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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...

Protein Organization

Protein Organization

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....

Conservation of Protein Domains

Conservation of Protein Domains

Conserved Binding Sites

Conserved Binding Sites

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.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

Conserved Binding Sites

Conserved Binding Sites

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Related Experiment Video

Updated: Mar 3, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

A Generative Angular Model of Protein Structure Evolution.

Michael Golden¹, Eduardo García-Portugués^2,3,4, Michael Sørensen³

¹Department of Statistics, University of Oxford, Oxford, United Kingdom.

Molecular Biology and Evolution

|April 29, 2017

Summary

This summary is machine-generated.

This study introduces a new evolutionary model for protein structure and sequence, improving evolutionary parameter estimation. The model captures both gradual and abrupt changes, revealing sequence-structure motifs.

Keywords:

directional statistics evolution probabilistic model protein structure

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

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

Published on: July 16, 2017

Area of Science:

Computational Biology
Molecular Evolution
Structural Bioinformatics

Background:

Stochastic models of protein evolution increasingly incorporate structural information for better parameter estimation.
Previous models often lacked the ability to fully integrate sequence and structure dynamics.

Purpose of the Study:

To develop a generative, evolutionary model of protein structure and sequence valid on a local scale.
To investigate local dependencies between sequence and structure evolution in homologous proteins.
To provide a more realistic framework for understanding protein evolution.

Main Methods:

Modeled protein structure evolution as a random walk in dihedral angle space using an angular diffusion process.
Coupled sequence and structure evolution to model conformational changes.
Developed a generative model capable of simulating evolutionary trajectories.

Main Results:

The model successfully captures both smooth conformational changes and abrupt jumps conditioned on amino acid substitutions.
Identified a sequence-structure evolutionary motif in homologous protein pairs.
Demonstrated the model's ability to simulate protein evolution under various conditions.

Conclusions:

The new model offers more realistic insights into the interplay between protein sequence and structure evolution.
It improves the estimation of evolutionary parameters by integrating structural data.
The generative approach allows for robust model validation and simulation studies.