Inhibitors of prolyl endopeptidase: Characterization of the pharmacophoric pattern using conformational analysis and 3D-QSAR

Summary A structure-activity study has been carried out on several compounds known as inhibitors of the serine protease prolyl endopeptidase. Conformational analysis has been done using different molecular mechanics methods such as molecular dynamics, or a randomized conformational search method. The conformers obtained were classified using geometric and energetic criteria. A pattern recognition analysis was done in order to divide conformers according to families. The resulting dominant families, for all compounds investigated, showed very similar geometric features. Based on the lowest energy conformers obtained after randomized conformational analysis, a 3D-QSAR model was established using the CoMFA approach. The validity of this model was verified by prediciting correctly the activity of other molecules not used in the construction of this model.     

Pattern recognition display methods for the analysis of computed molecular properties

Summary Pattern recognition methods, particularly the unsupervised learning techniques, are well suited for the preliminary analysis of the large data sets produced by computer chemistry. The use of linear and non-linear display methods for such exploratory analysis are exemplified with the aid of two data sets of biologically active molecules. Advantages and disadvantages of these techniques are discussed.     

Empirical scoring functions. II. The testing of an empirical scoring function for the prediction of ligand-receptor binding affinities and the use of Bayesian regression to improve the quality of the model

This paper tests the performance of a simple empirical scoring function on a set of candidate designs produced by a de novo design package. The scoring function calculates approximate ligand-receptor binding affinities given a putative binding geometry. To our knowledge this is the first substantial test of an empirical scoring function of this type on a set of molecular designs which were then subsequently synthesised and assayed. The performance illustrates that the methods used to construct the scoring function and the reliance on plausible, yet potentially false, binding modes can lead to significant over-prediction of binding affinity in bad cases. This is anticipated on theoretical grounds and provides caveats on the reliance which can be placed when using the scoring function as a screen in the choice of molecular designs. To improve the predictability of the scoring function and to understand experimental results, it is important to perform subsequent Quantitative Structure-Activity Relationship (QSAR) studies. In this paper, Bayesian regression is performed to improve the predictability of the scoring function in the light of the assay results. Bayesian regression provides a rigorous mathematical framework for the incorporation of prior information, in this case information from the original training set, into a regression on the assay results of the candidate molecular designs. The results indicate that Bayesian regression is a useful and practical technique when relevant prior knowledge is available and that the constraints embodied in the prior information can be used to improve the robustness and accuracy of regression models. We believe this to be the first application of Bayesian regression to QSAR analysis in chemistry.     

Roles of K151 and D180 in L‐2‐haloacid dehalogenase from Pseudomonas sp. YL: Analysis by molecular dynamics and ab initio fragment molecular orbital calculations

L ‐2‐haloacid dehalogenase (L ‐DEX) catalyzes the hydrolytic dehalogenation of L ‐2‐haloalkanoic acids to produce the corresponding D ‐2‐hydroxyalkanoic acids. This enzyme is expected to be applicable to the bioremediation of environments contaminated with halogenated organic compounds. We analyzed the reaction mechanism of L ‐DEX from Pseudomonas sp. YL (L ‐DEX YL) by using molecular modeling. The complexes of wild‐type L ‐DEX YL and its K151A and D180A mutants with its typical substrate, L ‐2‐chloropropionate, were constructed by docking simulation. Subsequently, molecular dynamics (MD) and ab initio fragment molecular orbital (FMO) calculations of the complexes were performed. The ab initio FMO method was applied at the MP2/6‐31G level to estimate interfragment interaction energies. K151 and D180, which are experimentally shown to be important for enzyme activity, interact particularly strongly with L ‐2‐chloropropionate, catalytic water, nucleophile (D10), and with each other. Our calculations suggest that K151 stabilizes substrate orientation and balances the charge around the active site, while D180 stabilizes the rotation of the nucleophile D10, fixes catalytic water around D10, and prevents K151 from approaching D10. Further, D180 may activate catalytic water on its own or with K151, S175, and N177. These roles are consistent with the previous results. Thus, MD and ab initio FMO calculations are powerful tools for the elucidation of the mechanism of enzymatic reaction at the molecular level and can be applied to other catalytically important residues. The results obtained here will play an important role in elucidating the reaction mechanism and rational design of L ‐DEX YL with improved enzymatic activity or substrate specificity. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2009     

Pharmacophore model-aided virtual screening combined with comparative molecular docking and molecular dynamics for identification of marine natural products as SARS-CoV-2 papain-like protease inhibitors

Targeting SARS-CoV-2 papain-like protease using inhibitors is a suitable approach for inhibition of virus replication and dysregulation of host anti-viral immunity. Engaging all five binding sites far from the catalytic site of PLpro is essential for developing a potent inhibitor. We developed and validated a structure-based pharmacophore model with 9 features of a potent PLpro inhibitor. The pharmacophore model-aided virtual screening of the comprehensive marine natural product database predicted 66 initial hits. This hit library was downsized by filtration through a molecular weight filter of ≤ 500 g/mol. The 50 resultant hits were screened by comparative molecular docking using AutoDock and AutoDock Vina. Comparative molecular docking enables benchmarking docking and relieves the disparities in the search and scoring functions of docking engines. Both docking engines retrieved 3 same compounds at different positions in the top 1 % rank, hence consensus scoring was applied, through which CMNPD28766, aspergillipeptide F emerged as the best PLpro inhibitor. Aspergillipeptide F topped the 50-hit library with a pharmacophore-fit score of 75.916. Favorable binding interactions were predicted between aspergillipeptide F and PLpro similar to the native ligand XR8-24. Aspergillipeptide F was able to engage all the 5 binding sites including the newly discovered BL2 groove, site V. Molecular dynamics for quantification of Cα-atom movements of PLpro after ligand binding indicated that it exhibits highly correlated domain movements contributing to the low free energy of binding and a stable conformation. Thus, aspergillipeptide F is a promising candidate for pharmaceutical and clinical development as a potent SARS-CoV-2 PLpro inhibitor.     

QSAR based virtual screening derived identification of a novel hit as a SARS CoV-229E 3CLpro Inhibitor: GA-MLR QSAR modeling supported by molecular Docking,molecular dynamics simulation and MMGBSA calculation approaches

Congruous coronavirus drug targets and analogous lead molecules must be identified as quickly as possible to produce antiviral therapeutics against human coronavirus (HCoV SARS 3CLpro) infections. In the present communication, we bear recognized a HIT candidate for HCoV SARS 3CLpro inhibition. Four Parametric GA-MLR primarily based QSAR model (R2:0.84, R2adj:0.82, Q2loo: 0.78) was once promoted using a dataset over 37 structurally diverse molecules along QSAR based virtual screening (QSAR-VS), molecular docking (MD) then molecular dynamic simulation (MDS) analysis and MMGBSA calculations. The QSAR-based virtual screening was utilized to find novel lead molecules from an in-house database of 100 molecules. The QSAR-vS successfully offered a hit molecule with an improved _PEC₅₀ value from 5.88 to 6.08. The benzene ring, phenyl ring, amide oxygen and nitrogen, and other important pharmacophoric sites are revealed via MD and MDS studies. Ile164, Pro188, Leu190, Thr25, His41, Asn46, Thr47, Ser49, Asn189, Gln191, Thr47, and Asn141 are among the key amino acid residues in the S1 and S2 pocket. A stable complex of a lead molecule with the HCoV SARS 3CLpro was discovered using MDS. MM-GBSA calculations resulted from MD simulation results well supported with the binding energies calculated from the docking results. The results of this study can be exploited to develop a novel antiviral target, such as an HCoV SARS 3CLpro Inhibitor.     

A structural approach to investigate halogen substituted MAO-B inhibitors using QSAR modeling,molecular dynamics,and conceptual DFT analysis

Halogenated inhibitors showed robust, reversible, and selective monoamine oxidase-B (MAO-B) inhibitory efficacy in candidates that were derived from them. Our team has previously synthesized and assessed a panel of halogenated chalcones and coumarin for the study on MAO-B inhibition. The aim of this study was to build GA-MLR based QSAR models and predictive 3D Pharmacophore models, as well as to investigate the relationship between halogenated derivatives and MAO-B inhibitory activity. The robust statistical significance in the parameter (R² = 0.78 and Q² = 0.69) was demonstrated. Best Hypo1 contains one hydrophobic and two aromatic rings. The lead molecule for quantum mechanics was performed, and it was revealed that it would bind to proteins and provide stability. To determine the stability of the ligand-enzyme complex, a thorough molecular dynamics analysis of the lead compounds was accomplished.