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.

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