Abstract
Determining the catalytic residues in an enzyme is critical to our understanding the relationship between protein sequence, structure, function, and enhancing our ability to design novel enzymes and their inhibitors. Although many enzymes have been sequenced, and their primary and tertiary structures determined, experimental methods for enzyme functional characterization lag behind. Because experimental methods used for identifying catalytic residues are resource- and labor-intensive, computational approaches have considerable value and are highly desirable for their ability to complement experimental studies in identifying catalytic residues and helping to bridge the sequence–structure–function gap. In this study, we describe a new computational method called PREvaIL for predicting enzyme catalytic residues. This method was developed by leveraging a comprehensive set of informative features extracted from multiple levels, including sequence, structure, and residue-contact network, in a random forest machine-learning framework. Extensive benchmarking experiments on eight different datasets based on 10-fold cross-validation and independent tests, as well as side-by-side performance comparisons with seven modern sequence- and structure-based methods, showed that PREvaIL achieved competitive predictive performance, with an area under the receiver operating characteristic curve and area under the precision-recall curve ranging from 0.896 to 0.973 and from 0.294 to 0.523, respectively. We demonstrated that this method was able to capture useful signals arising from different levels, leveraging such differential but useful types of features and allowing us to significantly improve the performance of catalytic residue prediction. We believe that this new method can be utilized as a valuable tool for both understanding the complex sequence–structure–function relationships of proteins and facilitating the characterization of novel enzymes lacking functional annotations.
| Original language | English |
|---|---|
| Pages (from-to) | 125-137 |
| Number of pages | 13 |
| Journal | Journal of Theoretical Biology |
| Volume | 443 |
| DOIs | |
| Publication status | Published - 14 Apr 2018 |
Keywords
- Bioinformatics
- Enzyme catalytic residues
- Functional annotation
- Machine learning
- Pattern recognition
- Sequence analysis
- Sequence–structure–function relationship
Cite this
}
PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework. / Song, Jiangning; Li, Fuyi; Takemoto, Kazuhiro; Haffari, Gholamreza; Akutsu, Tatsuya; Chou, Kuo Chen; Webb, Geoffrey I.
In: Journal of Theoretical Biology, Vol. 443, 14.04.2018, p. 125-137.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework
AU - Song, Jiangning
AU - Li, Fuyi
AU - Takemoto, Kazuhiro
AU - Haffari, Gholamreza
AU - Akutsu, Tatsuya
AU - Chou, Kuo Chen
AU - Webb, Geoffrey I.
PY - 2018/4/14
Y1 - 2018/4/14
N2 - Determining the catalytic residues in an enzyme is critical to our understanding the relationship between protein sequence, structure, function, and enhancing our ability to design novel enzymes and their inhibitors. Although many enzymes have been sequenced, and their primary and tertiary structures determined, experimental methods for enzyme functional characterization lag behind. Because experimental methods used for identifying catalytic residues are resource- and labor-intensive, computational approaches have considerable value and are highly desirable for their ability to complement experimental studies in identifying catalytic residues and helping to bridge the sequence–structure–function gap. In this study, we describe a new computational method called PREvaIL for predicting enzyme catalytic residues. This method was developed by leveraging a comprehensive set of informative features extracted from multiple levels, including sequence, structure, and residue-contact network, in a random forest machine-learning framework. Extensive benchmarking experiments on eight different datasets based on 10-fold cross-validation and independent tests, as well as side-by-side performance comparisons with seven modern sequence- and structure-based methods, showed that PREvaIL achieved competitive predictive performance, with an area under the receiver operating characteristic curve and area under the precision-recall curve ranging from 0.896 to 0.973 and from 0.294 to 0.523, respectively. We demonstrated that this method was able to capture useful signals arising from different levels, leveraging such differential but useful types of features and allowing us to significantly improve the performance of catalytic residue prediction. We believe that this new method can be utilized as a valuable tool for both understanding the complex sequence–structure–function relationships of proteins and facilitating the characterization of novel enzymes lacking functional annotations.
AB - Determining the catalytic residues in an enzyme is critical to our understanding the relationship between protein sequence, structure, function, and enhancing our ability to design novel enzymes and their inhibitors. Although many enzymes have been sequenced, and their primary and tertiary structures determined, experimental methods for enzyme functional characterization lag behind. Because experimental methods used for identifying catalytic residues are resource- and labor-intensive, computational approaches have considerable value and are highly desirable for their ability to complement experimental studies in identifying catalytic residues and helping to bridge the sequence–structure–function gap. In this study, we describe a new computational method called PREvaIL for predicting enzyme catalytic residues. This method was developed by leveraging a comprehensive set of informative features extracted from multiple levels, including sequence, structure, and residue-contact network, in a random forest machine-learning framework. Extensive benchmarking experiments on eight different datasets based on 10-fold cross-validation and independent tests, as well as side-by-side performance comparisons with seven modern sequence- and structure-based methods, showed that PREvaIL achieved competitive predictive performance, with an area under the receiver operating characteristic curve and area under the precision-recall curve ranging from 0.896 to 0.973 and from 0.294 to 0.523, respectively. We demonstrated that this method was able to capture useful signals arising from different levels, leveraging such differential but useful types of features and allowing us to significantly improve the performance of catalytic residue prediction. We believe that this new method can be utilized as a valuable tool for both understanding the complex sequence–structure–function relationships of proteins and facilitating the characterization of novel enzymes lacking functional annotations.
KW - Bioinformatics
KW - Enzyme catalytic residues
KW - Functional annotation
KW - Machine learning
KW - Pattern recognition
KW - Sequence analysis
KW - Sequence–structure–function relationship
UR - http://www.scopus.com/inward/record.url?scp=85042362561&partnerID=8YFLogxK
U2 - 10.1016/j.jtbi.2018.01.023
DO - 10.1016/j.jtbi.2018.01.023
M3 - Article
AN - SCOPUS:85042362561
VL - 443
SP - 125
EP - 137
JO - Journal of Theoretical Biology
JF - Journal of Theoretical Biology
SN - 0022-5193
ER -