Developing Techniques for the Identification of Non-Canonical RNA Pairing and Analysis of LC-MS Datasets

Non-canonical pairing dynamics in ribonucleic acid (RNA) structureand statistical analysis of metabolomics liquid chromatography mass spectrometry (LC-MS) datasets are two difficult problems that stand as open challenges. RNA folding algorithms are used across a variety of disciplines to predict structures when experimental elucidation techniques are inconvenient or impractical. Though successful and widely adopted, folding algorithms make simplifying assumptions for loop regions due to their complex interactions and associated difficulty with generating energy parameters for relevant non-canonical pairing interactions. Modeling assumptions and a lack of energy parameters for loops limit accuracy in these functional critical regions of RNA. This work describes a new technique for probing non-canonical loop interactions through the combined analysis of dimethyl sulfate (DMS) and three-dimensional crystallographic data. We demonstrate that DMS data encodes information about non-canonical pairing which describes these interactions in an efficient, high throughput manner. Metabolomics aims to understand biological processes through the analysis of small molecule metabolites. The field primarily uses 1H nuclear magnetic resonance (NMR) spectroscopy as well as LC-MS to identify and quantitate metabolites. With even simple samples having hundreds or thousands of metabolites, researchers in the field have developed software pipelines to make metabolomics studies a tractable task. Numerous packages exist for the analysis of either 1H NMR or LC-MS data, but current offerings force researchers to use multiple packages to analyze both data types. To address the need for a metabolomics package capable of analyzing both, we have developed new LC-MS functionality for the NMR metabolomics package MVAPACK. Advisor: Joseph D. Yesselma

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 DMSMaPseq analysis will dramatically expand studies of RNA structure and aid in the design of structurebased therapeutics

Crystallization at Droplet Interfaces for the Fabrication of Geometrically Programmed Synthetic Magnetosomes

Many organisms rely on the precise growth, assembly, and/or organization of inorganic crystals to achieve vital functions, for example, three-dimensional structural support (i.e., skeletal systems based on calcite) or environmental sensing (i.e., magnetosomes based on magnetite). Mimicking the production of the complex products observed in these biomineralization processes, synthetically, remains challenging. Herein, a method for the synthesis of artificial magnetosomes with programmable magnetic domains was developed. Specifically, precursors were compartmentalized inside different surfactant-stabilized aqueous-phase droplets suspended in oil and microfluidic technologies were implemented to control their interactions precisely. When reactive droplets were brought into contact with one another, a lipid bilayer formed, allowing transport of reagents between droplets. This process led to interface-confined magnetite growth. These polarized magnetic domains were used to manipulate the synthetic magnetosomes using external magnetic fields, thus providing a convenient method for droplet manipulation and transport. This method of producing synthetic magnetosomes provides a route toward useful materials with applications in areas such as drug delivery and microfluidics

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 Alexas 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

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 can seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins by interacting with a computational machine, similar to how we use Amazon Alexa in these days. The technical foundation of Mutexa has been established through the development of database that integrates enzyme structures with 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 non-electrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and challenges in our endeavor to develop new Mutexa applications that facilitate the selection of beneficial mutants in enzyme engineering