Exploiting Biomimetic Recognition between Polymers & Surfaces to Design Nanoscale Separation Processes

利用聚合物之间的仿生识别

• 批准号: 0001304
• 负责人: Arup Chakraborty
• 金额: $ 25万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-08-01 至 2004-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0001304&HistoricalAwards=false
• 关键词: Exploiting; Biomimetic; Recognition; between; Polymers

CTS-0001304Arup K. ChakrabortyUniversity of California at BerkeleyExploiting Biomimetic Recognition between Polymers and Surfaces to Design Nanoscale Separation ProcessesABSTRACTMany vital biological processes, such as transmembrane signaling and pathogen-host interactions, are initiated by a protein recognizing a particular pattern of binding sites on part of a surface bearing receptors. The development of synthetic systems that can mimic such recognition between polymers and surfaces could have significant impact on applications such as the development of nano-scale separation processes and synthetic viral inhibition agents. Can such biomimetic systems which exhibit the hallmarks of recognition be designed? This project explores the question by studying the interactions of disordered heteropolymers (DHPs) with surfaces bearing patterns of binding sites. DHPs are copolymers with more than one type of segment. The sequence in which the segments are arranged is aperiodic and is described statistically. Thus, these molecules may be considered to carry a statistical pattern encoded in the sequence distribution. By studying the interactions of DHPs with surfaces bearing multiple types of sites distributed in a manner that is also described statistically, one can examine whether synthetic systems can mimic the hallmarks of recognition when the statistics characterizing the DHP sequence and that of the surface pattern are related in a special way. It appears that recognition due to statistical pattern matching can be achieved through proper design of the DHP sequence and surface-site distribution statistics. This result indicates that hierarchical organization of structural patterns on scales much larger than the monomeric units is crucial for recognition to occur. The findings motivate research aimed toward exploiting the phenomenon of recognition due to statistical pattern matching in practical applications such as the development of nanoscale separation systems. Several important questions to be addressed are: 1] In adsorption applications where one wishes to separate a mixture of macromolecules, what is the highest solution concentration of DHPs that can be processed while maintaining a high separation (recognition) efficiency based on statistical pattern matching? 2] Given a particular DHP sequence, how can one design the optimal surface pattern for efficient recognition? Is there an algorithm that can be used routinely to carry out such design? 3] Is there an analytical model that provides insight into the kinetic processes that have been revealed by statistical simulations? 4] Can one use matching of shapes between DHP conformations and surface patterns to augment recognition? Research aimed toward addressing these questions using field-theoretic methods and computer simulations is the focus of this project. Synergy between the proposed efforts and experimental work being carried out in other provides an understanding of the basic principles that lead to creation of biomimetic recognition in synthetic systems. Successful results of this research can help scientists and engineers develop nanoscale separation devices, sensors, and viral inhibitors faster and less expensively. The basic notion to be exploited is that pattern recognition can be elicited in man-made materials by statistical matching, beyond the specific matching exhibited by living systems. Scientists and engineers wanting to design macromolecules that recognize a target pattern may use the approaches developed in this project to speed up their search. Similarly, appropriate patterns of active sites on sorbents for specific separations may be identified by use of the statistical concepts and lead to potentially useful nanostructures.

利用聚合物和表面之间的仿生识别设计纳米级分离过程【摘要】许多重要的生物过程，如跨膜信号传导和病原体-宿主相互作用，都是由蛋白质识别表面承载受体上特定结合位点的模式启动的。能够模拟聚合物和表面之间这种识别的合成系统的发展可能对纳米级分离过程和合成病毒抑制剂的发展等应用产生重大影响。这种具有识别特征的仿生系统能被设计出来吗？本项目通过研究无序杂多聚合物（DHPs）与结合位点的表面承载模式的相互作用来探索这个问题。DHPs是具有一种以上类型节段的共聚物。片段排列的顺序是非周期性的，用统计方法描述。因此，这些分子可以被认为携带序列分布中编码的统计模式。通过研究DHP与表面的相互作用，表面上有多种类型的位点分布，这种分布方式也被统计描述，人们可以检查当DHP序列的统计特征和表面模式的统计特征以一种特殊的方式相关时，合成系统是否可以模拟识别的标志。通过适当设计DHP序列和表面位点分布统计，可以实现统计模式匹配的识别。这一结果表明，在比单体单位大得多的尺度上，结构模式的层次组织对识别的发生至关重要。这些发现激发了旨在利用统计模式匹配在实际应用中的识别现象的研究，例如纳米级分离系统的开发。需要解决的几个重要问题是：1]在希望分离大分子混合物的吸附应用中，在保持基于统计模式匹配的高分离（识别）效率的同时，可以处理的DHPs的最高溶液浓度是多少？[2]给定一个特定的DHP序列，如何设计最优的表面模式以实现有效的识别？是否有一种算法可以常规地执行这种设计？[3]是否存在一种分析模型，可以提供对统计模拟所揭示的动力学过程的洞察？[4]可以使用DHP构象和表面模式之间的形状匹配来增强识别吗？研究旨在解决这些问题，利用场论方法和计算机模拟是本项目的重点。提出的努力和正在进行的实验工作之间的协同作用提供了对导致在合成系统中创建仿生识别的基本原理的理解。这项研究的成功结果可以帮助科学家和工程师更快、更便宜地开发纳米级分离设备、传感器和病毒抑制剂。要利用的基本概念是，模式识别可以通过统计匹配在人造材料中引出，而不是生命系统所表现出的特定匹配。科学家和工程师想要设计识别目标模式的大分子，可以使用本项目开发的方法来加速他们的搜索。类似地，吸附剂上用于特定分离的活性位点的适当模式可以通过使用统计概念来确定，并导致潜在有用的纳米结构。