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Protein Organization

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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....

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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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Updated: Nov 1, 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

Learning the protein language: Evolution, structure, and function.

Tristan Bepler¹, Bonnie Berger²

¹Simons Machine Learning Center, New York Structural Biology Center, New York, NY, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA.

|June 17, 2021

Summary

This summary is machine-generated.

Protein language models (PLMs) distill evolutionary, structural, and functional insights from massive sequence data. These machine learning models enhance protein design and property prediction through transfer learning.

Keywords:

contact prediction deep neural networks inductive bias language models natural language processing protein sequences proteins transfer learning transmembrane region prediction

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Area of Science:

Bioinformatics
Computational Biology
Machine Learning

Background:

Massive protein sequence databases offer rich information.
Language models (LMs) are powerful machine learning tools for data analysis.

Purpose of the Study:

To explore the application of protein language models (PLMs) in understanding protein sequence data.
To demonstrate how PLMs can encode evolutionary, structural, and functional information.
To investigate methods for enriching PLMs with biological knowledge for improved downstream tasks.

Main Methods:

Utilizing language models to encode amino acid sequences into vector representations.
Developing approaches to incorporate protein structural knowledge into learned representations.
Applying transfer learning to enhance protein function prediction.

Main Results:

PLMs successfully capture evolutionary, structural, and functional properties from sequence data alone.
Learned representations from PLMs enable evaluation of sequence variant fitness.
Enriching PLMs with biological and structural knowledge improves downstream function prediction accuracy.

Conclusions:

Deep protein language models are revolutionizing protein biology and design.
Further research is needed to integrate biological priors and enhance model accessibility.