Identification of ZINC inhibitors for HIV-1 Protease through Structure-Based Pharmacophore, Virtual Screening, ADMET and Molecular Dynamics Simulation
Andrew Rabontsi Motsilanyane, Zimbili Mkhize, Sphelele Sosibo
937
ABSTRACT
Treating
HIV is made difficult by the evolution of antimicrobial resistant mutants of
that occurs in the life cycle of this virus. This has led to the evolution of
numerous inhibition mechanism that intends to challenges the replication of
this virus. One such approach is the application of molecular modelling in drug
discovery. In this study the application of computer aided drug design were
employed in finding an inhibitor that can contribute in finding a suitable drug
candidate. Firstly, the ZINC database was screened using the Lipinski’s rule of
five, and compounds having similar biological features were classified and
grouped together into a compound library. Secondly, the co-crystallized
structure of 1rv7 wild type of HIV-1 protease was used to construct a pharmacophore
model that was used to screen further, the ZINC database Ligand stored in the
compound library. Thestructure-basedpharmacophore was created by mimicking the three
main active features of the co-crystallized structure from the Protein Data
Bank, namely, 82 and 84, I50 and I150 residues.Thirdly, a total of twenty-six
of the compounds that had less than 0,5 RMSD were identified and selected. Fourthly,
based on the best docking ligands, ten of these compounds having high binding
affinity were selected. These compounds are ZINC_001456687980 (-8.0), ZINC_001445792073
(-7.8) and ZINC_001461099137 (-7.4), successively. However, ZINC_001461099137
was eliminated based on unfavorable donor-donor interaction. The subsequent compound,
ZINC_000015276352 was then placed on the top three leading compounds with a
binding affinity of (-6.9). Fifthly, the ADMET property analysis was used on
the top ten compounds, and the compounds that satisfied the analysis were, ZINC_001456687980,
ZINC_000015276352, ZINC_001359888321 and ZINC_001460445290. The main properties
that included the BBB permeation, the gastrointestinal absorption, pan assay
interference structure (PAINS), were analyzed. ZINC_001456687980 had two
violation (mw >350, XLOGP3>3.5),whiles ZINC_000015276352 had three
violations (mw > 350, XLOGP3 > 3.5, rotatable > 7). Lastly, only two
top compound underwent molecular dynamics simulations. Molecular dynamics
simulation revealed the formation of numerous bond interactions between
ZINC_001456687980 and ZINC_000015276352 together with the target protein.
ABSTRACT
Treating
HIV is made difficult by the evolution of antimicrobial resistant mutants of
that occurs in the life cycle of this virus. This has led to the evolution of
numerous inhibition mechanism that intends to challenges the replication of
this virus. One such approach is the application of molecular modelling in drug
discovery. In this study the application of computer aided drug design were
employed in finding an inhibitor that can contribute in finding a suitable drug
candidate. Firstly, the ZINC database was screened using the Lipinski’s rule of
five, and compounds having similar biological features were classified and
grouped together into a compound library. Secondly, the co-crystallized
structure of 1rv7 wild type of HIV-1 protease was used to construct a pharmacophore
model that was used to screen further, the ZINC database Ligand stored in the
compound library. Thestructure-basedpharmacophore was created by mimicking the three
main active features of the co-crystallized structure from the Protein Data
Bank, namely, 82 and 84, I50 and I150 residues.Thirdly, a total of twenty-six
of the compounds that had less than 0,5 RMSD were identified and selected. Fourthly,
based on the best docking ligands, ten of these compounds having high binding
affinity were selected. These compounds are ZINC_001456687980 (-8.0), ZINC_001445792073
(-7.8) and ZINC_001461099137 (-7.4), successively. However, ZINC_001461099137
was eliminated based on unfavorable donor-donor interaction. The subsequent compound,
ZINC_000015276352 was then placed on the top three leading compounds with a
binding affinity of (-6.9). Fifthly, the ADMET property analysis was used on
the top ten compounds, and the compounds that satisfied the analysis were, ZINC_001456687980,
ZINC_000015276352, ZINC_001359888321 and ZINC_001460445290. The main properties
that included the BBB permeation, the gastrointestinal absorption, pan assay
interference structure (PAINS), were analyzed. ZINC_001456687980 had two
violation (mw >350, XLOGP3>3.5),whiles ZINC_000015276352 had three
violations (mw > 350, XLOGP3 > 3.5, rotatable > 7). Lastly, only two
top compound underwent molecular dynamics simulations. Molecular dynamics
simulation revealed the formation of numerous bond interactions between
ZINC_001456687980 and ZINC_000015276352 together with the target protein.