Protein complexes are macromolecules essential to the functioning and
well-being of all living organisms. As the structure of a protein complex, in
particular its region of interaction between multiple protein subunits (i.e.,
chains), has a notable influence on the biological function of the complex,
computational methods that can quickly and effectively be used to refine and
assess the quality of a protein complex's 3D structure can directly be used
within a drug discovery pipeline to accelerate the development of new
therapeutics and improve the efficacy of future vaccines. In this work, we
introduce the Equivariant Graph Refiner (EGR), a novel E(3)-equivariant graph
neural network (GNN) for multi-task structure refinement and assessment of
protein complexes. Our experiments on new, diverse protein complex datasets,
all of which we make publicly available in this work, demonstrate the
state-of-the-art effectiveness of EGR for atomistic refinement and assessment
of protein complexes and outline directions for future work in the field. In
doing so, we establish a baseline for future studies in macromolecular
refinement and structure analysis.

本文介绍了一种新的 E (3)-equivariant 图神经网络 (EGR)，旨在用于多任务结构细化和评估蛋白质复合物的效果最优，证实其在蛋白质复合物原子精细化和评估方面的效果最好，并针对未来研究进行了简要展示和总结，为大分子细化和结构分析领域建立了一个基准。

EGR: 三维蛋白质复合物结构的等变图形细化和评估

EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures

We propose a novel algorithm for the joint refinement of structure and motion
parameters from image data directly without relying on fixed and known
correspondences. In contrast to traditional bundle adjustment (BA) where the
optimal parameters are determined by minimizing the reprojection error using
tracked features, the proposed algorithm relies on maximizing the photometric
consistency and estimates the correspondences implicitly. Since the proposed
algorithm does not require correspondences, its application is not limited to
corner-like structure; any pixel with nonvanishing gradient could be used in
the estimation process. Furthermore, we demonstrate the feasibility of refining
the motion and structure parameters simultaneously using the photometric in
unconstrained scenes and without requiring restrictive assumptions such as
planarity. The proposed algorithm is evaluated on range of challenging outdoor
datasets, and it is shown to improve upon the accuracy of the state-of-the-art
VSLAM methods obtained using the minimization of the reprojection error using
traditional BA as well as loop closure.

本文提出了一种新颖的算法，用于直接从图像数据中联合优化结构和运动参数，而无需依赖固定和已知的对应关系。