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Evolution and physics in comparative protein structure modeling.

András Fiser¹, Michael Feig, Charles L Brooks

¹Laboratory of Molecular Biophysics, Pels Family Center for Biochemistry and Structural Biology, The Rockefeller University, 1230 York Avenue, New York, New York 10021, USA.

Accounts of Chemical Research

|June 19, 2002

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Protein structure prediction uses physical forces and evolution. This review explores comparative modeling, enhancing it with physical insights like solvation effects for accurate protein structure determination.

Area of Science:

Biophysics
Computational Biology
Structural Biology

Background:

Protein native structure arises from physical forces during folding and evolutionary processes.
Two main approaches exist for protein structure prediction: de novo prediction and comparative modeling.

Purpose of the Study:

To review comparative protein structure modeling techniques.
To discuss integrating physical principles into protein structure prediction.
To highlight the role of solvation effects in loop modeling.

Main Methods:

Review of comparative protein structure modeling.
Discussion of incorporating physical considerations.
Illustrative example using solvation effects in loop modeling.

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Main Results:

Comparative modeling by satisfaction of spatial restraints offers a robust starting point.
Integrating physical factors, such as solvation, improves model accuracy.
Solvation effects are crucial for accurate modeling of protein loops.

Conclusions:

Comparative modeling is a powerful tool for protein structure prediction.
Incorporating physical principles enhances the accuracy and reliability of predicted structures.
Further research into physical considerations will advance protein structure prediction.