pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou's General PseAAC and IHTS Treatment to Balance Training Dataset.

Xuan Xiao, Xiang Cheng, Genqiang Chen, Qi Mao, Kuo-Chen Chou

Journal: Medicinal chemistry (Shariqah (United Arab Emirates)) 2019;15(5):496-509

PMID: 30556503

Abstract

BACKGROUND/OBJECTIVE

Knowledge of protein subcellular localization is vitally important for both basic research and drug development. Facing the avalanche of protein sequences emerging in the post-genomic age, it is urgent to develop computational tools for timely and effectively identifying their subcellular localization based on the sequence information alone. Recently, a predictor called "pLoc-mVirus" was developed for identifying the subcellular localization of virus proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with multi-label systems in which some proteins, known as "multiplex proteins", may simultaneously occur in, or move between two or more subcellular location sites. Despite the fact that it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mVirus was trained by an extremely skewed dataset in which some subset was over 10 times the size of the other subsets. Accordingly, it cannot avoid the biased consequence caused by such an uneven training dataset.

METHODS

Using the Chou's general PseAAC (Pseudo Amino Acid Composition) approach and the IHTS (Inserting Hypothetical Training Samples) treatment to balance out the training dataset, we have developed a new predictor called "pLoc_bal-mVirus" for predicting the subcellular localization of multi-label virus proteins.

RESULTS

Cross-validation tests on exactly the same experiment-confirmed dataset have indicated that the proposed new predictor is remarkably superior to pLoc-mVirus, the existing state-of-theart predictor for the same purpose.

CONCLUSION

Its user-friendly web-server is available at http://www.jci-bioinfo.cn/pLoc_balmVirus/, by which the majority of experimental scientists can easily get their desired results without the need to go through the detailed complicated mathematics. Accordingly, pLoc_bal-mVirus will become a very useful tool for designing multi-target drugs and in-depth understanding of the biological process in a cell.

Address: Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.; Gordon Life Science Institute, Boston, MA 02478, United States.; College of Chemistry, Chemical Engineering and Biotechnology, Donghua University, Shanghai 201620, China.; College of Information Science and Technology, Donghua University, Shanghai, China.; Gordon Life Science Institute, Boston, MA 02478, United States.; Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China.

DOI: 10.2174/1573406415666181217114710

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MeSH Terms: Computational Biology, Datasets as Topic, Internet, Machine Learning, Software, Subcellular Fractions, Viral Proteins, Viruses