Development of computational tools for studying protein sequences, structures and signaling networks

开发用于研究蛋白质序列、结构和信号网络的计算工具

• 批准号: 283170-2008
• 负责人: Truong, Kevin
• 金额: $ 1.31万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2010
• 资助国家: 加拿大
• 起止时间: 2010-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=456426
• 关键词: Development; computational; tools; studying; protein

Cells are composed of protein signaling networks that perform biological functions such as regulating cell growth or catalyzing biochemical reactions. As a result, the malfunction of proteins often causes human illnesses such as Alzheimer's disease, heart disease and cancer. My long term research goal is to create synthetic protein signaling networks that will allow us to one day manipulate cell biology with the same precision as electrical circuits and computer networks. To accomplish this goal, my proposal will focus on developing computational tools for studying protein sequences, structures and signaling networks. First, to infer the function of a protein sequence, the Smith Waterman (SW) algorithm is used to find its similarity to proteins of known function. As sequence databases grow larger, faster sequence comparison approaches are required such as using accelerated field programmable gate array (FPGA) hardware. To make the FPGA solution more affordable, I will develop FPGA hardware for accelerating the SW algorithm using fewer resources while maintaining a comparable speed. Next, to study the protein signaling kinetics within cells, fluorescent protein biosensors are powerful tools but the design of these biosensors is often trial and error. Using a computational tool to model the conformational space of protein biosensors, I improved the design however the tool was not quantitative. To address that problem, I will include molecular factors that select preferred biosensor conformations. Lastly, to design synthetic protein networks or model larger existing networks, I will develop a computational tool for simulating the spatial and temporal kinetics of protein signaling networks. Together this work will yield insights into protein sequences and their networks that will ultimately aid in developing therapies for human illnesses.

细胞由蛋白质信号网络组成，这些信号网络具有调节细胞生长或催化生化反应等生物学功能。因此，蛋白质的功能失调经常导致人类疾病，如阿尔茨海默病、心脏病和癌症。我的长期研究目标是创造合成蛋白质信号网络，这将使我们有一天能够像电路和计算机网络一样精确地操纵细胞生物学。为了实现这一目标，我的建议将集中于开发用于研究蛋白质序列、结构和信号网络的计算工具。首先，为了推断蛋白质序列的功能，使用Smith Waterman （SW）算法寻找其与已知功能蛋白质的相似性。随着序列数据库的增长，需要更快的序列比较方法，如使用加速现场可编程门阵列（FPGA）硬件。为了使FPGA解决方案更加经济实惠，我将开发FPGA硬件，以使用更少的资源加速SW算法，同时保持相当的速度。其次，为了研究细胞内的蛋白质信号动力学，荧光蛋白生物传感器是强大的工具，但这些生物传感器的设计往往是反复试验的。使用计算工具对蛋白质生物传感器的构象空间进行建模，改进了设计，但该工具不是定量的。为了解决这个问题，我将包括选择首选生物传感器构象的分子因素。最后，为了设计合成蛋白质网络或为更大的现有网络建模，我将开发一个计算工具来模拟蛋白质信号网络的时空动力学。总之，这项工作将对蛋白质序列及其网络产生深入的了解，最终将有助于开发人类疾病的治疗方法。