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

CTS-0001304 Arup K.Chakraborty加州大学伯克利分校利用聚合物和表面之间的仿生识别来设计纳米级分离过程摘要许多重要的生物过程，如跨膜信号和病原体与宿主的相互作用，都是由识别表面受体部分上结合位点的特定模式的蛋白质启动的。能够模拟聚合物和表面之间的这种识别的合成系统的开发可能对诸如纳米级分离过程和合成病毒抑制剂的开发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？这个项目通过研究无序杂化聚合物(DHPs)与带有结合位图案的表面的相互作用来探索这个问题。DHP是具有一种以上类型链段的共聚物。分段排列的顺序是非周期性的，并且被统计地描述。因此，这些分子可以被认为携带在序列分布中编码的统计模式。通过研究DHP与以统计方式描述的具有多种类型位置分布的表面的相互作用，可以检验当表征DHP序列的统计特性和表面图案的统计以特殊方式相关时，合成系统是否能够模拟识别的特征。看来，通过适当设计DHP序列和表面位置分布统计，可以实现由于统计模式匹配而产生的识别。这一结果表明，比单体单位大得多的尺度上的结构模式的分层组织对于识别的发生至关重要。这些发现推动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DHP的最高溶液浓度是多少？2)给定特定的DHP序列，如何设计用于有效识别的最佳表面图案？有没有可以常规使用的算法来进行这种设计？3]有没有一个分析模型可以洞察统计模拟揭示的动力学过程？4)是否可以使用DHP构象和表面图案之间的形状匹配来增强识别？旨在利用场论方法和计算机模拟解决这些问题的研究是本项目的重点。拟议的努力与其他国家正在进行的实验工作之间的协同作用有助于理解导致在合成系统中创造仿生识别的基本原则。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。要开发的基本概念是，在生物系统表现出的特定匹配之外，可以通过统计匹配在人造材料中引发模式识别。想要设计识别目标模式的大分子的科学家和工程师可以使用这个项目中开发的方法来加快他们的搜索速度。同样，通过使用统计学概念，可以确定特定分离的吸附剂上的活性中心的适当模式，并导致潜在有用的纳米结构。

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.