Have you ever been troubled by the complexity and computational cost of SE(3)
protein structure modeling and been amazed by the simplicity and power of
language modeling? Recent work has shown promise in simplifying protein
structures as sequences of protein angles; therefore, language models could be
used for unconstrained protein backbone generation. Unfortunately, such
simplification is unsuitable for the constrained protein inpainting problem,
where the model needs to recover masked structures conditioned on unmasked
ones, as it dramatically increases the computing cost of geometric constraints.
To overcome this dilemma, we suggest inserting a hidden \textbf{a}tomic
\textbf{d}irection \textbf{s}pace (\textbf{ADS}) upon the language model,
converting invariant backbone angles into equivalent direction vectors and
preserving the simplicity, called Seq2Direct encoder ($\text{Enc}_{s2d}$).
Geometric constraints could be efficiently imposed on the newly introduced
direction space. A Direct2Seq decoder ($\text{Dec}_{d2s}$) with mathematical
guarantees is also introduced to develop a \textbf{SDS}
($\text{Enc}_{s2d}$+$\text{Dec}_{d2s}$) model. We apply the SDS model as the
denoising neural network during the conditional diffusion process, resulting in
a constrained generative model--\textbf{DiffSDS}. Extensive experiments show
that the plug-and-play ADS could transform the language model into a strong
structural model without loss of simplicity. More importantly, the proposed
DiffSDS outperforms previous strong baselines by a large margin on the task of
protein inpainting.

提出了 Seq2Direct 编码器和 Direct2Seq 解码器，用于生成具有约束条件的氨基酸序列。DiffSDS 模型使用了 Seq2Direct 和 Direct2Seq，采用 ADS 来构建一个强大的蛋白质生成模型，具有更高的性能。

DiffSDS：一种适用于受几何条件和约束影响的蛋白质主链填补的语言扩散模型

DiffSDS: A language diffusion model for protein backbone inpainting under geometric conditions and constraints

A protein performs biological functions by folding to a particular 3D
structure. To accurately model the protein structures, both the overall
geometric topology and local fine-grained relations between amino acids (e.g.
side-chain torsion angles and inter-amino-acid orientations) should be
carefully considered. In this work, we propose the Directed Weight Neural
Network for better capturing geometric relations among different amino acids.
Extending a single weight from a scalar to a 3D directed vector, our new
framework supports a rich set of geometric operations on both classical and
SO(3)--representation features, on top of which we construct a perceptron unit
for processing amino-acid information. In addition, we introduce an equivariant
message passing paradigm on proteins for plugging the directed weight
perceptrons into existing Graph Neural Networks, showing superior versatility
in maintaining SO(3)-equivariance at the global scale. Experiments show that
our network has remarkably better expressiveness in representing geometric
relations in comparison to classical neural networks and the (globally)
equivariant networks. It also achieves state-of-the-art performance on various
computational biology applications related to protein 3D structures.

本文提出了一种基于蛋白质氨基酸之间的几何关系的指向性权重神经网络，结合了传统神经网络和等变神经网络，提供更好的蛋白质结构建模方法，并在多个计算生物学应用中取得了最佳表现。