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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,...

Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...

Protein-Protein Interfaces

Protein-Protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...

Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Proteomics, networks and connectivity indices.

Humberto González-Díaz¹, Yenny González-Díaz, Lourdes Santana

¹Unit of Bioinformatics and Connectivity Analysis, Institute of Industrial Pharmacy, and Department of Organic Chemistry, University of Santiago de Compostela, Santiago de Compostela, Spain. gonzalezdiazh@yahoo.es

|February 26, 2008

Summary

Connectivity Indices (CIs) numerically describe biological networks in proteomics. This review explores CIs

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Area of Science:

Proteomics
Bioinformatics
Network Science

Background:

Networks are essential for modeling biological systems, representing components (nodes) and their relationships (edges).
In proteomics, nodes can represent amino acids, proteins, or other biological entities, with edges denoting functional or geometric links.
Connectivity Indices (CIs) provide a numerical representation of these networks, facilitating computational analysis.

Purpose of the Study:

To review and discuss current challenges and emerging trends in the definition and application of Connectivity Indices (CIs) within proteomics.
To highlight the utility of CIs in uncovering structure-function relationships in biological systems.

Main Methods:

Review of existing literature on Connectivity Indices (CIs) in proteomics.
Categorization of CIs based on dimensionality (1-D, 2-D, 3-D) and application areas.
Discussion of specific CI types, including topographic indices (TPGIs) and pseudo 3-D CIs.

Main Results:

Emphasis on 1-D CIs for DNA/protein sequences and 2-D CIs for RNA secondary structures.
Application of 3-D-TPGIs for protein function annotation and drug-target interaction studies (drug-protein, drug-RNA).
Exploration of CIs for protein interaction networks, RNA co-expression networks, and proteomic data (2-DE maps, mass spectra).

Conclusions:

CIs offer a powerful mathematical framework for analyzing complex biological networks in proteomics.
The development and application of diverse CIs are crucial for advancing structure-function relationship studies and drug discovery.
Future trends point towards advanced CIs for molecular recognition and network analysis.