ArticlePloS one2012
Predicting secretory proteins of malaria parasite by incorporating sequence evolution information into pseudo amino acid composition via grey system model.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 51 citations in OpenAlex.
- Adipokinetic hormone signaling in the malaria vector Anopheles gambiae facilitates Plasmodium falciparum sporogony.Communications biology · 2023Article
- Computational Prediction of Ubiquitination Proteins Using Evolutionary Profiles and Functional Domain Annotation.Current genomics · 2019Article
- Detecting Succinylation sites from protein sequences using ensemble support vector machine.BMC bioinformatics · 2018Article
- iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples.Molecular genetics and genomics : MGG · 2016Article
- Sequence-specific flexibility organization of splicing flanking sequence and prediction of splice sites in the human genome.Chromosome research : an international journal on the molecular, supramolecular and evolutionary aspects of chromosome biology · 2014Article
- iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking.International journal of molecular sciences · 2014Article
- iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components.International journal of molecular sciences · 2014Article
- Article
- A multilabel model based on Chou's pseudo-amino acid composition for identifying membrane proteins with both single and multiple functional types.The Journal of membrane biology · 2013Article
- Article
- iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking.PloS one · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The malaria disease has become a cause of poverty and a major hindrance to economic development. The culprit of the disease is the parasite, which secretes an array of proteins within the host erythrocyte to facilitate its own survival. Accordingly, the secretory proteins of malaria parasite have become a logical target for drug design against malaria. Unfortunately, with the increasing resistance to the drugs thus developed, the situation has become more complicated. To cope with the drug resistance problem, one strategy is to timely identify the secreted proteins by malaria parasite, which can serve as potential drug targets. However, it is both expensive and time-consuming to identify the secretory proteins of malaria parasite by experiments alone. To expedite the process for developing effective drugs against malaria, a computational predictor called "iSMP-Grey" was developed that can be used to identify the secretory proteins of malaria parasite based on the protein sequence information alone. During the prediction process a protein sample was formulated with a 60D (dimensional) feature vector formed by incorporating the sequence evolution information into the general form of PseAAC (pseudo amino acid composition) via a grey system model, which is particularly useful for solving complicated problems that are lack of sufficient information or need to process uncertain information. It was observed by the jackknife test that iSMP-Grey achieved an overall success rate of 94.8%, remarkably higher than those by the existing predictors in this area. As a user-friendly web-server, iSMP-Grey is freely accessible to the public at http://www.jci-bioinfo.cn/iSMP-Grey. Moreover, for the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated mathematical equations involved in this paper.
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