College of Life Sciences,
Zhejiang Sci-Tech University
  
 

PSCP-PSSE: Protein Structural Class Prediction Using Statistical Features of Predicted Secondary Structures

   
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Overview

PSCP-PSSE is an integrated computational software which implements sixteen statistical features of predicted secondary structures from content to position for protein structural class prediction. The content-based statistical features of secondary structural elements (CBF-PSSEs) have been widely used for protein structural class prediction. But the position distribution of the successive occurrences of an element hasn't been used until now. It is necessary to extract some appropriate position features of secondary structural elements for the prediction task. This software proposed a new way to explore position features of predicted secondary structural elements (PBF-PSSEs) and proposed seven position-based statistical features of secondary structural elements. PSCP-PSSE can be used to predict protein structural classes based on the predicted secondary structure sequences.

PSCP-PSSE was written in Matlab, compiled in Windows, and run on this platform. We have supplied a versions of PSCP-PSSE. Bioinformatic programs often perform computation on large data sets and therefore require much CPU time. This can cause problems like http connection timeouts during online usage. To avoid such problems, we suggest you to download the software and run task on your PC.

The output of PSCP-PSSE consists of accuracies of each class and overall accuracies.


Reference

Qi Dai*, Yan Li, Xiaoqing Liu, Yuhua Yao, Yunjie Cao, Pingan He. Comparison study on statistical features of predicted secondary structures for protein structural class prediction: From content to position. To be submitted to BMC bioinformatics.

 

 

   If you have any difficulty in using the programs, please contact Qi Dai.
Last updated 3/11/2013
  
 

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