Single-molecule protein sequencing by detection and identification of N-terminal amino acids

摘要-蛋白质表达的细微变化对于正常的生长和发育至关重要，但不适当的是- 爱滋病会造成有害的细胞效应或大规模的生物功能障碍。使用以下工具对样本进行排序 蛋白质的复杂混合物可以极大地加速蛋白质功能和生物学的研究，但 目前还没有在单一氨基酸分辨率下进行蛋白质测序的有效和成本效益高的策略。 用于蛋白质测序的商业方法有两种。在第一种情况下，“埃德曼退化”，散装量子- 通过裂解第一个(N-末端)氨基酸和化学物质，对整个蛋白质或纯化片段的连接进行测序。 从技术上识别它。在第二种方法中，基于质谱学，单个蛋白质或蛋白质混合物 碎片，并分析了每个碎片的相对分子质量和电荷。将这些信息进行比较 已知的蛋白质序列来推断输入蛋白质的身份。这两种方法都需要大约100万英镑 每种蛋白质的分子，以及埃德曼降解，目前不能用于异质蛋白质混合物。 现有的单分子蛋白质测序方法受到氨基酸数量和多样性的阻碍 酸，以及干扰其一侧化学识别的氨基酸之间的相互作用 锁链。苛刻的变性剂可以缓解一些问题，但它们可能会危及身份识别系统 他们自己。此外，变性剂只能去除蛋白质分子内的部分相互作用。 GYPHY生物技术公司正在开发一种新的策略来对单个蛋白质分子进行完整测序 从一个不同种类的样本中。这一过程是基于将N-末端的氨基酸连接到一个可切割的化学- ICAL连接物，随后将其局部系于表面。连接子的切割去除了N-末端 来自蛋白质的氨基酸，用于高灵敏的鉴定，不受蛋白质结构或ADJA的干扰。 分氨基酸。对随后的每一种氨基酸重复这个过程，产生蛋白质序列。这个 这种方法可以在几个小时内同时对数百万到数十亿个单独的蛋白质分子进行测序，这将 使大规模蛋白质测序变得可行、廉价和常规，从而使蛋白质分析发生革命性变化。 目前的提议重点是开发专门检测蛋白质N端氨基酸的试剂， 允许通过这种N端分离策略对氨基酸进行数字识别。在目标1中，我们将生成 识别至少10种不同分离氨基酸的抗体-足以识别~90%的蛋白质组 10轮测序。在目标2中，我们将进一步优化抗体，并论证使用 对未修饰蛋白背景中的个别蛋白进行测序。 这些目标的成功将使糖蛋白测序平台能够检测、量化和测序 在复杂的蛋白质混合物中以公正的方式表达单一蛋白质--事先不知道它们的身份 甚至是他们的存在。商业化后，它将使基于蛋白质的疾病临床诊断成为可能 存在于患者样本中，并允许识别独特的蛋白质，以寻找迄今未知的生物标志物。

SUMMARY - Subtle changes in protein expression are critical for proper growth and development, but irregu- larities can cause deleterious cellular effects or large-scale biological dysfunction. Sequencing samples with complex mixtures of proteins could greatly accelerate research into protein function and biology, but there is currently no efficient and cost-effective strategy for protein sequencing at single-amino-acid resolution. Two methods are commercially available for protein sequencing. In the first, “Edman degradation”, bulk quanti- ties of whole protein or purified fragments are sequenced by cleaving the first (N-terminal) amino acid and chem- ically identifying it. In the second method, based on mass spectrometry, a single protein or mixture of proteins is fragmented, and the molecular mass and charge of each fragment are analyzed. This information is compared known protein sequences to infer the identity of the input proteins. Both of these methods require ~1 million molecules of each protein, and Edman degradation cannot currently be used on heterogenous protein mixtures. Existing approaches for single molecule protein sequencing are hindered by the number and diversity of amino acids, as well as the interactions between amino acids that interfere with chemical identification of their side chains. Harsh denaturation agents can mitigate some issues, but they can compromise the identification systems themselves. In addition, denaturation agents only remove some of the intramolecular interactions of proteins. Glyphic Biotechnologies is developing a novel strategy to sequence individual protein molecules in their entirety from a heterogeneous sample. This process is based on ligating the N-terminal amino acid to a cleavable chem- ical linker, which subsequently tethers it locally to the surface. Cleavage of the linker removes the N-terminal amino acid from the protein for highly sensitive identification with no interference from protein structure or adja- cent amino acids. The process is repeated for each subsequent amino acid, yielding the protein sequence. The approach may simultaneously sequence millions to billions of individual protein molecules in hours, which will revolutionize protein analysis by making large-scale protein sequencing feasible, inexpensive, and routine. The current proposal focuses on developing reagents specifically to detect the N-terminal amino acid of proteins, allowing amino acids to be digitally identified via this N-terminal isolation strategy. In Aim 1 we will generate antibodies to recognize at least 10 different isolated amino acids – enough to identify ~90% of the proteome after 10 sequencing rounds. In Aim 2 we will further optimize the antibodies and demonstrate the feasibility of using them to sequence individual proteins among a background of non-modified proteins. Success of these Aims will enable the Glyphic protein sequencing platform to detect, quantify, and sequence single proteins in complex protein mixtures in an unbiased fashion - without any prior knowledge of their identity or even their existence. When commercialized, it will enable clinical diagnosis of disease based on the proteins present in a patient sample and allow identification of unique proteins to for as-yet unknown biomarkers.