At the early stages, ROS generation was inhibited by SSHE

d the following data sets: 1. Protein RMSD. It has the pairwise RMSD distance between the first and every MD’s structure, considering all structure residues as applied by . 2. Cavity RMSD. This data set contains the RMSD distance between the first and every MD’s structure, considering the residues that enclose the substrate-binding cavity of the InhA 5 / 25 An Approach for Clustering MD Trajectory Using MedChemExpress LY-411575 Cavity-Based Features enzyme in complex with NADH and the C16 substrate analog enzyme. Application examples of this measure of similarity are in . 3. Cavity Attributes. It was built by using a set of features extracted from the substrate-binding cavity of the MD trajectory. This is the proposed data set and a more detailed explanation is given in this section. The first two data sets were generated from typical measures of similarity for clustering MD simulations. Our purpose in using these data sets is to compare the quality of partitions between them and the Cavity Attributes data set. For generating the Cavity Attributes data set, we extract structural properties from the substrate-binding cavity of each conformation generated by an MD simulation. CASTp is an online software tool that allows us to obtain information from all cavities in a structural manner through a free access to the source code of the results page. It relies on the alpha-shape method to enclose the substrate cavity on proteins. This method uses the solvent-accessible surface area model and the molecular surface model with a probe sphere of radius 1.4. To identify the substrate cavity on an ensemble of conformations generated by MD simulation, we developed a heuristic function based on the number of heavy atoms present in the substrate-binding cavity of the 1BVR structure. The substrate analog, which is inside the 1BVR crystallographic structure, allowed us to identify the substrate cavity and the largest number of atoms, considering the residues that encloses it. Thus, we calculated the volume and the number of heavy atoms of the substrate cavity for each snapshot based on the substrate analog, according to the cavities present in the 1BVR and selected by CASTp.The volume from the substrate-binding cavity was chosen as one of attributes from Cavity Attributes data set since it varies considerably along the MD simulation. This is 6 / 25 An Approach for Clustering MD Trajectory Using Cavity-Based Features Fig 2. Substrate-binding cavity of the InhA enzyme identified by the CASTp software tool. On the left, the PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/19747578 substrate-binding cavity of the 1BVR structure represented by molecular surface and colored by atom types. PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/19748727 The projection displays all residues from the binding pocket in stick representation. doi:10.1371/journal.pone.0133172.g002 evidenced by analyzing the substrate-binding cavity volumes generated by CASTp, which ranged from 45.4 3 to 2,852.9 3 for the entire 20 ns MD simulation trajectory. We also note that the volumes of the substrate-binding cavity from the MD trajectory comprise proportionally those found in the boundaries of the InhA crystal structure. For instance, cavity volumes from 2B37 and 4OXN structures are 445.1 3 and 2,032.8 3, respectively, pointing out to significantly different volume values in the MD trajectory. doi:10.1371/journal.pone.0133172.t001 7 / 25 An Approach for Clustering MD Trajectory Using Cavity-Based Features Although the volume allows us to identify the biggest accessible surface of the substratebinding cavity, we also conside