DSPMP: Discriminating secretory proteins of malaria parasite by hybridizing different descriptors of <scp>C</scp>hou's pseudo amino acid patterns

Fan, Guoliang; Zhang, Xiaoyan; Liu, Yanling; Nang, Y.; Wang, Hui

doi:10.1002/jcc.24210

Cited by 39 publications

(5 citation statements)

References 66 publications

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“…Identifying drug targets in the proteome of the malaria parasite is also a key step in the treatment of malaria. Scholars have designed various methods to identify mitochondrial proteins [35][36][37][38][39] and secreted proteins [40][41][42][43] of the malaria parasite.…”

Section: F I G U R E 1 Structures Of a Selection Of Known Antimalariamentioning

confidence: 99%

Multistage inhibitors of the malaria parasite: Emerging hope for chemoprotection and malaria eradication

Poonam

Gupta

et al. 2018

Medicinal Research Reviews

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Over time, several exciting advances have been made in the treatment and prevention of malaria; however, this devastating disease continues to be a major global health problem and affects millions of people every year. Notably, the paucity of new efficient drug molecules and the inevitable drug resistance of the malaria parasite, Plasmodium falciparum, against frontline therapeutics are the foremost struggles facing malaria eradication initiatives. According to the malaria eradication agenda, the discovery of new chemical entities that can destroy the parasite at the liver stage, the asexual blood stage, the gametocyte stage, and the insect ookinete stage of the parasite life cycle (i.e., compounds exhibiting multistage activity) are in high demand, preferably with novel and multiple modes of action. Phenotypic screening of chemical libraries against the malaria parasite is certainly a crucial step toward overcoming these crises. In the last few years, various research groups, including industrial research laboratories, have performed large-scale phenotypic screenings that have identified a wealth of chemical entities active against multiple life stages of the malaria parasite. Vital scientific and technological developments have led to the discovery of multistage inhibitors of the malaria parasite; these compounds, considered highly valuable starting points for subsequent drug discovery and eradication of malaria, are reviewed.

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Section: F I G U R E 1 Structures Of a Selection Of Known Antimalariamentioning

confidence: 99%

Multistage inhibitors of the malaria parasite: Emerging hope for chemoprotection and malaria eradication

Poonam

Gupta

et al. 2018

Medicinal Research Reviews

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“…28–32 therein. Accordingly, the jackknife test has been widely recognized and increasingly used by investigators to examine the quality of various predictors (see, e.g., . In view of this, the jackknife test method has also been used in the current study.…”

Section: Resultsmentioning

confidence: 99%

iPhos‐PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory

Qiu

Sun

Xiao

et al. 2016

Molecular Informatics

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Protein phosphorylation plays a critical role in human body by altering the structural conformation of a protein, causing it to become activated/deactivated, or functional modification. Given an uncharacterized protein sequence, can we predict whether it may be phosphorylated or may not? This is no doubt a very meaningful problem for both basic research and drug development. Unfortunately, to our best knowledge, so far no high throughput bioinformatics tool whatsoever has been developed to address such a very basic but important problem due to its extremely complexity and lacking sufficient training data. Here we proposed a predictor called iPhos-PseEvo by (1) incorporating the protein sequence evolutionary information into the general pseudo amino acid composition (PseAAC) via the grey system theory, (2) balancing out the skewed training datasets by the asymmetric bootstrap approach, and (3) constructing an ensemble predictor by fusing an array of individual random forest classifiers thru a voting system. Rigorous jackknife tests have indicated that very promising success rates have been achieved by iPhos-PseEvo even for such a difficult problem. A user-friendly web-server for iPhos-PseEvo has been established at http://www.jci-bioinfo.cn/iPhos-PseEvo, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. It has not escaped our notice that the formulation and approach presented here can be used to analyze many other problems in protein science as well.

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“…Among these three, however, the jackknife test is deemed the least arbitrary that can always yield a unique outcome for a given benchmark dataset as elucidated in [41] and demonstrated by Eqs.28–32 therein. Accordingly, the jackknife test has been widely recognized and increasingly used by investigators to examine the quality of various predictors (see, e.g., [84–87, 120–127]). However, to reduce the computational time, in this study we adopted the K-fold cross-validation, as done by most investigators with SVM and random forests algorithms as the prediction engine.…”

Section: Methodsmentioning

confidence: 99%

iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC

et al. 2016

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Carbonylation is a posttranslational modification (PTM or PTLM), where a carbonyl group is added to lysine (K), proline (P), arginine (R), and threonine (T) residue of a protein molecule. Carbonylation plays an important role in orchestrating various biological processes but it is also associated with many diseases such as diabetes, chronic lung disease, Parkinson's disease, Alzheimer's disease, chronic renal failure, and sepsis. Therefore, from the angles of both basic research and drug development, we are facing a challenging problem: for an uncharacterized protein sequence containing many residues of K, P, R, or T, which ones can be carbonylated, and which ones cannot? To address this problem, we have developed a predictor called iCar-PseCp by incorporating the sequence-coupled information into the general pseudo amino acid composition, and balancing out skewed training dataset by Monte Carlo sampling to expand positive subset. Rigorous target cross-validations on a same set of carbonylation-known proteins indicated that the new predictor remarkably outperformed its existing counterparts. For the convenience of most experimental scientists, a user-friendly web-server for iCar-PseCp has been established at http://www.jci-bioinfo.cn/iCar-PseCp, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. It has not escaped our notice that the formulation and approach presented here can also be used to analyze many other problems in computational proteomics.

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DSPMP: Discriminating secretory proteins of malaria parasite by hybridizing different descriptors of Chou's pseudo amino acid patterns

Cited by 39 publications

References 66 publications

Multistage inhibitors of the malaria parasite: Emerging hope for chemoprotection and malaria eradication

Multistage inhibitors of the malaria parasite: Emerging hope for chemoprotection and malaria eradication

iPhos‐PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory

iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC

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