Deep learning methods to accelerate discoveryof drugs targeting gene regulatory proteins

深度学习方法加速发现针对基因调节蛋白的药物

• 批准号: 10599781
• 负责人: William Ellis Fondrie
• 金额: $ 39.84万
• 依托单位: TALUS BIOSCIENCE, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-20 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10599781
• 关键词: 3-Dimensional; Acceleration; Affect; Algorithms; Behavior; Binding; Bioinformatics; Biological Assay; Cells; Chromatin; Code; Communities; Complex; Computer software; Consumption; DNA sequencing; Data; Data Set; Development; Diabetes Mellitus; Disease; Drug Compounding; Drug Targeting; Event; Future; Genomics; Human; Image; Image Compression; Individual; Intervention; Knowledge; Laboratories; Lead; Learning; Licensing; Liquid substance; Malignant Neoplasms; Mass Spectrum Analysis; Measurement; Measures; Methods; Modeling; Pattern; Peptides; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Physiological Processes; Physiology; Process; Protein Analysis; Proteins; Proteome; Proteomics; Regulator Genes; Research; Research Personnel; Running; Sampling; Services; Set protein; Signal Transduction; Small Business Innovation Research Grant; Speed; Systems Analysis; Talus; Techniques; Technology; Testing; Therapeutic; Time; Training; United States National Institutes of Health; X-Ray Computed Tomography; blind; cell type; chromatin protein; commercialization; cost; deep learning; design; digital imaging; drug candidate; drug development; drug discovery; experimental study; genetic regulatory protein; genomic data; improved; instrument; learning strategy; machine learning algorithm; machine learning method; novel therapeutics; open source; programs; protein complex; protein expression; screening services

SUMMARY To evaluate how a drug candidate affects cells, researchers often study how the abundance or behavior of a specific set of proteins is changed by treatment with each compound. However, it is not currently possible to test the effect of every possible drug compound (>500,000) on every human protein (~20,000) in hundreds of different types of cells. Even the most advanced protein analysis systems available today could only measure and process a tiny fraction of these combinations in a feasible timeframe. One method of measuring the abundance of all the proteins in a cell sample is mass spectrometry, but available instruments can only analyze several samples per day. To increase the throughput of these mass spectrometry experiments, in Aim 1 of the proposed project we will develop a machine learning algorithm that will reconstruct the peptide composition of a large number of samples from measurements of a smaller number of mixtures of those samples. This technology, called “compressed sensing” was developed for digital imaging to reduce (com- press) the file size of an image. Importantly, it can also “decompress” a low amount of collected information to reconstruct an image with surprisingly high detail. Similarly, we will develop a compressed sensing algorithm to extract the individual protein profiles from mixtures of multiple combined samples. Initially, this approach will analyze 1,000 samples from 250 measurements of mixtures of those samples, providing a 4-fold increase in speed. Ultimately, with a much higher number of samples, it may allow a 100-fold increase in samples analyzed. To accelerate interpretation of this type of data for drug discovery, we will create a machine learning algorithm to simplify complex patterns of interactions between test compounds and the proteins within various types of cells. Previously acquired data will be modeled to learn the effects of individual compounds on various proteins. By learning from a large number of these data sets that describe interactions between specific compounds and proteins, in many different cell types, the model will be able to predict the effect of untested compounds on proteins within various types of cells. In addition, it will be able to indicate which experiments would be most useful to perform in the future, to obtain information on classes of compounds or proteins that are lacking in the current data sets. The combination of these two techniques has the potential to greatly accelerate development of novel drugs by providing a potentially huge increase in protein abundance measurements, along with a powerful method to predict how drugs will alter the expression of proteins in cells.

概括 为了评估候选药物如何影响细胞，研究人员经常研究 特定的蛋白质集可以通过每种化合物的处理来改变。但是，目前不可能测试 每种可能的药物化合物（> 500,000）对数百种不同的人蛋白（约20,000）的影响 细胞类型。即使是当今可用的最先进的蛋白质分析系统，也只能衡量和处理 在可行的时间范围内，这些组合中的一小部分。 一种测量细胞样品中所有蛋白质抽象的抽象的方法是质谱法，但可用 仪器每天只能分析几个样本。增加这些质谱的吞吐量 实验，在拟议项目的目标1中，我们将开发一种机器学习算法，该算法将重建 大量样品的胡椒组成是从较少数量的混合物的测量中 那些样本。这项称为“压缩灵敏度”的技术是为数字成像开发的，以减少（com- 按）图像的文件大小。重要的是，它还可以“解压缩”低量的收集信息 重建一个令人惊讶的高细节的图像。同样，我们将开发一种压缩感应算法到 从多个组合样品的混合物中提取单个蛋白质谱。最初，这种方法将 分析从250种样品混合物测量的1,000个样品，从而增加4倍 速度。最终，随着样品数量更高，分析的样品可能会增加100倍。 为了加快对药物发现数据的解释，我们将创建一种机器学习算法 简化测试化合物和蛋白质之间的相互作用的复杂模式 细胞。以前获得的数据将被建模，以了解各个化合物对各种蛋白质的影响。 通过从大量数据集中学习，这些数据集描述了特定化合物之间的相互作用 蛋白质，在许多不同的细胞类型中，模型将能够预测未经测试化合物对 各种细胞内的蛋白质。此外，它将能够指出哪些实验最多 将来可以执行，以获取有关缺乏的化合物或蛋白质类别的信息 当前数据集。 这两种技术的结合有可能通过 提供蛋白质丰度测量的潜在巨大增加，以及一种有力的方法 预测药物将如何改变细胞中蛋白质的表达。