Structural data science methods and software to study immunotherapeutic proteins

研究免疫治疗蛋白质的结构数据科学方法和软件

• 批准号: 10703139
• 负责人: Philippe Youkharibache
• 金额: $ 15.14万
• 依托单位: DIVISION OF BASIC SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10703139
• 关键词: 2019-nCoV; Algorithms; Amino Acid Transporter; Animals; Antibodies; Antibody Binding Sites; Antigens; Antiviral Agents; Antiviral Therapy; Big Data; Binding; CCL21 gene; CD19 gene; CD28 gene; CD8B1 gene; CTLA4 gene; Cell Communication; Cell Surface Proteins; Cell Surface Receptors; Cell membrane; Cell surface; Cells; Clinical Trials; Collaborations; Communities; Computer software; Coronavirus; Crystallization; Data; Data Science; Databases; Development; Endogenous Retroviruses; Engineering; Epitopes; Extramural Activities; G-Protein-Coupled Receptors; Goals; Group Structure; Heart; Human; Immune; Immune system; Immunoglobulin Domain; Immunologic Receptors; Immunooncology; Immunotherapeutic agent; Immunotherapy; In Vitro; Individual; Institutes; Integral Membrane Protein; Knowledge; Ligands; Light; Link; Machine Learning; Mediating; Membrane Proteins; Metabolic; Methods; Middle East Respiratory Syndrome; Mining; Molecular; Molecular Conformation; Nutrient; Online Systems; Pattern; Property; Protein Conformation; Protein Databases; Proteins; Protocols documentation; Reproducibility; Research; Research Support; Retroviridae; Route; SARS-CoV-2 antibody; Sequence Homology; Severe Acute Respiratory Syndrome; Signal Transduction; Structure; System; T-Lymphocyte; Techniques; Tertiary Protein Structure; Therapeutic; Time; United States National Institutes of Health; Viral; Viral Proteins; Vision; Visualization; anti-cancer therapeutic; antibody engineering; base; betacoronavirus; chimeric antigen receptor; chimeric antigen receptor T cells; computational platform; data repository; data sharing; data streams; data visualization; design; diverse data; extracellular; flexibility; hackathon; immunological synapse; improved; in silico; in vivo; inhibitor; innovation; molecular subtypes; nanobodies; neutralizing antibody; novel strategies; open source; programmed cell death ligand 1; programmed cell death protein 1; programs; protein complex; protein folding; protein protein interaction; receptor; receptor binding; scaffold; scientific computing; software development; structural biology; tool

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.

我们开发了一种高效的交互式网络软件，用于分子可视化和结构分析（iCn3D）[Wang 等人。 2020，王等人。 2022]通过与 NCBI 结构小组的初步合作。我们成功地应用该软件来研究病毒蛋白与细胞表面受体的结构和相互作用[Youkharibache 等人。 2020]。 iCn3D 软件现已成为一个协作研究平台，正如我们最近对 SARS-CoV-2 和其他 β 冠状病毒的序列结构分析所证明的那样，我们在受体结合域/基序 (RBD/RBM) 中发现了特定的序列结构微观同源性从 SARS 到 MERS、OC43、HKU1、HKU4 和 MHV 冠状病毒的超二级结构 [Youkharibache et al. 2020]用于通过中和抗体或其他治疗分子进行靶向。在进行此分析时，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在改进结构方面的价值。我们通过 iCn3D 中的 F.A.I.R 机制实现了创新的数据共享功能。事实上，我们比数据共享更进一步，因为整个分析协议都嵌入可共享的永久链接中，以实现可重复性、可扩展性和协作研究。随着软件变得跨学科，它也正在成为集成不同数据流的平台。软件开发本身正在发展成为一个协作的开源中心，来自校内和校外社区的新开发团队加入其中，并通过黑客马拉松共同接触更广泛的开发者社区 [https://www.iscb.org /ismb2020-program/ismb2020-hackathon]，与校内和校外合作者共同组织。我研究的基本基础是研究分子系统（尤其是蛋白质）的自关联决定因素，如分子组织多个层面的结构对称性所揭示的那样 [Youkharibache 2019; Youkharibache、Tran 和 Abrol 2020]。我们正在开发的用于研究分子相互作用的软件以及我们现在正在处理的应用程序已经开始捕捉这一愿景，我们正在探索对称分析作为数据组织机制的初步实现，旨在开发基于分子相互作用知识的治疗方法。例如，虽然抗体的重链和轻链对称性众所周知，但单个免疫球蛋白结构域本身由本质上伪对称的原型结构域组成[Youkharibache 2019]，这一特性在很大程度上被忽视，但却可以为抗体工程（尤其是纳米抗体）开辟新途径。同时，许多细胞表面蛋白受体，从 T 细胞到其靶细胞（TCR、CD4、CD8、CD28、CTLA4、PD1、PDL1 等）均由通过寡聚伪对称相互作用的 Ig 结构域组成。这些排列揭示了蛋白质结构域关联的决定因素，特别是 Ig 结构域。我们正在构建一个以 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提供宝贵的数据。 Ig 结构域是迄今为止最常见的免疫组结构折叠，其伪对称组装模式对于理解和设计抑制剂和调节剂具有宝贵的指导作用。然而，细胞表面还有其他重要的折叠：GPCR、MFS、SLC 等，它们用作免疫细胞相互作用、代谢调节或病毒进入的受体。我们已经证明，包括 GPCR 和 SLC 在内的多种多胞膜蛋白确实是通过伪对称组装机制形成的 [Youkharibache, Tran, and Abrol 2020]。其次，对于基于 Ig 的蛋白质，GPCR 代表了细胞表面组/免疫组中最重要的分子支架子集，而 SLC 也在列表中名列前茅。我们之前已经确定，在 20% 的已知结构中发现了蛋白质结构域的伪对称性，但在完整膜蛋白中发现了更高比例的准对称性 [Youkharibache, Tran, and Abrol 2020]，而且我们现在看到了甚至表面组蛋白质的百分比更高，尤其是免疫细胞上的蛋白质。我们的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，我们正在积极开发方法和数据库，以便能够根据这些想法设计新的基于 Ig 的受体作为抗癌疗法。基于灵活性分析的抗 CD19 和抗 BCMA CAR 的表征使我们能够支持正在进行的临床试验中的观察结果 [Brudno 等人，2017 年。 2020]；同时，我们观察到晶体中 CAR-T scFv 自发重排的形成[PDBid：7JO8 Cheung 等人。 2020] 由 Ig 结构域关联的准对称性介导 [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复合物的柔性部分，以研究蛋白质折叠和展开、构象变化，最重要的是，我们的一些应用将灵活性与其潜在的序列结构决定因素联系起来。我们还在开发一个带注释的免疫蛋白数据库，重新组合所有已知的包含相互作用的免疫球蛋白结构域的结构，以研究细胞之间免疫突触核心的界面，主要涉及 T 细胞及其受体。