Amir Homayoun Keihan

Introduction:Staphylococcus aureus is a dangerous pathogen which causes a vast selection of infections. Antimicrobial peptides have been demonstrated as a new hope for developing antibiotic agents against multi-drug-resistant bacteria such as S. aureus. Yet, most studies on developing classification tools for antimicrobial peptide activities do not focus on any specific species, and therefore, their applications are limited.Methods: Here, by using an up-to-date dataset, we have developed a hierarchical machine learning model for classifying peptides with antimicrobial activity against S. aureus. The first-level model classifies peptides into AMPs and non-AMPs. The second-level model classifies AMPs into those active against S. aureus and those not active against this species.Results: Results from both classifiers demonstrate the effectiveness of the hierarchical approach. A comprehensive set of physicochemical and linguistic-based features has been used, and after feature selection steps, only some physicochemical properties were selected. The final model showed the F1-score of 0.80, recall of 0.86, balanced accuracy of 0.80, and specificity of 0.73 on the test set.Discussion: The susceptibility to a single AMP is highly varied among different target species. Therefore, it cannot be concluded that AMP candidates suggested by AMP/non-AMP classifiers are able to show suitable activity against a specific species. Here, we addressed this issue by creating a hierarchical machine learning model which can be used in practical applications for extracting potential antimicrobial peptides against S. aureus from peptide libraries.

The human immunodeficiency virus type 1 protease (HIV-1 PR) is an important enzyme in the life cycle of the HIV virus. It cleaves inactive pre-proteins of the virus and changes them into active proteins. Darunavir (DRV) suppresses the wild-type HIV-1 PR (WT-Pr) activity but cannot inhibit some mutant resistant forms (MUT-Pr). Increasing knowledge about the resistance mechanism can be helpful for designing more effective inhibitors. In this study, the mechanism of resistance of a highly MUT-Pr strain against DRV was investigated. For this purpose, complexes of DRV with WT-Pr (WT-Pr-D) and MUT-Pr (MUT-Pr-D) were studied by all-atom molecular dynamics simulation in order to extract the dynamic and energetic properties. Our data revealed that mutations increased the flap-tip flexibility due to the reduction of the flap-flap hydrophobic interactions. So, the protease’s conformation changed from a closed state to a semi-open state that can facilitate the disjunction of DRV from the active site. On the other hand, energy analysis limited to the final basins of the energy landscape indicated that the entropy of binding of DRV to MUT-Pr was more favorable than that of WT-Pr. However, the enthalpy penalty overcomes it and makes binding more unfavorable relative to the WT-Pr. The unfavorable interaction of DRV with R8, I50, I84, D25′, and A28′ residues in MUT-Pr-D relative to WT-Pr-D is the reason for this enthalpy penalty. Thus, mutations drive resistance to DRV. The hydrogen bond analysis showed that compared with WT-Pr, the hydrogen bonds between DRV and the active-site residues of MUT-Pr were disrupted.

Background and Objectives: The severity and mortality of coronavirus disease 19 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 are positively associated with underlying diseases such as hypertension, diabetes, and chronic kidney disease (CKD). In this regard, the current study aimed to evaluate the clinical characteristics, laboratory findings, and outcomes of coronavirus disease 2019 (COVID-19) hospitalized patients with and without CKD. Methods: This cross-sectional matched study was conducted on hospitalized confirmed COVID-19 patients with and without CKD admitted to Baqiyatallah Hospital in Tehran, Iran, from February 26, 2020 to March 26, 2020. The patients were homogenized in terms of age, gender, body mass index, and underlying diseases such as hypertension and diabetes. Demographic data, clinical symptoms, and laboratory and radiological findings were collected from patients’ medical records and compared between the patients based on their CKD status. Results: Among the COVID-19 patients, 56 and 97 cases with and without CKD were investigated, respectively. In general, 111 (72.5%) patients with a mean age of 55 years were males. Patients with CKD had higher levels of blood urea nitrogen, creatinine, and red cell distribution width (p<0.05). No differences were found regarding chest computed tomography findings, ICU admission, and death among COVID-19 patients with and without CKD. Conclusions: Overall, the findings support the use of red cell distribution width, blood urea nitrogen, and creatinine for monitoring the COVID-19 patients with CKD and assessing the risk of disease progression. Eventually, managing comorbidities including hypertension and diabetes will reduce COVID-19 severity in CKD patients.

Semiconductor photocatalysis is an effective method used to degrade organophosphorus compounds. Here, the potential of a commonly mixed oxide semiconductor, ZnO/CuO, has been examined to degrade methyl parathion. Sono-coprecipitation method was used to provide ZnO/CuO nanocomposites, and it was applied to photocatalytic and sono-photocatalytic degradation of methyl parathion under solar light irradiation. Powder x-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), the Brunauer-Emmett-Teller (BET) surface area, field emission scanning electron microscopy (FE-SEM), and transmission electron microscopy (TEM) were used to characterize the synthesized samples. The optimal experimental conditions such as ZnO/CuO photocatalyst 90:10 M ratios, the initial concentration of 20 mg/L parathion, 1 g/L photocatalyst loading, no compressed air sparging, pH of 8, and ultrasonic power (60 W and 80 kHz) were used to degrade the parathion effectively. The parathion was fully (100% removal) degraded after 60 min sono-photoirradiation in the optimal experimental conditions. A real water sample was used to examine the ability of the ZnO/CuO photocatalyst 90:10 to remove the parathion in the water-soluble ions. Graphical abstract

Lactate dehydrogenase (LDH) is a tetrameric enzyme which is composed of two subunits known as LDHA and LDHB, which are encoded by the LDHA and LDHB genes respectively. LDH catalyses the last step in anaerobic glycolysis through the reversible conversion of pyruvate to lactate via coupled oxidation of NADH cofactor. The LDHA plays an important regulatory role in anaerobic glycolysis, by catalysing the final step of the process. Therefore, it is likely that increases in the expression level of LDHA in cancer cells could facilitate the efficiency of anaerobic glycolysis. Measuring the level of serum LDHA is a key step in the diagnosis of many cancer types. In this study, the adsorption, stability, and dynamics of LDHA on the surface of pristine graphene (PG) and carboxylated graphene (COOH-Graphene) were investigated using its molecular dynamics simulation. Variations in root mean square deviation, root mean square fluctuation, solvent accessible surface area and adsorption energy of the LDHA during the simulation were calculated to analyse the effect of PG and COOH-Graphene on the overall conformation of LDHA. Results showed that the adsorption of LDHA on COOH-Graphene is mostly mediated by electrostatic interactions, whereas on the PG, both Van der Waals and π-π interactions are prominent. ABBREVIATIONS: COOH-Graphene: Carboxylated Graphene; MD simulation: Molecular Dynamics Simulation; MM/PBSA: Molecular mechanics/Poisson- Boltzmann Surface Area; PG: Pristine Graphene; PDB: Protein data bank; RMSD: Root mean square deviation; RMSF: Root mean square fluctuation; SASA: Solvent accessible surface area