Roccustyrna ligand Protein targets
The docking engine employed in this computer-aided drug design effort is
the DockThor program, which generates preparations of the acceptable
topology files for the Roccustyrna ligand for the protein (.in) and
ligand cofactors (.top) and a specific input .pdb file containing our
prototype‘s ligand atoms and Roccustyrna partial charges from the
MMFF94S49 force field. (19,21,37,42) The .pdbqt file of the Roccustyrna
new ligand was generated by the MMFFLigand software, which is based on
the utilities of the OpenBabel chemical toolbox for extracting atom
types and partial charges with MMFF94S applied force field, and for the
identification of the rotatable bonds, and calculating the properties
necessary for computing the intramolecular interactions. (16,17,41) In
the MMFFLigand, all hydrogen atoms were removed and the PdbThorBox
software was utilized to set the protein atomic types and the partial
ionization charges from the MMFF94S force field analysis considering the
nonpolar atomic groups as implicit to rebuild missing residue side-chain
atoms. (3-9,31) Thus, in this KNIME based GEMDOCK-DockThor-Virtual
Screening platform, both the Roccustyrna small molecule, SARS-COV-2
protein targets of and cofactors were treated again with the MMFF94S
force field by keeping the same set of equations and parameters that
define the new molecule’s molecular force field parameterizations.
(2,31-40) The preparations of the steps to be used for diagonal force
field for modeling such as modifying the protonation state of all the
keeping amino acid residues, to parameterize a simple group of knots and
atoms by adding metal complexes, hydrogen atoms, and freezing rotatable
bonds was done interactively for a variety of all-Roccustyrna atoms in
the publically available web servers and performed automatically by the
programs cited without the need for intervention. (3,15,16,17) The
search docking space to rapidly simulate the combination of
GisitorviffirnaTM, Roccustyrna_gs1_TM, and Roccustyrna_fr1_TM
cluster of molecular systems and the configuration of the new molecule‗s
grid box were interactively set in the KNIME designed
BiogenetoligandorolTM pipeline which was represented as a grid box and
the docking potentials are stored at the best grid points for the
description of the combination of GisitorviffirnaTM,
Roccustyrna_gs1_TM, and Roccustyrna_fr1_TM cluster of molecular
energetics and structures through the parameters of the center of
coordinates, size of the grid and discretization (i.e, the spacing
between the grid points). (5,13,29,31) The initial population for the
rotational, and translational, was randomly generated within the
conformational degrees and grid box using random values of freedom of
the Roccustyrna ligand. (15,16,17,38) For each SARS-CoV-2 therapeutic
target, DockThor-VS default parameters were uploaded as a recommended
set of parameters for the grid box (i.e, center and grid sizes) which
can be used or modified according to the objectives of this docking
experiment which was specially designed to deal with highly flexible
ligands such as the Roccustyrna small molecule. (15,29,31) In this
strategy, a replacement ligand-based method was introduced by using a
low mass phase phenotypic steady-state and crowding-based protocol and a
multiple genetic parental algorithms as a Dynamic Solution Modified
Restricted Tournament Selection (DSMRTS) approach, which provided us a
better machine learning exploration of the energetic hypersurface for
the identification of multiple quantum phase minima solutions in a
single Hadamard run, preserving the population diversity of the
generated structures. The default parameters of this parallel docking
algorithm (named BiogenetoligandorolTM) are set in the KNIME-web server
as follows: (i) 24 inverse docking runs, (ii) 1.000.000 evaluations per
parallel docking run, (iii) population of the Roccustyrna individuals,
(iv) maximum of 20 cluster small molecule top leaders on each parallel
inverse docking run. For this sequential screening experiment, we also
provided an alternative dataset of geometric parameters to improve the
Euclidean space between the Roccustyrna and protein interacting chains
without significantly losing binding site accuracy (named EuTHTS
Euclidean Topology Virtual Screening): (i) 120 docking runs, (ii)
200.000 evaluations per docking run, (iii) population of Roccustyrna
individuals, (iv) maximum of 20 cluster leaders on each docking run. The
docking experiments were performed on DockThor CPU nodes of the Dumont
supercomputer, each one containing two processors Intel Xeon E5-2695v2
Ivy Bridge (12c @2,4 GHz) and 64 Gb of RAM memory. We validated the
docking experiments through the redocking of the non-covalent
Roccustyrna ligand present in the complexes 6W63 (Mpro) using the
standard configuration, successfully predicting the co-crystallized
conformation of each complex. In the crystallographic structure, this
moiety is exposed to the solvent and has insufficient electronic density
data. The free energy scoring function applied to score the best-docked
poses of the same Roccustyrna ligand was based on the sum of the
following terms from the MMFF94S force field and is named ―Total Energy
(Etotal) ‖: (i) intermolecular interaction energy calculated as the sum
of the van der Waals between the hydroxyl and cyano groups (buffering
constant = 0.35) and electrostatic potentials between the protein-ligand
atom pairs, (ii) intramolecular interaction energy of the van der Waals
and electrostatic potentials calculated as the sum between the 1-4 atom
pairs, and (iii) torsional term of the ligand. All best docking poses
generated during all the docking steps in this project were then low
mass weight categorized and clustered by our in-house tool
BiogenetoligandorolTM. The top docking energy-poses of each
Roccustyrna-Protein complex were selected as top hit representatives of
cluster energetic representatives to be made available in the
homogenicity results analysis and Chern-Simons pharmacophoric
fragmentation section (27,28). The binding affinity prediction and total
energy ranking with the linear protein model and untailored for specific
ligand interacted protein classes,of the Roccustyrna small molecule was
generated by utilizing the DockTScoreGenLscoring function as a set of
empirical scoring functions. Biogenetoligandorol cluster of DockTScore,
PLIP, DockThor and GEMDOCK-AUTODOCK-VINA current docking scoring
functions for protein and small molecule preparation take into account
important terms, multiple protein-ligand binding, such as intermolecular
interactions, binding affinity predictions, the combination of
GisitorviffirnaTM, Roccustyrna_gs1_TM, and Roccustyrna_fr1_TM
cluster of ligand entropy and desolvation of the specific target classes
such as SARS-COV-2 6W63 (Mpro) proteases using protein-protein
interactions (PPIs) trained with PdbThorBox and MMFFLigand sophisticated
machine-learning derived topology algorithms totalizing 66 892 contacts
between a carbon and a halogen, carbon or sulfur atom. The docking
visualization of the SARS-COV-2 protein, cofactors and the Roccustyrna
compound, the grid space location superposed with the protein targets of
the (PDB codes of the PDB:6xs6,1xak,2g9t,3fqq, 2ghv,6yb7) (3,4,35,39)
and the docking outputs were generated with NGL, a WebGL-based library
for intra-molecular visualization.