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Conservation of Protein Domains Over Different Proteins

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain. Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...

Conservation of Protein Domains

Conservation of Protein Domains

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

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Published on: July 16, 2017

Enhancing Protein Conformational Space Sampling Using Distance Profile-Guided Differential Evolution.

Gui-Jun Zhang, Xiao-Gen Zhou, Xu-Feng Yu

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|January 24, 2017

Summary

This summary is machine-generated.

This study introduces a new algorithm for protein structure prediction. It uses both energy and residue-distance information to find more accurate protein structures, improving upon traditional methods.

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Area of Science:

Computational Biology
Biophysics
Structural Bioinformatics

Background:

De novo protein structure prediction seeks low-energy conformations based on the thermodynamics hypothesis.
Native protein conformations may not always be at the global energy minimum due to energy model inaccuracies.

Purpose of the Study:

To develop an effective algorithm for sampling protein conformations with reasonable structures.
To improve de novo protein structure prediction accuracy by incorporating distance information.

Main Methods:

A differential evolution algorithm with a distance profile-based selection strategy was developed.
The algorithm uses both energy and residue-residue distance errors to guide conformation sampling.
Distance acceptance probability is calculated based on distance profiles for trial conformations.

Main Results:

The algorithm effectively samples conformations with lower energies and more reasonable structures.
Experimental results on 28 benchmark proteins demonstrate effective near-native structure prediction.
Dual constraints of energy and distance improve sampling efficiency and accuracy.

Conclusions:

The proposed algorithm enhances de novo protein structure prediction by integrating energy and distance-based criteria.
This approach offers a more robust method for identifying near-native protein structures.
The distance profile-based selection strategy is a valuable addition to protein structure prediction algorithms.