Structure-Based Design and in Silico Screening of Virtual Combinatorial Library of Benzamides Inhibiting 2-trans Enoyl-Acyl Carrier Protein Reductase of Mycobacterium tuberculosis with Favorable Predicted Pharmacokinetic Profiles

Kouman, Koffi Charles; Keita, Melalie; N'Guessan, Raymond Kre; Owono, L. C. Owono; Megnassan, Eugène; Frecer, Vladimı́r; Miertus, Stanislav

doi:10.3390/ijms20194730

Cited by 7 publications

(6 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The library was built using a 6-fluoroquinolone skeleton with structural variations in positions 1, 7, and 8. Similarly, Kouman et al designed a VCL based on a benzamide scaffold to identify new Mycobacterium tuberculosis 2-trans enoyl-acyl carrier protein reductase inhibitors with favorable pharmacokinetic profiles [26]. Lauro et al have also built a library containing approximately 2.0 × 10 4 virtual compounds by following a multicomponentbased chemical route for the decoration of the 2,4-thiazolidinedione core [27].…”

Section: Types Of Combinatorial Librariesmentioning

confidence: 99%

Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design

Suay-García

Bueso-Bordils

Falcó

et al. 2022

IJMS

View full text Add to dashboard Cite

Traditionally, drug development involved the individual synthesis and biological evaluation of hundreds to thousands of compounds with the intention of highlighting their biological activity, selectivity, and bioavailability, as well as their low toxicity. On average, this process of new drug development involved, in addition to high economic costs, a period of several years before hopefully finding a drug with suitable characteristics to drive its commercialization. Therefore, the chemical synthesis of new compounds became the limiting step in the process of searching for or optimizing leads for new drug development. This need for large chemical libraries led to the birth of high-throughput synthesis methods and combinatorial chemistry. Virtual combinatorial chemistry is based on the same principle as real chemistry—many different compounds can be generated from a few building blocks at once. The difference lies in its speed, as millions of compounds can be produced in a few seconds. On the other hand, many virtual screening methods, such as QSAR (Quantitative Sturcture-Activity Relationship), pharmacophore models, and molecular docking, have been developed to study these libraries. These models allow for the selection of molecules to be synthesized and tested with a high probability of success. The virtual combinatorial chemistry–virtual screening tandem has become a fundamental tool in the process of searching for and developing a drug, as it allows the process to be accelerated with extraordinary economic savings.

show abstract

Section: Types Of Combinatorial Librariesmentioning

confidence: 99%

Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design

Suay-García

Bueso-Bordils

Falcó

et al. 2022

IJMS

View full text Add to dashboard Cite

show abstract

“…Evaluation of the relationship between structure and biological activity of compounds by PLS method can quantitate the reliability of the model [32]. The analysis was divided into two parts, the training set database were cross-validated by using the leave-one-out method to calculate the cross-validation correlation coe cient (Q 2 ) and the optimum number of components (ONC) [33].…”

Section: Partial Least Squares (Pls) Analysismentioning

confidence: 99%

Computational investigation of adenosine 5′-(α,β-methylene)-diphosphate (AMPCP) derivatives as ecto-5′-nucleotidase (CD73) inhibitors by using 3D-QSAR, molecular docking, and molecular dynamics simulations

et al. 2022

View full text Add to dashboard Cite

CD73, as a surface enzyme anchored on the outside of the cell membrane via glycosylphosphatidylinositol (GPI), can convert the AMP in the tumor cell microenvironment into adenosine to promote the growth of tumor cells. It has been overexpressed in many different types of human tumors, such as gastric cancer, pancreatic cancer, liver cancer and other tumor cells. Therefore, targeted inhibitors of CD73 are considered as potential tumor treatment methods. Due to the low bioavailability of nucleoside CD73 inhibitors, it is necessary to develop new inhibitors. In this study, through molecular docking, three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular dynamics (MD) simulations, a series of CD73 inhibitors were calculated and studied to reveal their structure-activity relationships. Through molecular docking studies, explore the possible mode of interaction between inhibitors and protein. Subsequently, a 3D-QSAR model was established by comparative molecular eld analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). For the best CoMFA model, the Q 2 and R 2 values are 0.708 and 0.983, respectively, while for the best CoMSIA model, the Q 2 and R 2 values are 0.809 and 0.992, respectively. In addition, the stability of the complex formed by the two inhibitors and CD73 was evaluated by molecular dynamics simulation, and the results are consistent with the results of molecular docking and 3D-QSAR research. Finally, the binding free energy was calculated by the surface area method (MM-GBSA), and the results are consistent with the activities that Van Der Waals and Coulomb contribute the most during the binding process of the molecule to the CD73 protein. In conclusion, our research provides valuable information for the further development of CD73 inhibitors.

show abstract

“…3D models of the enzyme-inhibitor (E:I) complexes were built starting from the free enzyme (E) and the free inhibitors (I), both derived from a well -refined X-ray crystal structure (PDB ID: 3BWK) of the co-crystallised potent inhibitor K11017 (or Mu-Leu-Hph-VSPh, where VSPh: phenyl vinyl sulfone; Hph:homophenylalanyl;Mu:morpholino urea) retrieved from the PDB [32]. Chain A was employed in all computations and modellingwas carried out on the graphical user in-terface of Discovery Studio 2.5 [33], using a previously well described protocol [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48]. Thus, the pH values were kept at 7.0, while all N-and C-terminal residues were kept neutral.…”

Section: Molecular Modelingmentioning

confidence: 99%

Computer-Aided Design of Peptidomimetic Inhibitors of Falcipain-3: QSAR and Pharmacophore Models

et al. 2021

Self Cite

View full text Add to dashboard Cite

In this work, antiparasitic peptidomimetics inhibitors (PEP) of falcipain-3 (FP3) of Plasmodium falciparum (Pf) are proposed using structure-based and computer-aided molecular design. Beginning with the crystal structure of PfFP3-K11017 complex (PDB ID: 3BWK), three-dimensional (3D) models of FP3-PEPx complexes with known activities () were prepared by in situ modification, based on molecular mechanics and implicit solvation to compute Gibbs free energies (GFE) of inhibitor-FP3 complex formation. This resulted in a quantitative structure–activity relationships (QSAR) model based on a linear correlation between computed GFE () and the experimentally measured . Apart from the structure-based relationship, a ligand-based quantitative pharmacophore model (PH4) of novel PEP analogues where substitutions were directed by comparative analysis of the active site interactions was derived using the proposed bound conformations of the PEPx. This provided structural information useful for the design of virtual combinatorial libraries (VL), which was virtually screened based on the 3D-QSAR PH4. The end results were predictive inhibitory activities falling within the low nanomolar concentration range.

show abstract

Structure-Based Design and in Silico Screening of Virtual Combinatorial Library of Benzamides Inhibiting 2-trans Enoyl-Acyl Carrier Protein Reductase of Mycobacterium tuberculosis with Favorable Predicted Pharmacokinetic Profiles

Cited by 7 publications

References 40 publications

Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design

Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design

Computational investigation of adenosine 5′-(α,β-methylene)-diphosphate (AMPCP) derivatives as ecto-5′-nucleotidase (CD73) inhibitors by using 3D-QSAR, molecular docking, and molecular dynamics simulations

Computer-Aided Design of Peptidomimetic Inhibitors of Falcipain-3: QSAR and Pharmacophore Models

Contact Info

Product

Resources

About