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Computing tertiary structures of proteins.

¹Department of Microbiology and Immunology, State University of New York, Syracuse 13210.

Journal of Protein Chemistry

|April 1, 1990

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A new computational method predicts protein tertiary structures from sequence data alone. This approach uses residue distances and hydrophobicity to generate low-resolution protein models with potential for further refinement.

Area of Science:

Computational biology
Structural bioinformatics
Protein structure prediction

Background:

Accurate protein tertiary structure prediction is crucial for understanding protein function.
Existing methods often require extensive experimental data or significant computational resources.

Purpose of the Study:

To develop a novel computational method for predicting low-resolution protein tertiary structures using only sequence information.
To assess the accuracy and applicability of the proposed method for proteins of varying sizes.

Main Methods:

Estimating residue distances from the molecular centroid using hydrophobicity and geometric constraints.
Constructing a binary distance matrix based on the molecular radius.
Optimizing the matrix to generate a three-dimensional protein structure.

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Main Results:

The method successfully computes low-resolution tertiary structures for proteins with 164 residues, achieving correlation coefficients (r) between 0.5 and 0.7.
Accurate inference of a few long-range contacts significantly improves structural prediction accuracy (r values of 0.8-0.9).
A criterion for evaluating input matrix quality without knowing the native structure was developed.

Conclusions:

The described method offers a computationally efficient approach to protein structure prediction from sequence data.
The ability to infer long-range contacts enhances the accuracy of predicted protein structures.
This method provides a valuable tool for initial protein structure modeling and analysis.