scholarly journals Application of Hybrid Functional Groups to Predict ATP Binding Proteins

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Andreas N. Mbah
Keyword(s):  
Amino Acid ◽  
Binding Proteins ◽  
Stress Proteins ◽  
Atp Binding ◽  
Sequence Information ◽  
Hybrid Functional ◽  
Diverse Groups ◽  
Atp Binding Protein ◽  
Fold Cross Validation ◽  
Better Than

The ATP binding proteins exist as a hybrid of proteins with Walker A motif and universal stress proteins (USPs) having an alternative motif for binding ATP. There is an urgent need to find a reliable and comprehensive hybrid predictor for ATP binding proteins using whole sequence information. In this paper the open source LIBSVM toolbox was used to build a classifier at 10-fold cross-validation. The best hybrid model was the combination of amino acid and dipeptide composition with an accuracy of 84.57% and Mathews Correlation Coefficient (MCC) value of 0.693. This classifier proves to be better than many classical ATP binding protein predictors. The general trend observed is that combinations of descriptors performed better and improved the overall performances of individual descriptors, particularly when combined with amino acid composition. The work developed a comprehensive model for predicting ATP binding proteins irrespective of their functional motifs. This model provides a high probability of success for molecular biologists in predicting and selecting diverse groups of ATP binding proteins irrespective of their functional motifs.

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.

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>

scholarly journals CirBiTree: Citrullination Site Inference Based on a Fuzzy Neural Network and Flexible Neural Tree

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Chuandong Song ◽  
Haifeng Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Classification Model ◽  
Peptide Sequence ◽  
Sequence Information ◽  
Post Translational Modification ◽  
Fuzzy Neural ◽  
Human Complex ◽  
Better Than

Emerging evidence demonstrates that post-translational modification plays an important role in several human complex diseases. Nevertheless, considering the inherent high cost and time consumption of classical and typical in vitro experiments, an increasing attention has been paid to the development of efficient and available computational tools to identify the potential modification sites in the level of protein. In this work, we propose a machine learning-based model called CirBiTree for identification the potential citrullination sites. More specifically, we initially utilize the biprofile Bayesian to extract peptide sequence information. Then, a flexible neural tree and fuzzy neural network are employed as the classification model. Finally, the most available length of identified peptides has been selected in this model. To evaluate the performance of the proposed methods, some state-of-the-art methods have been employed for comparison. The experimental results demonstrate that the proposed method is better than other methods. CirBiTree can achieve 83.07% in sn%, 80.50% in sp, 0.8201 in F1, and 0.6359 in MCC, respectively.

scholarly journals Three distinct anti-allergic drugs, amlexanox, cromolyn and tranilast, bind to S100A12 and S100A13 of the S100 protein family

1999 ◽  
Vol 338 (3) ◽  
pp. 583-589 ◽  
Author(s):  
Tsuyoshi SHISHIBORI ◽  
Yuhta OYAMA ◽  
Osamu MATSUSHITA ◽  
Kayoko YAMASHITA ◽  
Hiromi FURUICHI ◽  
...  
Keyword(s):  
Amino Acid ◽  
Amino Acid Sequence ◽  
Calcium Binding ◽  
Binding Proteins ◽  
S100 Protein ◽  
Reversed Phase ◽  
Molecular Probes ◽  
Calcium Binding Proteins ◽  
Ige Mediated ◽  
Human And Mouse

To investigate the roles of calcium-binding proteins in degranulation, we used three anti-allergic drugs, amlexanox, cromolyn and tranilast, which inhibit IgE-mediated degranulation of mast cells, as molecular probes in affinity chromatography. All of these drugs, which have different structures but similar function, scarcely bound to calmodulin in bovine lung extract, but bound to the same kinds of calcium-binding proteins, such as the 10-kDa proteins isolated in this study, calcyphosine and annexins I–V. The 10-kDa proteins obtained on three drug-coupled resins and on phenyl-Sepharose were analysed by reversed-phase HPLC. It was found that two characteristic 10-kDa proteins, one polar and one less polar, were bound with all three drugs, although S100A2 (S100L), of the S100 family, was bound with phenyl-Sepharose. The cDNA and deduced amino acid sequence proved our major polar protein to be identical with the calcium-binding protein in bovine amniotic fluid (CAAF1, S100A12). The cDNA and deduced amino acid sequence of the less-polar protein shared 95% homology with human and mouse S100A13. In addition, it was demonstrated that the native S100A12 and recombinant S100A12 and S100A13 bind to immobilized amlexanox. On the basis of these findings, we speculate that the three anti-allergic drugs might inhibit degranulation by binding with S100A12 and S100A13.

scholarly journals Isotope-Coded ATP Probe for Quantitative Affinity Profiling of ATP-Binding Proteins

2013 ◽  
Vol 85 (15) ◽  
pp. 7478-7486 ◽  
Author(s):  
Yongsheng Xiao ◽  
Lei Guo ◽  
Yinsheng Wang
Keyword(s):  
Binding Proteins ◽  
Atp Binding

scholarly journals Increasing Ceftriaxone Resistance and Multiple Alterations of Penicillin-Binding Proteins among Penicillin-Resistant Streptococcus pneumoniae Isolates in Taiwan

2007 ◽  
Vol 51 (9) ◽  
pp. 3404-3406 ◽  
Author(s):  
Cheng-Hsun Chiu ◽  
Lin-Hui Su ◽  
Yhu-Chering Huang ◽  
Jui-Chia Lai ◽  
Hsiu-Ling Chen ◽  
...  
Keyword(s):  
Streptococcus Pneumoniae ◽  
Amino Acid ◽  
Binding Proteins ◽  
Binding Protein ◽  
Penicillin Binding Protein ◽  
Penicillin Binding Proteins

ABSTRACT The rate of nonsusceptibility of penicillin-resistant Streptococcus pneumoniae strains to ceftriaxone increased significantly in Taiwan in 2005. Approximately 90% of the ceftriaxone-nonsusceptible isolates were found to be of four major serotypes (serotypes 6B, 14, 19F, and 23F). Seven amino acid alterations in the penicillin-binding protein 2B transpeptidase-encoding region specifically contributed to the resistance.

Sign in / Sign up

Export Citation Format

Share Document