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Protein Networks

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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.
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Protein Families

Protein Families

Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism. Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members. If these new proteins contain similar amino acids in key locations, 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...

Proteomics

Proteomics

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Mechanical Protein Function

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Proteins perform many mechanical functions in a cell. These proteins can be classified into two general categories- proteins that generate mechanical forces and proteins that are subjected to mechanical forces. Proteins providing mechanical support to the structure of the cell, such as keratin, are subjected to mechanical force, whereas proteins involved in cell movement and transport of molecules across cell membranes, such as an ion pump, are examples of generating mechanical force.

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Related Experiment Video

Updated: Jun 21, 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

Identification of protein functions using a machine-learning approach based on sequence-derived properties.

Bum Ju Lee¹, Moon Sun Shin, Young Joon Oh

¹Industrial Research Center, Jungwon University, Chungbuk, Republic of Korea. jupiter-lee@hanmail.net

Proteome Science

|August 12, 2009

Summary

This study introduces a novel machine learning method for predicting protein function using only protein sequence properties. The new approach achieves high accuracy, outperforming traditional methods when sequence similarity is low.

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Last Updated: Jun 21, 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

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

Bioinformatics
Computational Biology
Proteomics

Background:

Predicting protein function is crucial in bioinformatics.
Sequence similarity methods are limited by weak or absent sequence homology.
Existing methods struggle with proteins lacking clear sequence or structural similarity to known ones.

Purpose of the Study:

Develop an accurate protein function prediction method.
Overcome limitations of sequence similarity-based approaches.
Identify function irrespective of sequence and structural similarities.

Main Methods:

Developed a machine learning model using protein sequence properties.
Introduced 33 novel features representing local and global sequence differences.
Utilized 484 sequence-derived features for model training.
Employed random forests with feature selection for prediction.

Main Results:

Achieved high accuracy (94.23%–100%) in predicting functions for 11 diverse proteins.
Demonstrated the broad applicability of local sequence information.
Identified a compact and effective feature subset for function prediction.

Conclusions:

Proposed an accurate machine learning method based solely on protein sequence properties.
Introduced new PNPRD (positively and/or negatively charged residues) features.
Sequence-based classifiers show strong performance across various proteins.
The proposed features enhance discriminative power for specific protein functions.