DSPMP: Discriminating secretory proteins of malaria parasite by hybridizing different descriptors of Chou’s pseudo amino acid patterns. Identification of the proteins secreted by the malaria parasite is important for developing effective drugs and vaccines against infection. Therefore, we developed an improved predictor called ”DSPMP” (Discriminating Secretory Proteins of Malaria Parasite) to identify the secretory proteins of the malaria parasite by integrating several vector features using support vector machine-based methods. DSPMP achieved an overall predictive accuracy of 98.61%, which is superior to that of the existing predictors in this field. We show that our method is capable of identifying the secretory proteins of the malaria parasite and found that the amino acid composition for buried and exposed sequences, denoted by AAC(b/e), was the most important feature for constructing the predictor. This article not only introduces a novel method for detecting the important features of sample proteins related to the malaria parasite but also provides a useful tool for tackling general protein-related problems. The DSPMP webserver is freely available at http://18.104.22.168:8032/fuwu/DSPMP/index.asp.
Keywords for this software
References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Arif, Muhammad; Hayat, Maqsood; Jan, Zahoor: IMem-2LSAAC: a two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into Chou’s pseudo amino acid composition (2018)
- Jia, Jianhua; Liu, Zi; Xiao, Xuan; Liu, Bingxiang; Chou, Kuo-Chen: pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach (2016)