Evaluating protein binding specificity of titanium surfaces through mass spectrometry–based proteomics

CLINICAL ORAL INVESTIGATIONS(2020)

引用 8|浏览15
暂无评分
摘要
Objectives To evaluate whether surface characteristics of different titanium modifications may influence the composition of the salivary pellicle on each surface by analyzing the salivary proteome through mass spectrometry–based proteomics. Materials and methods Titanium discs with three surfaces modifications (PT (machined titanium), SLA (sandblasted/large-grit/acid-etched), and SLActive (modified SLA)) were characterized (topography, chemistry, and energy) prior to being exposed to saliva for 2 h to form a protein pellicle. The resultant protein layer was retrieved and analyzed through mass spectrometry (nLC-ESI-MS/MS) to examine the surface specificity for protein binding, while the proteome profile of each surface was classified. Results The proteome analysis showed that the salivary pellicle composition was more complex on rough surfaces (SLA and SLActive). Although variability in protein composition was observed between surfaces, most proteins were detected on more than one surface, indicating a limited surface specificity for protein binding. Additionally, the salivary pellicle formed on the SLActive presented a larger number of proteins associated with immune response, biological adhesion, and biomineralization. Conclusions Although topography, chemistry, and energy differed between the surfaces, they were not determinant to produce a salivary pellicle with high surface specificity. Also, we showed that several salivary proteins adsorbed on Ti surfaces are involved in biological functions important to the biointegration. Clinical relevance This study sheds light on the necessity for the development of bioactive surfaces that favors the formation of a specific protein layer that can enhance tissue response to assist the biointegration of dental implants.
更多
查看译文
关键词
Salivary pellicle, Salivary proteins, Proteomics, Mass spectrometry, Titanium, Proteins
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要