Simulated LC-MS Data Set for Assessing the Metabolomics Data Processing Pipeline Implemented into MVAPACK.

Metabolomics commonly relies on using one-dimensional (1D) 1H NMR spectroscopy or liquid chromatography-mass spectrometry (LC-MS) to derive scientific insights from large collections of biological samples. NMR and MS approaches to metabolomics require, among other issues, a data processing pipeline. Quantitative assessment of the performance of these software platforms is challenged by a lack of standardized data sets with "known" outcomes. To resolve this issue, we created a novel simulated LC-MS data set with known peak locations and intensities, defined metabolite differences between groups (i.e., fold change > 2, coefficient of variation ≤ 25%), and different amounts of added Gaussian noise (0, 5, or 10%) and missing features (0, 10, or 20%). This data set was developed to improve benchmarking of existing LC-MS metabolomics software and to validate the updated version of our MVAPACK software, which added gas chromatography-MS and LC-MS functionality to its existing 1D and two-dimensional NMR data processing capabilities. We also included two experimental LC-MS data sets acquired from a standard mixture andMycobacterium smegmatiscell lysates since a simulated data set alone may not capture all the unique characteristics and variability of real spectra needed to assess software performance properly. Our simulated and experimental LC-MS data sets were processed with the MS-DIAL and XCMSOnline software packages and our MVAPACK toolkit to showcase the utility of our data sets to benchmark MVAPACK against community standards. Our results demonstrate the enhanced objectivity and clarity of software assessment that can be achieved when both simulated and experimental data are employed since distinctly different software performances were observed with the simulated and experimental LC-MS data sets. We also demonstrate that the performance of MVAPACK is equivalent to or exceeds existing LC-MS software programs while providing a single platform for processing and analyzing both NMR and MS data sets.

Mutexa: A Computational Ecosystem for Intelligent Protein Engineering.

Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexa's role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering.

High-throughput computational investigation of protein electrostatics and cavity for SAM-dependent methyltransferases.

S-adenosyl methionine (SAM)-dependent methyl transferases (MTases) are a ubiquitous class of enzymes catalyzing dozens of essential life processes. Despite targeting a large space of substrates with diverse intrinsic reactivity, SAM MTases have similar catalytic efficiency. While understanding of MTase mechanism has grown tremendously through the integration of structural characterization, kinetic assays, and multiscale simulations, it remains elusive how these enzymes have evolved to fit the diverse chemical needs of their respective substrates. In this work, we performed a high-throughput molecular modeling analysis of 91 SAM MTases to better understand how their properties (i.e., electric field [EF] strength and active site volumes) help achieve similar catalytic efficiency toward substrates of different reactivity. We found that EF strengths have largely adjusted to make the target atom a better methyl acceptor. For MTases that target RNA/DNA and histone proteins, our results suggest that EF strength accommodates formal hybridization state and variation in cavity volume trends with diversity of substrate classes. Metal ions in SAM MTases contribute negatively to EF strength for methyl donation and enzyme scaffolds tend to offset these contributions.

Web-based platform for analysis of RNA folding from high throughput chemical probing data.

RNA structures play critical roles in regulating gene expression across all domains of life and viruses. Chemical probing methods coupled with massively parallel sequencing have revolutionized the RNA structure field by enabling the assessment of many structures in their native, physiological context. Previously, we developed Dimethyl-Sulfate-based Mutational Profiling and Sequencing (DMS-MaPseq), which uses DMS to label the Watson-Crick face of open and accessible adenine and cytosine bases in the RNA. We used this approach to determine the genome-wide structures of HIV-1 and SARS-CoV-2 in infected cells, which permitted uncovering new biology and identifying therapeutic targets. Due to the simplicity and ease of the experimental procedure, DMS-MaPseq has been adopted by labs worldwide. However, bioinformatic analysis remains a substantial hurdle for labs that often lack the necessary infrastructure and computational expertise. Here we present a modern web-based interface that automates the analysis of chemical probing profiles from raw sequencing files (http://rnadreem.org). The availability of a simple web-based platform for DMS-MaPseq analysis will dramatically expand studies of RNA structure and aid in the design of structure-based therapeutics.

Crystallization at droplet interfaces for the fabrication of geometrically programmed synthetic magnetosomes.

Biological systems demonstrate exquisite three dimensional (3D) control over crystal nucleation and growth using soft micro/nanoenvironments, such as vesicles, for reagent transport and confinement. It remains challenging to mimic such biomineralization processes using synthetic systems. A synthetic mineralization strategy applicable to the synthesis of artificial magnetosomes with programmable magnetic domains is described. This strategy relies on the compartmentalization of precursors in surfactant-stabilized liquid microdroplets which, when contacted, spontaneously form lipid bilayers that support reagent transport and interface-confined magnetite nucleation and growth. The resulting magnetic domains are polarized and thus readily manipulated using magnetic fields or assembled using droplet-droplet interactions. This strategy presents a new, liquid phase procedure for the synthesis of vesicles with geometrically controlled inorganic features that would be difficult to produce otherwise. The artificial magnetosomes demonstrated could find use in, for example, drug/cargo delivery, droplet microfluidics, and formulation science.