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GENERALIST: A latent space based generative model for protein sequence families
Hoda Akl1, Brooke Emison2, Xiaochuan Zhao1
1Department of Physics, University of Florida, Gainesville, Florida, United States of America.
Plos Computational Biology
|November 27, 2023
Summary
We developed GENERALIST, a novel generative model for protein sequences. It accurately models protein families, even with limited data, and generates realistic sequence ensembles.
Area of Science:
- Computational biology
- Protein engineering
- Bioinformatics
Background:
- Generative models are crucial for protein science but struggle with large proteins and low-coverage families.
- Existing methods face challenges in inference, accuracy, and overfitting.
Purpose of the Study:
- To introduce GENERALIST, a new generative model for protein sequences.
- To address limitations of current generative approaches in protein modeling.
Main Methods:
- GENERALIST employs a nonlinear tensor factorization approach.
- The model is designed for ease of learning, tunability, and accuracy.
Main Results:
- GENERALIST accurately captures high-order amino acid covariation statistics.
- It predicts stable protein structures and generates sequence ensembles closely matching natural ones.
- The model creates an informative latent space for protein sequences.
Conclusions:
- GENERALIST offers an accurate and efficient method for modeling protein sequence variability.
- It overcomes key limitations of existing generative models.
- This tool will advance the study of protein sequence diversity and function.
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