Trends and features of the informatization of higher education modern stage

Problem statement. Nowadays, people are increasingly talking not about the progressive development of higher education in the context of the use of digital technologies, but about its more drastic digital transformation. The problem of identifying and systematizing patterns characterizing the development of universities in the context of the mass introduction of new digital technologies, as well as changes in external influences on student training systems, is urgent. The solution of this problem is significant for determining further scientific and pedagogical research, as well as ways to develop teacher training systems. The purpose of the study is to identify on the basis of domestic and foreign analytical data (OECD, UNESCO and others) directions, characteristics, problems and prospects of informatization of higher education. Methodology. The analysis of scientific publications on the development of didactics and the use of modern teaching tools in universities is based on the study of the species composition and specifics of existing digital resources, the use of mathematical methods for processing numerical data and technologies for their visualization. Results. It is shown that modern informatization of higher education is characterized by the spread of online courses, collections of digital resources, a decrease in the number of computer equipment in universities, the penetration of technologies of the new industrial revolution and many other factors. The development of a fundamental component of higher education that is invariant with respect to the development of technologies, the preparation of students for the use of promising technologies in professional activities, the pooling of resources into a single digital educational environment are significant. Conclusion. Research should be continued to ensure and evaluate the quality of all types of learning tools, to identify theoretical and practical approaches to the integration and unification of disparate information systems. There is a need for a scientifically based substantive and methodological update of the systems of training and retraining of all specialists working in the conditions of digital transformation of higher education

Advanced Precursor Ion Selection Algorithms for Increased Depth of Bottom-Up Proteomic Profiling

Conventional TopN data-dependent acquisition (DDA) LC–MS/MS analysis identifies only a limited fraction of all detectable precursors because the ion-sampling rate of contemporary mass spectrometers is insufficient to target each precursor in a complex sample. TopN DDA preferentially targets high-abundance precursors with limited sampling of low-abundance precursors and repeated analyses only marginally improve sample coverage due to redundant precursor sampling. In this work, advanced precursor ion selection algorithms were developed and applied in the bottom-up analysis of HeLa cell lysate to overcome the above deficiencies. Precursors fragmented in previous runs were efficiently excluded using an automatically aligned exclusion list, which reduced overlap of identified peptides to ∼10% between replicates. Exclusion of previously fragmented high-abundance peptides allowed deeper probing of the HeLa proteome over replicate LC–MS runs, resulting in the identification of 29% more peptides beyond the saturation level achievable using conventional TopN DDA. The gain in peptide identifications using the developed approach translated to the identification of several hundred low-abundance protein groups, which were not detected by conventional TopN DDA. Exclusion of only identified peptides compared with the exclusion of all previously fragmented precursors resulted in an increase of 1000 (∼10%) additional peptide identifications over four runs, suggesting the potential for further improvement in the depth of proteomic profiling using advanced precursor ion selection algorithms

Empirical multi-dimensional space for scoring peptide spectrum matches in shotgun proteomics

Data-dependent tandem mass spectrometry (MS/MS) is one of the main techniques for protein identification in shotgun proteomics. In a typical LC MS/MS workflow, peptide product ion mass spectra (MS/MS spectra) are compared with those derived theoretically from a protein sequence database. Scoring of these matches results in peptide identifications. A set of peptide identifications is characterized by false discovery rate (FDR), which determines the fraction of false identifications in the set. The total number of peptides targeted for fragmentation is in the range of 10 000 to 20 000 for a several-hour LC MS/MS run. Typically, <50% of these MS/MS spectra result in peptide-spectrum matches go (PSMs). A small fraction of PSMs pass the preset FDR level (commonly 1%) giving a list of identified proteins, yet a large number of correct PSMs corresponding to the peptides originally present in the sample are left behind in the "grey area" below the identity threshold. Following the numerous efforts to recover these correct PSMs, here we investigate the utility of a scoring scheme based on the multiple PSM descriptors available from the experimental data. These descriptors include retention time, deviation between experimental and theoretical mass, number of missed cleavages upon in-solution protein digestion, precursor ion fraction (PIF), PSM count per sequence, potential modifications, median fragment mass error, C-13 isotope mass difference, charge states, and number of PSMs per protein. The proposed scheme utilizes a set of metrics obtained for the corresponding distributions of each of the descriptors. We found that the proposed PSM scoring algorithm differentiates equally or more efficiently between correct and incorrect identifications compared with existing postsearch validation approaches

MS/MS-Free Protein Identification in Complex Mixtures Using Multiple Enzymes with Complementary Specificity

In this work, we present the results of evaluation of a workflow that employs a multienzyme digestion strategy for MS1-based protein identification in “shotgun” proteomic applications. In the proposed strategy, several cleavage reagents of different specificity were used for parallel digestion of the protein sample followed by MS1 and retention time (RT) based search. Proof of principle for the proposed strategy was performed using experimental data obtained for the annotated 48-protein standard. By using the developed approach, up to 90% of proteins from the standard were unambiguously identified. The approach was further applied to HeLa proteome data. For the sample of this complexity, the proposed MS1-only strategy determined correctly up to 34% of all proteins identified using standard MS/MS-based database search. It was also found that the results of MS1-only search were independent of the chromatographic gradient time in a wide range of gradients from 15–120 min. Potentially, rapid MS1-only proteome characterization can be an alternative or complementary to the MS/MS-based “shotgun” analyses in the studies, in which the experimental time is more important than the depth of the proteome coverage

MS/MS-Free Protein Identification in Complex Mixtures Using Multiple Enzymes with Complementary Specificity

In this work, we present the results of evaluation of a workflow that employs a multienzyme digestion strategy for MS1-based protein identification in “shotgun” proteomic applications. In the proposed strategy, several cleavage reagents of different specificity were used for parallel digestion of the protein sample followed by MS1 and retention time (RT) based search. Proof of principle for the proposed strategy was performed using experimental data obtained for the annotated 48-protein standard. By using the developed approach, up to 90% of proteins from the standard were unambiguously identified. The approach was further applied to HeLa proteome data. For the sample of this complexity, the proposed MS1-only strategy determined correctly up to 34% of all proteins identified using standard MS/MS-based database search. It was also found that the results of MS1-only search were independent of the chromatographic gradient time in a wide range of gradients from 15–120 min. Potentially, rapid MS1-only proteome characterization can be an alternative or complementary to the MS/MS-based “shotgun” analyses in the studies, in which the experimental time is more important than the depth of the proteome coverage

IdentiPy: An Extensible Search Engine for Protein Identification in Shotgun Proteomics

We present an open-source, extensible search engine for shotgun proteomics. Implemented in Python programming language, IdentiPy shows competitive processing speed and sensitivity compared with the state-of-the-art search engines. It is equipped with a user-friendly web interface, IdentiPy Server, enabling the use of a single server installation accessed from multiple workstations. Using a simplified version of X!Tandem scoring algorithm and its novel “autotune” feature, IdentiPy outperforms the popular alternatives on high-resolution data sets. Autotune adjusts the search parameters for the particular data set, resulting in improved search efficiency and simplifying the user experience. IdentiPy with the autotune feature shows higher sensitivity compared with the evaluated search engines. IdentiPy Server has built-in postprocessing and protein inference procedures and provides graphic visualization of the statistical properties of the data set and the search results. It is open-source and can be freely extended to use third-party scoring functions or processing algorithms and allows customization of the search workflow for specialized applications

First Cage-Like Pentanuclear Co(II)-Silesquioxane

International audienc

Empirical Multidimensional Space for Scoring Peptide Spectrum Matches in Shotgun Proteomics

Data-dependent tandem mass spectrometry (MS/MS) is one of the main techniques for protein identification in shotgun proteomics. In a typical LC–MS/MS workflow, peptide product ion mass spectra (MS/MS spectra) are compared with those derived theoretically from a protein sequence database. Scoring of these matches results in peptide identifications. A set of peptide identifications is characterized by false discovery rate (FDR), which determines the fraction of false identifications in the set. The total number of peptides targeted for fragmentation is in the range of 10 000 to 20 000 for a several-hour LC–MS/MS run. Typically, <50% of these MS/MS spectra result in peptide-spectrum matches (PSMs). A small fraction of PSMs pass the preset FDR level (commonly 1%) giving a list of identified proteins, yet a large number of correct PSMs corresponding to the peptides originally present in the sample are left behind in the “grey area” below the identity threshold. Following the numerous efforts to recover these correct PSMs, here we investigate the utility of a scoring scheme based on the multiple PSM descriptors available from the experimental data. These descriptors include retention time, deviation between experimental and theoretical mass, number of missed cleavages upon in-solution protein digestion, precursor ion fraction (PIF), PSM count per sequence, potential modifications, median fragment mass error, ¹³C isotope mass difference, charge states, and number of PSMs per protein. The proposed scheme utilizes a set of metrics obtained for the corresponding distributions of each of the descriptors. We found that the proposed PSM scoring algorithm differentiates equally or more efficiently between correct and incorrect identifications compared with existing postsearch validation approaches

A heterometallic (Fe6Na8) cage-like silsesquioxane: synthesis, structure, spin glass behavior and high catalytic activity.

International audienceThe exotic "Asian Lantern" heterometallic cage silsesquioxane [(PhSiO 1.5) 20 (FeO 1.5) 6 (NaO 0.5) 8 (n-BuOH) 9.6 (C 7 H 8)] (I) was obtained and characterized by X-ray diffraction, EXAFS, topological analyses and DFT calculation. The magnetic property investigations revealed that it shows an unusual spin glass-like behavior induced by a particular triangular arrangement of Fe(III) ions. Cyclohexane and other alkanes as well as benzene can be oxidized to the corresponding alkyl hydroperoxides and phenol, respectively, by hydrogen peroxide in air in the presence of catalytic amounts of complex I and nitric acid. The Icatalyzed reaction of cyclohexane, cC 6 H 12 , with H 2 16 O 2 in an atmosphere of 18 O 2 gave a mixture of labeled and non-labeled cyclohexyl hydroperoxides, cC 6 H 11-16 O-16 OH and cC 6 H 11-18 O-18 OH, respectively, with an 18 O incorporation level of ca. 12%. Compound I also revealed high efficiency in the oxidative amidation of alcohols into amides: in the presence of complex I, only 500 ppm of iron was allowed to reach TON and TOF values of 1660 and 92 h À1