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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...

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Related Experiment Video

Updated: Aug 19, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

LambdaPP: Fast and accessible protein-specific phenotype predictions.

Tobias Olenyi^1,2, Céline Marquet^1,2, Michael Heinzinger^1,2

¹TUM (Technical University of Munich) Department of Informatics, Bioinformatics- & Computational Biology-i12, Garching, Germany.

Protein Science : a Publication of the Protein Society

|December 1, 2022

Summary

This summary is machine-generated.

LambdaPP is a new webserver that uses artificial intelligence (AI) to quickly predict protein structure and function. It offers detailed protein insights, revolutionizing molecular biology research.

Keywords:

artificial intelligence protein annotation protein function prediction protein language models protein structure prediction web server

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Published on: January 26, 2024

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An Integrated Approach for Microprotein Identification and Sequence Analysis

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Published on: July 12, 2022

Area of Science:

Computational Biology
Bioinformatics
Artificial Intelligence in Molecular Biology

Background:

The first AI protein prediction server, PredictProtein, launched in 1992.
Accurate AI solutions for protein prediction are transforming molecular biology.
LambdaPP aims to advance protein prediction capabilities.

Purpose of the Study:

To introduce LambdaPP, a novel webserver for rapid and comprehensive protein analysis.
To provide accessible visualizations of protein 3D structure and residue-level predictions.
To offer predictions for protein function, including Gene Ontology and subcellular localization.

Main Methods:

LambdaPP utilizes ColabFold and MMseqs2 for protein structure prediction.
Other predictions leverage the pLM ProtT5 model.
Input is a protein sequence; output includes 3D structure, protein-level, and residue-level predictions.

Main Results:

LambdaPP provides near-instantaneous predictions for protein structure and function.
It offers detailed residue-level insights like binding sites, conservation, and disorder.
Accessible visualizations and downloadable results are available.

Conclusions:

LambdaPP offers a significant advancement over existing protein prediction tools.
The webserver is freely available, promoting wider accessibility in research.
Local execution options via Python package or Docker image support high-throughput applications.