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Comparing the Efficiency of 3-ketosteroid delta(1)-dehydrogenase and Cholesterol Oxidase at Degrading 7-Ketocholesterol
Medical bioremediation is a process that uses microbial enzymes to supplement the body’s natural catabolic processes in order to dispose of damaging substances. This study compares the efficiencies of two microbial enzymes: Cholesterol Oxidase (CO) from Chromobacterium sp. DS-1 and 3-ketosteroid delta(1)-dehydrogenase (KstD) from Rhodococcus erythropolis, at degrading one such harmful substance, 7-ketocholesterol (7KC). Based on existing literature, we hypothesized that hydrophobic interactions between the active sites of these enzymes and 7KC likely dictate enzyme efficiency. Two metrics of enzyme efficiency were analyzed: binding affinity for the ligand and Gibbs free energy of the enzymatic reaction. KstD was found to have a greater binding affinity with a pKd value of 7.88 (std. ± 0.63) compared to 6.55 (std. ± 0.35) for CO and a lower ΔG of -10.64 kcal/mol (std. ± 0.85) compared to -8.84 kcal/mol (std. ± 0.47) for CO. These values suggest that KstD is more efficient at degrading 7KC than CO. Results from this study may be used to design a more effective treatment for age-related diseases associated with the build-up of 7KC.
Experimental and Real-World Analysis of the Relationship Between Wind Speed and Atmospheric Pollution Concentration
In this project, we investigated the relationship between wind speed and atmospheric pollution concentration by looking at experimental and real-world data. For our experimental data, we had flume tank videos provided by FSU’s Geophysical Fluid Dynamics Institute. A plume of dyed liquid was released into the flume to represent air pollution. We analyzed these videos by taking 20 frames, separating them into light, medium, and dark red pixels, plotting them with Python’s PyPlot package, and conducting several t-tests. All of this pointed to an inverse relationship between wind speed and air pollution concentration (i.e. lower wind speeds result in higher air pollution concentrations and vice versa). For our real-world analysis, we collected wind speed and air quality index (AQI) data for three pairs of cities across the United States: Tallahassee and Orlando, Boston and Philadelphia, and Los Angeles and San Francisco. All pairs were similar in climate, urbanization, and region, but had different wind speeds. For each pair, we conducted t-tests that showed that, for two out of the three pairs, there was a significant difference between their wind speeds and AQIs (suggesting they are related). Finally, we also plotted AQI vs. wind speed data for each city, which again showed an inverse relationship between wind speed and air pollution. More research needs to be conducted with larger samples of real-world data, but this study does serve as an initial step towards discovering the factors that affect the dispersion of air pollution.
Experimental and Real-World Analysis of the Relationship Between Wind Speed and Atmospheric Pollution Concentration
Although previously studied through field testing and the use of wind tunnels, the relationship between wind speed and atmospheric pollution concentration has not been simulated within a water flume. The aim of the research was to implement such a simulation and compare the data it provided to real world data compiled from previous records of average wind speed and AQI measurements within 3 pairs of climatically similar cities. A statistical analysis of video-data containing the water flume simulations, with obstructions and without, was conducted. The simulation consisted of a red dye plume being released into a laminar water flow at two varying rates: 0.04 m/s and 0.15 m/s. Real world data from Los Angeles, San Francisco, Philadelphia, Boston, Tallahassee, and Orlando, was compiled from open-source data-bases and was also statistically analyzed. The findings partially confirmed the correlation between wind speed and concentration of atmospheric pollution by means of the water flume simulation. The flume’s idealistic nature though, did not transfer completely into the real world as the results of the statistical analysis of the three pairs of cities did not all display the same pattern. Tallahassee and Orlando did not show the relationship between win speed and atmospheric pollution. Even with drastically different average wind speeds, their AQI was very similar. The other two pairs did show this relationship however, and therefore the water flume cannot be completely discredited.
The Effects of Mutations in SARS-CoV-2 Variants On Viral Infectivity
The SARS-CoV-2 virus is a positive strand RNA virus whose genome encodes four structural proteins including the spike protein. The Spike protein is a type I fusion protein responsible for receptor binding and membrane fusion. As a result of the emergence of new variants of SARS-CoV-2, we decided to investigate how the interactions between the different spike proteins of new variants and the human ACE2 receptor affect the infectivity of each virus, specifically researching the four variants of concern in the United States classified by the CDC: Alpha, Beta, Gamma, and Delta, variants. Stronger attraction between the ACE2 receptor and the variant’s spike protein increases infectivity, so we predicted that factors that influence the strength of attraction between the two structures would produce differently infectious variants and that these factors would be accurate measures of infectivity. To answer this, we downloaded the sequences of the spike proteins of concern from open access data sources including NCBI/RCSB. We then collected and analysed data on the differences between the spike proteins and their interactions with the ACE2 receptor using sequence alignment tool, Clustal Omega, and modeling tools on Chimera X. We determined the infectivity of a variant by analyzing: bond length, surface charge, isoelectricity, aromaticity, and amino acid substitutions. Early findings indicated that the Delta variant was the most infectious and that the studied molecular characteristics were correlated with the variant’s level of infectivity, where Delta resulted in most infective in ¾ studied areas. Extensions of this research project include finding which of the molecular properties is most significant in determining the infectivity of the variants, which could advance treatment and prevention methods for this virus which target this property .
Molecular Dynamics Simulation Discovery Of Cryptic Sites In SARS-CoV-2 Main Protease
The research project uses Molecular Dynamics (MD) Simulations to determine sites of conformational change within the SARS-CoV-2 main protease. Within these conformed sites, the project determines the possibility of cryptic site formation.
Experimental and Real-World Analysis of the Relationship Between Wind Speed and Atmospheric Pollution Concentration
Through many recent fluctuations in expanding industry and other natural causes, air quality has seen a decline which may detrimentally affect the health of populations. Recent studies have also shown both the speeding up and slowing down of wind in certain regions of the world, due to both natural climate cycles and global warming. These correlations between wind speed and AQI, or air quality index, were examined using experimental water flume video data gathered by FSU’s Geophysical Institute and real-world wind speed and AQI data. These experiments were analyzed using Python computer vision techniques and heuristically calculated dark pixel concentrations were compared between trials with flume speeds of 0.04 m/s and 0.15 m/s. In terms of real-world data, 3 pairs of cities were selected and their monthly wind speed and AQI data were gathered for the year 2019 using online repositories. Using two-sample t-tests, we found a statistically significant difference between the dark pixel concentrations between the two flume speeds combined with a decrease in the mean dark pixel concentration with an increase in flume speed, which represents more dispersion of particles with higher flume speeds. Using the same method, a statistically significant difference was also found between the wind speeds and the AQIs for 2 out of the 3 pairs of cities that were analyzed combined with an inverse relationship between mean wind speed and mean AQI being found for all pairs of cities. These results do indeed show that there is a negative correlation between wind speed and AQI, and the data collected serves as a good warning sign as to the future that might be without science delving deeper into the topic.
Epidemiology Research
The purpose of the study was focused on what the spread of COVID variants let us know about the spread of the Delta variant and how we can apply this information onto future variants. Throughout the research, we plotted data from a COVID lab at FSU and modeled the cumulative findings of the alpha and delta variants. We also used Contactome to view how unique contacts play a key role in the kinetics of COVID-19. In the plot, it can be discerned that the alpha variant became the dominant strain specifically in April 2021 to June 2021. However, as the delta variant begins to rise exponentially during June 2021, and with the Alpha variant slowing in its rise, its inferred that the Delta variant will quickly become the new dominant strain. The sharp increase in cumulative occurrences observed in the delta variant can be credited to its greater R0. In conclusion, the emergence of the delta variant has been the most dangerous variant yet due to its increase in transmissibility. Furthermore, without any measures to slow the spread of delta, it will quickly become very difficult to manage. It is important to note that with universities reopening, we may see a sharp increase in cases because of the large number of unique contacts every student has. We expect delta to become the dominant variant in the next few months because it not only is following trends set by alpha, but is also following it at a much faster rate.
Analyzing the Unique Properties of PETase in Comparison to Other PET-Degrading Enzymes
Polyethylene Terephthalate (PET) is a common thermoplastic polymer used in the manufacturing of materials such as water bottles and polyester fibers. Since PET is so durable, it does not fully break down, causing major concerns for ocean ecosystems and landfills. In ocean ecosystems, PET is broken down into microplastics which are consumed by aquatic organisms, causing the death of these organisms due to endocrine disruption. In landfills, PET plastics accumulate for long periods of time without breaking down. In sludge samples in 2016 in Japan, however, an organism with PET-degrading abilities was discovered: Ideonella sakaiensis. This bacteria degrades PET through a novel enzyme known as PETase, the most efficient PET-degrading enzyme with the unique ability to convert PET into energy. We studied the amino acid sequence of PETase, in comparison to that of cutinase from Saccharomonospora viridis and polyester hydrolase from Pseudomonas aestusnigri, to find differences that would contribute to PETase’s unique properties. Using Clustal Omega’s sequencing software, we analyzed the sequences of these three enzymes at key sites: Subsite I, Subsite II, and the Catalytic Triad. Through comparing the amino acids and their properties at key locations, we found that PETase has unique amino acids at 238, 246, and 280, part of subsite II. We concluded that one of the main reasons for PETase’s unique catalytic capabilities is its unique amino acids at key points. These findings have clear applications for future research involving PETase, as it could be bioengineered to further maximize its efficiency at degrading PET plastics.
