Abstract:While small molecule internal strain is crucial to molecular docking, using it in evaluating ligand scores has remained elusive. Here, we investigate a technique that calculates strain using relative torsional populations in the Cambridge Structural Database, enabling fast precalculation of these energies. In retrospective studies of large docking screens of the dopamine D4 receptor and of AmpC β-lactamase, where close to 600 docking hits were tested experimentally, including such strain energies improved hit … Show more
“…The torsional energy is calculated by multiplying a weight factor (Wtor) with the torsion number of the ligand (Ntor). As the torsional energy (ΔG torsion ) increases, it lowers the binding energy [ 38 , 39 ]. On comparing the torsional energy (ΔG torsion ) of silydianin (+2.49 kcal/mol) with tolbutamide (+1.39 kcal/mol), but also the intermolecular energy as well as the van der Waals, the electrostatic and internal energies were much higher in the silydianin-bound protein complex, which minimized the high torsional energy penalty as compared to tolbutamide.…”
The increase in the number of cases of type 2 diabetes mellitus (T2DM) and the complications associated with the side effects of chemical/synthetic drugs have raised concerns about the safety of the drugs. Hence, there is an urgent need to explore and identify natural bioactive compounds as alternative drugs. Protein tyrosine phosphatase 1B (PTP1B) functions as a negative regulator and is therefore considered as one of the key protein targets modulating insulin signaling and insulin resistance. This article deals with the screening of a database of polyphenols against PTP1B activity for the identification of a potential inhibitor. The research plan had two clear objectives. Under first objective, we conducted a quantitative structure–activity relationship analysis of flavonoids with PTP1B that revealed the strongest correlation (R2 = 93.25%) between the number of aromatic bonds (naro) and inhibitory concentrations (IC50) of PTP1B. The second objective emphasized the binding potential of the selected polyphenols against the activity of PTP1B using molecular docking, molecular dynamic (MD) simulation and free energy estimation. Among all the polyphenols, silydianin, a flavonolignan, was identified as a lead compound that possesses drug-likeness properties, has a higher negative binding energy of −7.235 kcal/mol and a pKd value of 5.2. The free energy-based binding affinity (ΔG) was estimated to be −7.02 kcal/mol. MD simulation revealed the stability of interacting residues (Gly183, Arg221, Thr263 and Asp265). The results demonstrated that the identified polyphenol, silydianin, could act as a promising natural PTP1B inhibitor that can modulate the insulin resistance.
“…The torsional energy is calculated by multiplying a weight factor (Wtor) with the torsion number of the ligand (Ntor). As the torsional energy (ΔG torsion ) increases, it lowers the binding energy [ 38 , 39 ]. On comparing the torsional energy (ΔG torsion ) of silydianin (+2.49 kcal/mol) with tolbutamide (+1.39 kcal/mol), but also the intermolecular energy as well as the van der Waals, the electrostatic and internal energies were much higher in the silydianin-bound protein complex, which minimized the high torsional energy penalty as compared to tolbutamide.…”
The increase in the number of cases of type 2 diabetes mellitus (T2DM) and the complications associated with the side effects of chemical/synthetic drugs have raised concerns about the safety of the drugs. Hence, there is an urgent need to explore and identify natural bioactive compounds as alternative drugs. Protein tyrosine phosphatase 1B (PTP1B) functions as a negative regulator and is therefore considered as one of the key protein targets modulating insulin signaling and insulin resistance. This article deals with the screening of a database of polyphenols against PTP1B activity for the identification of a potential inhibitor. The research plan had two clear objectives. Under first objective, we conducted a quantitative structure–activity relationship analysis of flavonoids with PTP1B that revealed the strongest correlation (R2 = 93.25%) between the number of aromatic bonds (naro) and inhibitory concentrations (IC50) of PTP1B. The second objective emphasized the binding potential of the selected polyphenols against the activity of PTP1B using molecular docking, molecular dynamic (MD) simulation and free energy estimation. Among all the polyphenols, silydianin, a flavonolignan, was identified as a lead compound that possesses drug-likeness properties, has a higher negative binding energy of −7.235 kcal/mol and a pKd value of 5.2. The free energy-based binding affinity (ΔG) was estimated to be −7.02 kcal/mol. MD simulation revealed the stability of interacting residues (Gly183, Arg221, Thr263 and Asp265). The results demonstrated that the identified polyphenol, silydianin, could act as a promising natural PTP1B inhibitor that can modulate the insulin resistance.
“…The top 1 million scored compounds from each screen were investigated for intramolecular strain (total strain <7.5 TEU, maximum single torsion strain <2.5 TEU ( 41 )) and hydrogen bonding with Asp22, Ile23, Gly48, Val49, Phe156 and Asp157. Molecules with unsatisfied hydrogen bond donors or more than three unsatisfied acceptors were not considered for experimental evaluation.…”
The nonstructural protein 3 (NSP3) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) contains a conserved macrodomain enzyme (Mac1) that is critical for pathogenesis and lethality. While small molecule inhibitors of Mac1 have great therapeutic potential, few have been described. Here, we report the structure-based development of several chemical scaffolds exhibiting low- to sub-micromolar affinity for Mac1 through iterations of computer-aided design, structural characterization by ultra-high resolution X-ray protein crystallography, and binding evaluation with in-solution assays. Potent scaffolds were designed with in silico linkage of previously obtained fragment hits and ultra-large library docking screens of more than 450 million molecules. In total, 160 hits comprising 119 different scaffolds were discovered and 152 Mac1-ligand complex crystal structures were determined, typically to 1 Å resolution or better. The structure-activity-relationships emerging from this study may template future drug development against Mac1.
“…An average of 4358 orientations was sampled, and for each orientation about 187 conformations—over 1.57 x 10 11 ligand configurations in total were sampled in 121,018 core hours (or 5 days over 1000 cores). High ranking molecules were filtered for interactions with Tyr95, Asp98, Tyr176, Ile172, Asn177, Phe335 and Phe341, those adopting strained conformations (Gu et al, 2021) were deprioritized, as were molecules that topologically resembled ∼28,000 annotated aminergic ligands acting at serotonin, dopamine and adrenergic receptors as well as known inhibitors of SERT, DAT or NET with ECFP4-based Tanimoto coefficients (Tcs) < 0.35, based on molecules annotated in ChEMBL20 (Gaulton et al, 2017). Of the remaining molecules, the top ranking 300,000 were clustered for similarity to one-another.…”
The serotonin transporter (SERT) removes synaptic serotonin and is the target of anti-depressant drugs. SERT adopts three conformations: outward-open, occluded, and inward-open. All known inhibitors target the outward-open state except ibogaine, which has an unusual anti-depressant profile and stabilizes the inward-open conformation. Unfortunately, ibogaine is promiscuous and cardiotoxic, limiting understanding of inward-open state ligands. We computationally docked over 200 million small molecules against the ibogaine stabilized inward-open state of SERT. Thirty-six top-ranking compounds were synthesized and thirteen inhibited with potencies ranging from 29 to 5000 nM. Structure-based optimization led to two novel inhibitors with Ki values down to 3 nM. The new molecules stabilized an outward-closed state of the transporter and had little activity against off-targets. A cryo-EM structure of one of these bound to SERT confirmed the predicted geometry. In mouse behavioral assays, both had anxiolytic and anti- depressant activity, with potencies up to 200 better than fluoxetine.
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