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.

Unsymmetrical dimethylhydrazine (UDMH) is a widely used rocket propellant. Entering the environment or being stored in uncontrolled conditions, UDMH easily forms an enormous variety (at least many dozens) of transformation products. Environmental pollution by UDMH and its transformation products is a major problem in many countries and across the Arctic region. Unfortunately, previous works often use only electron ionization mass spectrometry with a library search, or they consider only the molecular formula to propose the structures of new products. This is quite an unreliable approach. It was demonstrated that a newly proposed artificial intelligence-based workflow allows for the proposal of structures of UDMH transformation products with a greater degree of certainty. The presented free and open-source software with a convenient graphical user interface facilitates the non-target analysis of industrial samples. It has bundled machine learning models for the prediction of retention indices and mass spectra. A critical analysis of whether a combination of several methods of chromatography and mass spectrometry allows us to elucidate the structure of an unknown UDMH transformation product was provided. It was demonstrated that the use of gas chromatographic retention indices for two stationary phases (polar and non-polar) allows for the rejection of false candidates in many cases when only one retention index is not enough. The structures of five previously unknown UDMH transformation products were proposed, and four previously proposed structures were refined.

We conduct quantum studies of adsorption of diazine heterocycles on graphene to discuss experimental thermodynamics of gas-phase adsorption of pyridazine, pyrimidine and pyrazine on graphitized thermal carbon black, as reported previously. Using Born–Oppenheimer molecular dynamics and density functional studies, we characterize structural and electronic tendencies of the heterocycles on graphene. The theoretical studies predict the adsorption of pyridazine, pyrazine and pyrimidine to cause electronic perturbations of dipole, quadrupole and mixed spatial characters, respectively, resulting in a red shift of the electronic components of the heterocycles to modulate graphene electronics upon admixing of diazine orbital components with the π_z states of the substrate. Investigating the thermodynamics of adsorption further involves calculating Henry's constant with the expression of the uniform surface limit: using experimental data, we estimate binding energies and force derivatives with respect to the surface normal. The extracted association energies agree with the results of Lennard-Jones potential calculations. Together, the reported pyridazine anomalous retention required the association force constant to be lower compared with values for the other diazines. Exploring energies of intermolecular relations, we ascribe the pyridazine anomalous retention to possibility of the formation of pyridazine dimers: when on the surface, only for pyridazine, the computed benefit of pairing is larger than the energy of molecular association with graphene.

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.

The adsorption of 1H-pyrazole, 1H-imidazole, and 1H-1,2,4-triazole on a homogeneous graphene surface has been studied using density functional theory. Calculated the atomic charges by the Mulliken method for individual azoles, according to which the electronic structure of 1H-imidazole with a strong dipole is the most favorable for adsorption on polarizable graphene. Lennard-Jones potential curves were constructed, from which the values of the adsorption enthalpies of the azoles were found. The electronic perturbations arising as changes in the electron density during binding to graphene are estimated. The results obtained were compared with the literature data on the nature of the adsorption of azoles on nonpolar sorbents. The need to take into account the distribution of electron density when explaining the mechanism of adsorption on the graphene surface was noted.