ANGLOR: A Composite Machine-Learning Algorithm for Protein Backbone Torsion Angle Prediction

By Sitao Wu, Yang Zhang. 2008

87 citations recorded by the source corpus.

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°/46°, which is ∼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 <1.0×10−300 (or <1.0×10−148) 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°) 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.

Subfields: Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. Robin Pearce
2. Yang Zhang
3. Sheng Wang
4. Jinbo Xu
5. Chengxin Zhang
6. Yang Li
7. Wei Zheng
8. S. Mortuza

In Molecular Biology

1. Robin Pearce
2. Yang Zhang
3. Chengxin Zhang
4. Sheng Wang
5. Yang Li
6. Wei Zheng
7. Jinbo Xu
8. S. Mortuza

Citations in the corpus

• RaptorX-Angle: real-value prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning (2018, methodology): “Wu et al.proposed a composite machine-learning algorithm called ANGLOR to predict real-value protein backbone torsion angles from protein sequences [24].”
• STRUM: structure-based prediction of protein stability changes upon single-point mutation (2016, methodology): “These include secondary structure prediction by PSSpred (Yan et al. , 2013), specified by three states of coil, helix and strand; solvent accessibility by MUSTER (Wu and Zhang, 2008 b), and the backbone torsional angle ( / ; w ) by ANGLOR (Wu and Zhang, 2008a).”
• DeepMSA: constructing deep multiple sequence alignment to improve contact prediction and fold-recognition for distant-homology proteins (2019, methodology): “The HHsearch and MUSTER template database is constructed from the 71 684 non-redundant (pair-wise sequence identity 70”
• Toward the solution of the protein structure prediction problem (2021, background): “While this may be one of the first claims of end-to-end learning, the idea is similar to predicting the backbone torsion angles for a given query sequence, which is a longstanding idea in the field (193).”