Mallet Vincent, Rapisarda Chiara, Minoux Hervé, Ovsjanikov Maks
LIX, Ecole Polytechnique, IPP Paris, Palaiseau, 91120, France.
Integrated Drug Discovery, Structural Biology and Biophysics, Sanofi, Vitry-sur-Seine, 94400, France.
Bioinformatics. 2025 May 6;41(5). doi: 10.1093/bioinformatics/btaf157.
Therapeutic antibodies have emerged as a prominent class of new drugs due to their high specificity and their ability to bind to several protein targets. Once an initial antibody has been identified, its design and characteristics are refined using structural information, when it is available. Cryo-EM is currently the most effective method to obtain 3D structures. It relies on well-established methods to process raw data into a 3D map, which may, however, be noisy and contain artifacts. To fully interpret these maps the number, position, and structure of antibodies and other proteins present must be determined. Unfortunately, existing automated methods addressing this step have limited accuracy, require additional inputs and high-resolution maps, and exhibit long running times.
We propose the first fully automatic and efficient method dedicated to finding antibodies in cryo-EM maps: CrAI. This machine learning approach leverages the conserved structure of antibodies and a dedicated novel database that we built to solve this problem. Running a prediction takes only a few seconds, instead of hours, and requires nothing but the cryo-EM map, seamlessly integrating within automated analysis pipelines. Our method can find the location and pose of both Fabs and VHHs at resolutions up to 10 Å and is significantly more reliable than existing approaches.
We make our method available both in open source github.com/Sanofi-Public/crai and as a ChimeraX bundle (crai).
治疗性抗体因其高特异性以及与多种蛋白质靶点结合的能力，已成为一类重要的新型药物。一旦鉴定出初始抗体，若有结构信息，便会利用其对抗体的设计和特性进行优化。冷冻电镜是目前获取三维结构最有效的方法。它依靠成熟的方法将原始数据处理成三维图谱，然而该图谱可能存在噪声并包含伪影。为了全面解读这些图谱，必须确定其中存在的抗体及其他蛋白质的数量、位置和结构。不幸的是，现有的解决此步骤的自动化方法准确性有限，需要额外输入和高分辨率图谱，且运行时间长。
我们提出了首个专门用于在冷冻电镜图谱中寻找抗体的全自动高效方法：CrAI。这种机器学习方法利用了抗体的保守结构以及我们为解决此问题构建的一个专门的新型数据库。进行一次预测仅需几秒而非数小时，并且只需要冷冻电镜图谱，可无缝集成到自动化分析流程中。我们的方法能够在高达10埃的分辨率下找到Fab片段和VHH的位置与姿态，并且比现有方法可靠得多。
我们将我们的方法以开源形式发布在github.com/Sanofi - Public/crai上，同时也作为ChimeraX软件包（crai）提供。
