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Updated: Dec 30, 2025

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

Protein structure predictions by enhanced conformational sampling methods.

Yuko Okamoto^1,2,3,4,5

¹Department of Physics, Graduate School of Science, Nagoya University, Nagoya, Aichi 464-8602, Japan.

Biophysics and Physicobiology

|January 28, 2020

Summary

This summary is machine-generated.

This review covers advanced methods for protein structure prediction, including generalized-ensemble algorithms and parallel computing techniques. These computational approaches enhance conformational sampling for more accurate tertiary structure predictions.

Keywords:

genetic algorithm molecular simulation multicanonical algorithm protein structure prediction replica-exchange method

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Last Updated: Dec 30, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

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Published on: July 16, 2017

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

Area of Science:

Computational Biology
Biophysics
Structural Biology

Background:

Accurate protein tertiary structure prediction is crucial for understanding biological function.
Traditional methods often face challenges in exploring the vast conformational space of proteins.

Purpose of the Study:

To review enhanced conformational sampling methods for protein structure prediction.
To highlight the utility of generalized-ensemble algorithms and parallel computing in this field.

Main Methods:

Review of generalized-ensemble algorithms (e.g., multicanonical algorithm, replica-exchange method).
Discussion of parallel Monte Carlo and molecular dynamics methods with genetic crossover.
Application examples of these methods to protein tertiary structure prediction.

Main Results:

Enhanced conformational sampling methods significantly improve the efficiency and accuracy of protein structure prediction.
Generalized-ensemble algorithms effectively overcome energy barriers in complex protein landscapes.
Parallel computing approaches accelerate the exploration of conformational space.

Conclusions:

Advanced computational methods, particularly generalized-ensemble algorithms and parallel techniques, are powerful tools for protein structure prediction.
These methods offer a viable path towards more accurate and efficient determination of protein tertiary structures.
The reviewed techniques represent significant advancements in computational structural biology.