@inproceedings{
huguet2024sequenceaugmented,
title={Sequence-Augmented {SE}(3)-Flow Matching For Conditional Protein Generation},
author={Guillaume Huguet and James Vuckovic and Kilian FATRAS and Eric Thibodeau-Laufer and Pablo Lemos and Riashat Islam and Cheng-Hao Liu and Jarrid Rector-Brooks and Tara Akhound-Sadegh and Michael M. Bronstein and Alexander Tong and Joey Bose},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=paYwtPBpyZ}
}
Proteins are essential for almost all biological processes and derive their diverse functions from complex $3 \rm D$ structures, which are in turn determined by their amino acid sequences. 
In this paper, we exploit the rich biological inductive bias of amino acid sequences and introduce FoldFlow++, a novel sequence-conditioned $\text{SE}(3)$-equivariant flow matching model for protein structure generation. FoldFlow++ presents substantial new architectural features over the previous FoldFlow family of models including a protein large language model to encode sequence, a new multi-modal fusion trunk that combines structure and sequence representations, and a geometric transformer based decoder. To increase 
diversity and novelty of generated samples -- crucial for de-novo drug design -- we
train FoldFlow++ at scale on a new dataset 
that is an order of magnitude 
larger than PDB datasets of prior works, containing both known proteins in PDB and high-quality synthetic structures achieved through filtering. We further demonstrate the ability to align FoldFlow++ to arbitrary rewards, e.g. increasing secondary structures diversity, by introducing a Reinforced Finetuning (ReFT) objective. We empirically observe that FoldFlow++ outperforms previous state-of-the-art protein structure-based generative models, improving over RFDiffusion in terms of unconditional generation across all metrics including designability, diversity, and novelty across all protein lengths, as well as exhibiting generalization on the task of equilibrium conformation sampling. Finally, we demonstrate that a fine-tuned FoldFlow++ makes progress on challenging conditional design tasks such as designing scaffolds for the VHH nanobody.
Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Generation
Guillaume Huguet James Vuckovic Kilian FATRAS Eric Thibodeau-Laufer Pablo Lemos Riashat Islam Cheng-Hao Liu Jarrid Rector-Brooks Tara Akhound-Sadegh Michael M. Bronstein Alexander Tong Joey Bose