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Protein Organization

Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....

Protein Organization

Protein Organization

Protein Organization

Protein Organization

Protein Organization

Protein Organization

Protein and Protein Structure

Protein and Protein Structure

Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme...

Protein Folding

Protein Folding

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Updated: Mar 29, 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

Protein Structure Prediction: The Next Generation.

Michael C Prentiss¹, Corey Hardin¹, Michael P Eastwood¹

¹Center for Theoretical Biological Physics, La Jolla, California 92093, Department of Chemistry and Biochemistry, University of California at San Diego, La Jolla, California 92093, Department of Physics, University of California, La Jolla, California 92093, and Department of Chemistry, University of Illinois, Urbana [Formula: see text] Champaign, 600 South Mathews Avenue, Urbana, Illinois 61801.

Journal of Chemical Theory and Computation

|December 3, 2015

Summary

This summary is machine-generated.

Statistical mechanics provides insights into protein folding kinetics and energy landscapes, improving protein structure prediction models. Simulated structures enhance energy functions for more accurate predictions.

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Area of Science:

Computational biology
Biophysics
Statistical mechanics

Background:

Recent advances in statistical mechanics have illuminated the chemical reactions governing protein folding.
Energy landscape concepts derived from protein folding kinetics have significantly advanced protein structure prediction.
Coarse-grained models have emerged as a key development in this field.

Purpose of the Study:

To survey the outcomes of blind protein structure prediction studies.
To investigate the development of second-generation prediction energy functions.
To explore the integration of information from simulated structures into energy functions.

Main Methods:

Utilizing statistical mechanics and energy landscape principles.
Developing coarse-grained models for protein structure prediction.
Employing an ensemble of previously simulated structures to inform energy functions.
Comparing simulated structures with experimentally determined structures for input refinement.

Main Results:

Blind structure prediction results demonstrate the utility of current methodologies.
Second-generation energy functions incorporating simulated structural data show promise.
The principle of minimal frustration is upheld through the assumption of a funneled energy landscape.
First-generation simulated structures offer superior input for associative memory energy functions compared to sequence-alignment-selected experimental structures.

Conclusions:

The integration of simulated structural ensembles represents a promising direction for enhancing protein structure prediction energy functions.
The energy landscape paradigm remains a robust framework for understanding and predicting protein folding.
Further refinement of energy functions using computational data can lead to more accurate protein structure predictions.