Structural data science methods and software to study immunotherapeutic proteins

我们开发了一个高效的交互式基于网络的分子生物学软件， 通过可视化和结构分析（iCn 3D）[Wang等人，2020，Wang等人，2022] 与NCBI结构组的初步合作。我们成功地将该软件应用于 研究病毒蛋白质的结构和与细胞表面受体的相互作用 [Youkharibache等人，2020年]。iCn 3D软件现在正在成为一个合作研究 我们最近对SARS-CoV-2和其他病毒的序列结构分析表明， 我们在β冠状病毒中发现了特定的序列结构微同源性， SARS冠状病毒的受体结合域/基序（RBD/RBM）超二级结构 MERS、OC 43、HKU 1、HKU 4和MHV [Youkharibache et al. 2020]，用于通过中和靶向 抗体或其它治疗分子。在进行分析的同时，我们还提出了 隐藏序列同源性的结构校正，证明了 综合分析方法，以改善结构。我们已经实施了一个创新的数据 通过iCn 3D中的F.A.I.R机制共享功能。事实上，我们比数据更进一步 共享，因为整个分析协议嵌入在可共享的永久链接中， 可重复性、可扩展性和协作研究。随着软件成为 由于跨学科，它也正在成为整合各种数据流的平台。软件 开发本身正在演变成一个协作的、开放源码的中心， 来自校内和校外社区的团体， 通过黑客松接触更广泛的开发者社区 [https：//www.iscb.org/ismb2020-program/ismb2020-hacksign]，与校内和 校外合作者我研究的基本基础是 分子系统，特别是蛋白质的自缔合决定簇，如它们的 分子组织的几个层次上的结构对称性[Youkharibache 2019; Youkharibache，Tran和Abrol 2020]。我们正在开发的研究分子的软件 我们现在正在处理的交互和应用程序开始捕捉这一愿景， 我们正在探索对称分析作为数据组织的初步实现， 机制，旨在开发基于分子相互作用知识的治疗方法。为 例如，虽然抗体的重链和轻链对称性是众所周知的，但个体 免疫球蛋白结构域本身由本质上假对称的原结构域组成 [Youkharibache 2019]，一种基本上被忽视的特性，可以为抗体开辟新的途径 工程，尤其是纳米抗体。与此同时，许多细胞表面蛋白 受体，从T细胞到其靶细胞（TCR、CD 4、CD 8、CD 28、CTLA 4、PD 1、PDL 1等）。 由通过寡聚伪对称排列相互作用的IG结构域组成 揭示了蛋白质结构域关联的决定因素，特别是IG结构域。我们 组装以IG为中心的数据库，该数据库将为设计新的基于IG的 免疫受体和抑制剂。Ig结构域是迄今为止最常见的结构折叠， 免疫组学及其伪对称组装模式是理解和 设计抑制剂和调节剂。然而，细胞表面还有其他重要的褶皱： GPCR、MFS、SLC等，其用作免疫细胞相互作用、代谢和免疫应答的受体。 调节或用于病毒进入。我们已经证明，广泛的多位膜 蛋白质，包括GPCR和SLC，确实是通过假对称组装形成的 机制[Youkharibache，Tran和Abrol 2020]。其次，对于Ig基蛋白，GPCR代表 细胞表面组/免疫组中最重要的分子支架子集，SLC是 也在名单上名列前茅。我们之前已经确定蛋白质结构域的伪对称性 在20%的已知结构中发现，但准对称性的比例更高 在整合膜蛋白[Youkharibache，Tran和Abrol 2020]中，我们现在看到 甚至更高的百分比跨越表面组的蛋白质，特别是在免疫细胞上。我们 对称性分析为我们提供了一个研究分子相互作用的解码框架， 积极开发能够设计新的基于Ig的受体的方法和数据库 作为基于这些想法的抗癌疗法。抗-CD 19和 基于灵活性分析的反BCMA汽车使我们能够支持以下观察结果： 正在进行的临床试验[Brudno et al. 2020];同时，我们观察到 晶体中CAR-T scFv的自发重排[PDBid：7 JO 8 Cheung et al. 2020] 通过IG结构域缔合的准对称性介导[Youkharibache 2019]。我们目前正在 开发一种算法来检测和表征蛋白质和蛋白质的柔性部分 复合物研究蛋白质折叠和展开，构象变化， 重要的是，对于我们的一些应用程序来说， 序列结构决定子我们还在开发一个带注释的免疫蛋白数据库 重组所有已知的含有相互作用的免疫球蛋白结构域的结构，以研究 在细胞之间的免疫突触的核心接口，主要涉及T细胞和 他们的受体。

We have developed a highly efficient interactive web-based software for molecular visualization and structural analysis (iCn3D) [Wang et al. 2020, Wang et al. 2022] through an initial collaboration with the NCBI structure group. We successfully applied the software to study the structure and interactions of viral proteins with cell surface receptors [Youkharibache et al. 2020]. The iCn3D software is now becoming a collaborative research platform as demonstrated by our recent sequence-structure analysis of SARS-CoV-2 and other beta coronaviruses where we identified specific sequence-structure micro-homologies in receptor binding domains/motifs (RBD/RBM) supersecondary structures of coronaviruses from SARS to MERS, OC43, HKU1, HKU4, and MHV [Youkharibache et al. 2020] for targeting by neutralizing antibodies or other therapeutic molecules. While performing this analysis, we also proposed structure corrections that were hiding sequence homologies, demonstrating the value of an integrated analysis approach to improve structures. We have implemented an innovative data sharing capability through a F.A.I.R mechanism in iCn3D. In fact, we go further than data sharing, as entire analysis protocols are embedded in sharable permanent links for reproducibility, extensibility, and collaborative research. As the software becomes cross-disciplinary, it is also becoming a platform to integrate diverse data streams. Software development itself is evolving into a collaborative, open-source hub with new development groups joining in from both in the intramural and extramural community, and collectively reaching out to a broader developers' community through hackathons [https://www.iscb.org/ismb2020-program/ismb2020-hackathon], co-organized with intramural and extramural collaborators. The fundamental basis of my research has been the study of self-association determinants of molecular systems, especially proteins, as revealed by their structural symmetries at several levels of molecular organization [Youkharibache 2019; Youkharibache, Tran, and Abrol 2020]. The software we are developing to study molecular interactions and the applications we are now tackling are beginning to capture this vision and we are exploring the initial implementations of symmetry analysis as a data organizing mechanism, aiming at developing therapeutics based on molecular interactions knowledge. For example, while antibodies' heavy and light chain symmetries are well known, the individual Immunoglobulin domains consist themselves of intrinsically pseudo-symmetric protodomains [Youkharibache 2019], a property largely ignored that can open new routes to antibody engineering, especially nanobodies. At the same time, many of the cell surface protein receptors, from T-cells to their target cells (TCRs, CD4, CD8, CD28, CTLA4, PD1, PDL1, etc.) are composed of Ig domains interacting through oligomeric pseudo-symmetric arrangements revealing the determinants of protein domain association, and Ig domains in particular. We are assembling an Ig-centric database that will provide invaluable data to design new Ig-based immunoreceptors and inhibitors. The Ig-domain is by far the most common structural fold of the immunome, and its pseudo symmetric assembly patterns are an invaluable guide to understand and design inhibitors and modulators. There are, however, other important folds on cell surfaces: GPCRs, MFS, SLCs, etc. that are used as receptors for immune cell interactions, metabolic modulations, or for viral entry. We have demonstrated that a wide range of polytopic membrane proteins, including GPCRs and SLCs, are indeed formed through a pseudo-symmetric assembly mechanism [Youkharibache, Tran, and Abrol 2020]. Second, to Ig-based proteins, GPCRs represent the most important subset of molecular scaffolds in the cell surfaceome/immunome, and SLCs are also high up in the list. We had established earlier that protein domains' pseudo symmetries are found in 20% of known structures overall, yet quasi-symmetry is found in higher proportion in integral membrane proteins [Youkharibache, Tran, and Abrol 2020], and we are now seeing an even higher percentage across the proteins of the surfaceome, especially on immune cells. Our symmetry analysis gives us a decoding framework to study molecular interactions, and we are actively developing methods and databases that can enable the design of new Ig-based receptors as anti-cancer therapeutics based on these ideas. The characterization of anti-CD19 and anti-BCMA CARs based on flexibility analysis have enabled us to support observations in ongoing clinical trials [Brudno et al. 2020]; at the same time, we have observed the formation of a spontaneous rearrangement of a CAR-T scFv in a crystal [PDBid: 7JO8 Cheung et al. 2020] mediated by quasi-symmetry of Ig domains association [Youkharibache 2019]. We are currently developing an algorithm to detect and characterize flexible parts of proteins and protein complexes to study protein folding and unfolding, conformational changes, and, most importantly, for some of our applications to relate flexibility to their underlying sequence-structure determinants. We are also developing an annotated Immunoproteins database regrouping all known structures containing Immunoglobulin domains in interaction to study the interfaces at the heart of immune synapses between cells, and primarily involving T-cells and their receptors.