Motif scaffolding seeks to design scaffold structures for constructing
proteins with functions derived from the desired motif, which is crucial for
the design of vaccines and enzymes. Previous works approach the problem by
inpainting or conditional generation. Both of them can only scaffold motifs
with fixed positions, and the conditional generation cannot guarantee the
presence of motifs. However, prior knowledge of the relative motif positions in
a protein is not readily available, and constructing a protein with multiple
functions in one protein is more general and significant because of the
synergies between functions. We propose a Floating Anchor Diffusion (FADiff)
model. FADiff allows motifs to float rigidly and independently in the process
of diffusion, which guarantees the presence of motifs and automates the motif
position design. Our experiments demonstrate the efficacy of FADiff with high
success rates and designable novel scaffolds. To the best of our knowledge,
FADiff is the first work to tackle the challenge of scaffolding multiple motifs
without relying on the expertise of relative motif positions in the protein.
Code is available at this https URL

浮动锚扩散（FADiff）模型用于构建蛋白质的结构，使其具有所需的基序，以实现疫苗和酶的设计。FADiff 模型可以让基序在扩散过程中自由浮动，从而保证基序的存在并自动设计基序位置。

多图范式支撑的浮动锚扩散模型

Floating Anchor Diffusion Model for Multi-motif Scaffolding

Enzymes are genetically encoded biocatalysts capable of accelerating chemical
reactions. How can we automatically design functional enzymes? In this paper,
we propose EnzyGen, an approach to learn a unified model to design enzymes
across all functional families. Our key idea is to generate an enzyme's amino
acid sequence and their three-dimensional (3D) coordinates based on
functionally important sites and substrates corresponding to a desired
catalytic function. These sites are automatically mined from enzyme databases.
EnzyGen consists of a novel interleaving network of attention and neighborhood
equivariant layers, which captures both long-range correlation in an entire
protein sequence and local influence from nearest amino acids in 3D space. To
learn the generative model, we devise a joint training objective, including a
sequence generation loss, a position prediction loss and an enzyme-substrate
interaction loss. We further construct EnzyBench, a dataset with 3157 enzyme
families, covering all available enzymes within the protein data bank (PDB).
Experimental results show that our EnzyGen consistently achieves the best
performance across all 323 testing families, surpassing the best baseline by
10.79% in terms of substrate binding affinity. These findings demonstrate
EnzyGen's superior capability in designing well-folded and effective enzymes
binding to specific substrates with high affinities.

通过挖掘酶数据库中的功能性重要位点和底物来生成酶的氨基酸序列和三维坐标，EnzyGen 提出了一种学习酶设计统一模型的方法，实验结果表明，EnzyGen 在设计与特定底物具有高亲和力的酶方面具有卓越能力。