Protein Structure, Stability, and Amyloid Formation

蛋白质结构、稳定性和淀粉样蛋白形成

• 批准号: 10702352
• 负责人: Ruth Nussinov
• 金额: $ 69.45万
• 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/10702352
• 关键词: Adaptor Signaling Protein; Address; Adopted; Affect; Alleles; Artificial Intelligence; BRAF gene; Bypass; Calmodulin; Categories; Cell Cycle; Cell Proliferation; Cell division; Cell membrane; Cells; Chromatin; Clinical; Colorectal Cancer; Communities; DNA Sequence Alteration; Data; Databases; Disease; Drug resistance; Embryo; Essential Genes; Event; FGFR3 gene; FRAP1 gene; Feedback; Fibroblast Growth Factor Receptors; GIT1 gene; Gene Fusion; Gene Mutation; Genes; High-Risk Cancer; Human; Immune system; Immunity; Individual; Inflammation; Intercept; Interleukin-1; KRAS2 gene; Lead; Life; Lipids; MAP Kinase Gene; MAP2K1 gene; MEKs; Malignant Neoplasms; Maps; Measures; Membrane; Methods; Mission; Modeling; Molecular; Molecular Conformation; Monomeric GTP-Binding Proteins; Mutation; Mutation Analysis; NF1 gene; Neoplasm Metastasis; Nervous System; Neurodevelopmental Disorder; Neurons; Non-Small-Cell Lung Carcinoma; Oncogenic; Oncologist; PTEN gene; PTPN11 gene; Pancreatic Ductal Carcinoma; Pathology; Pathway interactions; Pharmaceutical Preparations; Pharmacological Treatment; Pharmacology; Phenotype; Phosphotransferases; Physiological; Play; Point Mutation; Predisposition; Protein Inhibition; Protein Kinase; Proteins; RAF1 gene; RET gene; Regulation; Resistance; Role; Signal Pathway; Signal Transduction; Somatic Mutation; Structure; Testing; Therapeutic; Tumor Suppressor Proteins; amyloid formation; antitumor agent; base; brain cell; cancer cell; cancer drug resistance; cell growth; cell type; combinatorial; cytokine; cytotoxic; driver mutation; drug discovery; experimental study; falls; grasp; inhibitor; innovation; insight; interest; loss of function mutation; microbiota; mimicry; morphogens; mutant; neoplastic cell; neurodevelopment; overexpression; pathogen; precision medicine; protein activation; protein protein interaction; protein structure; resistance mechanism; resistance mutation; response; simulation; tool; tumor; tumor microenvironment

the oncogenic network of Ras signaling. We have been seeking to decipher the mechanisms of the proteins, their autoinhibition and activation, at the detailed conformational level, aiming to understand and to discover their pharmacological susceptibilities. Our broad outlook further aims to uncover their detailed interactions with other proteins and with the cell membrane, and (felled) regulation. We have also been interested in their signaling pathways. We aim to determine the scenarios of exactly how key signaling nodes are activated (or repressed, in repressors) by oncogenic driver mutations, and regulated under normal control, and decipher the hallmarks of the propagation of their signaling to the cell cycle. Uncontrolled cell proliferation is a hallmark of cancer leading us to ask what the determinants that influence signaling strength are. For cell proliferation, signaling should be sufficiently strong. This wide-ranging scope motivates us to investigate protein kinases in solution, lipid kinase (at the membrane), adaptor proteins, and their dynamic associations. To address this daunting task, we focus on two major Ras pathways that feed into the cell cycle, MAPK and PI3K/mTOR. MAPK acts in cell division; PI3K/mTOR in cell growth. Both are required in proliferation. We aim to unravel allosteric activation and inhibition mechanisms of oncogenic proteins, allosteric therapeutics and signaling. Protein activation and inhibition involve conformational changes, which are the hallmarks of allostery. We seek to understand how allostery controls physiological function; how it can play a role in cancer, and how it can be harnessed by drugs. We consider the conformational ensembles of the proteins in solution and when membrane-anchored, and their assemblies. We also ask: What is productive signaling? How to define it, how to measure it, and what are the parameters that determine it? Further, what determines the strength of signaling from an upstream to a downstream protein in a specific cell such that it leads to cell proliferation? These questions have either not been considered or not resolved. Recently we also took up the compelling question of neurodevelopmental disorders (NDDs) and their connection to cancer. We ask the puzzling questions of how same-gene mutations can drive both cancer and NDDs and why individuals with NDD have a higher risk of cancer. Ras, MEK, PI3K, PTEN, and SHP2 are among the oncogenic proteins that can harbor mutations that encode diseases other than cancer. Understanding why some of their mutations can promote cancer, whereas others promote NDDs, and why even the same mutations may promote both phenotypes, has important clinical ramifications. Our thesis is that the immune and nervous systems co-evolve as the embryo develops. Immunity can release cytokines that activate MAPK signaling in neural cells. In specific embryonic brain cell types, dysregulated signaling that results from germline or embryonic mutations can promote changes in chromatin organization and gene accessibility, and thus expression levels of essential genes in neurodevelopment. In cancer, dysregulated signaling can emerge from sporadic somatic mutations during human life. NDDs and cancer share similarities. We suggested that in NDDs, immunity, and cancer, there appears an almost invariable involvement of small GTPases (e.g., Ras, RhoA, Rac) and their pathways. TLRs, IL-1, GIT1, and FGFR signaling pathways, all can be dysregulated in NDDs and cancer. While there are signaling similarities, decisive differentiating factors are timing windows, and cell type specific perturbation levels, pointing to chromatin reorganization. Pharmacological treatment can inhibit the action of the mutant protein; however, drug resistance almost invariably emerges. Multiple studies revealed that cancer drug resistance is based upon a plethora of distinct mechanisms. Drug resistance mutations can occur in the same protein or in different proteins; as well as in the same pathway or in parallel pathways, bypassing the intercepted signaling. The dilemma that the clinical oncologist is facing is that not all the genomic alterations as well as alterations in the tumor microenvironment that facilitate cancer cell proliferation are known, and neither are the alterations that are likely to promote metastasis. For example, the common KRasG12C driver mutation emerges in different cancers. Most occur in NSCLC, but some occur, albeit to a lower extent, in colorectal cancer and pancreatic ductal carcinoma. The responses to KRasG12C inhibitors are variable and fall into three categories, (i) new point mutations in KRas, or multiple copies of KRAS G12C which lead to higher expression level of the mutant protein; (ii) mutations in genes other than KRAS; (iii) original cancer transitioning to other cancer(s). Resistance to adagrasib, an experimental antitumor agent exerting its cytotoxic effect as a covalent inhibitor of the G12C KRas, indicated that half of the cases present multiple KRas mutations as well as allele amplification. Redundant or parallel pathways included MET amplification; emerging driver mutations in NRAS, BRAF, MAP2K1, and RET; gene fusion events in ALK, RET, BRAF, RAF1, and FGFR3; and loss-of-function mutations in NF1 and PTEN tumor suppressors. We explore the molecular mechanisms underlying drug resistance while focusing on those emerging to common targeted cancer drivers. We also address questions of why cancers with a common driver mutation are unlikely to evolve a common drug resistance mechanism, and whether one can predict the likely mechanisms that the tumor cell may develop. These vastly important and tantalizing questions in drug discovery, and broadly in precision medicine, are the focus of our present review. We suggest that target combinations should preferentially be selected and prioritized with the help of the emerging massive compute power which enables artificial intelligence, and the increased gathering of data to overcome its insatiable needs. We aim to predict which proteins can interact and how, through a structure-based interface mimicry strategy. Efficient and reliable prediction of new interactions can allow identification of potential targets. Powerful protein-protein interaction prediction tools can map interactions and predict how pathogens can hijack signaling in the host cell, which can be tested by experiment. Available experimental structural data are scant, and the combinatorial landscape of host protein-pathogen interactions is vast. We are working to further enhance our server to include more interactions and modeled proteins with AI-adopted prediction methods.

Ras信号的致癌网络。我们一直试图在详细的构象水平上破译蛋白质的机制，它们的自抑制和激活，旨在了解和发现它们的药理学敏感性。我们的广阔前景进一步旨在揭示它们与其他蛋白质和细胞膜的详细相互作用，以及（下降）调节。我们对它们的信号通路也很感兴趣。我们的目标是确定关键信号节点如何被致癌驱动突变激活（或抑制，在阻遏物中），并在正常控制下进行调节，并破译其信号传导到细胞周期的特征。不受控制的细胞增殖是癌症的一个标志，这使我们想知道影响信号强度的决定因素是什么。对于细胞增殖，信号传导应该足够强。这种广泛的范围促使我们研究溶液中的蛋白激酶，脂质激酶（在膜上），接头蛋白及其动态关联。为了解决这个艰巨的任务，我们专注于两个主要的Ras通路，MAPK和PI3K/mTOR进入细胞周期。MAPK参与细胞分裂；PI3K/mTOR在细胞生长中的作用。两者都是扩散所必需的。我们的目标是揭示变构激活和抑制机制的致癌蛋白，变构治疗和信号。蛋白质的激活和抑制涉及构象变化，这是变构的标志。我们试图理解变构如何控制生理功能；它如何在癌症中发挥作用，以及它如何被药物利用。我们考虑了蛋白质在溶液中和膜锚定时的构象集合，以及它们的组装。我们还会问：什么是有效的信号？如何定义它，如何测量它，决定它的参数是什么？此外，是什么决定了特定细胞中从上游到下游蛋白质的信号强度，从而导致细胞增殖？这些问题不是没有考虑到就是没有解决。最近，我们还讨论了神经发育障碍（ndd）及其与癌症的关系这一引人注目的问题。我们提出了一些令人困惑的问题，即相同的基因突变如何同时驱动癌症和NDD，以及为什么NDD患者患癌症的风险更高。Ras、MEK、PI3K、PTEN和SHP2都是致癌蛋白，它们可以携带编码癌症以外疾病的突变。了解为什么它们的一些突变会促进癌症，而另一些则会促进ndd，以及为什么即使是相同的突变也可能促进两种表型，具有重要的临床意义。我们的论点是免疫系统和神经系统随着胚胎的发育而共同进化。免疫可以释放激活神经细胞中MAPK信号的细胞因子。在特定的胚胎脑细胞类型中，由种系或胚胎突变引起的信号失调可以促进染色质组织和基因可及性的变化，从而促进神经发育中必需基因的表达水平。在癌症中，信号失调可以从人类生命中零星的体细胞突变中出现。ndd和癌症有相似之处。我们认为，在ndd、免疫和癌症中，小gtp酶（如Ras、RhoA、Rac）及其途径几乎总是参与其中。TLRs、IL-1、GIT1和FGFR信号通路在ndd和癌症中都可能失调。虽然存在信号相似性，但决定性的区分因素是时间窗口和细胞类型特定的扰动水平，指向染色质重组。药物治疗可抑制突变蛋白的作用；然而，耐药性几乎总是会出现。多项研究表明，癌症耐药性是建立在多种不同机制的基础上的。耐药突变可以发生在同一蛋白质或不同蛋白质中；也可以在相同或平行的通路中，绕过被截获的信号。临床肿瘤学家面临的困境是，并非所有促进癌细胞增殖的基因组改变以及肿瘤微环境的改变都是已知的，也不是所有可能促进转移的改变都是未知的。例如，常见的KRasG12C驱动突变出现在不同的癌症中。大多数发生在非小细胞肺癌中，但也有一些发生在结直肠癌和胰管癌中，尽管发生率较低。对KRasG12C抑制剂的反应是可变的，可分为三类，(i) KRas中出现新的点突变，或KRasG12C的多个拷贝导致突变蛋白的更高表达水平；（ii） KRAS以外基因的突变；（iii）原有癌症向其他癌症转移。阿达格拉西是一种实验性抗肿瘤药物，作为G12C KRas的共价抑制剂发挥其细胞毒性作用，对阿达格拉西的耐药性表明，一半的病例出现多重KRas突变和等位基因扩增。冗余或平行通路包括MET扩增；NRAS、BRAF、MAP2K1和RET中出现的驱动突变；ALK、RET、BRAF、RAF1和FGFR3中的基因融合事件；以及NF1和PTEN肿瘤抑制基因的功能缺失突变。我们探索了耐药性的分子机制，同时关注了那些常见的靶向癌症驱动因素。我们还解决了为什么具有共同驱动突变的癌症不太可能进化出共同的耐药机制的问题，以及是否可以预测肿瘤细胞可能发展的可能机制。这些在药物发现中非常重要和诱人的问题，广泛地说，在精确医学中，是我们目前综述的重点。我们建议，在新兴的大规模计算能力的帮助下，目标组合应该优先选择和优先排序，从而实现人工智能，并增加数据的收集，以克服其永不满足的需求。我们的目标是通过基于结构的界面模拟策略来预测哪些蛋白质可以相互作用以及如何相互作用。有效和可靠地预测新的相互作用可以使潜在目标的识别成为可能。强大的蛋白质-蛋白质相互作用预测工具可以绘制相互作用图并预测病原体如何劫持宿主细胞中的信号，这可以通过实验进行验证。可用的实验结构数据很少，而宿主蛋白质-病原体相互作用的组合景观是巨大的。我们正在努力进一步增强我们的服务器，以包含更多的交互和采用人工智能预测方法的建模蛋白质。