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IntFOLD: an integrated web resource for high performance protein structure and function prediction
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 CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens
Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.Peer reviewe
The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens
BackgroundThe Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.ResultsHere, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory.ConclusionWe conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.</p