Study Of Active Peptide-based Materials And Its Application

Peptides widely exist in nature and living organisms.They can be modified,and have enzyme-responsiveness,reversibility and good cell biocompatibility,and used in many biomedical fields.Based on responsiveness and self-assemble ability of peptide,we focused on enzyme-activatable cell penetrating peptide and artificial intelligence assisted self-assemble peptides,and we expected to archive specific biological functions through de novo design.The main research contents of the paper are briefly described as follows:Part one: According to the differential of tumor microenvironments,We designed a cell penetrating peptide(controlled peptide(pen M)and enzyme-responsive peptide(pen ME and pen MEE))based on the classic CPP Penetratin.It can selectively permeate the membrane on the surface of cancer cells with high expression of γ-glutaminetransferase,so as to achieve the purpose of selective and efficient tumor suppression.Experments proved that pen ME and pen MEE had the ability to response the enzyme reaction.At the cellular level,we used different contents of enzyme to verify the cell selectivity of the designed peptide.As the same time,the peptides were fluorescently labeled with FITC,and the visualization of the transmembrane efficiency was qualitatively and quantitatively explored by laser confocal microscopy and flow cytometry.The experimental results confirmed that the peptides have good enzymatic responsiveness and cell selectivity.In addition,in the case of adding exogenous enzymes to cells with low expression of γ-glutaminase,it was found that the peptides can enter the cells well,which further indicated that the peptides have excellent enzyme response characteristics.Finally,the internalization mechanism of the peptide was explored and found that its internalization crossed the cell membrane through various patterns such as macropinocytosisPart two: We developed a machine learning-based network(HYGEN)capable of generating new supramolecular hydrogels by capturing images of existing hydrogels and designing prior networks to learn commonalities.After the HYGEN network was generated,the SVM model was used for pre-judgment and scoring,and 1381 molecules candidate library were obtained and preliminarily demonstrated the advantages and potential application prospects of machine learning in the prediction of peptide gelat ion properties.We random Ly selected 17 candidate molecules for gel formation and verification,resulted in obtaining 9 molecules that could form supramolecular hydrogels under different conditions.Then,we rando m Ly selected 4 of the 9 forminggel molecules for characterization of hydrogel properties and cell experiments.Two were found to be biocompatible,and the other two had cancer cell suppression effects.This paper explores different forms of peptides(CPP and assemblies)and provides a better direction and strategy for the practical application of peptides.