Literature DB >> 31060481

Advance in predicting subcellular localization of multi-label proteins and its implication for developing multi-target drugs.

Kuo-Chen Chou1.   

Abstract

The smallest unit of life is a cell, which contains numerous protein molecules. Most of the functions critical to the cell's survival are performed by these proteins located in its different organelles, usually called ''subcellular locations". Information of subcellular localization for a protein can provide useful clues about its function. To reveal the intricate pathways at the cellular level, knowledge of the subcellular localization of proteins in a cell is prerequisite. Therefore, one of the fundamental goals in molecular cell biology and proteomics is to determine the subcellular locations of proteins in an entire cell. It is also indispensable for prioritizing and selecting the right targets for drug development. Unfortunately, it is both time-consuming and costly to determine the subcellular locations of proteins purely based on experiments. With the avalanche of protein sequences generated in the post-genomic age, it is highly desired to develop computational methods for rapidly and effectively identifying the subcellular locations of uncharacterized proteins based on their sequences information alone. Actually, considerable progresses have been achieved in this regard. This review is focused on those methods, which have the capacity to deal with multi-label proteins that may simultaneously exist in two or more subcellular location sites. Protein molecules with this kind of characteristic are vitally important for finding multi-target drugs, a current hot trend in drug development. Focused in this review are also those methods that have use-friendly web-servers established so that the majority of experimental scientists can use them to get the desired results without the need to go through the detailed mathematics involved. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

Keywords:  5-step rules; Multi-label proteins; Multi-target drugs; Globalzzm321990accuracy and metrics; Local accuracy and metrics; Absolute true rate;zzm321990Web-server

Year:  2019        PMID: 31060481     DOI: 10.2174/0929867326666190507082559

Source DB:  PubMed          Journal:  Curr Med Chem        ISSN: 0929-8673            Impact factor:   4.530


  16 in total

Review 1.  Some illuminating remarks on molecular genetics and genomics as well as drug development.

Authors:  Kuo-Chen Chou
Journal:  Mol Genet Genomics       Date:  2020-01-01       Impact factor: 3.291

2.  Computational Identification of Lysine Glutarylation Sites Using Positive-Unlabeled Learning.

Authors:  Zhe Ju; Shi-Yun Wang
Journal:  Curr Genomics       Date:  2020-04       Impact factor: 2.236

3.  Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy.

Authors:  Md Mehedi Hasan; Sho Tsukiyama; Jae Youl Cho; Hiroyuki Kurata; Md Ashad Alam; Xiaowen Liu; Balachandran Manavalan; Hong-Wen Deng
Journal:  Mol Ther       Date:  2022-05-06       Impact factor: 12.910

4.  i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation.

Authors:  Md Mehedi Hasan; Balachandran Manavalan; Watshara Shoombuatong; Mst Shamima Khatun; Hiroyuki Kurata
Journal:  Plant Mol Biol       Date:  2020-03-05       Impact factor: 4.076

5.  csDMA: an improved bioinformatics tool for identifying DNA 6 mA modifications via Chou's 5-step rule.

Authors:  Ze Liu; Wei Dong; Wei Jiang; Zili He
Journal:  Sci Rep       Date:  2019-09-11       Impact factor: 4.379

6.  RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou's five-step rule.

Authors:  Lei Zheng; Shenghui Huang; Nengjiang Mu; Haoyue Zhang; Jiayu Zhang; Yu Chang; Lei Yang; Yongchun Zuo
Journal:  Database (Oxford)       Date:  2019-01-01       Impact factor: 3.451

7.  iMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAAC via Chou's 5-steps Rule.

Authors:  Sarah Ilyas; Waqar Hussain; Adeel Ashraf; Yaser Daanial Khan; Sher Afzal Khan; Kuo-Chen Chou
Journal:  Curr Genomics       Date:  2019-05       Impact factor: 2.236

8.  PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method.

Authors:  Phasit Charoenkwan; Sakawrat Kanthawong; Nalini Schaduangrat; Janchai Yana; Watshara Shoombuatong
Journal:  Cells       Date:  2020-02-03       Impact factor: 6.600

9.  Characterization of the relationship between FLI1 and immune infiltrate level in tumour immune microenvironment for breast cancer.

Authors:  Shiyuan Wang; Yakun Wang; Chunlu Yu; Yiyin Cao; Yao Yu; Yi Pan; Dongqing Su; Qianzi Lu; Wuritu Yang; Yongchun Zuo; Lei Yang
Journal:  J Cell Mol Med       Date:  2020-04-05       Impact factor: 5.310

10.  HeteroDualNet: A Dual Convolutional Neural Network With Heterogeneous Layers for Drug-Disease Association Prediction via Chou's Five-Step Rule.

Authors:  Ping Xuan; Hui Cui; Tonghui Shen; Nan Sheng; Tiangang Zhang
Journal:  Front Pharmacol       Date:  2019-11-08       Impact factor: 5.810

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