scholarly journals PSORTdb--an expanded, auto-updated, user-friendly protein subcellular localization database for Bacteria and Archaea

2010 ◽  
Vol 39 (Database) ◽  
pp. D241-D244 ◽  
Author(s):  
N. Y. Yu ◽  
M. R. Laird ◽  
C. Spencer ◽  
F. S. L. Brinkman
Keyword(s):  
Subcellular Localization ◽  
Protein Subcellular Localization ◽  
User Friendly

pLoc_bal-mPlant: Predict Subcellular Localization of Plant Proteins by General PseAAC and Balancing Training Dataset

2019 ◽  
Vol 24 (34) ◽  
pp. 4013-4022 ◽  
Author(s):  
Xiang Cheng ◽  
Xuan Xiao ◽  
Kuo-Chen Chou
Keyword(s):  
Subcellular Localization ◽  
Basic Research ◽  
Training Dataset ◽  
Sequence Information ◽  
Plant Proteins ◽  
Protein Subcellular Localization ◽  
Computational Tools ◽  
Validation Tests ◽  
User Friendly ◽  
Better Than

Knowledge of protein subcellular localization is vitally important for both basic research and drug development. With the avalanche of protein sequences emerging in the post-genomic age, it is highly desired to develop computational tools for timely and effectively identifying their subcellular localization based on the sequence information alone. Recently, a predictor called “pLoc-mPlant” was developed for identifying the subcellular localization of plant 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, called “multiplex proteins”, may simultaneously occur in two or more subcellular locations. Although it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mPlant was trained by an extremely skewed dataset in which some subsets (i.e., the protein numbers for some subcellular locations) were more than 10 times larger than the others. Accordingly, it cannot avoid the biased consequence caused by such an uneven training dataset. To overcome such biased consequence, we have developed a new and bias-free predictor called pLoc_bal-mPlant by balancing the training dataset. Cross-validation tests on exactly the same experimentconfirmed dataset have indicated that the proposed new predictor is remarkably superior to pLoc-mPlant, the existing state-of-the-art predictor in identifying the subcellular localization of plant proteins. To maximize the convenience for the majority of experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_bal-mPlant/, by which users can easily get their desired results without the need to go through the detailed mathematics.

An efficient transient assays system using Agrobacterium-mediated transformation of onion (Allium cepa) epidermal cells

2020 ◽  
Vol 80 (03) ◽  
Author(s):  
Yu-Miao Zhang ◽  
Jun Wang ◽  
Tao Wu
Keyword(s):  
Subcellular Localization ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Epidermal Cells ◽  
Cyclin Dependent Kinase ◽  
Protein Subcellular Localization ◽  
Protein Protein Interactions ◽  
Efficient System ◽  
Protein Protein Interaction ◽  
Onion Epidermal Cells

In this study, the Agrobacterium infection medium, infection duration, detergent, and cell density were optimized. The sorghum-based infection medium (SbIM), 10-20 min infection time, addition of 0.01% Silwet L-77, and Agrobacterium optical density at 600 nm (OD600), improved the competence of onion epidermal cells to support Agrobacterium infection at >90% efficiency. Cyclin-dependent kinase D-2 (CDKD-2) and cytochrome c-type biogenesis protein (CYCH), protein-protein interactions were localized. The optimized procedure is a quick and efficient system for examining protein subcellular localization and protein-protein interaction.

pLoc_bal-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by General PseAAC and Quasi-balancing Training Dataset

2019 ◽  
Vol 15 (5) ◽  
pp. 472-485 ◽  
Author(s):  
Kuo-Chen Chou ◽  
Xiang Cheng ◽  
Xuan Xiao
Keyword(s):  
Drug Development ◽  
Subcellular Localization ◽  
Basic Research ◽  
The Other ◽  
Training Dataset ◽  
Sequence Information ◽  
Eukaryotic Proteins ◽  
Validation Tests ◽  
User Friendly ◽  
Better Than

<P>Background/Objective: Information of protein subcellular localization is crucially important for both basic research and drug development. With the explosive growth of protein sequences discovered in the post-genomic age, it is highly demanded to develop powerful bioinformatics tools for timely and effectively identifying their subcellular localization purely based on the sequence information alone. Recently, a predictor called “pLoc-mEuk” was developed for identifying the subcellular localization of eukaryotic proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with multi-label systems where many proteins, called “multiplex proteins”, may simultaneously occur in two or more subcellular locations. Although it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mEuk was trained by an extremely skewed dataset where some subset was about 200 times the size of the other subsets. Accordingly, it cannot avoid the biased consequence caused by such an uneven training dataset. </P><P> Methods: To alleviate such bias, we have developed a new predictor called pLoc_bal-mEuk by quasi-balancing the training dataset. Cross-validation tests on exactly the same experimentconfirmed dataset have indicated that the proposed new predictor is remarkably superior to pLocmEuk, the existing state-of-the-art predictor in identifying the subcellular localization of eukaryotic proteins. It has not escaped our notice that the quasi-balancing treatment can also be used to deal with many other biological systems. </P><P> Results: To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_bal-mEuk/. </P><P> Conclusion: It is anticipated that the pLoc_bal-Euk predictor holds very high potential to become a useful high throughput tool in identifying the subcellular localization of eukaryotic proteins, particularly for finding multi-target drugs that is currently a very hot trend trend in drug development.</P>

Integrating Second-order Moving Average and Over-sampling Algorithm to Predict Apoptosis Protein Subcellular Localization

2020 ◽  
Vol 15 (6) ◽  
pp. 517-527
Author(s):  
Yunyun Liang ◽  
Shengli Zhang
Keyword(s):  
Subcellular Localization ◽  
Moving Average ◽  
Subcellular Location ◽  
Second Order ◽  
Test Method ◽  
Support Vector ◽  
Protein Subcellular Localization ◽  
Protein Subcellular Location ◽  
Apoptosis Protein ◽  
Leibler Divergence

Background: Apoptosis proteins have a key role in the development and the homeostasis of the organism, and are very important to understand the mechanism of cell proliferation and death. The function of apoptosis protein is closely related to its subcellular location. Objective: Prediction of apoptosis protein subcellular localization is a meaningful task. Methods: In this study, we predict the apoptosis protein subcellular location by using the PSSMbased second-order moving average descriptor, nonnegative matrix factorization based on Kullback-Leibler divergence and over-sampling algorithms. This model is named by SOMAPKLNMF- OS and constructed on the ZD98, ZW225 and CL317 benchmark datasets. Then, the support vector machine is adopted as the classifier, and the bias-free jackknife test method is used to evaluate the accuracy. Results: Our prediction system achieves the favorable and promising performance of the overall accuracy on the three datasets and also outperforms the other listed models. Conclusion: The results show that our model offers a high throughput tool for the identification of apoptosis protein subcellular localization.

Sign in / Sign up

Export Citation Format

Share Document