Peptideinformatics-establishment of newly screening and design method of functional peptide-

多肽通过与细胞表面的受体相互作用来调节多种细胞事件，引起了人们的注意。在医药、药物输送、组织工程、生物成像、食品添加剂等领域，人们已经开发出了大量偶联功能肽的生物材料。然而，通过穷举筛选20个氨基酸的所有可能组合来识别功能多肽是非常耗时的，本研究提出了将新的多肽筛选方法与计算分析相结合的多肽信息学。该方法基于这样的概念，即即使是有限的数据，也可以通过使用计算分析得出的模型来提高筛选效率，该模型用作筛选的指南，并将其与后续的重复实验相结合。在这里，我们将重点放在细胞黏附肽作为这种肽筛选策略的一个模型应用。通过使用基于细胞的多肽阵列分析来筛选细胞粘附肽。从一个有限的、随机的5聚体文库(643个序列)中获得的筛选数据出发，通过模糊神经网络(FMN)分析提取了关于细胞粘附肽结构特征的规则。根据这一规则，从随机序列中剔除在某些位置具有不受欢迎残基的导致无效结合的多肽。在限制性第二随机文库(273个序列)中，与非限制性随机文库(20%)相比，粘附率为基础阵列载体的1.5倍以上的细胞粘附肽的产率(31%)显著高。在限制性第三文库(50个序列)中，细胞黏附肽的得率提高到84%。我们的结论是，FNN的规则提取特性可以帮助重复一轮实验筛选有限数量的多肽。

Peptides have attracted attention for their ability to regulate a variety of cellular events by interacting with receptors on the cell surface. A large number of biomaterials that conjugate functional peptides have been developed for medicine, drug delivery, tissue engineering, bio-imaging, and food additives. However, identifying functional peptides by exhaustive screening that covers all the possible combinations of 20 amino acids is terribly time-consuming.In this research, “Peptideinformatics" was proposed, which is combined new peptide screening method combined with computational analysis. The method is based on the concept that screening efficiency can be enhanced from even limited data by use of a model derived from computational analysis that serves as a guide to screening and combining it with subsequent repeated experiments. Here we focus on cell adhesion peptides as a model application of this peptide-screening strategy. Cell adhesion peptides were screened by use of a cell-based assay of a peptide array. Starting with the screening data obtained from a limited, random 5-mer library (643 sequences), a rule regarding structural characteristics of cell adhesion peptides was extracted by fuzzy neural network (FMN) analysis. According to this rule, peptides with nnfavored residues in certain positions that led to inefficient binding were eliminated from the random sequences. In the restricted, second random library (273 sequences), the yield of cell adhesion peptides having an adhesion rate more than 1.5-fold to that of the basal array-support was significantly high (31%) compared to the unrestricted random library (20%). In the restricted third library (50 sequences), the yield of cell adhesion peptides increased to 84%. We conclude that a repeated cycle of experiments screening limited numbers of peptides can be assisted by the rule-extracting feature of FNN.