About the LMI equation (6) in your correlation_plus paper

[TianmingQu]

About the LMI equation (6) in your correlation_plus paper

[tekpinar]

Indeed, you are right. Linear mutual information is different from general mutual information. As indicated in the correlationplus paper as well, equation 6 in the paper comes from equation 10 of https://onlinelibrary.wiley.com/doi/10.1002/prot.20784.

[TianmingQu]

Also, have you ever considered applying PCA into the correlation analysis?

[tekpinar]

Unfortunately no, because I don't have time for that. However, I'll happily accept if someone adds it. At the end of the day, this is a completely open source scientific project.

[TianmingQu]

Thank you for your so quick reply! I got one last question, could you give me some help on how to interpret this fig?

[tekpinar]

I can only give some general comments since I don't know the features of your system.

This seems like a typical distribution of normalized LMIs in an MD trajectory:

As expected, you have many short-distance (<20 correlations) nLMI values. They are due to short physical interaction distance and/or similar secondary structural elements.
In addition, you have many long-distance (>20 upto 60) high correlations, which may indicate allosteric interactions. However, you have to be sure about the oligomeric state of your protein to conclude for sure if they are due to dynamics of the protein or short distances within the oligomeric state.
You can further investigate these high-nLMI and long-distance values with 'correlationplus visualize' script and project them onto your protein. In this way, you can have a better sense on their potential/probable implications.