Protein threading using residue co-variation and deep learning

By Jianwei Zhu, Sheng Wang, Dongbo Bu, Jinbo Xu. 2018

70 citations recorded by the source corpus.

Motivation Template‐based modeling, including homology modeling and protein threading, is a popular method for protein 3D structure prediction. However, alignment generation and template selection for protein sequences without close templates remain very challenging. Results We present a new method called DeepThreader to improve protein threading, including both alignment generation and template selection, by making use of deep learning (DL) and residue co‐variation information. Our method first employs DL to predict inter‐residue distance distribution from residue co‐variation and sequential information (e.g. sequence profile and predicted secondary structure), and then builds sequence‐template alignment by integrating predicted distance information and sequential features through an ADMM algorithm. Experimental results suggest that predicted inter‐residue distance is helpful to both protein alignment and template selection especially for protein sequences without very close templates, and that our method outperforms currently popular homology modeling method HHpred and threading method CNFpred by a large margin and greatly outperforms the latest contact‐assisted protein threading method EigenTHREADER. Availability and implementation http://raptorx.uchicago.edu/

Subfields: Representation & Self-Supervised Learning, Computational Biology & Bioinformatics

How researchers see this paper

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

In Representation & Self-Supervised Learning

1. Tie-Yan Liu
2. Yang Zhang

In Computational Biology & Bioinformatics

1. Robin Pearce
2. Jianwei Zhu
3. Dongbo Bu
4. Tie-Yan Liu
5. Yang Zhang
6. Fusong Ju
7. Jianyi Yang
8. Liam J. McGuffin

Citations in the corpus

• CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction (2020, methodology): “For example, DeepThreader[21] improves threading by incorporating inter-residue distances into scoring function.”
• Toward the solution of the protein structure prediction problem (2021, background): “In fact, contact and distance map-guided threading approaches represent the state of the art in fold recognition, achieving superior accuracy to traditional pro fi le or local structural feature-based threading approaches (95, 96, 187).”