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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
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Updated: Jul 7, 2026

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

MUSTER: Improving protein sequence profile-profile alignments by using multiple sources of structure information.

Sitao Wu¹, Yang Zhang

¹Center for Bioinformatics and Department of Molecular Bioscience, University of Kansas, 2030 Becker Dr, Lawrence, Kansas 66047, USA.

|February 6, 2008

Summary

MUSTER, a novel protein threading algorithm, enhances fold recognition by integrating sequence and structural data. This new method significantly improves alignment accuracy compared to previous approaches, aiding in protein structure prediction.

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A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

An Integrated Approach for Microprotein Identification and Sequence Analysis

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Published on: July 12, 2022

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Area of Science:

Bioinformatics
Computational Biology
Structural Biology

Background:

Protein structure prediction is crucial for understanding protein function.
Existing sequence profile-profile alignment methods have limitations in accuracy.

Purpose of the Study:

To develop a new protein threading algorithm, MUSTER.
To improve the accuracy of protein fold recognition by integrating diverse sequence and structural information.

Main Methods:

Extended the PPA (sequence profile-profile alignment) method.
Incorporated sequence profiles, secondary structures, fragment profiles, solvent accessibility, torsion angles, and hydrophobic scoring.
Optimized weighting parameters using a grading search based on average TM-score.
Tested on 500 nonhomologous proteins.

Main Results:

MUSTER achieved higher accuracy in identifying correct protein folds compared to PPA.
Significant improvements in TM-score (5.1-6.3%) were observed, even after stringent homology removal.
Statistical analysis confirmed the significance of the improvements (P < 1.0 x 10(-13)).

Conclusions:

MUSTER effectively utilizes integrated sequence and structural information for superior protein fold recognition.
The algorithm demonstrates robust performance on independent test sets.
The MUSTER server is available for academic use.