What is it about?

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 machinebased methods.

Featured Image

Why is it important?

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://202.207.14.87:8032/fuwu/ DSPMP/index.asp.

Perspectives

We determined the dispensable features for accurate prediction, which were analyzed using the leave-one-feature-out strategy. Based on the analysis of differentiation scores, we determined the mechanism contributing to how the AAC(b/e) feature improves the prediction accuracy. In addition, this feature could be used in other protein classification problems and for prediction of subcellular locations to achieve improved predictive accuracy.

Dr Guo-Liang Fan

Read the Original

This page is a summary of: DSPMP: Discriminating secretory proteins of malaria parasite by hybridizing different descriptors of Chou's pseudo amino acid patterns, Journal of Computational Chemistry, October 2015, Wiley,
DOI: 10.1002/jcc.24210.
You can read the full text:

Read

Contributors

The following have contributed to this page