Faculty Opinions recommendation of LOCSVMPSI: a web server for subcellular localization of eukaryotic proteins using SVM and profile of PSI-BLAST.

Author(s):  
Darryl Nishimura
2005 ◽  
Vol 33 (Web Server) ◽  
pp. W105-W110 ◽  
Author(s):  
D. Xie ◽  
A. Li ◽  
M. Wang ◽  
Z. Fan ◽  
H. Feng

2019 ◽  
Vol 15 (5) ◽  
pp. 472-485 ◽  
Author(s):  
Kuo-Chen Chou ◽  
Xiang Cheng ◽  
Xuan Xiao

<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>


2020 ◽  
Vol 1 (2) ◽  
pp. 01-05
Author(s):  
Kuo Chou

In 2019 a very powerful web-server, or AI (Artificial Intelligence) tool, has been developed for predicting the subcellular localization of human proteins purely according to their information for the multi-label systems, in which a same protein may appear or travel between two or more locations and hence its identification needs the multi-label mark.


2012 ◽  
Vol 45 (3) ◽  
pp. 598-602
Author(s):  
Frank H. Zucker ◽  
Hae Young Kim ◽  
Ethan A. Merritt

The growth of diffracting crystals from purified proteins is often a major bottleneck in determining structures of biological and medical interest. ThePROSPEROweb server, http://skuld.bmsc.washington.edu/prospero, is intended both to provide a means of organizing the potentially large numbers of experimental characterizations measured from such proteins, and to provide useful guidance for structural biologists who have succeeded in purifying their target protein but have reached an impasse in the difficult and poorly understood process of turning purified protein into well diffracting crystals. These researchers need to decide which of many possible rescue options are worth pursuing, given finite resources. This choice is even more crucial when attempting to solve high-priority but relatively difficult structures of eukaryotic proteins. The site currently uses theHyGX1predictor, which was trained and validated on protein samples from pathogenic protozoa (eukaryotes) using results from six types of experiment.PROSPEROallows users to store, analyze and display multiple results for each sample, to group samples into projects, and to share results and predictions with collaborators.


2020 ◽  
pp. 1-4
Author(s):  
Kuo-Chen Chou ◽  

In 2019 a very powerful web-server, or AI (Artificial Intelligence) tool, has been developed for predicting the subcellular localization of human proteins purely according to their information for the multi-label systems [1], in which a same protein may appear or travel between two or more locations and hence its identification needs the multi-label mark [2].


2010 ◽  
Vol 38 (suppl_2) ◽  
pp. W497-W502 ◽  
Author(s):  
Sebastian Briesemeister ◽  
J�rg Rahnenf�hrer ◽  
Oliver Kohlbacher

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