Therapeutic antibodies have been extensively studied in drug discovery and
development in the past decades. Antibodies are specialized protective proteins
that bind to antigens in a lock-to-key manner. The binding strength/affinity
between an antibody and a specific antigen is heavily determined by the
complementarity-determining regions (CDRs) on the antibodies. Existing machine
learning methods cast in silico development of CDRs as either sequence or 3D
graph (with a single chain) generation tasks and have achieved initial success.
However, with CDR loops having specific geometry shapes, learning the 3D
geometric structures of CDRs remains a challenge. To address this issue, we
propose AntibodyFlow, a 3D flow model to design antibody CDR loops.
Specifically, AntibodyFlow first constructs the distance matrix, then predicts
amino acids conditioned on the distance matrix. Also, AntibodyFlow conducts
constraint learning and constrained generation to ensure valid 3D structures.
Experimental results indicate that AntibodyFlow outperforms the best baseline
consistently with up to 16.0% relative improvement in validity rate and 24.3%
relative reduction in geometric graph level error (root mean square deviation,
RMSD).

提出了一种名为 AntibodyFlow 的三维流模型，用于设计抗体 CDR 环。通过构建距离矩阵并基于该矩阵预测氨基酸，AntibodyFlow 能够学习 CDR 环的三维几何结构，实验证明 AntibodyFlow 在有效率和几何图形误差方面优于其他基线方法。

AntibodyFlow: 设计抗体互补决策区的标准流模型

AntibodyFlow: Normalizing Flow Model for Designing Antibody  Complementarity-Determining Regions

Antibodies are crucial proteins produced by the immune system in response to
foreign substances or antigens. The specificity of an antibody is determined by
its complementarity-determining regions (CDRs), which are located in the
variable domains of the antibody chains and form the antigen-binding site.
Previous studies have utilized complex techniques to generate CDRs, but they
suffer from inadequate geometric modeling. Moreover, the common iterative
refinement strategies lead to an inefficient inference. In this paper, we
propose a deep generative model that can co-design 1D sequences and 3D
structures of CDRs in a one-shot manner. To achieve this, we decouple the
antibody CDR design into two stages: (i) geometric modeling of protein
structures and (ii) sequence-structure co-learning. We develop a protein
complex invariant embedding that captures both intra- and inter-component
interactions among the backbone atoms including C$\alpha$, N, C, and O atoms to
achieve comprehensive geometric modeling. Then, we introduce a cross-gate MLP
for sequence-structure co-learning, which allows sequence and structure
representations to implicitly refine each other. This enables our model to
design desired sequences and structures in a one-shot manner. Extensive
experiments are conducted to evaluate our results at both the sequence and
structure level, which demonstrate that our model achieves superior performance
compared to the state-of-the-art antibody CDR design methods.

本文提出了一种深度生成模型，可在一次试验中协同设计抗体 CDR 的一维序列和三维结构，并通过广泛实验证明其性能优于现有方法。

蛋白复合物不变嵌入与交叉门多层感知器是一种一次性抗体设计工具

Protein Complex Invariant Embedding with Cross-Gate MLP is A One-Shot  Antibody Designer

Antibodies are versatile proteins that bind to pathogens like viruses and
stimulate the adaptive immune system. The specificity of antibody binding is
determined by complementarity-determining regions (CDRs) at the tips of these
Y-shaped proteins. In this paper, we propose a generative model to
automatically design the CDRs of antibodies with enhanced binding specificity
or neutralization capabilities. Previous generative approaches formulate
protein design as a structure-conditioned sequence generation task, assuming
the desired 3D structure is given a priori. In contrast, we propose to
co-design the sequence and 3D structure of CDRs as graphs. Our model unravels a
sequence autoregressively while iteratively refining its predicted global
structure. The inferred structure in turn guides subsequent residue choices.
For efficiency, we model the conditional dependence between residues inside and
outside of a CDR in a coarse-grained manner. Our method achieves superior
log-likelihood on the test set and outperforms previous baselines in designing
antibodies capable of neutralizing the SARS-CoV-2 virus.

本文提出了一种基于生成模型的方法来自动设计具有增强的结合特异性或中和能力的抗体的 CDRs。该方法系统地利用了序列和三维结构之间的关系，并在设计中具有更高的效率和灵活性，最终成功设计出能够中和 SARS-CoV-2 病毒的抗体。