PREDICTING PROTEIN TRANSLATION RATE FROM DNA SEQUENCE: A WEB SERVICE

根据 DNA 序列预测蛋白质翻译率：网络服务

• 批准号: 7956341
• 负责人: Howard M Salis
• 金额: $ 0.08万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7956341
• 关键词: Algorithms; Bacteria; Behavior; Binding Sites; Biomedical Research; Chemicals; Code; Communities; Computer Retrieval of Information on Scientific Projects Database; Computer software; DNA Sequence; DNA biosynthesis; Doctor of Philosophy; Engineering; Escherichia coli; Exhibits; Free Energy; Funding; Genetic Engineering; Grant; High Performance Computing; Human; Institution; Internet; Messenger RNA; Metabolic; Modeling; Occupations; Organism; Output; Plastics; Problem Solving; Procedures; Process; Production; Proteins; Pythons; RNA; Research; Research Personnel; Resort; Resources; Ribosomes; Running; Services; Societies; Source; Specific qualifier value; Techniques; Therapeutic; Transcript; Translation Initiation; Translations; United States National Institutes of Health; Writing; biological systems; cost; design; experience; graduate student; meetings; predictive modeling; programs; synthetic biology; tool; web-accessible

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. In the synthetic biology community, bacteria are re-engineered to carry out tasks that benefit human society, including the production of biofuels, therapeutics, plastics, and other important chemicals. With the latest advances in genetic engineering techniques, including DNA synthesis, the challenge is to identify the DNA sequence that reliably yields a desired behavior without resorting to trial-and-error procedures. Specifically, it is important to precisely control the production rate of a protein in order to design a biological system that exhibits a desired metabolic or regulatory behavior. To solve this problem, we have developed a design algorithm that generates a DNA sequence that causes a protein to be produced at a user-specified rate. A user inputs an arbitrary protein coding sequence and a target production rate and the algorithm outputs the RNA (DNA) sequence that yields the target rate. More specifically, the algorithm predicts the translation initiation rate of a protein coding sequence from a messenger RNA transcript by modeling the interactions between the ribosome binding site on the mRNA with the ribosome. Using the predictive model, a Monte Carlo optimization technique identifies the RNA (DNA) sequence that meets the user's target translation rate with the given protein coding sequence. These predictions and the design process have been experimentally validated in the bacterium Escherichia coli (a common industrial organism). The design algorithm predicts translation rates across four orders of magnitude and its accuracy is ~1 kT in terms of the Gibbs free energy. It is written in C and Python. We have created a web-accessible version of the design algorithm at http://voigtlab.ucsf.edu/software/. The computational cost of the design algorithm is moderate; a single design job can require between 5 and 30 minutes on an Intel 1.6Ghz dual core processor (using only 1 core) with 2Gb RAM. The synthetic biology community (at least 1000 PIs/postdocs/graduate students) would make frequent use of this tool. Accordingly, we request 30,000 SUs of Teragrid Roaming resources to perform the computations. Any combination of suitable resources would be appreciated, such as the Purdue Condor Pool, the IU Quarry IBM HS21 bladeserver, or the TACC Ranger. A single user may request 10-100 or more design jobs annually, yielding ~50 SU/user/year and up to ~600 users/year. From his PhD graduate studies, Howard Salis has extensive experience with developing algorithms and running jobs on the Teragrid, including parallel programming with MPI.

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此，可以在其他清晰的条目中表示。列出的机构是 对于中心，这不一定是调查员的机构。 在合成生物学界，对细菌进行了重新设计，以执行有益于人类社会的任务，包括生产生物燃料，治疗剂，塑料和其他重要化学物质。随着基因工程技术（包括DNA合成）的最新进展，挑战是确定可靠地产生所需行为的DNA序列，而无需诉诸试验和错误程序。具体而言，重要的是要精确控制蛋白质的生产速率，以设计具有所需代谢或调节行为的生物系统。为了解决此问题，我们开发了一种设计算法，该算法生成了DNA序列，该序列可导致以用户指定的速率生产蛋白质。用户输入任意蛋白质编码序列和目标生产率，算法输出了产生目标速率的RNA（DNA）序列。更具体地说，该算法通过模拟mRNA与核糖体上的核糖体结合位点之间的相互作用来预测蛋白质编码序列的翻译起始速率。使用预测模型，蒙特卡洛优化技术可以识别使用给定蛋白质编码序列满足用户目标翻译速率的RNA（DNA）序列。这些预测和设计过程已经在大肠菌（一种常见的工业生物）中实验验证。该算法预测了四个数量级的翻译速率，就吉布斯自由能而言，其精度约为1 kt。它用C和Python编写。我们已经在http://voigtlab.ucsf.edu/software/上创建了设计算法的Web访问版本。设计算法的计算成本中等；单个设计作业可能需要在Intel 1.6GHz双核处理器（仅使用1个核心）上使用2GB RAM的5到30分钟。合成生物学社区（至少有1000个PI/博士后/研究生）将经常使用此工具。因此，我们要求30,000个Teragrid漫游资源进行计算。任何合适的资源组合都将不胜感激，例如普渡大学的秃鹰池，IU Quarry IBM HS21 Bladeserver或TACC Ranger。单个用户每年可能要求10-100个或更多的设计作业，每年/年/年，每年约50个用户/年/年。霍华德·萨利斯（Howard Salis）从博士学位的研究生学习中，在开发算法和在Teragrid上运行工作的经验丰富，包括与MPI的平行编程。