Computational chemistry of oxidation inhibitors

In summary, the conversation discusses a project analyzing inhibitors of a heme enzyme that oxidizes an indole molecule. The data shows a negative correlation between electrstatic charge and better inhibitors in the first group, while the second group has a positive correlation. This suggests two different mechanisms at play. Suggestions for further analysis include kinetic analysis to determine inhibitor affinity and computational methods to study inhibitor-enzyme interactions.
  • #1
jrsmith
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I am currently working on a project analysing inhibitors of a heme enzyme which oxidises an indole molecule. We have data on halogenated and non halogenated indoles (methyl, hydroxy, amino and nitro). I have split them into these two classes because the first group have a negative correlation with electrstatic charge (DFT) in the benezene ring (better inhibitors have less electrons), whilst the latter class have a positive correlation (better inhibitor = more electrons in benzene ring). This seems to suggest that the molecules are acting via two different mechanisms. Can anyone suggest a computational analysis/experiment that could be used rather than just looking at partial charges? Some form of population analysis? I'm afraid I am somewhat out of my depth in this field due a complete lack of experience.
 
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  • #2
One experiment that could be useful is a kinetic analysis. Kinetic analysis involves measuring the rate of reaction of the enzyme with different inhibitors, and comparing the rate of reaction with different inhibitors to determine which inhibitors have the highest affinity for the enzyme. This type of experiment can provide insights into the mechanism of inhibition and how it correlates with the structure of the inhibitor. Additionally, one could use computational methods such as docking or molecular dynamics simulations to look at the interactions of the inhibitor with the enzyme, and gain insights into the mode of action of the inhibitor.
 

What is computational chemistry and how is it used in studying oxidation inhibitors?

Computational chemistry is a branch of chemistry that uses computer simulations to study chemical structures and reactions. In the case of oxidation inhibitors, computational chemistry can be used to model the structure and behavior of potential inhibitors and predict their effectiveness in preventing oxidation reactions.

What are the main techniques used in computational chemistry for studying oxidation inhibitors?

The main techniques used in computational chemistry for studying oxidation inhibitors include molecular dynamics simulations, density functional theory calculations, and quantum mechanics calculations. These techniques allow for the prediction of molecular structures, energies, and reaction mechanisms of oxidation inhibitors.

How accurate are the predictions made by computational chemistry in terms of oxidation inhibitor effectiveness?

The accuracy of predictions made by computational chemistry depends on the quality of the input data and the complexity of the system being studied. Generally, the predictions have a high degree of accuracy and can provide valuable insights into the behavior of oxidation inhibitors.

What are the potential benefits of using computational chemistry in the development of oxidation inhibitors?

Using computational chemistry in the development of oxidation inhibitors can save time and resources by allowing for the screening of a large number of potential inhibitors in a virtual environment. It can also provide a deeper understanding of the underlying mechanisms and interactions involved in oxidation inhibition.

What are some limitations of using computational chemistry in the study of oxidation inhibitors?

One limitation of using computational chemistry is the need for accurate and complete input data, which can be difficult to obtain for complex systems. Additionally, the simulations may not always accurately reflect real-world conditions, and experimental validation is still necessary to confirm the predictions made by computational chemistry.

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