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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Improving taxonomy-based protein fold recognition by using global and local features.

Jian-Yi Yang¹, Xin Chen

¹Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371. yang0241@ntu.edu.sg

|May 4, 2011

Summary

TAX FOLD, a new protein fold recognition method, uses sequence evolution and secondary structure information. It achieves high accuracy, outperforming existing taxonomic methods and showing promise for protein structure identification.

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Area of Science:

Bioinformatics
Structural Biology
Computational Biology

Background:

Protein fold recognition is crucial for determining protein structure and function.
Taxonomy-based methods offer a promising approach for protein fold recognition.
Extracting discriminative features from amino acid sequences is a key challenge.

Purpose of the Study:

To develop a novel taxonomy-based protein fold recognition method called TAXFOLD.
To improve the accuracy and efficiency of protein fold recognition using enhanced feature extraction.

Main Methods:

Exploited sequence evolution information from PSI-BLAST profiles.
Utilized secondary structure information from PSIPRED profiles.
Constructed a comprehensive set of 137 features capturing global and local profile characteristics.

Main Results:

Achieved recognition accuracies ranging from 79.6% to 90% across datasets.
Demonstrated an average 6.9% improvement over existing taxonomic methods.
Showed comparable performance to conventional template-based threading methods at the SCOP fold level.

Conclusions:

The developed TAXFOLD method significantly enhances protein fold recognition accuracy.
The comprehensive feature set is highly beneficial for depicting protein sequence characteristics.
TAX FOLD represents a valuable advancement in computational protein structure prediction.