ArticleNucleic acids research2019
IntFOLD: an integrated web resource for high performance protein structure and function prediction.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers.
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74 citing papers in PubMed.
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- Insight into Protein Engineering: FromCurrent pharmaceutical design · 2025Review
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- Embracing the future-is artificial intelligence already better? A comparative study of artificial intelligence performance in diagnostic accuracy and decision-making.European journal of neurology · 2024Observational
- Characterization of HMGA2 variants expands the spectrum of Silver-Russell syndrome.JCI insight · 2024Article
- Deep learning applications in protein crystallography.Acta crystallographica. Section A, Foundations and advances · 2024Review
14 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The IntFOLD server provides a unified resource for the automated prediction of: protein tertiary structures with built-in estimates of model accuracy (EMA), protein structural domain boundaries, natively unstructured or disordered regions in proteins, and protein-ligand interactions. The component methods have been independently evaluated via the successive blind CASP experiments and the continual CAMEO benchmarking project. The IntFOLD server has established its ranking as one of the best performing publicly available servers, based on independent official evaluation metrics. Here, we describe significant updates to the server back end, where we have focused on performance improvements in tertiary structure predictions, in terms of global 3D model quality and accuracy self-estimates (ASE), which we achieve using our newly improved ModFOLD7_rank algorithm. We also report on various upgrades to the front end including: a streamlined submission process, enhanced visualization of models, new confidence scores for ranking, and links for accessing all annotated model data. Furthermore, we now include an option for users to submit selected models for further refinement via convenient push buttons. The IntFOLD server is freely available at: http://www.reading.ac.uk/bioinf/IntFOLD/.
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