Statistical Pattern Matching Between Random Heteropolymers and Multifunctional Disordered Surfaces; Implications for Viral Inhibition and Chromatography

摘要提案编号：9711340提案类型：调查员发起首席调查员：奥雅纳·K·查克拉博蒂隶属关系：加州大学伯克利分校。这笔赠款是通过化学和运输系统司界面、运输和分离计划的分离和净化计划子要素授予的。首席研究员是加州大学伯克利分校的阿鲁普·查克拉博蒂博士。该项目旨在了解杂多聚合物在无序表面上的随机吸附，以及异官能团聚合物与吸附位置之间的模式匹配的合理设计。我们将结合复制场理论和系综格罗斯蒙特卡罗模拟来研究统计模式匹配问题。阐明统计模式匹配现象有望影响与生物医学应用相关的技术，如药物输送和药物设计、蛋白质分离过程以及聚合物对表面的吸附和黏附。这项研究的一个目标是随机杂聚序列统计(以及化学)的从头设计，它将以最佳方式抑制特定病毒与细胞的附着，并指导可以区分不同类型蛋白质序列的廉价层析材料的设计。

Abstract Proposal No.: 9711340 Proposal Type: Investigator Inititated Principal Investigator: Arup K. Chakraborty Affiliation: University of California Berkeley. This grant is awarded through the Separations and Purification Program sub-element of the Interfacial, Transport and Separations Program of the Chemical and Transport Systems Division. The principal investigator is Dr. Arup Chakraborty of the University of California Berkeley. The project is directed towards the understanding of random heteropolymer adsorption on disordered surfaces and the rational design of pattern matching between heterofunctional polymers and adsorption sites. A combination of replica field theory and ensemble groth Monte-Carlo simulations will be developed to examine the issues of statistical pattern matching. Elucidating the phenomenon of statitical pattern matching is expected to impact technologies relevant to biomedical applications such as drug delivery and drug design, Protein separation processes, and polymer adsorptioon and adhesion to surfaces. A goal of the research is the de novo design of random heteropolymer sequence statistics(and hence chemistry) that will optimally inhibit the attachment of specific viruses to cells, and to guide the design of inexpensive chromatographic materials that can discriminate between different types of protein sequences.