DSPMP: Discriminating secretory proteins of malaria parasite by hybridizing different descriptors of Chou's pseudo amino acid patterns. Issue 31 (20th October 2015)

• Record Type: Journal Article
• Authors: Fan, Guo‐Liang; Zhang, Xiao‐Yan; Liu, Yan‐Ling; Nang, Yi; Wang, Hui
• Is Part Of: Journal of computational chemistry. Volume 36:Issue 31(2015)
• Page Start: 2317
• Page End: 2327
• Publication Date: 2015-10-20
• Subjects: secretory proteins -- protein stickiness -- support vector machine -- chemical shift -- acid dissociation constant; Chemistry -- Data processing -- Periodicals; 542.85
• DOI: 10.1002/jcc.24210
• Languages: English
• ISSNs: 0192-8651

Abstract

Abstract : 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 athttp://202.207.14.87:8032/fuwu/DSPMP/index.asp . © 2015 Wiley Periodicals, Inc. Abstract : The identification of proteins secreted by the malaria parasite is important for developing effective drugs and vaccines against this infection. The amino acid composition for buried and exposed sequences (AAC( b / e )) is the most important feature for improving predictive accuracy. The differentiation scores of AAC( b / e ) were markedly different between secretory and non‐secretory proteins.