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Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

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CyclinPred: a SVM-based method for predicting cyclin protein sequences.

Mridul K Kalita¹, Umesh K Nandal, Ansuman Pattnaik

¹Structural and Computational Biology Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi, India.

|July 4, 2008

Summary

This study introduces CyclinPred, a Support Vector Machine (SVM) tool for identifying cyclin proteins. CyclinPred accurately predicts novel cyclins using Position Specific Scoring Matrix (PSSM) profiles, aiding computational biology research.

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A Protocol for Computer-Based Protein Structure and Function Prediction

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Area of Science:

Computational Biology
Bioinformatics
Protein Sequence Analysis

Background:

Functional annotation of low-similarity protein sequences is a significant challenge in post-genomic computational biology.
The cyclin protein family presents difficulties in discovering novel members and establishing orthologous relationships due to low sequence similarity.
Existing cyclin motifs and domains do not encompass all identified cyclin sequences.

Purpose of the Study:

To develop an efficient computational tool for predicting cyclin protein sequences.
To address the limitations of current methods in identifying novel cyclins and their relationships.
To improve the accuracy of cyclin sequence prediction using machine learning approaches.

Main Methods:

Development of a Support Vector Machine (SVM) based classifier named CyclinPred.
Training the SVM classifier using features including amino acid composition, dipeptide composition, secondary structure composition, and Position Specific Scoring Matrix (PSSM) profiles.
Validation using Leave-One-Out cross-validation, self-consistency, and holdout tests.

Main Results:

The SVM classifier trained with PSSM profiles demonstrated higher accuracy compared to classifiers using other features alone or hybrid features.
CyclinPred achieved high efficiency in predicting cyclin sequences.
The developed CyclinPred server provides a reliable tool for cyclin prediction.

Conclusions:

CyclinPred, an SVM-based tool utilizing PSSM profiles, offers a highly accurate method for cyclin sequence prediction.
This computational approach complements traditional methods for cyclin identification and functional annotation.
The tool facilitates the discovery of novel cyclins and enhances the understanding of the cyclin protein family.