COFACTOR: improved protein function prediction by combining structure, sequence and protein–protein interaction information

By Chengxin Zhang, Lydia Freddolino, Yang Zhang. Nucleic Acids Res., 2017

528 citations recorded by the source corpus.

Abstract The COFACTOR web server is a unified platform for structure-based multiple-level protein function predictions. By structurally threading low-resolution structural models through the BioLiP library, the COFACTOR server infers three categories of protein functions including gene ontology, enzyme commission and ligand-binding sites from various analogous and homologous function templates. Here, we report recent improvements of the COFACTOR server in the development of new pipelines to infer functional insights from sequence profile alignments and protein–protein interaction networks. Large-scale benchmark tests show that the new hybrid COFACTOR approach significantly improves the function annotation accuracy of the former structure-based pipeline and other state-of-the-art functional annotation methods, particularly for targets that have no close homology templates. The updated COFACTOR server and the template libraries are available at http://zhanglab.ccmb.med.umich.edu/COFACTOR/.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c51c2ac7a12c0c9c3516e0c8f74a0607264828f7

How researchers see this paper

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

In Computational Theory and Mathematics

1. Petr Skoda
2. Marián Novotný
3. David Hoksza
4. Radoslav Krivák
5. Chengxin Zhang
6. Yang Zhang
7. Lukás Jendele
8. Sheng Wang

In Molecular Biology

1. Chengxin Zhang
2. Yang Zhang
3. Sheng Wang
4. Xin Gao
5. Yu Li
6. Petr Skoda
7. Marián Novotný
8. David Hoksza

Citations in the corpus

• DEEPre: sequence-based enzyme EC number prediction by deep learning (2017, methodology): “The COFACTOR dataset is used for cross-dataset validation.”
• US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes (2022, methodology): “(2) Alignment of the secondary structures of the two chains by Needleman-Wunsch (NW) dynamic programming38, using a gap penalty of -1, a match score of 1, and a mismatch score of 0.”
• PrankWeb: a web server for ligand binding site prediction and visualization (2019, methodology): “This is the avenue taken by 3DLigandSite (11), COFAC-TOR (12,13), COACH (14) ISMBLAB-LIG (15) and LI-BRA (16).”
• Shape shifting: The multiple conformational substates of the PTEN N‐terminal PIP2 ‐binding domain (2022, methodology): “In previous work, we constructed a full-length model of PTEN21 using I-TASSER.22,23 Now, taking advantage of next-generation Deep Machine Learning methods, we have constructed full-length models of PTEN using AlphaFold224–26 and RoseTTAFold.27 Intriguingly, each of these methods predicted the PIP2-b”