Browsing by Subject "Enzyme"

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Open Access
Catalytic and bioactive nanostructures for regenerative medicine applications
(2016-04) Gülseren, Gülcihan
Peptide nanostructures provide a remarkable toolbox for designing nature inspired smart materials. Synthetic peptide nanomaterials can be tailored with chemical, physical and biological signals to be utilized in a wide range of applications in biomedicine. With the increasing demand for complex nanostructures with facile preparation methods, bioinspired smart nanomaterials have gained more importance for achieving multifunctional hybrid materials. In particular, biological or chemical cues can be integrated into supramolecular designs to generate bioactive materials. This thesis describes nature inspired combinatorial methods for designing peptide nanostructures, which display catalytic and biologically functional moieties. These multifunctional peptide nanostructures were synthesized by using solid phase peptide synthesis. Designed peptide units were in accordance with the relevant biological function and they self-assemble to form nanofibrous networks mimicking the extracellular matrix. This combinatorial approach allows a wide range of applications including artificial catalysis, cell cultivation, biomineralization and live-cell labeling. In this thesis, the self-assembled catalytic and bioactive peptide nanostructures were utilized in artificial enzyme studies, biomineralization and tissue regeneration. The results show that these new artificial enzymes display both catalytic and biological functions of their natural counterparts such as proteins. In the first chapter, basic concepts of self-assembly, artificial catalysis approach, biomineralization and bioactive peptide nanostructures were explained. In the second chapter, multicomponent artificial catalyst model formed by self-assembly was investigated. Designed artificial peptide molecules were characterized structurally and catalytic capability of this de novo system was shown with both model and actual substrate. In the third chapter, ALP inspired catalytic, ion coordinating and biomineralizable peptide nanostructures were examined, bioactivity of this enzyme inspired materials was shown with multiple cell lines and using 2D and 3D cell culture methods. In the fourth chapter, enzyme responsive dentin sialophospho-protein like materials were exhibited instead artificial catalyst. Multi-responsive material induced biomineralization similar to dentin sialophospho-protein which controls mineralization during dentinogenesis. In the fifth part, peptide nanostructures were applied as bioorthogonal catalyst for live-cell tagging study, fluorophore tagged living cells by peptide catalyst was imaged by confocal microscopy. The last chapter covers novel materials inspired by unique nature of collagen and its bioregenerative capacity investigated with stem cells and preliminary cartilage induction was obtained.
Open Access
Subsequence-based feature map for protein function classification
(Elsevier, 2008) Sarac, O. S.; Gürsoy-Yüzügüllü, O.; Cetin Atalay, R.; Atalay, V.
Automated classification of proteins is indispensable for further in vivo investigation of excessive number of unknown sequences generated by large scale molecular biology techniques. This study describes a discriminative system based on feature space mapping, called subsequence profile map (SPMap) for functional classification of protein sequences. SPMap takes into account the information coming from the subsequences of a protein. A group of protein sequences that belong to the same level of classification is decomposed into fixed-length subsequences and they are clustered to obtain a representative feature space mapping. Mapping is defined as the distribution of the subsequences of a protein sequence over these clusters. The resulting feature space representation is used to train discriminative classifiers for functional families. The aim of this approach is to incorporate information coming from important subregions that are conserved over a family of proteins while avoiding the difficult task of explicit motif identification. The performance of the method was assessed through tests on various protein classification tasks. Our results showed that SPMap is capable of high accuracy classification in most of these tasks. Furthermore SPMap is fast and scalable enough to handle large datasets. © 2007 Elsevier Ltd. All rights reserved.