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Related Experiment Video

Updated: Jul 12, 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

Masked inverse folding with sequence transfer for protein representation learning.

Kevin K Yang¹, Niccolò Zanichelli², Hugh Yeh³

¹Microsoft Research, 1 Memorial Drive, Cambridge, MA, USA.

Protein Engineering, Design & Selection : PEDS

|October 26, 2023

Summary

This study introduces a new protein language model that combines sequence and structure information for improved protein engineering. The model enhances protein function prediction by leveraging both existing sequences and structural data.

Keywords:

machine learning pretraining

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Published on: July 25, 2013

Area of Science:

Computational biology
Protein engineering
Machine learning

Background:

Self-supervised learning on protein sequences achieves state-of-the-art results in function and fitness prediction.
Sequence-only models overlook structural information, while inverse folding methods do not fully utilize available sequences without known structures.

Purpose of the Study:

To develop and evaluate a masked inverse folding protein language model that integrates structural information.
To investigate the impact of combining sequence-based and structure-based pretraining for protein engineering tasks.

Main Methods:

A masked inverse folding protein language model was trained as a structured graph neural network.
The model reconstructs corrupted protein sequences conditioned on backbone structure during pretraining.
Outputs from a sequence-only protein language model were used as input to the inverse folding model to assess performance improvements.

Main Results:

The proposed model demonstrates improved pretraining perplexity when incorporating sequence-only model outputs.
Evaluation on downstream protein engineering tasks shows the benefit of using structural information from experimental or predicted structures.
The study analyzes the performance gains attributed to the integration of structural data.

Conclusions:

Integrating structural information into protein language models significantly enhances performance on protein engineering tasks.
The developed masked inverse folding model offers a novel approach to leverage both sequence and structure data.
This work advances the application of machine learning in understanding and engineering protein functions.