Ionic Polyimides: New High Performance Polymers for Additive Manufacturing

There is currently a very limited set of engineering polymers that have been demonstrated as viable for use in 3-D printing. Additive manufacturing of custom components will require a much larger array of polymers, especially those with physical, thermal, chemical, and mechanical properties that can be tailor-made. The development of Ionic Polyimides offers a solution to this shortage by combining the well understood and widely accepted properties of conventional polyimides, with a new approach to polymer synthesis. Polyimides and polymeric ionic liquids (poly(ILs)) are at the forefront of advanced polymer materials, each with their own set of advantages and disadvantages. While it is clear that more types of polymer materials are needed for fused deposition modeling (FDM) additive manufacturing, there is a need to explore these classes of materials. The synthesis process developed by the Bara Research Group at the University of Alabama allows full control over polymer structure, nanostructure, thermal, electrical, and physical properties making them a prime candidate for use in the additive manufacturing process. Furthermore, the new process allows us to tailor-make a high strength polymer that can be used to fabricate filament feedstock instead of pellets for 3D printing. The primary objective of this proposal is to determine the relationship between molecular structure, physical properties, and performance of ionic polyimides. Further, we seek to determine their utility as materials suitable for additive manufacturing of components used in aerospace vehicles, with an emphasis on characterizing and simulating their thermal behaviors and properties. This proposal addresses the need for fundamental research on a customizable polymer filament feedstock for 3-D printing with tailor-made properties potentially making it superior to the commercial blends offered in industry today. The deliverables for this project are the creation of a database that will detail the relationships between the molecular structure and physical properties for the ionic polyimide of interest (e.g. Tg/Tm (Glass Transition Temperature divided by Melting Point)) relative to different ionic polyimide structures). This new database will provide a road map to the development of the first generation of materials and ultimately proof-of-concept

Protein Targets of Frankincense: A Reverse Docking Analysis of Terpenoids from Boswellia Oleo-Gum Resins

Background: Frankincense, the oleo-gum resin of Boswellia trees, has been used in traditional medicine since ancient times. Frankincense has been used to treat wounds and skin infections, inflammatory diseases, dementia, and various other conditions. However, in many cases, the biomolecular targets for frankincense components are not well established. Methods: In this work, we have carried out a reverse docking study of Boswellia diterpenoids and triterpenoids with a library of 16034 potential druggable target proteins. Results: Boswellia diterpenoids showed selective docking to acetylcholinesterase, several bacterial target proteins, and HIV-1 reverse transcriptase. Boswellia triterpenoids targeted the cancer-relevant proteins (poly(ADP-ribose) polymerase-1, tankyrase, and folate receptor β), inflammation-relevant proteins (phospholipase A2, epoxide hydrolase, and fibroblast collagenase), and the diabetes target 11β-hydroxysteroid dehydrogenase. Conclusions: The preferential docking of Boswellia terpenoids is consistent with the traditional uses and the established biological activities of frankincense

High Performance Computing Prediction of Potential Natural Product Inhibitors of SARS-CoV-2 Key Targets

This work describes using a supercomputer to perform virtual screening of natural products and natural products derivatives against several conformations of 3 proteins of SARS-CoC-2 : papain-like protease, main protease and spike protein. We analyze the common chemical features of the top molecules predicted to bind and describe the pharmacophores responsible for the predicted binding

The use of Gene Ontology terms for predicting highly-connected 'hub' nodes in protein-protein interaction networks

Background: Protein-protein interactions mediate a wide range of cellular functions and responses and have been studied rigorously through recent large-scale proteomics experiments and bioinformatics analyses. One of the most important findings of those endeavours was the observation that 'hub' proteins participate in significant numbers of protein interactions and play critical roles in the organization and function of cellular protein interaction networks (PINs) [1, 2]. It has also been demonstrated that such hub proteins may constitute an important pool of attractive drug targets. Thus, it is crucial to be able to identify hub proteins based not only on experimental data but also by means of bioinformatics predictions. Results A hub protein classifier has been developed based on the available interaction data and Gene Ontology (GO) annotations for proteins in the Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens genomes. In particular, by utilizing the machine learning method of boosting trees we were able to create a predictive bioinformatics tool for the identification of proteins that are likely to play the role of a hub in protein interaction networks. Testing the developed hub classifier on external sets of experimental protein interaction data in Methicillin-resistant Staphylococcus aureus (MRSA) 252 and Caenorhabditis elegans demonstrated that our approach can predict hub proteins with a high degree of accuracy. A practical application of the developed bioinformatics method has been illustrated by the effective protein bait selection for large-scale pull-down experiments that aim to map complete protein-protein interaction networks for several species. Conclusion The successful development of an accurate hub classifier demonstrated that highly-connected proteins tend to share certain relevant functional properties reflected in their Gene Ontology annotations. It is anticipated that the developed bioinformatics hub classifier will represent a useful tool for the theoretical prediction of highly-interacting proteins, the study of cellular network organizations, and the identification of prospective drug targets – even in those organisms that currently lack large-scale protein interaction data.Graduate and Postdoctoral StudiesInfectious Diseases, Division ofMedicine, Department ofMedicine, Faculty ofScience, Faculty ofReviewedFacult

Computational Prediction of Metabolites of Tobacco-Specific Nitrosamines by CYP2A13

A computational approach for the prediction of tobacco-specific nitrosamine (TSNA) metabolites by cytochrome P450s (CYPs) has been developed that currently predicts all of the known CYP2A13 metabolites of nicotine-derived nitrosamine ketone (NNK), N-nitrosonornicotine (NNN), and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) resulting from hydroxylations and heteroatom oxidations reported in metabolomics literature. This computational approach integrates 1) machine learning models trained on quantum-mechanically-derived molecular surface properties for a set of CYP substrates with known metabolites to identify sites of metabolism across CYP isoforms and 2) validation of machine learning predictions using ensemble docking of the TSNA parent molecules into CYP2A13’s binding site to identify the most likely TSNA reactive atoms. This method is generalizable to any CYP isoform for which there is structural information, opening the door to the prediction of P450-based metabolite prediction, as well as prediction and rationalization of metabolomics data.</div

Natural Products as New Treatment Options for Trichomoniasis: A Molecular Docking Investigation

Trichomoniasis, caused by the parasitic protozoan Trichomonas vaginalis, is the most common non-viral sexually-transmitted disease, and there can be severe complications from trichomoniasis. Antibiotic resistance in T. vaginalis is increasing, but there are currently no alternatives treatment options. There is a need to discover and develop new chemotherapeutic alternatives. Plant-derived natural products have long served as sources for new medicinal agents, as well as new leads for drug discovery and development. In this work, we have carried out an in silico screening of 952 antiprotozoal phytochemicals with specific protein drug targets of T. vaginalis. A total of 42 compounds showed remarkable docking properties to T. vaginalis methionine gamma-lyase (TvMGL) and to T. vaginalis purine nucleoside phosphorylase (TvPNP). The most promising ligands were polyphenolic compounds, and several of these showed docking properties superior to either co-crystallized ligands or synthetic enzyme inhibitors

Essential Oils as Antiviral Agents. Potential of Essential Oils to Treat SARS−CoV−2 Infection: An In−Silico Investigation

Essential oils have shown promise as antiviral agents against several pathogenic viruses. In this work we hypothesized that essential oil components may interact with key protein targets of the 2019 severe acute respiratory syndrome coronavirus 2 (SARS−CoV−2). A molecular docking analysis was carried out using 171 essential oil components with SARS−CoV−2 main protease (SARS−CoV−2 Mpro), SARS−CoV−2 endoribonucleoase (SARS−CoV−2 Nsp15/NendoU), SARS−CoV−2 ADP−ribose−1″−phosphatase (SARS−CoV−2 ADRP), SARS−CoV−2 RNA−dependent RNA polymerase (SARS−CoV−2 RdRp), the binding domain of the SARS−CoV−2 spike protein (SARS−CoV−2 rS), and human angiotensin−converting enzyme (hACE2). The compound with the best normalized docking score to SARS−CoV−2 Mpro was the sesquiterpene hydrocarbon (E)−β−farnesene. The best docking ligands for SARS−CoV Nsp15/NendoU were (E,E)−α−farnesene, (E)−β−farnesene, and (E,E)−farnesol. (E,E)−Farnesol showed the most exothermic docking to SARS−CoV−2 ADRP. Unfortunately, the docking energies of (E,E)−α−farnesene, (E)−β−farnesene, and (E,E)−farnesol with SARS−CoV−2 targets were relatively weak compared to docking energies with other proteins and are, therefore, unlikely to interact with the virus targets. However, essential oil components may act synergistically, essential oils may potentiate other antiviral agents, or they may provide some relief of COVID−19 symptoms