Biology subjects

Siegel, J. B.

Publications and source records attributed to Siegel, J. B..

34 records · Page 2Linked to original sources

bioRxiv2023

Environmental impacts of cultured meat: A cradle-to-gate life cycle assessment

Interest in animal cell-based meat (ACBM) or cultured meat as a viable environmentally conscious replacement for livestock production has been increasing, however a life cycle assessment for the current production methods of ACBM has not been conducted. Currently, ACBM products are being produced at a small scale and at an economic loss, however ACBM companies are intending to industrialize and scale-up production. This study assesses the potential environmental impact of near term ACBM production. Updated findings from recent technoeconomic assessments (TEAs) of ACBM and a life cycle assessment of Essential 8 were utilized to perform a life cycle assessment of near-term ACBM production. A scenario analysis was conducted utilizing the metabolic requirements examined in the TEAs of ACBM and a purification factor from the Essential 8 life cycle assessment was utilized to account for growth medium component processing. The results indicate that the environmental impact of near-term ACBM production is likely to be orders of magnitude higher than median beef production if a highly refined growth medium is utilized for ACBM production.

ecology↗

bioRxiv2023

Computationally Designed Carbonic Anhydrase Inhibitors Derived from Sulfonamides for Treatment of Glaucoma

Glaucoma is a known contributor to blindness in adults. This disease occurs as a result of overactivity of the carbonic anhydrase II (CAII) enzyme. Several of the commonly used sulfonamide drugs have been used to treat the symptoms of glaucoma but have been found to have low potency. To develop a more effective drug for the treatment of glaucoma, two novel CAII inhibitor drugs were designed through bioisoteric replacement and chemical intuition. This research discusses the development of these CAII inhibitor drugs from the existing glaucoma drug known as dichlorphenamide. The drugs created in this research were found to have a better docking score within the CAII binding site than that of dichlorphenamide. The drugs proposed in this paper would need to undergo further research to determine laboratory synthesis and potential clinical trialing.

pharmacology and toxicology↗

bioRxiv2022

Design of Novel Selective Serotonin Reuptake Inhibitors Using Computational Modeling Studies

Low levels of serotonin in the human brain have been associated with a variety of disorders including depression and anxiety. Selective serotonin reuptake inhibitors (SSRIs) are currently being prescribed to treat such conditions and examples of already marketed drugs include paroxetine, fluvoxamine, sertraline, and citalopram. Nevertheless, side effects such as nausea and drowsiness have been reported for these pharmaceuticals emphasizing the need for continuous development of new and improved lead molecules. In this study, chemical intuition and computational modeling were employed to propose two novel SSRI drug candidates with higher binding affinities to the ts3 human serotonin transporter (hSERT) than currently known SSRIs. Lastly, a homology analysis determined that Macaca fascicularis is a suitable model organism for future preclinical studies.

pharmacology and toxicology↗

bioRxiv2022

Computationally Guided Design of Two Novel Soluble Epoxide Hydrolase Inhibitors

The enzyme soluble epoxide hydrolase (sEH) has been found to play a role in many ailments such as inflammation, pain, renal function, pulmonary function, hypertension, and diabetes. Multiple sEH inhibitors have been developed to reduce the adverse effects of the ailments. Due to high inhibitory concentrations, there is urgent need for developing improved sEH inhibitors. In this study, two novel sEH inhibitors were designed via computational bioisosteric replacement and chemical intuition with the goal of increasing binding affinity, which can potentially decrease inhibitory concentration. The new drug candidates were found to have improved binding properties compared to existing drugs.

biochemistry↗

bioRxiv2022

Computationally Guided Design of Novel Angiotensin Converting Enzyme Inhibitors

Angiotensin-converting enzyme inhibitors (ACEI) such as Moexipril, Trandolapril, Ramipril, and Perindopril are used to treat hypertension. However, these drugs have undesired side effects. Using computational studies, two new novel drug candidates were designed with improved molecular interactions, ADMET properties, and docking scores compared to Moexipril. Homology analysis was done to determine the best animal model for preclinical studies, and it revealed that Pan troglodytes (chim-panzees) and Mus musculus (house mouse) were the best candidates.

biochemistry↗

bioRxiv2022

Computational Design of Two New Vitamin K Epoxide Reductase Inhibitors

Vitamin K epoxide reductase (VKOR) inhibitors are commonly used to treat atrial fibrillation and ischemic stroke. However, due to the side effects and poor absorbance of existing drugs, there is urgent need for developing better VKOR inhibitor drugs. In this work, computational bioisosteric replacement and chemical intuition were used to design two novel VKOR inhibitors. The new candidates are predicted to have improved ADMET and binding properties compared to existing drugs.

biochemistry↗

bioRxiv2020

Design to Data for mutants of β-glucosidase B from Paenibacillus polymyxa: L171M, H178M, M221L, E406W, N160E, F415M

Computational protein design is growing in popularity as a means to engineer enzymes. Currently, protein design algorithms can predict the stability and function of the enzymes to only a limited degree. Thus, further experimental data is required for training software to more accurately characterize the structure-function relationship of enzymes. To date, the Design2Data (D2D) database holds 129 single point mutations of {beta}-glucosidase B (BglB) characterized by kinetic and thermal stability biophysical parameters. In this study, we introduced six mutants into the BglB database and examined their catalytic activity and thermal stability: L171M, H178M, M221L, E406W, N160E, and F415M.

biochemistry↗

bioRxiv2020

Design to Data for mutants of β-Glucosidase B from Paenibacillus polymyxa: I45K, A357S, I20A, I20V, and I20E

The relatively small size and scope of most current datasets of biophysical mutation effects in enzymes limit the ability to develop data-driven algorithms enabling accurate generative modeling tools for designing novel enzyme function. Here, the Michaelis-Menten constants (kcat, KM, and kcat/KM) and thermal stability (TM) of five new mutations of {beta}-glucosidase B from Paenibacillus polymyxa (BglB) are characterized. Foldit software was used to create molecular models of the mutants, for which synthetic genes were constructed and the corresponding proteins produced and purified from E. coli. It was found that mutations that disrupted pre-existing hydrogen bonds near the active site had reduced expression in contrast to mutations at the same site that did not affect native hydrogen bonding. This is consistent with previous results showing the relationship between hydrogen bonding and enzyme functionality. These mutants contribute to a growing data set of >100 mutants that have been characterized for expression, kinetic, and thermal properties

biochemistry↗

bioRxiv2020

Computational design of two new soluble epoxide hydrolase (sEH) inhibitors

Inhibitors of soluble epoxide hydrolase (sEH) enzymes have shown great potential for the treatment of neuropathic pain. However, current sEH inhibitors have poor physicochemical properties and has not been proven to be safe for human treatments yet. New inhibitor designs could have the potential to improve current drugs efficacy, and so in this work, chemical intuition and bioisosteric replacement were used to computationally design two novel sEH inhibitors. These new candidates showed good pharmacokinetic properties and presented better docking scores compared to a known sEH inhibitor, t-TUCB, used in the treatment of pain in horses. Homology analysis revealed that Mus musculus may not be suitable organism for preclinical trials studies of these novel inhibitors.

pharmacology and toxicology↗

bioRxiv2020

Techno-economic assessment of animal cell-based meat

Interest in animal cell-based meat (ACBM) or laboratory grown meat has been increasing, however the economic viability of these potential products has not been thoroughly vetted. Recent studies suggest monoclonal antibody production technology can be adapted for the industrialization of ACBM production. This study provides a scenario-based assessment of the projected cost per kilogram of ACBM based on cellular metabolic requirements and process/chemical engineering conventions. A sensitivity analysis of the model identified the nine most influential cost factors for ACBM production out of 67 initial parameters. The results indicate that technological performance will need to approach technical limits for ACBM to achieve profitably as a commodity. However, the model also suggests that low-volume high-value specialty products could be viable based on current technology. One Sentence SummaryA model based upon cellular metabolism and engineering conventions was created to examine the economic viability of animal cell-based meat. Significance statementAnimal cell-based meat (ACBM) has received a significant amount of media attention (as well as corporate investment) in recent years based on its perceived potential to displace traditional meat production, whether beef, poultry, or fish. However, a robust techno-economic assessment (TEA) of these potential products is not publicly available. Our study examined the capital and operating expenditures for potential ACBM products based upon fundamental cellular attributes, the use of proposed near-term/existing technology, and process engineering conventions. Our findings suggest that the current production pathways are far from producing cost-competitive ACBM products, as well as highlight the technical metrics that must be achieved for an ACBM product to become economically viable.

bioengineering↗

bioRxiv2020

Computationally Guided Design of Novel Selective Serotonin Reuptake Inhibitors

Current selective serotonin reuptake inhibitor (SSRI) drugs such as paroxetine, fluoxetine and escitalopram are prescribed for mental health conditions such as anxiety and depression. However, there are still several unpleasant side effects which fuels the need for new designs. In this work, computational studies were conducted to design two novel drug candidates with improved docking scores than paroxetine, an existing SSRI drug. Homology analysis was carried out to determine the suitability of an organism for preclinical trials and revealed laboratory mouse (Mus musculus) to be a good candidate.

pharmacology and toxicology↗

bioRxiv2020

Computationally Guided Design of BCR-ABL Tyrosine Kinase Inhibitors

BCR-ABL tyrosine kinase inhibitors (TKI) are used to treat the chronic myeloid leukemia (CML). Many TKI have been developed as the primary treatment to the CML. Imatinib, a first generation TKI, directly targets BCR-ABL with effective results. As the disease becomes more advanced, patients start to develop resistance to imatinib. Due to this effect it is necessary to generate novel treatments for advanced stage CML. Computational tools can predict new drug candidates to target BCR-ABL. We have designed two new drug candidates with different levels of modification, based on the predicted structure activity relationships with BCR-ABL. These new drug candidates are predicted to have better binding affinities with BCR-ABL than imatinib, which can be more potent treatments of the disease.

pharmacology and toxicology↗

bioRxiv2019

Design to Data for mutants of β-glucosidase B from Paenibacillus polymyxa: Q22T, W123R, F155G, Y169M, W438D, V401A

A key goal of protein engineering is to accurately model the stability and catalytic activity of enzymes. However, the limitations of functional predictive abilities pose a major challenge for modeling algorithm design, and can be attributed to the lack of large data sets quantifying the functional properties of enzymes. Here, the thermal stability (TM) and Michaelis-Menten constants (kcat, KM, and kcat/KM) of six new variants of the {beta}-glucosidase B (BglB) protein are quantitatively characterized. Molecular stability of the enzyme variants were hypothesized using the Foldit software and BglB was synthesized in E. coli cells. Testing was done through a colorimetric kinetic assay and thermal stability fluorescence-based protein unfolding assay. Results from the assays suggest that all mutations, with the exception of variant Y169M, all experienced reduced catalytic efficiency compared to the wildtype. Assay results indicate that variant W123R is more thermally stable compared to the wildtype, while the differences in thermal stability between the other variants, and the wildtype are negligible. The collected thermal stability and catalytic efficiency data has been added to a data set with the aim of improving Rosetta algorithms for modeling and predicting the functional interactions between biomolecules through a machine learning algorithm and facilitate the precise engineering of protein catalysts.

biochemistry↗

bioRxiv2019

Evaluating molecular modeling tools for thermal stability using an independently generated dataset

Engineering proteins to enhance thermal stability is a widely utilized approach for creating industrially relevant biocatalysts. Computational tools that guide these engineering efforts remain an active area of research with new data sets and develop algorithms. To aid in these efforts, we are reporting an expansion of our previously published data set of mutants for a {beta}-glucosidase to include both measures of TM and {Delta}{Delta}G, to complement the previously reported measures of T50 and kinetic constants (kcat and KM). For a set of 51 mutants, we found that T50 and TM are moderately correlated with a Pearson correlation coefficient (PCC) of 0.58, indicated the two methods capture different physical features. The performance of predicted stability using five computational tools are also evaluated on the 51 mutants dataset, none of which are found to be strong predictors of the observed changes in T50, TM, or {Delta}{Delta}G. Furthermore, the ability of the five algorithms to predict the production of isolatable soluble protein is examined, which revealed that Rosetta {Delta}{Delta}G, ELASPIC, and DeepDDG are capable of predicting if a mutant could be produced and isolated as a soluble protein. These results further highlight the need for new algorithms for predicting modest, yet important, changes in thermal stability as well as a new utility for current algorithms for prescreening designs for the production of soluble mutants.

biophysics↗

bioRxiv2019

Design to Data for mutants of β-glucosidase B from Paenibacillus polymyxa: M319C, T431I, and K337D

The use of computational tools has become an increasingly popular tool for engineering protein function. While there are numerous examples of computational tools enabling the design of novel protein functions, there remains room for improvement in both prediction accuracy and success. To improve algorithms for functional and stability predictions, we have initiated the development of a data set designed to be used for training new computational algorithms for enzyme design. To date our dataset is composed of over 129 mutants with associated expression levels, kinetic data, and thermal stability for the enzyme {beta}-glucosidase B (BglB) from Paenibacillus polymyxa. In this study, we introduced three new variants (M319C, T431I, and K337D) to our existing dataset with the goal of cultivating a larger dataset to train new design algorithms and more broadly explore structure-function relationships in BglB.

biochemistry↗

bioRxiv2019

Computationally Guided Design of Dipeptidyl Peptidase-4 Inhibitors

The Type 2 Diabetes Mellitus (T2DM) epidemic undoubtedly creates a need for the development of new pharmaceuticals. With the goal of generating new therapeutics for this disease, computational studies were conducted to design novel dipeptidyl peptidase-4 (DPP-4) inhibitors. Two candidates, generated by chemical intuition-driven design and bioisosteric replacement, were found to have better docking scores than anagliptin, a currently available diabetes medication.

pharmacology and toxicology↗