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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 form...

Conservation of Protein Domains

Conservation of Protein Domains

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 form...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Covalently Linked Protein Regulators

Covalently Linked Protein Regulators

Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein.

Covalently Linked Protein Regulators

Covalently Linked Protein Regulators

Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein.

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Related Experiment Video

Updated: May 16, 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

Multiple graph regularized protein domain ranking.

Jim Jing-Yan Wang¹, Halima Bensmail, Xin Gao

¹Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia. xin.gao@kaust.edu.sa

BMC Bioinformatics

|November 20, 2012

Summary

This summary is machine-generated.

Multiple Graph regularized Ranking (MultiG-Rank) improves protein domain ranking by combining multiple graphs, overcoming limitations of single-graph methods. This approach enhances ranking performance and simplifies parameter selection in structural biology.

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

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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:

Structural biology
Bioinformatics
Computational biology

Background:

Protein domain ranking is crucial in structural biology.
Existing methods often overlook global data structure, relying on pairwise comparisons.
Current graph regularized ranking methods are sensitive to graph model and parameter choices.

Purpose of the Study:

To develop a robust protein domain ranking algorithm that addresses the limitations of existing methods.
To improve ranking performance by incorporating global manifold structure.
To reduce sensitivity to graph model and parameter selection.

Main Methods:

Developed the Multiple Graph regularized Ranking (MultiG-Rank) algorithm.
MultiG-Rank combines multiple initial graphs for regularization, approximating the protein domain distribution's intrinsic manifold.
Employs an iterative algorithm to jointly and automatically learn graph weights and ranking scores.

Main Results:

MultiG-Rank demonstrated superior ranking performance compared to single-graph regularized methods.
The algorithm outperformed traditional pairwise similarity-based ranking methods.
Experimental results were validated on a subset of the ASTRAL SCOP protein domain database.

Conclusions:

Combining multiple graphs effectively solves the challenge of graph model and parameter selection in protein domain ranking.
This multi-graph approach offers a generalized solution for protein domain ranking applications.
The study opens new avenues for applying multiple graphs in structural biology.