Inevitably, the hundreds of CVs monitored during the MD will have a lot of redundancies, as they were picked only by ranking coefficient of variance. This means if the distance between residue m and n is monitored, distances between residue m ± 1 and n ± 1 are also likely included. Handling these plentiful and redundant CVs can be challenging for subsequent analysis works. To address this, we integrated a method called Automatic Mutual Information Noise Omission (AMINO) to remove these redundant CVs. AMINO computes the mutual information across CVs using the timeseries data from MD to identify highly correlated one, and only keep the most representative CVs for further analysis. This usually reduces the number of CVs from a few hundred to fewer than 20, making the following works more manageable and interpretable.