Paper Info
Title
FoldRec-C2C: protein fold recognition by combining cluster-to-cluster model and protein similarity network
Abstract
As a key for studying the protein structures, protein fold recognition is playing an important role in predicting the protein structures associated with COVID-19 and other important structures. However, the existing computational predictors only focus on the protein pairwise similarity or the similarity between two groups of proteins from 2-folds. However, the homology relationship among proteins is in a hierarchical structure. The global protein similarity network will contribute to the performance improvement. In this study, we proposed a predictor called FoldRec-C2C to globally incorporate the interactions among proteins into the prediction. For the FoldRec-C2C predictor, protein fold recognition problem is treated as an information retrieval task in nature language processing. The initial ranking results were generated by a surprised ranking algorithm Learning to Rank, and then three re-ranking algorithms were performed on the ranking lists to adjust the results globally based on the protein similarity network, including seq-to-seq model, seq-to-cluster model and cluster-to-cluster model (C2C). When tested on a widely used and rigorous benchmark dataset LINDAHL dataset, FoldRec-C2C outperforms other 34 state-of-the-art methods in this field. The source code and data of FoldRec-C2C can be downloaded from http://bliulab.net/FoldRec-C2C/download.
Year
DOI
Venue
2021
10.1093/bib/bbaa144
BRIEFINGS IN BIOINFORMATICS
Keywords
DocType
Volume
protein fold recognition, seq-to-seq model, seq-to-cluster model, cluster-to-cluster model
Journal
22
Issue
ISSN
Citations 
3
1467-5463
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Jiangyi Shao100.68
Yan Ke22581191.93
Bin Liu341933.30