Unknown

Dataset Information

0

ANGLOR: a composite machine-learning algorithm for protein backbone torsion angle prediction.

ABSTRACT: We developed a composite machine-learning based algorithm, called ANGLOR, to predict real-value protein backbone torsion angles from amino acid sequences. The input features of ANGLOR include sequence profiles, predicted secondary structure and solvent accessibility. In a large-scale benchmarking test, the mean absolute error (MAE) of the phi/psi prediction is 28 degrees/46 degrees , which is approximately 10% lower than that generated by software in literature. The prediction is statistically different from a random predictor (or a purely secondary-structure-based predictor) with p-value <1.0 x 10(-300) (or <1.0 x 10(-148)) by Wilcoxon signed rank test. For some residues (ILE, LEU, PRO and VAL) and especially the residues in helix and buried regions, the MAE of phi angles is much smaller (10-20 degrees ) than that in other environments. Thus, although the average accuracy of the ANGLOR prediction is still low, the portion of the accurately predicted dihedral angles may be useful in assisting protein fold recognition and ab initio 3D structure modeling.

SUBMITTER: Wu S 

PROVIDER: S-EPMC2559866 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

Json Xml
altmetric image

Publications

ANGLOR: a composite machine-learning algorithm for protein backbone torsion angle prediction.

Wu Sitao S   Zhang Yang Y  

PloS one 20081015 10

We developed a composite machine-learning based algorithm, called ANGLOR, to predict real-value protein backbone torsion angles from amino acid sequences. The input features of ANGLOR include sequence profiles, predicted secondary structure and solvent accessibility. In a large-scale benchmarking test, the mean absolute error (MAE) of the phi/psi prediction is 28 degrees/46 degrees , which is approximately 10% lower than that generated by software in literature. The prediction is statistically d  ...[more]

Similar Datasets

Unknown SAINT-Angle: self-attention augmented inception-inside-inception network and transfer learning improve protein backbone torsion angle prediction.
| S-EPMC10115468 | biostudies-literature
Unknown TANGLE: two-level support vector regression approach for protein backbone torsion angle prediction from primary sequences.
| S-EPMC3271071 | biostudies-literature
Unknown Quantitative residue-specific protein backbone torsion angle dynamics from concerted measurement of 3J couplings.
| S-EPMC4318723 | biostudies-literature
Unknown Backbone Torsion Angle Determination Using Proton Detected Magic-Angle Spinning Nuclear Magnetic Resonance.
| S-EPMC8762656 | biostudies-literature
Unknown Classification and prediction of protein-protein interaction interface using machine learning algorithm.
| S-EPMC7815773 | biostudies-literature
Unknown Grid-based prediction of torsion angle probabilities of protein backbone and its application to discrimination of protein intrinsic disorder regions and selection of model structures.
| S-EPMC5796405 | biostudies-literature
Transcriptomics Prediction of Breast Cancer Estrogen Receptor Status using Machine Learning
2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress
Unknown Early Prediction of Sepsis Based on Machine Learning Algorithm.
| S-EPMC8526252 | biostudies-literature
Unknown RaptorX-Angle: real-value prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning.
| S-EPMC5998898 | biostudies-literature
Unknown Prediction of Protein Loop Conformations using the AGBNP Implicit Solvent Model and Torsion Angle Sampling.
| S-EPMC2533438 | biostudies-literature