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Protein Folding

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Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
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Related Experiment Video

Updated: Mar 11, 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

Enhanced fold recognition using efficient short fragment clustering.

Evgeny Krissinel¹

¹CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxon, OX11 0FA, United Kingdom.

Journal of Molecular Biochemistry

|November 25, 2016

Summary

A new protein structure alignment algorithm, GESAMT, overcomes limitations of existing tools for early-stage structure solution. It offers improved speed and fold recognition for structural bioinformatics.

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Last Updated: Mar 11, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

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

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Published on: July 16, 2017

Area of Science:

Structural Biology
Bioinformatics
Computational Biology

Background:

The Secondary Structure Matching (SSM) aligner in CCP4 has limitations in early protein structure solution stages.
Incomplete or fragmented mainchains hinder reliable secondary structure computation, impacting SSM's applicability.

Purpose of the Study:

To develop a novel algorithm for protein structure alignment and comparison in CCP4.
To overcome the limitations of SSM while maintaining quality and speed.
To enhance fold recognition capabilities for structural bioinformatics.

Main Methods:

Developed GESAMT (General Efficient Structural Alignment of Macromolecular Targets) algorithm.
Utilized local fragment similarity to infer global structure similarity.
Implemented technical solutions for increased computational speed.

Main Results:

GESAMT demonstrates improved speed compared to previous methods.
Comparative analysis revealed significant enhancements in fold recognition properties.
The algorithm effectively handles structural incompleteness and fragmented mainchains.

Conclusions:

GESAMT provides a faster and more sensitive alternative for protein structure alignment.
The new algorithm expands applications in structural bioinformatics.
GESAMT is integrated into the CCP4 Software Suite (version 6.3 and later).