We consider the problem of antibody sequence design given 3D structural
information. Building on previous work, we propose a fine-tuned inverse folding
model that is specifically optimised for antibody structures and outperforms
generic protein models on sequence recovery and structure robustness when
applied on antibodies, with notable improvement on the hypervariable CDR-H3
loop. We study the canonical conformations of complementarity-determining
regions and find improved encoding of these loops into known clusters. Finally,
we consider the applications of our model to drug discovery and binder design
and evaluate the quality of proposed sequences using physics-based methods.

给定 3D 结构信息，我们提出了一种经过精调的逆折叠模型，专门针对抗体结构进行优化，在抗体中的序列恢复和结构鲁棒性方面超越通用蛋白质模型，尤其在高变性 CDR-H3 环上改进显著。我们研究了互补决定区的规范构象，发现将这些环编码到已知簇中的方法有所改进。最后，我们考虑了我们模型在药物发现和结合物设计方面的应用，并利用基于物理的方法评估了所提出序列的质量。

利用深度学习进行抗体序列设计的逆折叠

Inverse folding for antibody sequence design using deep learning

Understanding the geometry and pose of objects in 2D images is a fundamental
necessity for a wide range of real world applications. Driven by deep neural
networks, recent methods have brought significant improvements to object pose
estimation. However, they suffer due to scarcity of keypoint/pose-annotated
real images and hence can not exploit the object's 3D structural information
effectively. In this work, we propose a data-efficient method which utilizes
the geometric regularity of intraclass objects for pose estimation. First, we
learn pose-invariant local descriptors of object parts from simple 2D RGB
images. These descriptors, along with keypoints obtained from renders of a
fixed 3D template model are then used to generate keypoint correspondence maps
for a given monocular real image. Finally, a pose estimation network predicts
3D pose of the object using these correspondence maps. This pipeline is further
extended to a multi-view approach, which assimilates keypoint information from
correspondence sets generated from multiple views of the 3D template model.
Fusion of multi-view information significantly improves geometric comprehension
of the system which in turn enhances the pose estimation performance.
Furthermore, use of correspondence framework responsible for the learning of
pose invariant keypoint descriptor also allows us to effectively alleviate the
data-scarcity problem. This enables our method to achieve state-of-the-art
performance on multiple real-image viewpoint estimation datasets, such as
Pascal3D+ and ObjectNet3D. To encourage reproducible research, we have released
the codes for our proposed approach.

利用物体分类内部几何规律的数据驱动姿态估计方法。首先，从 2D RGB 图像学习物体部分的姿态不变局部描述符。将这些描述符与来自固定３D 模板的关键点结合使用，生成给定单眼实际图像的关键点对应图。最后，使用这些关键点对应图，预测物体的３D 姿态。该方法在多个真实环境的视角估计数据集，如 Pascal3D + 与 ObjectNet3D 上的表现达到最佳水平，并且我们公开了所使用方法的代码以鼓励再生研究。