Wladimir Labeikovsky

The bottleneck for taking full advantage of metabolomics data is often the availability, awareness, and usability of analysis tools. Software tools specifically designed for metabolomics data are being developed at an increasing rate, with hundreds of available tools already in the literature. Many of these tools are open-source and freely available but are very diverse with respect to language, data formats, and stages in the metabolomics pipeline. To help mitigate the challenges of meeting the increasing demand for guidance in choosing analytical tools and coordinating the adoption of best practices for reproducibility, we have designed and built the MSCAT (Metabolomics Software CATalog) database of metabolomics software tools that can be sustainably and continuously updated. This database provides a survey of the landscape of available tools and can assist researchers in their selection of data analysis workflows for metabolomics studies according to their specific needs. We used machine learning (ML) methodology for the purpose of semi-automating the identification of metabolomics software tool names within abstracts. MSCAT searches the literature to find new software tools by implementing a Named Entity Recognition (NER) model based on a neural network model at the sentence level composed of a character-level convolutional neural network (CNN) combined with a bidirectional long-short-term memory (LSTM) layer and a conditional random fields (CRF) layer. The list of potential new tools (and their associated publication) is then forwarded to the database maintainer for the curation of the database entry corresponding to the tool. The end-user interface allows for filtering of tools by multiple characteristics as well as plotting of the aggregate tool data to monitor the metabolomics software landscape.

Protein motions on all timescales faster than molecular tumbling are encoded in the spectral density. The dissection of complex protein dynamics is typically performed using relaxation rates determined at high and ultra-high field. Here we expand this range of the spectral density to low fields through field cycling using the nucleocapsid protein of the SARS coronavirus as a model system. The field-cycling approach enables site-specific measurements of R ₁ at low fields with the sensitivity and resolution of a high-field magnet. These data, together with high-field relaxation and heteronuclear NOE, provide evidence for correlated rigid-body motions of the entire β-hairpin, and corresponding motions of adjacent loops with a time constant of 0.8 ns (mesodynamics). MD simulations substantiate these findings and provide direct verification of the time scale and collective nature of these motions.

A unique feature of chemical catalysis mediated by enzymes is that the catalytically reactive atoms are embedded within a folded protein. Although current understanding of enzyme function has been focused on the chemical reactions and static three-dimensional structures, the dynamic nature of proteins has been proposed to have a function in catalysis^1,2,3,4,5. The concept of conformational substates has been described⁶; however, the challenge is to unravel the intimate linkage between protein flexibility and enzymatic function. Here we show that the intrinsic plasticity of the protein is a key characteristic of catalysis. The dynamics of the prolyl cis–trans isomerase cyclophilin A (CypA) in its substrate-free state and during catalysis were characterized with NMR relaxation experiments. The characteristic enzyme motions detected during catalysis are already present in the free enzyme with frequencies corresponding to the catalytic turnover rates. This correlation suggests that the protein motions necessary for catalysis are an intrinsic property of the enzyme and may even limit the overall turnover rate. Motion is localized not only to the active site but also to a wider dynamic network. Whereas coupled networks in proteins have been proposed previously^3,7,8,9,10, we experimentally measured the collective nature of motions with the use of mutant forms of CypA. We propose that the pre-existence of collective dynamics in enzymes before catalysis is a common feature of biocatalysts and that proteins have evolved under synergistic pressure between structure and dynamics.

Creatine kinase (CK) is an abundant enzyme, important for maintenance of high-energy phosphate homeostasis in many tissues including heart. Double-knockout CK (DbKO-CK) mice missing both the muscle (MM) and sarcomeric mitochondrial (ScMit) isoforms of CK have recently been studied. Despite a large change in skeletal muscle function in DbKO-CK mice, there is little functional change in the heart. To investigate whether there are specific changes in cardiac mitochondrial proteins associated with the loss of MM- and ScMit-CK isoforms, we have used difference gel electrophoresis (DIGE) to compare mitochondrial proteins from wild-type and DbKO-CK mice. Mass spectrometry fingerprinting was used to identify 40 spots as known mitochondrial proteins. We have discovered that the loss of MM- and ScMit-CK isoforms did not cause large scale changes in heart mitochondrial proteins. The loss of ScMit-CK was readily detected in the DbKO-CK samples. We have also detected a large decrease in the precursor form of aconitase. Furthermore, two mitochondrial protein differences have been found in the parent mouse strains of the DbKO-CK mice.