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National Repository of Grey Literature 2 records found  Search took 0.01 seconds. 
Chemical modification of glycopeptides and prediction of their retention on hydrophilic liquid chromatography
 Pála, Anastasie ; Ječmen, Tomáš (advisor) ; Křížek, Tomáš (referee)
5 Abstract Glycoproteins are an integral part of many cellular processes, without which the complex life of higher organisms wouldn't be possible. Glycoproteins are also important from clinical perspective, since changes of physiological state caused by diseases lead to changes in protein glycosylation. Changes in the abundance of glycosylations or structures of synthesised glycans are associated with diseases such as Alzheimer's disease or cancer and as such can be used as biomarkers for diagnostic purposes. There are several approaches used in the study of glycoproteins, one of which is glycopeptide analysis of enzymatically cleaved glycoproteins. This approach allows analysis of glycoproteins even in complex matrices such as biological samples, as the glycan residues are still connected to their glycosylation sites. This makes the identification of glycoproteins along with their associated glycan structures possible, which oftentimes isn't possible with other analytical approaches. Glycopeptide analysis readily uses high performance liquid chromatography coupled with mass spectrometry. Hydrophilic interaction chromatography is often chosen as the separation mode for glycoproteomic studies, due to its ability to separate glycopeptides with different glycans attached. This chromatographic mode was used in...
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Extension of artificial intelligence DeepReI and its application in practice
 Hurychová, Nikola ; Sobotníková, Jana (advisor) ; Křížek, Tomáš (referee)
Diploma thesis deals with expanding the artificial intelligence DeepReI with the prediction of retention indices of substances in gas chromatography for standard non-polar and polar stationary phases. The theoretical part describes artificial intelligence, convolutional neural networks, and the principles of neural network learning. There is also a brief overview of the applications of neural networks in analytical chemistry. In the experimental part, the original DeepReI model was extended to predict the retention indices of substances for standard non-polar and polar stationary phases. Furthermore, more accurate predictions of retention indices were achieved for semi-standard non-polar stationary phases compared to existing models. The applicability of the model for substance identification was verified through non-targeted analysis of non-alcoholic beers using gas chromatography with mass detection.
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