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.

Development of Escherichia coli asparaginase II for the Treatment of Acute Lymphocytic Leukemia: In Silico Reduction of asparaginase II Side Effects by a Novel Mutant (V27F).

Acute lymphoblastic leukemia (ALL) is a common blood disease in children that is accountable for many deaths. Due to major improvements in treatment procedures in the past 50 years, the survivability of this disease has risen dramatically to about 90 percent today. L-asparaginase (ASNase) has been used to treat ALL. The glutaminase (GLNase) activity of this enzyme causes some side effects and is unnecessary for anticancer activity. This study investigated mutagenesis in Escherichia coli ASNase II to find a mutant with lower GLNase activity via molecular dynamics (MD) simulation. Residues with low binding energy to asparagine (Asn) and high binding energy to glutamine (Gln) were chosen for mutagenesis. A mutant with low free binding energy to Gln was then selected for molecular docking and MD studies. The results showed that V27F is a good candidate for reducing GLNase activity and that it has little effect on enzyme ASNase activity. A simulation analysis showed that the V27F mutant was more stable than the WT ASNase and that mutagenesis was quite successful.

Novel mutant of Escherichia coli asparaginase II to reduction of the glutaminase activity in treatment of acute lymphocytic leukemia by molecular dynamics simulations and QM-MM studies.

L-Asparaginases (ASNase) belong to a family of amidohydrolases, have both asparaginase and glutaminase activity. Acute lymphocytic leukemia (ALL) is an outrageous disease worldwide. Bacterial ASNase has been used for the treatment of ALL. Glutaminase activity of enzyme causes some side effect and it is not essential for anticancer activity. The aim of this study was engineering of Escherichia coli asparaginase II to find a mutant with reduced glutaminase activity by molecular docking, molecular dynamics (MD) and QM-MM (Quantum mechanics molecular dynamics) simulations. Residues with low free energy of binding to Asn and high free binding energy to Gln were chosen for mutagenesis. Then, a mutant with higher glutaminase free binding energy was selected for further studies. Additionally, the MD simulation and QM-MM computation of wild type (WT) were employed and the selected mutated ASNase were analyzed and discussed. Our data showed that V27T is a good candidate to reduction the glutaminase activity, while has no remarkable effect on asparaginase activity of the enzyme. The simulation analysis revealed that V27T mutant is more stable than WT and mutant simulation was successful completely. QM-MM results confirmed the successfulness of our mutagenesis.