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Updated: Aug 6, 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

Evolutionary-scale prediction of atomic-level protein structure with a language model.

Zeming Lin^1,2, Halil Akin¹, Roshan Rao¹

¹FAIR, Meta AI, New York, NY, USA.

Science (New York, N.Y.)

|March 17, 2023

Summary

Large language models can now directly infer atomic-level protein structure from primary sequences. This breakthrough accelerates structure prediction, enabling the creation of the ESM Metagenomic Atlas with over 617 million protein structures.

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Area of Science:

Computational biology
Artificial intelligence
Structural biology

Background:

Machine learning advances utilize evolutionary information in multiple sequence alignments for protein structure prediction.
Current methods often require extensive computational resources and time.

Purpose of the Study:

To demonstrate direct inference of full atomic-level protein structure from primary sequence using a large language model.
To achieve an order-of-magnitude acceleration in high-resolution structure prediction.
To enable large-scale structural characterization of metagenomic proteins.

Main Methods:

Utilized a large language model scaled up to 15 billion parameters.
Trained the model on protein sequences to learn atomic-resolution structural information within its representations.
Applied the model to predict structures for a large dataset of metagenomic protein sequences.

Main Results:

An atomic-resolution picture of protein structure emerged in the learned representations of the scaled-up language model.
Achieved an order-of-magnitude acceleration in high-resolution protein structure prediction.
Successfully constructed the ESM Metagenomic Atlas, predicting structures for over 617 million metagenomic protein sequences.
Identified over 225 million protein sequences with high-confidence structure predictions.

Conclusions:

Large language models offer a powerful and efficient approach for direct protein structure prediction from primary sequences.
This method significantly accelerates the process, enabling unprecedented large-scale structural characterization of biological data.
The ESM Metagenomic Atlas provides a valuable resource for exploring the diversity of natural proteins.