Browsing by Author "Thyer, Ross"

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    DLPacker: Deep learning for prediction of amino acid side chain conformations in proteins
    (Wiley, 2022) Misiura, Mikita; Shroff, Raghav; Thyer, Ross; Kolomeisky, Anatoly B.; Center for Theoretical Biological Physics
    Prediction of side chain conformations of amino acids in proteins (also termed “packing”) is an important and challenging part of protein structure prediction with many interesting applications in protein design. A variety of methods for packing have been developed but more accurate ones are still needed. Machine learning (ML) methods have recently become a powerful tool for solving various problems in diverse areas of science, including structural biology. In this study, we evaluate the potential of deep neural networks (DNNs) for prediction of amino acid side chain conformations. We formulate the problem as image-to-image transformation and train a U-net style DNN to solve the problem. We show that our method outperforms other physics-based methods by a significant margin: reconstruction RMSDs for most amino acids are about 20% smaller compared to SCWRL4 and Rosetta Packer with RMSDs for bulky hydrophobic amino acids Phe, Tyr, and Trp being up to 50% smaller.
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    Engineering Optogenetic Control of Bacterial Metabolism in Stationary Phase of Growth
    (2023-09-06) Lazar, John Tyler; Tabor, Jeffrey J; Thyer, Ross
    Genetically-encoded sensors are used to induce metabolite production in bacterial fermentations. However, these sensors are typically optimized for exponential growth phase rather than stationary phase where the majority of metabolite production occurs. In the first portion of this work, we find that our exponential phase-optimized green light-activated E. coli two-component gene regulatory system CcaSR is effectively non-functional in stationary phase. We show that the major causes of failure are stationary-phase specific mutation of the plasmid-borne biosynthetic pathway used to produce the required chromophore phycocyanobilin (PCB) and accumulation of the response regulator CcaR leading to very high leaky target gene expression. To address these problems, we move the PCB biosynthetic pathway into the chromosome and re-optimize expression of the component enzymes, and re-balance CcaR expression for stationary phase . The resulting CcaSRstat system exhibits low levels of leakiness and an 80-fold activation of target gene expression in stationary phase. Notably, our stationary phase-optimized CcaSRstat system is not functional in exponential phase, a feature that may have benefits for metabolic engineering and other applications. In the second portion of this work, we combine CcaSRstat with static and periodic illumination patterns to achieve high levels of production of several industrially-relevant phenylpropanoid metabolites such as p-coumaric acid and bisdemethoxycurcumin. We then proceed to demonstrate that our optimal light signals at the 0.5 mL light plate apparatus (LPA) volume scale to 25 mL optogenetic bioreactors. CcaSRstat is a useful tool for optimizing bacterial metabolite production and could be used to control bacterial behaviors in other non-growth environments such as the gastrointestinal tract or soil. Our work lays the foundation for increasing the exploration space of dynamic control of metabolic pathways while also providing valuable insight into design considerations of biosensors in the stationary phase of growth.
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    New Tools and Cultivation Protocols for Autotrophic and Diazotrophic Non-model Bacteria
    (2024-07-29) Alameri, Abdulaziz; Thyer, Ross; Verduzco, Rafael; Kampouri, Stavroula
    Anthropogenic climate change necessitates a societal shift toward sustainable manufacturing practices. The chemical and polymer industry, which accounts for a fifth of the global CO2 emissions, has been slow to adapt due to factors such as economic pressures and risk aversion. Biomanufacturing, which usually relies on microorganisms such as bacteria, offers a promising alternative by leveraging native biological pathways that can perform complex chemistry without the requirement for extremes of temperature and pressure, often needed in traditional chemical processes. Beyond the model bacterium E. coli, the diverse metabolisms of environmental bacteria can be harnessed to produce a wide range of chemicals, with some bacteria capable of directly fixing atmospheric CO2 (autotrophs) and N2 (diazotrophs) when provided with a source of reducing power. Biomolecular engineering and ‘domestication’ of these non-model bacteria is required to make sustainable biomanufacturing a reality. However, these efforts are hindered by a lack of genetic tools. Here, I present efforts to (i) address the challenges of introducing recombinant DNA into environmental Mycobacteriales, an industrially relevant yet largely genetically intractable clade of bacteria, (ii) investigate the metabolism of these bacteria under conditions of extreme nutrient limitation and isolate novel CO2 fixing species, and (iii) develop a modular DNA assembly framework for the model diazotroph Azotobacter vinelandii. From this work, we have developed a suite of new genetic tools which function in several Mycobacteriales genera, characterized a novel Gordonia species with potential autotrophic metabolism, and identified the presence of multiple restriction-modification systems as a dominant barrier to genetic manipulation and future biomolecular engineering efforts in these species.