Integrating co‐evolutionary signals and other properties of residue pairs to distinguish biological interfaces from crystal contacts

It remains challenging to accurately discriminate between biological and crystal interfaces. Most existing analyses and algorithms focused on the features derived from a single side of the interface. However, less attention has been paid to the properties of residue pairs across protein interfaces....

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Bibliographic Details
Published in:Protein science 2018-09, Vol.27 (9), p.1723-1735
Main Authors: Hu, Jian, Liu, Hui‐Fang, Sun, Jun, Wang, Jia, Liu, Rong
Format: Article
Language:English
Subjects:
Algorithms
Biological evolution
biological interface
co‐evolution
crystal contact
Crystals
Databases, Protein
Evolution
Image processing
Interfaces
Internet
machine learning
Properties (attributes)
Protein Binding
Protein Conformation
Proteins
Proteins - chemistry
Proteins - metabolism
residue pair
Servers
Tools for Protein Science
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Summary:It remains challenging to accurately discriminate between biological and crystal interfaces. Most existing analyses and algorithms focused on the features derived from a single side of the interface. However, less attention has been paid to the properties of residue pairs across protein interfaces. To address this problem, we defined a novel co‐evolutionary feature for homodimers through integrating direct coupling analysis and image processing techniques. The residue pairs across biological homodimeric interfaces were significantly enriched in co‐evolving residues compared to those across crystal contacts, resulting in a promising classification accuracy with area under the curves (AUCs) of >0.85. Considering the availability of co‐evolutionary feature, we also designed other residue pair based features that were useful for both homodimers and heterodimers. The most informative residue pairs were identified to reflect the interaction preferences across protein interfaces. Regarding the other extant properties, we designed the new descriptors at the interface residue level as well as at the pairwise contact level. Extensive validation showed that these single properties can be used to identify biological interfaces with AUCs ranging from 0.60 to 0.88. By integrating co‐evolutionary feature with other residue pair based properties, our final prediction model output excellent performance with AUCs of >0.91 on different datasets. Compared to existing methods, our algorithm not only yielded better or comparable results but also provided complementary information. An easy‐to‐use web server is freely accessible at http://liulab.hzau.edu.cn/RPAIAnalyst.
ISSN:0961-8368
1469-896X
DOI:10.1002/pro.3448