Mass Spectrometry Measurements for Complex PTMs

摘要： 重组蛋白表达技术彻底改变了人类健康研究，使 科学家们为小分子药物发现活动产生大量蛋白质 开发基于蛋白质的药物，以及研究蛋白质结构和功能。然而，尽管研究人员 他们擅长生产具有已知基因序列的蛋白质，但他们对以下方面的了解完全不足： 蛋白质的翻译后修饰，无论是在重组表达期间还是在蛋白质被 从内源性来源中分离出来。蛋白质翻译后修饰的改变可以改变 蛋白质从活性酶变成死酶；他们可以将基于蛋白质的药物从拯救生命的药物改变 分子变成无效或更糟糕的、危险的免疫原性的分子；这些看似简单的“附加” 蛋白质的“ons”可能是决定蛋白质是否按设计发挥作用的定义特征。 表征蛋白质的 PTM 是识别疾病状态、调节蛋白质的必要先决条件 结合和功能，并生产安全有效的基于蛋白质的药物和疫苗。长期目标 我的研究计划是加快蛋白质翻译后修饰 (PTM) 的分析， 使用这些分析工具来应对与健康相关的关键挑战，并广泛传播该技术。 我们正在追求近期的进步，例如开发一种强大的解决方案来量化异常 重组蛋白中的二硫键，以及蛋白质的方式发生长距离、革命性的变化 分析认为，通过奠定将人工智能解决方案引入该领域所需的基础设施 PTM 分析。我们将在有意义的生物问题的背景下开发和推出这些技术 如果没有有关蛋白质 PTM 的信息，就无法取得该领域的进展。领域的解决方案 HIV疫苗设计、癌症治疗和抗体治疗将首先成为目标。

Abstract: Recombinant protein expression technology has revolutionized human health research, enabling scientists to generate large quantities of protein for small-molecule drug discovery campaigns, for the development of protein-based drugs, and for studying protein structure and function. Yet while researchers are adept at producing proteins with known genetic sequences, they possess entirely inadequate knowledge about the protein's post-translational modifications, both during recombinant expression and when the proteins are isolated from endogenous sources. Alterations in a protein's post-translational modifications can change the protein from an active enzyme to a dead one; they can change a protein-based drug from a life-saving molecule to one that is either ineffective or worse, dangerously immunogenic; these seemingly simple “add- ons” to proteins can be the defining features that determine whether the protein functions as designed or not. Characterizing proteins' PTMs is a necessary prerequisite for identifying disease states, modulating protein binding and function, and producing safe and effective protein-based drugs and vaccines. The long term goals of my research program are to expedite the analysis of post-translational modifications (PTMs) on proteins, to use these analytical tools to answer critical health-related challenges, and to widely distribute the technology. We are pursuing both near-term advancements, such as developing a robust solution to quantifying aberrant disulfide bonds in recombinant proteins, as well as long-range, revolutionary changes in the way proteins are analyzed, by laying the foundational infrastructure needed to bring artificial intelligence solutions to the field of PTM analysis. We will develop and launch these technologies in the context of meaningful biological problems where field advancements cannot be made without information about proteins' PTMs. Solutions in the fields of HIV vaccine design, cancer treatment, and antibody therapy will be targeted first.