
- TargetingFrontiers in microbiology · 2021Article
- Computational-approach understanding the structure-function prophecy of Fibrinolytic Protease RFEA1 fromPeerJ · 2021Article
- Guardians of the Cell: State-of-the-Art of Membrane Proteins from a Computational Point-of-View.Methods in molecular biology (Clifton, N.J.) · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
Computational elucidation of membrane protein (MP) structures is challenging partially due to lack of sufficient solved structures for homology modeling. Here, we describe a high-throughput deep transfer learning method that first predicts MP contacts by learning from non-MPs and then predicts 3D structure models using the predicted contacts as distance restraints. Tested on 510 non-redundant MPs, our method has contact prediction accuracy at least 0.18 better than existing methods, predicts correct folds for 218 MPs, and generates 3D models with root-mean-square deviation (RMSD) less than 4 and 5 Å for 57 and 108 MPs, respectively. A rigorous blind test in the continuous automated model evaluation project shows that our method predicted high-resolution 3D models for two recent test MPs of 210 residues with RMSD ∼2 Å. We estimated that our method could predict correct folds for 1,345-1,871 reviewed human multi-pass MPs including a few hundred new folds, which shall facilitate the discovery of drugs targeting at MPs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.