L1pred: a sequence-based prediction tool for catalytic residues in enzymes with the L1-logreg classifier.

PloS One
Yongchao DouChi Zhang

Abstract

To understand enzyme functions, identifying the catalytic residues is a usual first step. Moreover, knowledge about catalytic residues is also useful for protein engineering and drug-design. However, to experimentally identify catalytic residues remains challenging for reasons of time and cost. Therefore, computational methods have been explored to predict catalytic residues. Here, we developed a new algorithm, L1pred, for catalytic residue prediction, by using the L1-logreg classifier to integrate eight sequence-based scoring functions. We tested L1pred and compared it against several existing sequence-based methods on carefully designed datasets Data604 and Data63. With ten-fold cross-validation, L1pred showed the area under precision-recall curve (AUPR) and the area under ROC curve (AUC) of 0.2198 and 0.9494 on the training dataset, Data604, respectively. In addition, on the independent test dataset, Data63, it showed the AUPR and AUC values of 0.2636 and 0.9375, respectively. Compared with other sequence-based methods, L1pred showed the best performance on both datasets. We also analyzed the importance of each attribute in the algorithm, and found that all the scores contributed more or less equally to the L1pred performance.

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Citations

Apr 12, 2016·BioMed Research International·Hamid D IsmailDukka B Kc
Dec 18, 2013·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Dong-Jun YuJing-Yu Yang
Oct 7, 2015·IEEE Transactions on Nanobioscience·Zhi-Sen WeiDong-Jun Yu
Aug 12, 2015·PloS One·Priyadarshini P PaiSukanta Mondal
May 31, 2019·Nucleic Acids Research·Gurdeep SinghFrancesco Raimondi
Mar 7, 2020·Computational and Structural Biotechnology Journal·Jingtian ZhaoLe Zhang
Jun 5, 2019·Cell·Asuka InoueRobert B Russell

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Software Mentioned

PSIPRED
BLAST
PSI
StAR
Blastclust
RVP
Consurf
VJSD
net
L1pred

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