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Most frequently, the identification of peptides in mass spectrometry-based proteomics is carried out using high-resolution tandem mass spectrometry. In order to increase the accuracy of analysis, additional information on the peptides such as chromatographic retention time and collision cross section in ion mobility spectrometry can be used. An accurate prediction of the collision cross section values allows erroneous candidates to be rejected using a comparison of the observed values and the predictions based on the amino acids sequence. Recently, a massive high-quality data set of peptide collision cross sections was released. This opens up an opportunity to apply the most sophisticated deep learning techniques for this task. Previously, it was shown that a recurrent neural network allows for predicting these values accurately. In this work, we present a deep convolutional neural network that enables us to predict these values more accurately compared with previous studies. We use a neural network with complex architecture that contains both convolutional and fully connected layers and comprehensive methods of converting a peptide to multi-channel 1D spatial data and vector. The source code and pre-trained model are available online.

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Porous graphitic carbon (PGC) is a widely used stationary phase for reversed-phase high-performance liquid chromatography (HPLC) that allows separation of structurally similar compounds retained in mixed form on a flat graphite surface. Such a stationary phase can be used in analytical chemistry to provide good separation and selectivity in pesticide monitoring. In this article, we studied the chromatographic behavior of five common triazine herbicides (simazine, atrazine, desmetryn, propazine, prometryn) on PGC vis-à-vis octadecyl-functionalized silica gel (ODS). It was found that the herbicides studied have an abnormal elution order on PGC compared to ODS. PGC was also characterized by higher selectivity of analyte separation. This behavior of triazine herbicides on PGC cannot be explained either with the help of existing theory or by mathematical modeling of adsorption processes on graphite. Therefore, we have proposed a possible retention mechanism, explaining the effects observed, due to the shielding of the amino group in the triazine ring by alkyl substituents, which decreases the “polar retention effect” of PGC. Satisfactory separation efficacy was obtained with the proposed analytical method, using convenient UV-detection and without resort to laborious techniques such as HPLC coupled with mass spectrometry.

No abstract