Proteomic Investigation Reveals Dominant Alterations of Neutrophil Degranulation and mRNA Translation Pathways in COVID-19 Patients. Bankar et. al.

Published: 28 January 2021| Version 1 | DOI: 10.17632/rnfn3vhg63.1
Sanjeeva Srivastava


This dataset comprises of supplementary files for the comprehensive proteomics-based investigation of nasopharyngeal swab samples from COVID-19 patients. The table S1 shows the clinical characteristics of COVID-19 patients, Related to Figure 1. The table S8 shows the refined list of transitions prepared using skyline software for MRM assay of clinical marker proteins, Related to Figure 3. The table S10 is related to the in-silico docking, showing the list of FDA approved drug and details of drug binding to the target proteins detected using mass-spectrometry analysis, Related to Figure 5.


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Table 8, the list of transitions was prepared for unique peptides of these selected proteins using Skyline (Ver In our study, we have docked the identified proteins from neutrophil degranulation, translation and interleukin pathway against 29 FDA approved, 9 clinical and 20 pre-clinical trial drugs (Table S10). The inhibitors well established in the literature (Gordon et al., 2020) were used as a positive control against these target proteins. The control inhibitor gives us a possible cut off for the docking score. The selection of drugs for the protein was done by considering a couple of criteria. First, the binding energy of the drug was expected to be equal or higher than that of the control inhibitor. Second, the biding pocket of the drug was expected to be similar to the control drug. The docking experiment was performed using AutoDock Vina 1.1.2 (Trott, O., Olson, 2019). The 3D structure of the drugs was obtained from the PubChem database (Kim et al., 2019) and the ZINC15 database. The drugs were converted from SDF format to PDB format using PyMOL software (Rigsby and Parker, 2016). The coordinates for the proteins were retrieved from the Protein Data Bank (PDB) (Berman et al., 2002). Using PyRx software, the PDB format of the protein and the drugs were converted to a PDBQT format, which is a readable file format for AutoDock Vina. The docking was done with the exhaustiveness value set to 50 using the blind docking method. In this method, the grid box is large enough to select the entire protein. This increases the chances of obtaining all possible ligand-receptor complexes. The docked complexes were visualized using PyMOL software. The binding interactions between the drug and the protein were calculated using the protein-ligand interaction profiler (PLIP) server (Salentin et al., 2015).


Indian Institute of Technology Bombay


Proteomics, Molecular Docking, Clinical Data Collection