Designing a natural inhibitor against human kynurenine aminotransferase type II and a comparison with PF-04859989: a computational effort against schizophrenia.

The Kynurenine aminotransferase II (KATII) enzyme has an essential role in L-kynurenine transmission to kynurenic acid (KYNA). High concentration of kynurenic acid associates with schizophrenia and some neurocognitive disorders. Decreasing KYNA production via inhibiting KATII would be an effective method for treating and understanding the related central nervous system (CNS) diseases. This study aimed to discover a potent inhibitor against human KATII (hKATII) in comparison with PF-04859989. We utilized the computational methods of molecular dynamics, virtual screening, docking, and binding free-energy calculations. Initially, the 58722 compounds from three drug libraries, including IBS library, DrugBank library, and Analyticon library, were obtained. At the next stage, these sets of compounds were screened by AutoDock Vina software, and a potent inhibitor (ZINC35466084) was selected. Following the screening, molecular dynamics simulations for both ZINC35466084 and PF-04859989 were performed by GROMACS software. MM-PBSA analysis showed that the amount of binding free energy for ZINC35466084 (-61.26 KJ mol-1) is more potent than PF-04859989 (-43.14 KJ mol-1). Furthermore, the ADME analysis results revealed that the pharmacokinetic parameters of ZINC35466084 are acceptable for human use. Eventually, our data demonstrated that ZINC35466084 is suitable for hKATII inhibition, and it is an appropriate candidate for further studies in the laboratory. Communicated by Ramaswamy H. Sarma.

Structural screening into the recognition of a potent inhibitor against non-structural protein 16: a molecular simulation to inhibit SARS-CoV-2 infection.

COVID-19 infection is caused by endemic crown infection (SARS-CoV-2) and is associated with lung damage and severe immune response. Non-Structural Proteins are the central components of coronaviral transcription and replication machinery in SARS-CoV-2 and also stimulate mRNA cap methylation to avoid the immune response. Non-Structural Protein 16 (NSP16) is one of the primary targets for the drug discovery of coronaviruses. Discovering an effective inhibitor against the NSP16 in comparison with Sinefungin was the main purpose of this investigation. Binding free-energy calculations, computational methods of molecular dynamics, docking, and virtual screening were utilized in this study. The ZINC and PubChem databases were applied to screen some chemical compounds regarding Sinefungin as a control inhibitor. Based on structural similarity to Sinefungin, 355 structures were obtained from the mentioned databases. Subsequently, this set of compounds were monitored by AutoDock Vina software, and ultimately the potent inhibitor (PUBCHEM512713) was chosen. At the next stage, molecular dynamics were carried out by GROMACS software to evaluate the potential elected compounds in a simulated environment and in a timescale of 100 nanoseconds. MM-PBSA investigation exhibited that the value of binding free energy for PUBCHEM512713 (-30.829 kJ.mol-1) is more potent than Sinefungin (-11.941 kJ.mol-1). Furthermore, the results of ADME analysis illustrated that the pharmacokinetics, drug-likeness, and lipophilicity parameters of PUBCHEM512713 are admissible for human utilization. Finally, our data suggested that PUBCHEM512713 is an effective drug candidate for inhibiting the NSP16 and is suitable for in vitro and in vivo studies.Communicated by Ramaswamy H. Sarma.

The SARS-Cov-2 Proliferation Blocked by a Novel and Potent Main Protease Inhibitor via Computer-aided Drug Design.

The recent prevalence of novel "coronavirus disease 2019" has expanded quickly globally, causing a universal pandemic. Herein, an effort was constructed to design a potent drug to inhibit the main protease of SARS-Cov-2 (3CLp) by means of structure-based drug design. A large library of the compounds was used for virtual screening. After molecular docking and ADME studies, we selected a compound with a better binding affinity to the 3CLp active site and acceptable ADME properties compared to the selected positive control drug. Molecular dynamic (MD) simulation (200 ns) and Molecular Mechanics-Poisson Boltzmann Surface Area (MM-PBSA) were used for further analysis. MD simulation outcomes have proved that the 3CLp-ZINC31157475 complex possesses a considerable value of dynamic properties such as flexibility, stability, compactness, and binding energy. Our MM-PBSA computation illustrates that ZINC31157475 is more potent (-88.03 kcal mol-1) than nelfinavir (-19.54 kcal mol-1) against COVID-19 3CLp. Further, we have determined that the main residues of the 3CLp interact with ligands from per-residue binding energy. In conclusion, we suggest that ZINC31157475 can potentially treat COVID-19 by inhibition of the 3CLp. However, in-vitro and in-vivo study is essential for approval of this suggestion.