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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.
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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...

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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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Genome Annotation and Assembly

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

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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...

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Related Experiment Video

Updated: Aug 10, 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

GO Bench: shared hub for universal benchmarking of machine learning-based protein functional annotations.

Andrew Dickson¹, Ehsaneddin Asgari^1,2, Alice C McHardy²

¹Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, CA 94720, USA.

Bioinformatics (Oxford, England)

|February 14, 2023

Summary

A new Gene Benchmarking database standardizes evaluation for gene annotation models. This resource provides accessible datasets and tools to clarify the significance of machine learning advancements in bioinformatics.

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Last Updated: Aug 10, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

An Integrated Approach for Microprotein Identification and Sequence Analysis

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Published on: July 12, 2022

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Published on: April 11, 2019

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Gene annotation, mapping proteins to Gene Ontology (GO) terms, is crucial for understanding protein functions.
It is a complex multi-label, multi-class classification problem with millions of proteins lacking functional characterization.
Current lack of a standard GO dataset hinders the evaluation of novel machine learning models in bioinformatics.

Purpose of the Study:

To introduce the Gene Benchmarking database, a centralized hub for developing and evaluating gene annotation models.
To address the need for a standardized dataset and evaluation framework within the bioinformatics community.
To facilitate the assessment of machine learning model performance in gene annotation tasks.

Main Methods:

Development of a web application for easy access to pre-specified datasets.
Implementation of custom presets for data preprocessing and filtering.
Creation of leaderboards for evaluating and displaying trained gene annotation models.

Main Results:

The Gene Benchmarking database offers an integrated platform for learning and evaluating gene annotation models.
It provides preprocessed and filtered datasets via a user-friendly web interface.
The platform enables model evaluation and comparison through leaderboards for annotation tasks.

Conclusions:

The Gene Benchmarking database establishes a crucial standard for evaluating gene annotation models.
It simplifies the process of benchmarking machine learning models, clarifying performance improvements.
This resource is vital for advancing the field of bioinformatics and protein function prediction.