Summary of Sequence-augmented Se(3)-flow Matching For Conditional Protein Backbone Generation, by Guillaume Huguet et al.
Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation
by Guillaume Huguet, James Vuckovic, Kilian Fatras, Eric Thibodeau-Laufer, Pablo Lemos, Riashat Islam, Cheng-Hao Liu, Jarrid Rector-Brooks, Tara Akhound-Sadegh, Michael Bronstein, Alexander Tong, Avishek Joey Bose
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Biomolecules (q-bio.BM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel sequence-conditioned SE(3)-equivariant flow matching model called FoldFlow-2 for generating protein structures. The model leverages the rich biological inductive bias of amino acid sequences and features a protein large language model, a multi-modal fusion trunk, and a geometric transformer-based decoder. To increase diversity and novelty, the authors train FoldFlow-2 on a new dataset containing both known proteins and high-quality synthetic structures. They also introduce a Reinforced Finetuning (ReFT) objective to align the model to arbitrary rewards. The results show that FoldFlow-2 outperforms previous state-of-the-art models in terms of unconditional generation, designability, diversity, and novelty. Additionally, the authors demonstrate the model’s ability to generalize on challenging conditional design tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to generate protein structures using information from their amino acid sequences. The researchers created a special kind of computer model called FoldFlow-2 that can make realistic pictures of proteins. They trained this model on a huge dataset containing many different types of proteins, including ones that have never been seen before. This allows the model to generate new and diverse protein structures. The authors also showed that their model can be fine-tuned to solve specific problems, like designing special scaffolds for proteins. |
Keywords
» Artificial intelligence » Decoder » Large language model » Multi modal » Transformer
