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Updated: May 17, 2025

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Protein sequence modelling with Bayesian flow networks.

Timothy Atkinson¹, Thomas D Barrett², Scott Cameron¹

¹InstaDeep, 5 Merchant Square, London, W2 1AY, England.

Nature Communications

|April 3, 2025

Summary

This summary is machine-generated.

Bayesian Flow Networks (BFNs) enable advanced protein sequence generation. Our ProtBFN model creates diverse, natural-like protein sequences, outperforming existing methods in both unconditional and conditional tasks.

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Last Updated: May 17, 2025

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Published on: July 16, 2017

Area of Science:

Computational biology
Protein engineering
Machine learning

Background:

Generative machine learning has advanced protein sequence modeling.
Existing models struggle with both unconditional and conditional generation.
Bayesian Flow Networks (BFNs) offer a novel framework for generative modeling.

Purpose of the Study:

To introduce Bayesian Flow Networks (BFNs) for protein sequence generation.
To develop and evaluate ProtBFN, a large-scale BFN model for protein sequences.
To assess the capability of BFNs in conditional generation for antibody design.

Main Methods:

Developed ProtBFN, a 650M parameter model based on Bayesian Flow Networks.
Trained ProtBFN on protein sequences from UniProtKB.
Fine-tuned ProtBFN on antibody heavy chains to create AbBFN for conditional generation tasks.

Main Results:

ProtBFN generates natural-like, diverse, structurally coherent, and novel protein sequences.
ProtBFN significantly outperforms leading autoregressive and discrete diffusion models.
The antibody-specific model, AbBFN, demonstrates competitive or superior zero-shot conditional generation compared to BERT-style models.

Conclusions:

Bayesian Flow Networks are highly effective for protein sequence generation.
ProtBFN represents a significant advancement in generative protein modeling.
BFNs show promise for specialized applications like antibody engineering and therapeutic protein design.