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

University Of California-Berkeley, Berkeley CA

Investigators

Abstract

CTS-0001304 Arup K. Chakraborty University of California at Berkeley Exploiting Biomimetic Recognition between Polymers and Surfaces to Design Nanoscale Separation Processes ABSTRACT Many 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.