The Protein Model Portal—a comprehensive resource for protein structure and model information

By J. Haas, Steven Roth, Konstantin Arnold, Florian Kiefer, Tobias Schmidt, Lorenza Bordoli, Torsten Schwede. 2013

286 citations recorded by the source corpus.

The Protein Model Portal (PMP) has been developed to foster effective use of 3D molecular models in biomedical research by providing convenient and comprehensive access to structural information for proteins. Both experimental structures and theoretical models for a given protein can be searched simultaneously and analyzed for structural variability. By providing a comprehensive view on structural information, PMP offers the opportunity to apply consistent assessment and validation criteria to the complete set of structural models available for proteins. PMP is an open project so that new methods developed by the community can contribute to PMP, for example, new modeling servers for creating homology models and model quality estimation servers for model validation. The accuracy of participating modeling servers is continuously evaluated by the Continuous Automated Model EvaluatiOn (CAMEO) project. The PMP offers a unique interface to visualize structural coverage of a protein combining both theoretical models and experimental structures, allowing straightforward assessment of the model quality and hence their utility. The portal is updated regularly and actively developed to include latest methods in the field of computational structural biology. Database URL: http://www.proteinmodelportal.org

Subfields: Materials Chemistry, Molecular Biology, Evaluation & Benchmarks, Computational Biology & Bioinformatics

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Citations in the corpus

The SWISS-MODEL Repository—new features and functionality (2016, methodology): “The PMP Protein Model Portal aggregates information from different model providers and allows for a comparative analysis of independently generated models.”
I-TASSER server: new development for protein structure and function predictions (2015, background): “A live-bench based platform has been recently embarked to assess the ligand-binding site prediction (15), which demonstrated the considerable accuracy and usefulness of automated protein function annotations (16).”
Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks (2017, methodology): “…of DeepContact on the ASTRAL validation set (660 proteins), on a set of 228 previous CASP targets (CASP228), on a set of 220 CAMEO targets, and by participating in the blind prediction tasks from the well-established CASP12 experiment (Fox et al., 2013; Moult et al., 2014, 2016; Haas et al., 2013).”
SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information (2014, methodology): “The performance and reliability of the SWISS-MODEL server is continuously evaluated by the CAMEO project (8).”
Folding membrane proteins by deep transfer learning (2017, methodology): “CAMEO(Haas et al., 2013b) can be interpreted as a fully-automated CASP(Moult et al., 2014), but has a smaller number (about 32) of participating servers since many CASP-participating servers are not fully automated.”
ProQ3D: improved model quality assessments using deep learning (2016, neutral): “Here, we provide results that are analogical to the ones in the main text but on another data set - CAMEO (Haas et al., 2013).”
Analysis of deep learning methods for blind protein contact prediction in CASP12 (2017, methodology): “After CASP12, we have been testing our method in a fully automated, weekly online benchmark CAMEO.(22) The blind test in CAMEO indicates that ab initio folding using our predicted contacts as restraints can fold large proteins without similar structures in PDB and many sequence homologs.”
Deep convolutional networks for quality assessment of protein folds (2018, methodology): “The “CAMEO” dataset contains all structural models published on the CAMEO-QE webpage [50] in the 6-month period prior to December 10, 2017.”
Unexpected features of the dark proteome (2015, methodology): “This fraction was largest for viral dark proteins, possibly because of their rapid mutation rates (29), which would tend to increase darkness by undermining the sequence-based structure prediction used in this work (2, 19).”
ModFOLD6: an accurate web server for the global and local quality estimation of 3D protein models (2017, methodology): “The ModFOLD6 server is continuously independently benchmarked for local QE performance using the CAMEO resource (29).”
Outcome of the First wwPDB Hybrid/Integrative Methods Task Force Workshop (2015, unknown): “The molecular architecture of the SAGA transcription coactivator complex was determined with 199 inter- and 240 intrasubunit crosslinks, several comparative models based on X-ray crystal structures, and a TFIID core EM map at 31 Å resolution (Han et al., 2014).”