Generating Highly Designable Proteins with Geometric Algebra Flow Matching
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912110854799360 |
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| author | Wagner, Simon Seute, Leif Viliuga, Vsevolod Wolf, Nicolas Gräter, Frauke Stühmer, Jan |
| author_facet | Wagner, Simon Seute, Leif Viliuga, Vsevolod Wolf, Nicolas Gräter, Frauke Stühmer, Jan |
| contents | We introduce a generative model for protein backbone design utilizing geometric products and higher order message passing. In particular, we propose Clifford Frame Attention (CFA), an extension of the invariant point attention (IPA) architecture from AlphaFold2, in which the backbone residue frames and geometric features are represented in the projective geometric algebra. This enables to construct geometrically expressive messages between residues, including higher order terms, using the bilinear operations of the algebra. We evaluate our architecture by incorporating it into the framework of FrameFlow, a state-of-the-art flow matching model for protein backbone generation. The proposed model achieves high designability, diversity and novelty, while also sampling protein backbones that follow the statistical distribution of secondary structure elements found in naturally occurring proteins, a property so far only insufficiently achieved by many state-of-the-art generative models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05238 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generating Highly Designable Proteins with Geometric Algebra Flow Matching Wagner, Simon Seute, Leif Viliuga, Vsevolod Wolf, Nicolas Gräter, Frauke Stühmer, Jan Machine Learning We introduce a generative model for protein backbone design utilizing geometric products and higher order message passing. In particular, we propose Clifford Frame Attention (CFA), an extension of the invariant point attention (IPA) architecture from AlphaFold2, in which the backbone residue frames and geometric features are represented in the projective geometric algebra. This enables to construct geometrically expressive messages between residues, including higher order terms, using the bilinear operations of the algebra. We evaluate our architecture by incorporating it into the framework of FrameFlow, a state-of-the-art flow matching model for protein backbone generation. The proposed model achieves high designability, diversity and novelty, while also sampling protein backbones that follow the statistical distribution of secondary structure elements found in naturally occurring proteins, a property so far only insufficiently achieved by many state-of-the-art generative models. |
| title | Generating Highly Designable Proteins with Geometric Algebra Flow Matching |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2411.05238 |