Milp-hyperbox classification for structure-based drug design in the discovery of small molecule inhibitors of SIRTUIN6

Abstract

Virtual screening of chemical libraries following experimental assays of drug candidates is a common procedure in structure-based drug discovery. However, virtual screening of chemical libraries with millions of compounds requires a lot of time for computing and data analysis. A priori classification of compounds in the libraries as low-and high-binding free energy sets decreases the number of compounds for virtual screening experiments. This classification also reduces the required computational time and resources. Data analysis is demanding since a compound can be described by more than one thousand attributes that make any data analysis very challenging. In this paper, we use the hyperbox classification method in combination with partial least squares regression to determine the most relevant molecular descriptors of the drug molecules for an efficient classification. The effectiveness of the approach is illustrated on a target protein, SIRT6. The results indicate that the proposed approach outperforms other approaches reported in the literature with 83.55% accuracy using six common molecular descriptors (SC-5, SP-6, SHBd, minHaaCH, maxwHBa, FMF). Additionally, the top 10 hit compounds are determined and reported as the candidate inhibitors of SIRT6 for which no inhibitors have so far been reported in the literature.

In silico identification of widely used and well-tolerated drugs as potential SARS-CoV-2 3C-like protease and viral RNA-dependent RNA polymerase inhibitors for direct use in clinical trials

“…SARS-CoV RdRp (PDB ID: 6NUR) and 3CL pro (PDB ID: 6Y84) (Owen et al., 2020 ) structures were retrieved from The Protein Data Bank ( www.rcsb.org ) and used in docking studies. Proteins were prepared for docking simulations via UCSF Chimera, Dock Prep module as described in (Tardu et al., 2016 ). Excess water molecules and, in case of presence of alternate locations for residues, the ones with the lower occupancies were removed from structures.…”

In Silico Drug Repositioning Against Human NRP1 to Block SARS-CoV-2 Host Entry

“…High-resolution human NRP1 structure (PDB ID: 6FMC) was retrieved from The Protein Data Bank (www.rcsb.org) (Powell et al, 2018). Protein was prepared for docking simulations via Dock Prep module of UCSF Chimera as described previously (Doruk et al, 2020;Gul et al, 2020;Tardu et al, 2016). Water molecules were removed and if alternate locations are available for residues, the ones with the higher occupancies were selected.…”

“…AutoDock Vina program was used to calculate binding energy and predict the binding mode of drugs to the target pocket as described before (Gul et al, 2020;Tardu et al, 2016). During the docking simulations, receptor (NRP1) was treated as a rigid body and drugs were allowed to sample different conformations in the CendR binding pocket.…”

In silico identification of widely used and well-tolerated drugs as potential SARS-CoV-2 3C-like protease and viral RNA-dependent RNA polymerase inhibitors for direct use in clinical trials

“…SARS-CoV RdRp (PDB ID: 6NUR) and 3CL pro (PDB ID: 6Y84) (Owen, 2020) structures were retrieved from The Protein Data Bank (www.rcsb.org) and used in docking studies. Proteins were prepared for docking simulations via UCSF Chimera, Dock Prep module as described in (Tardu et al, 2016). Excess water molecules and, in case of presence of alternate locations for residues, the ones with the lower occupancies were removed from structures.…”