PSEApred
From DrugPedia: A Wikipedia for Drug discovery
(New page: '''PSEApred''' is a web-server for predicting proteins secreted by Malarial Parasite ''P.falciparum'' into infected-erythrocyte. ==Background== Malaria parasite secretes various proteins ...) |
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'''PSEApred''' is a web-server for predicting proteins secreted by Malarial Parasite ''P.falciparum'' into infected-erythrocyte. | '''PSEApred''' is a web-server for predicting proteins secreted by Malarial Parasite ''P.falciparum'' into infected-erythrocyte. | ||
| + | [[Image:ProteinTraffic1.jpg|frame|Schematic representation of malarial protein transportation]] | ||
==Background== | ==Background== | ||
Revision as of 10:45, 19 August 2008
PSEApred is a web-server for predicting proteins secreted by Malarial Parasite P.falciparum into infected-erythrocyte.
Contents |
Background
Malaria parasite secretes various proteins in infected RBC for its growth and survival. Thus identification of these secretory proteins is important for developing vaccine/drug against malaria. The existing motif-based methods have got limited success due to lack of universal motif in all secretory proteins of malaria parasite.
Results
In this study a systematic attempt has been made to develop a general method for predicting secretory proteins of malaria parasite. All models were trained and tested on a nonredundant dataset of 252 secretory and 252 non-secretory proteins. We developed SVM models and achieved maximum MCC 0.72 with 85.65% accuracy and MCC 0.74 with 86.45% accuracy using amino acid and dipeptide composition respectively. SVM models were developed using split-amino acid and split-dipeptide composition and achieved maximum MCC 0.74 with 86.40% accuracy and MCC 0.77 with accuracy 88.22% respectively. In this study, for the first time PSSM profiles obtained from PSI-BLAST, have been used for predicting secretory proteins. We achieved maximum MCC 0.86 with 92.66% accuracy using PSSM based SVM model. All models developed in this study were evaluated using 5-fold cross-validation technique.
Conclusion
This study demonstrates that secretory proteins have different residue composition than non-secretory proteins. Thus, it is possible to predict secretory proteins from its residue composition-using machine learning technique. The multiple sequence alignment provides more information than sequence itself. Thus performance of method based on PSSM profile is more accurate than method based on sequence composition.
Availability
A web server PSEApred has been developed for predicting secretory proteins of malaria parasites. PSEApred
