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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.
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Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Updated: May 12, 2025

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

M-DeepAssembly: enhanced DeepAssembly based on multi-objective multi-domain protein conformation sampling.

Xinyue Cui¹, Yuhao Xia¹, Minghua Hou¹

¹College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.

BMC Bioinformatics

|May 5, 2025

Summary

This summary is machine-generated.

M-DeepAssembly improves multi-domain protein structure prediction by using a novel multi-objective conformation sampling algorithm. This method enhances accuracy, especially for proteins with weak evolutionary signals or large structures.

Keywords:

Conformation sampling Multi-domain protein assembly Multi-objective energy model Protein structure prediction

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Last Updated: May 12, 2025

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Area of Science:

Structural Biology
Computational Biology
Biophysics

Background:

Protein structure prediction is crucial for understanding function and drug design.
Deep learning has advanced single-domain prediction, but multi-domain prediction remains challenging.
Weak evolutionary signals and large protein sizes hinder accurate multi-domain structure prediction.

Purpose of the Study:

To develop a novel computational protocol for accurate multi-domain protein structure prediction.
To address limitations in predicting structures of large multi-domain proteins and those with weak inter-domain evolutionary signals.

Main Methods:

Proposed M-DeepAssembly, a protocol utilizing a multi-objective protein conformation sampling algorithm.
Extracted inter-domain interactions and sequence distance features using DeepAssembly and AlphaFold2.
Constructed a multi-objective energy model and employed a sampling algorithm to generate conformational ensembles.
Utilized an in-house model quality assessment algorithm for final structure selection.

Main Results:

M-DeepAssembly achieved a 15.4% higher average TM-score than AlphaFold2 and 2.0% higher than DeepAssembly on a test set of 164 multi-domain proteins.
Ensembles contained models with significantly higher accuracy, outperforming baseline methods by up to 20.3%.
Demonstrated performance advantages over AlphaFold2 on CASP15 multi-domain targets.

Conclusions:

M-DeepAssembly offers a novel approach to multi-domain protein assembly, overcoming challenges of weak evolutionary signals and large structures.
The method generates diverse ensembles through multi-objective conformation sampling, aiding in predicting complex protein structures.
Contributes to exploring multi-domain protein functions and provides insights into proteins with multiple conformational states.