I-TASSER server for protein 3D structure prediction.
TLDR
The I-TASSER server has been developed to generate automated full-length 3D protein structural predictions where the benchmarked scoring system helps users to obtain quantitative assessments of the I- TASSER models.Abstract:
Prediction of 3-dimensional protein structures from amino acid sequences represents one of the most important problems in computational structural biology. The community-wide Critical Assessment of Structure Prediction (CASP) experiments have been designed to obtain an objective assessment of the state-of-the-art of the field, where I-TASSER was ranked as the best method in the server section of the recent 7th CASP experiment. Our laboratory has since then received numerous requests about the public availability of the I-TASSER algorithm and the usage of the I-TASSER predictions. An on-line version of I-TASSER is developed at the KU Center for Bioinformatics which has generated protein structure predictions for thousands of modeling requests from more than 35 countries. A scoring function (C-score) based on the relative clustering structural density and the consensus significance score of multiple threading templates is introduced to estimate the accuracy of the I-TASSER predictions. A large-scale benchmark test demonstrates a strong correlation between the C-score and the TM-score (a structural similarity measurement with values in [0, 1]) of the first models with a correlation coefficient of 0.91. Using a C-score cutoff > -1.5 for the models of correct topology, both false positive and false negative rates are below 0.1. Combining C-score and protein length, the accuracy of the I-TASSER models can be predicted with an average error of 0.08 for TM-score and 2 A for RMSD. The I-TASSER server has been developed to generate automated full-length 3D protein structural predictions where the benchmarked scoring system helps users to obtain quantitative assessments of the I-TASSER models. The output of the I-TASSER server for each query includes up to five full-length models, the confidence score, the estimated TM-score and RMSD, and the standard deviation of the estimations. The I-TASSER server is freely available to the academic community at http://zhang.bioinformatics.ku.edu/I-TASSER
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Citations
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Journal ArticleDOI
I-TASSER: a unified platform for automated protein structure and function prediction
TL;DR: The iterative threading assembly refinement (I-TASSER) server is an integrated platform for automated protein structure and function prediction based on the sequence- to-structure-to-function paradigm.
Journal ArticleDOI
I-TASSER server: new development for protein structure and function predictions
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TL;DR: Focuses have been made on the introduction of new methods for atomic-level structure refinement, local structure quality estimation and biological function annotations, which are designed to address the requirements from the user community and to increase the accuracy of modeling predictions.
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References
More filters
Journal ArticleDOI
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.
Stephen F. Altschul,Thomas L. Madden,Alejandro A. Schäffer,Jinghui Zhang,Zheng Zhang,Webb Miller,David J. Lipman +6 more
TL;DR: A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original.
Journal ArticleDOI
The Protein Data Bank
Helen M. Berman,John D. Westbrook,Zukang Feng,Gary L. Gilliland,Talapady N. Bhat,Helge Weissig,Ilya N. Shindyalov,Philip E. Bourne +7 more
TL;DR: The goals of the PDB are described, the systems in place for data deposition and access, how to obtain further information and plans for the future development of the resource are described.
Journal ArticleDOI
A general method applicable to the search for similarities in the amino acid sequence of two proteins
TL;DR: A computer adaptable method for finding similarities in the amino acid sequences of two proteins has been developed and it is possible to determine whether significant homology exists between the proteins to trace their possible evolutionary development.
Journal ArticleDOI
Identification of common molecular subsequences.
TL;DR: This letter extends the heuristic homology algorithm of Needleman & Wunsch (1970) to find a pair of segments, one from each of two long sequences, such that there is no other Pair of segments with greater similarity (homology).
Journal ArticleDOI
A solution for the best rotation to relate two sets of vectors
TL;DR: In this paper, a simple procedure is derived which determines a best rotation of a given vector set into a second vector set by minimizing the weighted sum of squared deviations, which is generalized for any given metric constraint on the transformation.