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Updated: Feb 10, 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

A method for partitioning the information contained in a protein sequence between its structure and function.

Andrea Possenti^1,2, Michele Vendruscolo², Carlo Camilloni³

¹Center for Complexity and Biosystems and Department of Physics, Università degli Studi di Milano and INFN, via Celoria 16, 20133, Milan, Italy.

|May 24, 2018

Summary

This summary is machine-generated.

This study quantifies the information needed for protein structure, revealing an

Keywords:

designed proteins information content intrinsically disordered proteins protein folding/function structure prediction

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Last Updated: Feb 10, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

Determination of Plasma Membrane Partitioning for Peripherally-associated Proteins

Determination of Plasma Membrane Partitioning for Peripherally-associated Proteins

Published on: June 15, 2018

Area of Science:

Biophysics
Computational Biology
Molecular Biology

Background:

Proteins function based on genetic code information translated into sequences.
Protein function relies on the balance between sequence information and structural constraints.

Purpose of the Study:

To estimate the information required for protein structure formation, considering thermodynamic properties.
To investigate the 'information gap'—sequence information remaining after structure encoding—and its relation to protein function.

Main Methods:

Information theory applied to protein sequences and structures.
Thermodynamic modeling of protein folding.
Predicting the information gap from amino acid sequences.

Main Results:

The information gap is closely aligned with the information needed for protein function and interactions.
Predicting the information gap from sequences can distinguish ordered from disordered proteins.
This approach aids in identifying unknown protein functions and optimizing designed proteins.

Conclusions:

Information theory provides a framework to understand protein sequence-structure-function relationships.
The 'information gap' is a key metric for predicting protein behavior and applications.
Predictive models based on the information gap can advance protein engineering and functional annotation.