CASP 11 target classification.
Lisa N. Kinch,Wenlin Li,R. Dustin Schaeffer,Roland L. Dunbrack,Bohdan Monastyrskyy,Andriy Kryshtafovych,Nick V. Grishin +6 more
TLDR
Protein target structures for the Critical Assessment of Structure Prediction round 11 (CASP11) and CASP ROLL were split into domains and classified into categories suitable for assessment of template‐based modeling (TBM) and free modeling (FM) based on their evolutionary relatedness to existing structures classified by theECOD database.Abstract:
Protein target structures for the Critical Assessment of Structure Prediction round 11 (CASP11) and CASP ROLL were split into domains and classified into categories suitable for assessment of template-based modeling (TBM) and free modeling (FM) based on their evolutionary relatedness to existing structures classified by the Evolutionary Classification of Protein Domains (ECOD) database. First, target structures were divided into domain-based evaluation units. Target splits were based on the domain organization of available templates as well as the performance of servers on whole targets compared to split target domains. Second, evaluation units were classified into TBM and FM categories using a combination of measures that evaluate prediction quality and template detectability. Generally, target domains with sequence-related templates and good server prediction performance were classified as TBM, whereas targets without sequence-identifiable templates and low server performance were classified as FM. As in previous CASP experiments, the boundaries for classification were blurred due to the presence of significant insertions and deteriorations in the targets with respect to homologous templates, as well as the presence of templates with partial coverage of new folds. The FM category included 45 target domains, which represents an unprecedented number of difficult CASP targets provided for modeling. Proteins 2016; 84(Suppl 1):20-33. © 2016 Wiley Periodicals, Inc.read more
Citations
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DNCON2: improved protein contact prediction using two-level deep convolutional neural networks.
TL;DR: The improved performance of DNCON2 is attributed to the inclusion of short- and medium-range contacts into training, two-level approach to prediction, use of the state-of-the-art optimization and activation functions, and a novel deep learning architecture that allows each filter in a convolutional layer to access all the input features of a protein of arbitrary length.
Journal ArticleDOI
DeepSF: deep convolutional neural network for mapping protein sequences to folds
TL;DR: A deep 1D‐convolution neural network (DeepSF) is developed to directly classify any protein sequence into one of 1195 known folds, which is useful for both fold recognition and the study of sequence‐structure relationship.
Journal ArticleDOI
Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12
Jürgen Haas,Alessandro Barbato,Dario Behringer,Gabriel Studer,Steven Roth,Martino Bertoni,Khaled Mostaguir,Rafal Gumienny,Torsten Schwede +8 more
TL;DR: The “Continuous Automated Model EvaluatiOn (CAMEO)” platform complements the CASP experiment by conducting fully automated blind prediction assessments based on the weekly pre‐release of sequences of structures, which are going to be published in the next release of the PDB Protein Data Bank.
Journal ArticleDOI
ProQ3: Improved model quality assessments using Rosetta energy terms.
TL;DR: Two novel methods are introduced, inspired by the state-of-art method ProQ2, but using a completely different description of a protein model, based on Rosetta energies, and it is shown that combining the input features from all three predictors, the resulting predictor ProQ3 performs better than any of the individual methods.
Posted Content
ProQ3: Improved model quality assessments using Rosetta energy terms
TL;DR: In this article, the authors examined if these features can be exchanged with energy terms calculated from Rosetta and if a combination of these terms can improve the quality of a protein model.
References
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