Functional Geometry Guided Protein Sequence and Backbone Structure
Co-Design
- URL: http://arxiv.org/abs/2310.04343v3
- Date: Mon, 8 Jan 2024 19:40:24 GMT
- Title: Functional Geometry Guided Protein Sequence and Backbone Structure
Co-Design
- Authors: Zhenqiao Song, Yunlong Zhao, Wenxian Shi, Yang Yang, Lei Li
- Abstract summary: We propose a model to jointly design Protein sequence and structure based on automatically detected functional sites.
NAEPro is powered by an interleaving network of attention and equivariant layers, which can capture global correlation in a whole sequence.
Experimental results show that our model consistently achieves the highest amino acid recovery rate, TM-score, and the lowest RMSD among all competitors.
- Score: 12.585697288315846
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Proteins are macromolecules responsible for essential functions in almost all
living organisms. Designing reasonable proteins with desired functions is
crucial. A protein's sequence and structure are strongly correlated and they
together determine its function. In this paper, we propose NAEPro, a model to
jointly design Protein sequence and structure based on automatically detected
functional sites. NAEPro is powered by an interleaving network of attention and
equivariant layers, which can capture global correlation in a whole sequence
and local influence from nearest amino acids in three dimensional (3D) space.
Such an architecture facilitates effective yet economic message passing at two
levels. We evaluate our model and several strong baselines on two protein
datasets, $\beta$-lactamase and myoglobin. Experimental results show that our
model consistently achieves the highest amino acid recovery rate, TM-score, and
the lowest RMSD among all competitors. These findings prove the capability of
our model to design protein sequences and structures that closely resemble
their natural counterparts. Furthermore, in-depth analysis further confirms our
model's ability to generate highly effective proteins capable of binding to
their target metallocofactors. We provide code, data and models in Github.
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