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Structural Protein Function

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Structural proteins are a category of proteins responsible for functions ranging from cell shape and movement to providing support to major structures such as bones, cartilage, hair, and muscles. This group includes proteins such as collagen, actin, myosin, and keratin.
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Updated: Feb 12, 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

Functional classification of protein structures by local structure matching in graph representation.

Caitlyn L Mills¹, Rohan Garg², Joslynn S Lee¹

¹Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts.

Protein Science : a Publication of the Protein Society

|April 1, 2018

Summary

A new computational method, Graph Representation of Active Sites for Prediction of Function (GRASP-Func), rapidly and accurately predicts protein function. This validated tool enhances the value of structural genomics data by assigning biochemical functions to previously uncharacterized proteins.

Keywords:

6-Hairpin Glycosidase (6-HG) superfamily Concanavalin A-like Lectins/Glucanase (CAL/G) superfamily Graph Representation of Active Sites for Prediction of Function (GRASP-Func)Ribulose Phosphate Binding Barrel (RPBB) superfamily Structurally Aligned Local Sites of Activity (SALSA)protein function annotation

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A Protocol for Computer-Based Protein Structure and Function Prediction

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Crystallizing Membrane Proteins for Structure Determination using Lipidic Mesophases

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Contrast-Matching Detergent in Small-Angle Neutron Scattering Experiments for Membrane Protein Structural Analysis and Ab Initio Modeling

Contrast-Matching Detergent in Small-Angle Neutron Scattering Experiments for Membrane Protein Structural Analysis and Ab Initio Modeling

Published on: October 21, 2018

Area of Science:

Structural biology
Genomics
Computational biology

Background:

High-throughput structural genomics initiatives have solved over 14,400 protein structures.
Reliable functional information for these Structural Genomics (SG) proteins is often lacking.
Accurate functional predictions are crucial for maximizing the value of obtained structural data.

Purpose of the Study:

To develop and validate a fast and accurate computational method for predicting protein biochemical function.
To compare the performance of the new Graph Representation of Active Sites for Prediction of Function (GRASP-Func) method with a previous approach (SALSA).
To apply the validated method to predict functions for uncharacterized SG proteins.

Main Methods:

Representing residues at predicted local active sites as graphs, rather than Cartesian coordinates, for function prediction.
Utilizing the Ribulose Phosphate Binding Barrel (RPBB), 6-Hairpin Glycosidase (6-HG), and Concanavalin A-like Lectins/Glucanase (CAL/G) superfamilies for method validation.
Comparing the GRASP-Func method against the Structurally Aligned Local Sites of Activity (SALSA) method.

Main Results:

GRASP-Func demonstrated comparable accuracy to SALSA in classifying characterized proteins within the tested superfamilies.
GRASP-Func significantly outperforms SALSA in terms of speed.
Both methods successfully classified numerous SG proteins into their respective functional families, including 41 in RPBB, 9 in 6-HG, and 1 in CAL/G.

Conclusions:

The GRASP-Func method provides a validated, faster, and reliable computational approach for predicting protein biochemical function.
This method significantly enhances the utility of structural genomics data by assigning functions to uncharacterized proteins.
The improved prediction capabilities offer broad applicability for the scientific community.