Investigation of the antibacterial and biofilm inhibitory activities ofPrangos acaulis(DC.) Bornm in nanoparticulated formulation.

Here in, a chitosan-based nanoformulation ofP.acauliswas evaluated for its antibacterial and antibiofilm inhibitory activities against some known food-borne bacteria. The FTIR, FE-SEM, DLS and zeta-potential analysis were performed for confirming loading process, morphological appearance, hydrodynamic diameter and surface charge of the nanoparticles respectively. The results confirmed that, the nanoparticles had semi-spherical shape with the mean hydrodynamic diameter and surface charge of 89.8 ± 5.8 nm and 10.78 ± 2.7 mv respectively. Furthermore, the FTIR analysis approved that the nanoparticles were successfully loaded with ethyl acetate fraction fromP.acaulis. The antibacterial and biofilm inhibitory activities of the nanoformulated fraction were significantly increased against the tested Gram positive strains than free sample. The results also confirmed that the fraction release from the nanoparticles follows a sustained manner release after 30 h in a logarithmic pattern. Based on the obtained results, chitosan based nanoformulation ofP. acauliscan be considered for more evaluations to serve as an alternative natural antibiotic.

In silico screening of ssDNA aptamer against Escherichia coli O157:H7: A machine learning and the Pseudo K-tuple nucleotide composition based approach.

This study was planned to in silico screening of ssDNA aptamer against Escherichia coli O157:H7 by combination of machine learning and the PseKNC approach. For this, firstly a total numbers of 47 validated ssDNA aptamers as well as 498 random DNA sequences were considered as positive and negative training data respectively. The sequences then converted to numerical vectors using PseKNC method through Pse-in-one 2.0 web server. After that, the numerical vectors were subjected to classification by the SVM, ANN and RF algorithms available in Orange 3.2.0 software. The performances of the tested models were evaluated using cross-validation, random sampling and ROC curve analyzes. The primary results demonstrated that the ANN and RF algorithms have appropriate performances for the data classification. To improve the performances of mentioned classifiers the positive training data was triplicated and re-training process was also performed. The results confirmed that data size improvement had significant effect on the accuracy of data classification especially about RF model. Subsequently, the RF algorithm with accuracy of 98% was selected for aptamer screening. The thermodynamics details of folding process as well as secondary structures of the screened aptamers were also considered as final evaluations. The results confirmed that the selected aptamers by the proposed method had appropriate structure properties and there is no thermodynamics limit for the aptamers folding.

Using Chou's General Pseudo Amino Acid Composition to Classify Laccases from Bacterial and Fungal Sources via Chou's Five-Step Rule.

Laccases are a group of enzymes with a critical activity in the degradation process of both phenolic and non-phenolic compounds. These enzymes present in a diverse array of species, including fungi and bacteria. Since this enzyme is in the market for different usages from industry to medicine, having a better knowledge of its structures and properties from diverse sources will be useful to select the most appropriate candidate for different purposes. In the current study, sequence- and structure-based characteristics of these enzymes from fungi and bacteria, including pseudo amino acid composition (PseAAC), physicochemical characteristics, and their secondary structures, are being compared and classified. Autodock 4 software was used for docking analysis between these laccases and some phenolic and non-phenolic compounds. The results indicated that features including molecular weight, aliphatic, extinction coefficient, and random coil percentage of these protein groups present high degrees of diversity in most cases. Categorization of these enzymes by the notion of PseAAC, showed over 96% accuracy. The binding free energy between fungal laccases and their substrates showed to be considerably higher than those of bacterial ones. According to the outcomes of the current study, data mining methods by using different machine learning algorithms, especially neural networks, could provide valuable information for a fair comparison between fungal and bacterial laccases. These results also suggested an association between efficacy and physicochemical features of laccase enzymes from different sources.

Introducing of an integrated artificial neural network and Chou's pseudo amino acid composition approach for computational epitope-mapping of Crimean-Congo haemorrhagic fever virus antigens.

This study was aimed to introduce a novel algorithm for determining linear B- and T-cell epitopes from Crimean-Congo haemorrhagic fever virus (CCHFV) antigens. To this end, 387 approved B- and T-cell epitopes, as well as 331 non-epitope peptides from different serotypes of the virus were collected from IEDB database for generating of the train datasets. After that, the physicochemical properties of the epitopes were expressed as the numeric vectors using Chou's pseudo amino acid composition method. The vectors then were used for training of four machine learning algorithms including artificial neural network (ANN), k-nearest neighbors (kNN), support vector machine (SVM) and Random forest (RF). The results confirmed that ANN was the most accurate algorithm for discriminating between the epitopes and non-epitopes with the accuracy of 0.90. Furthermore, for evaluating the performance of the ANN algorithm, an epitope prediction challenge was performed to a random peptide library from envelopment polyprotein of CCHFV. Moreover, the efficiency of the predicted epitopes in term of antigenicity and affinity to MHC-II were compared to the predicted epitope by standard epitope prediction tools based on their VaxiJen 2.0 score and molecular docking outputs. Finally, the ability of the screened epitopes to stimulation of humoral and cellular responses was evaluated by an in silico immune simulation process thought C-Immsim 10.1 server. The results confirmed that this method has more accuracy for epitope-mapping than the standard tools and could considered as an effective algorithm to develop a serotype independent one-click automated epitope based vaccine design tool.

Designing a multi-epitope vaccine for cross-protection against Shigella spp: An immunoinformatics and structural vaccinology study.

Shigellosis is a severe diarrheal disease with high mortality and morbidity rate. Until now, there is no approved vaccine against the disease. Therefore, the present study was planned to design a novel multi-epitope vaccine against Shigella spp., the causative agents of the disease based on the immunoinformatic tools. For this end, firstly seven conserved antigens of the bacteria, including IpaA, IpaB, IpaC, IpaD, OmpC, OmpF and VirG were selected. Then, linear B-cell epitope mapping of these proteins was carried out and top-ranked and shared epitopes were selected based on antigenicity, allergenicity, stability, toxicity and physicochemical properties for further analysis. In next step, B-cell derived T-cell epitopes were determined and appropriate epitopes were selected for incorporation into the final construct. Moreover, the selected epitopes and two mucosal adjuvants including ctxB and LT-IIc were joined using appropriate linkers. The three dimensional structure of the final construct was modeled and evaluated in term of structural quality and presence of conformational B-cell epitopes. Furthermore, binding affinity of the proposed vaccine to MHC I and II molecules were evaluated through molecular docking method using Hex 8.0. as well as the stability of the vaccine-MHC complexes was monitored by molecular dynamics method using the NAMD graphical user interface embedded in visual molecular dynamics. Finally, to evaluate the immunogenicity of the designed protein, the protein was administered to BALB/c mice and the serum IgG was determined by ELISA. The results indicated that the proposed vaccine has high structural quality and binding affinity to both MHC I and II molecules. Moreover, molecular dynamics studies confirmed that the vaccine-MHC docked complexes were stable during simulation time. Animal study showed that the proposed protein is able to evoke mice's humoral immune response. In sum, the results suggested that the proposed candidate vaccine could be considered as a promising anti-shigellosis vaccine.

Application of an Artificial Neural Network in the Diagnosis of Chronic Lymphocytic Leukemia.

Introduction Chronic lymphocytic leukemia (CLL) is one of the most common types of leukemia, and the early diagnosis of patients coincides with their proper treatment and survival. If patients are diagnosed late or proper treatment is not applied, it may lead to harmful results. Several methods could be used for the diagnosis of leukemia; some of these include complete blood count (CBC), immunophenotyping, lymph node biopsy, chest X-ray, computerized tomography (CT) scan, and ultrasound. Most of these methods are time-consuming and an application of more than one method will result as intended. This acknowledgment stresses the necessity of rapid and proper diagnosis for leukemia based on clinical and medical findings, inasmuch as it was decided to apply the artificial neural network (ANN) in order to identify a molecular biomarker for rapid leukemia diagnosis from blood samples and evaluate its potential for the detection of cancer. Materials & methods The independent sample t-test was applied with the Statistical Package for the Social Sciences (SPSS; IBM Corp, Armonk, NY, US) software on the microarray gene expression data of Gene Expression Omnibus (GEO) datasets (GSE22529); 12 genes that had shown the highest differences (among parameters whose p-value was less than 0.01) were selected for further ANN analysis. The selected genes of 53 patients were applied to the training network algorithm, with a learning rate of 0.1. Results The results showed a high accuracy of the relationship between the output of the trained network and the test data. The area under the receiver operating characteristic (ROC) curve was 0.991, which provides proof of the precision and the relationship with identifying Gelsolin as a potential biomarker for this research. Conclusions With these results, it was concluded that the training process of the ANN could be applied to rapid CLL diagnosis and finding a potential biomarker. Besides, it is suggested that this method could be performed to diagnose other forms of cancer in order to get a rapid and reliable outcome.

Towards the first multi-epitope recombinant vaccine against Crimean-Congo hemorrhagic fever virus: A computer-aided vaccine design approach.

Crimean-Congo hemorrhagic fever (CCHF) is considered one of the major public health concerns with case fatality rates of up to 80%. Currently, there is no effective approved vaccine for CCHF. In this study, we used a computer-aided vaccine design approach to develop the first multi-epitope recombinant vaccine for CCHF. For this purpose, linear B-cell and T-cell binding epitopes from two structural glycoproteins of CCHF virus including Gc and Gn were predicted. The epitopes were further studied regarding their antigenicity, allergenicity, hydrophobicity, stability, toxicity and population coverage. A total number of seven epitopes including five T-cell and two B-cell epitopes were screened for the final vaccine construct. Final vaccine construct composed of 382 amino acid residues which were organized in four domains including linear B-cell, T-cell epitopes and cholera toxin B-subunit (CTxB) along with heat labile enterotoxin IIc B subunit (LT-IIc) as adjuvants. All the segments were joined using appropriate linkers. The physicochemical properties as well as the presence of IFN-γ inducing epitopes in the proposed vaccine, was also checked to determining the vaccine stability, solubility and its ability to induce cell-mediated immune responses. The 3D structure of proposed vaccine was subjected to the prediction of computational B-cell epitopes and molecular docking studies with MHC-I and II molecules. Furthermore, molecular dynamics stimulations were performed to study the vaccine-MHCs complexes stability during stimulation time. The results suggest that our proposed vaccine was stable, well soluble in water and potentially antigenic. Results also demonstrated that the vaccine can induce both humoral and cell-mediated immune responses and could serve as a promising anti-CCHF vaccine candidate.

Inhibition of Human Immunodeficiency Type 1 Virus (HIV-1) Life Cycle by Different Egg White Lysozymes.

Lysozyme is a relatively small enzyme with different biological activities, which is found in tears, saliva, egg white, and human milk. In the study, the anti-HIV-1 activity of lysozymes purified from quail, Meleagris, and hen egg white has been determined. For this end, a time-of-drug-addition assay was performed to identify the target of anti-HIV-1 agents and for determination of probable anti HIV-1 mechanism of the studied lysozyme, the binding affinity of the lysozymes to the human CD4 receptor was studied by molecular docking method. To define structural differences between studied lysozymes, structural motifs of them were predicted by MEME tool. Quail, hen, and Meleagris lysozymes showed potent anti-HIV-1 activity with EC50 of 7.5, 10, and 55 nM, respectively. The time-of-drug-addition study demonstrated that the inhibitory effect of all purified lysozymes is before HIV-1 infection. The frequency and intensity of CD4 expression in PBMCs decreased in the presence of all mentioned lysozymes. Also, the expression level of C-C chemokine receptor type 5 (CCR5) and chemokine receptor type 4 (CXCR4) on CD4+ T cells was not changed in cells treated with these lysozymes. The results of in silico study confirmed that the binding energy of quail lysozyme with CD4 was more than that of other studied lysozymes. The results revealed that these lysozymes restrict HIV-1 attachment to host cell CD4.

Frangulosid as a novel hepatitis B virus DNA polymerase inhibitor: a virtual screening study.

Hepatitis B virus (HBV) infects more than 400 million humans Worldwide. Currently, development of new anti-HBV agents is focused on inhibiting of HBV DNA polymerase activity. The natural components of medicinal plant have a broad spectrum of biological activities with therapeutic properties which can be exploited in various steps of drug discovery. Currently, in silico analyses have been introduced as alternative or supplements methods for drug discovery. This study was planned to in silico screening novel HBV DNA polymerase inhibitor(s) from R. palmatum, R. coreanus and S. officinalis. For this purpose, a set of dominant phytochemicals from mentioned plants were retrieved from PubChem database and primary screening was performed with molecular docking method using iGemdock 2.1 software. SwissADME and MedChem Designer 3.0 were used to calculate the drug-likeness parameters of the ligands. Furthermore, the genotoxicity of the studied ligands was predicted using Toxtree 2.6.6 software. Final analysis of screened compounds was done using Autodock 4 software. Result confirmed that Frangulosid and Lindleyin acid have most and least efficacy in HBV DNA polymerase inhibition with the inhibition constant of 2.97 and 53.83 µM, respectively. Results also showed that, the amino acids, involved in interaction, were different for each compound. In this regards, results revealed that the main amino acids residues of the receptor, involved in interaction with Quercetin-3-glucuronide, Frangulosid and Lindleyin separately, located in 420-424, 606-615 and 512-542 spectra, respectively. In conclusion, Frangulosid can be considered as a good candidate for more investigation of its anti-HBV activity.

Analysis and comparison of lignin peroxidases between fungi and bacteria using three different modes of Chou's general pseudo amino acid composition.

Lignin peroxidases (LiPs) are important enzymes in the degradation process of lignin which are presented in different species of fungi and bacteria. In the present study, sequence and structure-based properties of LPs in fungi and bacteria are compared. These properties include pseudo amino acid composition (PseAAC), physicochemical properties and the secondary structure. Autodock 4 has been used for docking between LiPs and lignan. The motifs of LiP were predicted by MEME tool. Statistical analysis and Multinomial Naïve Bayes (MNB) algorithm were used for the classification of two LiP protein groups. The results demonstrated that molecular weight, isoelectric point, aliphatic, extinction coefficient and random coil percentage of LiPs in fungi and bacteria were significantly different between these two groups. The classification of these two groups based on the concept of PseAAC showed over 80% accuracy. The binding free energy between bacterial LiPs and lignan is significantly more than fungi LiP and ligand. The aliphatic and instability of most important motifs of bacteria and fungi were significantly different. In conclusion, the results indicated that computational techniques could provide useful information for comparing fungal and bacterial LiPs. These results can also explain that there is a relationship between efficacy and physicochemical properties of LiPs.