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: 10262834
• 负责人: Philippe Youkharibache
• 金额: $ 11.44万
• 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/10262834
• 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; 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; chimeric antigen receptor; chimeric antigen receptor T cells; computational platform; data sharing; data streams; data visualization; data warehouse; design; diverse data; extracellular; flexibility; hackathon; immunological synapse; improved; in silico; in vivo; inhibitor/antagonist; 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; structured data; tool

We have developed a highly efficient interactive web-based software for molecular visualization and structural analysis (iCn3D) Wang et al. 2020] 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.

通过与NCBI结构组的初步合作，我们开发了一种用于分子可视化和结构分析的高效交互式网络软件（iCn 3D）Wang et al. 2020]。我们成功应用该软件研究了病毒蛋白与细胞表面受体的结构和相互作用[Youkharibache et al. 2020]。iCn 3D软件现在正在成为一个合作研究平台，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SARS到MERS，OC 43，HKU 1，HKU 4的冠状病毒的受体结合域/基序（RBD/RBM）超二级结构中鉴定了特定的序列结构微同源性，和MHV [Youkharibache et al. 2020]，用于通过中和抗体或其他治疗分子靶向。在进行这种分析的同时，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对改善结构的价值。我们通过iCn 3D中的F.A.I.R机制实现了创新的数据共享功能。事实上，我们比数据共享走得更远，因为整个分析协议嵌入在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，它也正在成为一个平台，以整合不同的数据流。软件开发本身正在演变成一个协作的开源中心，新的开发团队从校内和校外社区加入，并通过与校内和校外合作者共同组织的黑客马拉松[https：//www.iscb.org/ismb2020-program/ismb2020-hacksons]集体接触更广泛的开发人员社区。我研究的基本基础是研究分子系统的自缔合决定因素，特别是蛋白质，正如它们在分子组织的几个层次上的结构对称性所揭示的那样[Youkharibache 2019; Youkharibache，Tran和Abrol 2020]。我们正在开发的研究分子相互作用的软件和我们现在正在处理的应用程序开始捕捉这一愿景，我们正在探索对称性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是众所周知的，但单个免疫球蛋白结构域本身由固有的伪对称原结构域组成[Youkharibache 2019]，这一特性在很大程度上被忽视，可以为抗体工程开辟新的途径，特别是纳米抗体。与此同时，从T细胞到其靶细胞的许多细胞表面蛋白受体（TCR、CD 4、CD 8、CD 28、CTLA 4、PD 1、PDL 1等）也可以在细胞内表达。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缔合的决定因素，特别是IG结构域。我们正在建立一个以免疫球蛋白为中心的数据库，这将为设计新的免疫球蛋白免疫受体和抑制剂提供宝贵的数据。Ig结构域是迄今为止免疫组最常见的结构折叠，其伪对称组装模式是理解和设计抑制剂和调节剂的宝贵指南。然而，细胞表面上还有其他重要的折叠：GPCR、MFS、SLC等，它们被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，广泛的多位膜蛋白，包括GPCR和SLC，确实是通过假对称组装机制形成的[Youkharibache，Tran和Abrol 2020]。其次，对于Ig基蛋白，GPCR代表了细胞表面组/免疫组中最重要的分子支架子集，SLC也在列表中名列前茅。我们早些时候已经确定，蛋白质结构域的伪对称性在20%的已知结构中发现，但在完整的膜蛋白中发现了更高比例的准对称性[Youkharibache，Tran和Abrol 2020]，我们现在在表面组的蛋白质中看到了更高的百分比，特别是在免疫细胞上。我们的对称性分析为我们提供了一个解码框架来研究分子相互作用，我们正在积极开发方法和数据库，以便能够基于这些想法设计新的基于Ig的受体作为抗癌疗法。基于柔性分析的抗CD 19和抗BCMA汽车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 et al. 2020];同时，我们观察到晶体中CAR-T scFv的自发重排形成[PDBid：7 JO 8 Cheung et al. 2020]，其由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，对于我们的一些应用程序，将柔性与其潜在的序列结构决定因素联系起来。我们还正在开发一个注释的免疫蛋白数据库，重新组合所有已知的含有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。