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.

摘要 为了评估候选药物如何影响细胞，研究人员经常研究一种药物的丰度或行为如何 每种化合物的处理都会改变一组特定的蛋白质。然而，目前还不可能测试 每种可能的药物化合物(&gt；500,000)对数百种不同的人类蛋白质(~20,000)的影响 单元格类型。即使是当今最先进的蛋白质分析系统也只能测量和处理 在一个可行的时间框架内，这些组合中的一小部分。 一种测量细胞样本中所有蛋白质丰度的方法是质谱仪，但也是可行的。 仪器每天只能分析几个样本。为了提高这些质谱学的吞吐量 实验，在提议的项目的目标1中，我们将开发一种机器学习算法，该算法将重建 大量样品的多肽组成通过测量较少数量的混合物 那些样本。这项被称为“压缩传感”的技术是为数字成像而开发的，以减少(COM- 按)图像的文件大小。重要的是，它还可以“解压缩”少量收集的信息以 以惊人的高细节重建图像。同样，我们将开发一种压缩感知算法来 从多个组合样本的混合物中提取单独的蛋白质图谱。最初，这种方法将 从250个样本的混合物中分析1,000个样本，提供4倍的增长 速度。最终，随着样本数量的增加，分析的样本数量可能会增加100倍。 为了加快对药物发现的这类数据的解释，我们将创建一个机器学习算法 为了简化测试化合物与各种类型的蛋白质之间复杂的相互作用模式 细胞。以前获得的数据将被模型化，以了解单个化合物对各种蛋白质的影响。 通过从大量描述特定化合物之间相互作用的数据集学习 蛋白质，在许多不同的细胞类型中，该模型将能够预测未经测试的化合物对 不同类型细胞中的蛋白质。此外，它将能够指出哪些实验将是最多的 有助于在未来执行，以获得有关化合物或蛋白质类别的信息，这些化合物或蛋白质在 当前数据集。 这两种技术的结合有可能通过以下方式极大地加速新药的开发 为蛋白质丰度测量提供了潜在的巨大增长，以及一种强大的方法来 预测药物将如何改变细胞中蛋白质的表达。