Novel surface biochemical modifications of urinary catheters to prevent catheter-associated urinary tract infections.

More than 70% of hospital-acquired urinary tract infections are related to urinary catheters, which are commonly used for the treatment of about 20% of hospitalized patients. Urinary catheters are used to drain the bladder if there is an obstruction in the tube that carries urine out of the bladder (urethra). During catheter-associated urinary tract infections, microorganisms rise up in the urinary tract and reach the bladder, and cause infections. Various materials are used to fabricate urinary catheters such as silicone, polyurethane, and latex. These materials allow bacteria and fungi to develop colonies on their inner and outer surfaces, leading to bacteriuria or other infections. Urinary catheters could be modified to exert antibacterial and antifungal effects. Although so many research have been conducted over the past years on the fabrication of antibacterial and antifouling catheters, an ideal catheter needs to be developed for long-term catheterization of more than a month. In this review, we are going to introduce the recent advances in fabricating antibacterial materials to prevent catheter-associated urinary tract infections, such as nanoparticles, antibiotics, chemical compounds, antimicrobial peptides, bacteriophages, and plant extracts.

Using several pseudo amino acid composition types and different machine learning algorithms to classify and predict archaeal phospholipases.

Phospholipases, as important lipolytic enzymes, have diverse industrial applications. Regarding the stability of extremophilic archaea's proteins in harsh conditions, analyses of unusual features of their proteins are significantly important for their utilization. This research was accomplished to in silico study of archaeal phospholipases' properties and to develop a pioneering method for distinguishing these enzymes from other archaeal enzymes via machine learning algorithms and Chou's pseudo-amino acid composition concept. The non-redundant sequences of archaeal phospholipases were collected. BioSeq-Analysis sever was used with Support Vector Machine (SVM), Random Forests (RF), Covariance Discrimination (CD), and Optimized Evidence-Theoretic K-nearest Neighbor (OET-KNN) as powerful machine learnings algorithms. Also, different Chou's pseudo-amino acid composition modes were performed and then, 5-fold cross-validation was applied to the sequences. Based on our results, the OET-KNN predictor, with 96% accuracy, yields the best performance in SC-PseAAC mode by 5-fold cross-validation. This predictor also achieved very high values of specificity (95%), sensitivity (96%), Matthews's correlation coefficient (0.92), and accuracy (96%). The present investigation yielded a robust anticipatory model for the archaeal phospholipase prediction utilizing the tenets PseAAC and OET-KNN machine learning algorithm.

Investigating the anti-streptococcal biofilm effect of ssDNA aptamer-silver nanoparticles complex on a titanium-based substrate.

Streptococcus mutans is a commensal and opportunistic pathogen that causes several diseases by forming a biofilm in humans and animals in many areas such as nasopharyngeal, cardiac valves, lungs, and oral cavity. Biofilms are very important in prosthetic infections associated with medical implants. The use of nanoparticles is one of the evolving fields in biofilm targeting. Silver nanoparticles can be used for biofilm targeting due to their inherent antimicrobial properties. Hybridization of nanoparticles with small molecules increases their biological properties and makes them multifunctional. The present investigation aimed to design an appropriate silver nanoparticles-aptamer complex that binds to the surface receptors of streptococcal strains. For this reason, silver nanoparticles with particle sizes in a range of 50 to 70 nm were synthesized and connected to a designed aptamer with a streptavidin-biotin linker. Then, the effect of the complex was investigated on the S. mutans biofilm formed on the surface of a medical-grade titanium substrate. The silver nanoparticles-aptamer complex at a concentration of 100 µg mL-1 after 48 h inhibited 43% of the biofilm formation and degraded 63% of the formed biofilm. Also, the cell availability reached 96% and the complex was stable in cell medium culture for 360 min. It was concluded that this complex could be a good candidate for removing the formed biofilms on the surface of titanium implants.

Conjugation of VEGFR1/R2-targeting peptide with gold nanoparticles to enhance antiangiogenic and antitumoral activity.

BACKGROUND: Inhibition of tumor angiogenesis through simultaneous targeting of vascular endothelial growth factor receptor (VEGFR)-1 and -2 is highly efficacious. An antagonist peptide of VEGFA/VEGFB, referred to as VGB3, can recognize and neutralize both VEGFR1 and VEGFR2 on the endothelial and tumoral cells, thereby inhibits angiogenesis and tumor growth. However, improved efficacy and extending injection intervals is required for its clinical translation. Given that gold nanoparticles (GNPs) can enhance the efficacy of biotherapeutics, we conjugated VGB3 to GNPs to enhance its efficacy and extends the intervals between treatments without adverse effects. RESULTS: GNP-VGB3 bound to VEGFR1 and VEGFR2 in human umbilical vein endothelial (HUVE) and 4T1 mammary carcinoma cells. GNP-VGB3 induced cell cycle arrest, ROS overproduction and apoptosis and inhibited proliferation and migration of endothelial and tumor cells more effectively than unconjugated VGB3 or GNP. In a murine 4T1 mammary carcinoma tumor model, GNP-VGB3 more strongly than VGB3 and GNP inhibited tumor growth and metastasis, and increased animal survival without causing weight loss. The superior antitumor effects were associated with durable targeting of VEGFR1 and VEGFR2, thereby inhibiting signaling pathways of proliferation, migration, differentiation, epithelial-to-mesenchymal transition, and survival in tumor tissues. MicroCT imaging and inductively coupled plasma mass spectrometry showed that GNP-VGB3 specifically target tumors and exhibit greater accumulation within tumors than the free GNPs. CONCLUSION: Conjugation to GNPs not only improved the efficacy of VGB3 peptide but also extended the intervals between treatments without adverse effects. These results suggest that GNP-VGB3 is a promising candidate for clinical translation.

In silico design of quadruplex aptamers against the spike protein of SARS-CoV-2.

Nucleic acid aptamers are short sequences of nucleic acid ligands that bind to a specific target molecule. Aptamers are experimentally nominated using the well-designed SELEX (systematic evolution of ligands by exponential enrichment) method. Here, we designed a new method for diagnosis and blocking SARS-CoV-2 based on G-quadruplex aptamer. This aptamer was developed against the receptor-binding domain (RBD) region of the spike protein. In the current study, ten quadruplex DNA aptamers entitled AP1, AP2, AP3, AP4, AP5, AP6, AP7, AP8, AP9, and AP10 were designed in silico and had high HADDOCK scores. One quadruplex aptamer sequence (AP1) was selected based on the interaction with RBD of SARS-CoV-2. Results showed that AP1 aptamer could be used as an agent in the diagnosis and therapy of SARS-CoV-2, although more works are still needed.

Metabolic engineering of Deinococcus radiodurans for pinene production from glycerol.

BACKGROUND: The objective of this work was to engineer Deinococcus radiodurans R1 as a microbial cell factory for the production of pinene, a monoterpene molecule prominently used for the production of fragrances, pharmaceutical products, and jet engine biofuels. Our objective was to produce pinene from glycerol, an abundant by-product of various industries. RESULTS: To enable pinene production in D. radiodurans, we expressed the pinene synthase from Abies grandis, the geranyl pyrophosphate (GPP) synthase from Escherichia coli, and overexpressed the native 1-deoxy-D-xylulose 5-phosphate synthase. Further, we disrupted the deinoxanthin pathway competing for the substrate GPP by either inactivating the gene dr0862, encoding phytoene synthase, or substituting the native GPP synthase with that of E. coli. These manipulations resulted in a D. radiodurans strain capable of producing 3.2 ± 0.2 mg/L pinene in a minimal medium supplemented with glycerol, with a yield of 0.13 ± 0.04 mg/g glycerol in shake flask cultures. Additionally, our results indicated a higher tolerance of D. radiodurans towards pinene as compared to E. coli. CONCLUSIONS: In this study, we successfully engineered the extremophile bacterium D. radiodurans to produce pinene. This is the first study demonstrating the use of D. radiodurans as a cell factory for the production of terpenoid molecules. Besides, its high resistance to pinene makes D. radiodurans a suitable host for further engineering efforts to increase pinene titer as well as a candidate for the production of the other terpenoid molecules.

In silico design of a multi-epitope peptide construct as a potential vaccine candidate for Influenza A based on neuraminidase protein.

Designing an effective vaccine against different subtypes of Influenza A virus is a critical issue in the field of medical biotechnology. At the current study, a novel potential multi-epitope vaccine candidate based on the neuraminidase proteins for seven subtypes of Influenza virus was designed, using the in silico approach. Potential linear B-cell and T-cell binding epitopes from each neuraminidase protein (N1, N2, N3, N4, N6, N7, N8) were predicted by in silico tools of epitope prediction. The selected epitopes were joined by three different linkers, and physicochemical properties, toxicity, and allergenecity were investigated. The final multi-epitope construct was modeled using GalaxyWEB server, and the molecular interactions with immune receptors were investigated and the immune response simulation assay was performed. A multi-epitope construct with GPGPGPG linker with the lowest allergenicity and highest stability was selected. The molecular docking assay indicated the interactions with immune system receptors, including HLA1, HLA2, and TLR-3. Immune response simulation detected both humoral and cellular response, including the elevated count of B-cells, T-cell, and Nk-cells. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40203-021-00095-w.

Analysis and comparison of physiochemical properties, mutations and glycosylation patterns between RNA polymerase and membrane protein of SARS-CoV and SARS-CoV-2.

SARS-CoV-2 is a member of ß-genus of the coronavirus subfamily, alongside the virus that causes SARS (Severe Acute Respiratory Syndrome). As implied by their names, SARS-CoV-2 and SARS-CoV genome sequences have close kinship (about 79% genomic sequence similarity). In the current research, sequence-based physiochemical properties of RNA polymerase and membrane glycoprotein of SARS-CoV-2 and SARS-CoV were compared. In addition, impacts of substitution mutations on stability and glycosylation patterns of these proteins were studied. In comparison of physiochemical features of membrane and RNA polymerase proteins, only instability index of membrane protein was difference between SARS-CoV and SARS-CoV-2. Mutation analysis showed increase in stability of RNA polymerase and decrease in stability of membrane protein in SARS-CoV-2. Glycosylation pattern analysis showed glycosylation enhancement in both membrane and RNA polymerase proteins of SARS-CoV-2 in comparison to SARS-CoV. In conclusion, more glycosylation and stability of SARS-CoV-2 RNA polymerase could be one of the reasons of high pathogenicity property and host immune system evasion of SARS-CoV-2.

A Comparative Analysis of Allergen Proteins between Plants and Animals Using Several Computational Tools and Chou's PseAAC Concept.

BACKGROUND: A large number of allergens are derived from plant and animal proteins. A major challenge for researchers is to study the possible allergenic properties of proteins. The aim of this study was in silico analysis and comparison of several physiochemical and structural features of plant- and animal-derived allergen proteins, as well as classifying these proteins based on Chou's pseudo-amino acid composition (PseAAC) concept combined with bioinformatics algorithms. METHODS: The physiochemical properties and secondary structure of plant and animal allergens were studied. The classification of the sequences was done using the PseAAC concept incorporated with the deep learning algorithm. Conserved motifs of plant and animal proteins were discovered using the MEME tool. B-cell and T-cell epitopes of the proteins were predicted in conserved motifs. Allergenicity and amino acid composition of epitopes were also analyzed via bioinformatics servers. RESULTS: In comparison of physiochemical features of animal and plant allergens, extinction coefficient was different significantly. Secondary structure prediction showed more random coiled structure in plant allergen proteins compared with animal proteins. Classification of proteins based on PseAAC achieved 88.24% accuracy. The amino acid composition study of predicted B- and T-cell epitopes revealed more aliphatic index in plant-derived epitopes. CONCLUSIONS: The results indicated that bioinformatics-based studies could be useful in comparing plant and animal allergens.

In silico analyzing the molecular interactions of plant-derived inhibitors against E6AP, p53, and c-Myc binding sites of HPV type 16 E6 oncoprotein.

Human papillomaviruses (HPV) are a group of strong human carcinogen viruses considered to be the fourth leading cause of mortality among women in the world. HPV is the most important cause of cervical cancer, which is the second most common cancer in women living in low and middle-income countries. To date, there is no effective cure for an ongoing HPV infection; therefore, it is required to investigate anticancer drugs against this life-threatening infection. In this study, we collected more than 100 plant-derived compounds with anti-cancer and antiviral potentials from a variety of papers. Smile formats of these compounds (ligand), were harvested from PubChem database and examined based on the absorption, distribution, metabolism, excretion, and toxicity properties by programs such as Swiss ADME, admetSAR, and pkCSM. Twenty compounds, which were likely to be the HPV16E6 inhibitor, were selected for docking calculations. We examined these natural inhibitors against the HPV16 E6 oncogenic protein. Eventually, three of these compounds were used as the most potent inhibitors (Ginkgetin (peculiarly), Hypericin and Apigetrin) were probably used as the possible source of cancer treatment caused by E6 oncoprotein. In this research, we conducted the docking calculations by Autodock 4.2.6 software. Docking analysis showed the interaction of these plant-originated inhibitors with E6AP, p53, and Myc binding sites on the E6 oncoprotein which support the normal function of E6AP, p53, and Myc.

In Silico Selection of Gp120 ssDNA Aptamer to HIV-1.

Nucleic acid aptamers that specifically bind to other molecules are mostly obtained through the systematic evolution of ligands by exponential enrichment (SELEX). Because SELEX is a time-consuming procedure, the in silico design of specific aptamers has recently become a progressive approach. HIV-1 surface glycoprotein gp120, which is involved in the early stages of HIV-1 infection, is an attractive target for RNA and DNA aptamer selection. In this study, four single-stranded DNA aptamers, referred to as HD2, HD3, HD4, and HD5, that had the ability of HIV-1 inhibition were designed in silico. In a proposed non-SELEX approach, some parts of the B40 aptamer sequence, which interacted with gp120, were isolated and considered as a separate aptamer sequence. Then, to obtain the best docking scores of the HDOCK server and Hex software, some modifications, insertions, and deletions were applied to each selected sequence. Finally, the cytotoxicity and HIV inhibition of the selected aptamers were evaluated experimentally. Results demonstrated that the selected aptamers could inhibit HIV-1 infection by up to 80%, without any cytotoxicity. Therefore, this new non-SELEX approach could be considered a simple, fast, and efficient method for aptamer selection.

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.

Analysis and comparison of alkaline and acid phosphatases of Gram-negative bacteria by bioinformatic and colorimetric methods.

Alkaline phosphatase (ALP) and acid phosphatase (ACP) are two important phosphatase enzymes that play fundamental roles in Gram-negative bacteria. Additionally, they are useful for various biotechnological and industrial applications. In the present study, different aspects of bacterial ALPs and ACPs such as pseudo amino acid composition (PseAAC), amino acid composition, dipeptide composition, physicochemical properties, secondary structures and structural motifs were studied. The binding affinity of the phosphomonoesters to ALP and ACP enzymes was predicted by docking, and the activity of ALPs and ACPs were measured using colorimetric assay. ROC curve statistical analysis the machine learning algorithms were applied for classification of these two phosphatase protein groups. The results indicated that the physicochemical properties of ALPs and ACPs were not significantly different, although the aliphatic index and Extinction coefficient of motifs of these two enzymes were significantly different. Classification based on the concept of PseAAC and dipeptide composition also indicated high accuracy. The result of docking demonstrated that the binding free energy of ALPs was less than ACPs and the experimental results demonstrated that the activity of ACPs was more than ALPs. In conclusion, there is a relationship between efficiency and PseAAC and dipeptide compositions of these two enzymes.

Studying the effects of several heat-inactivated bacteria on colon and breast cancer cells.

A great number of researches over the last years are allocated to know cancer reasons, prevention and treatment strategies. Bacterial infections are one of the promoting factors in cancer development. The present study was carried out to study effects of heat-killed bacteria on cancer cell lines MCF7 and HT-29. To this purpose, four bacterial strains including Salmonella typhi, Staphylococcus epidermidis, Escherichia coli and Pseudomonas aeruginosa were assayed. Thermal inactivation method was used to kill the bacteria and preserve the bacterial surface proteins unchangeable. The concentrations of 0.01, 0.1, 0.5 and 1 mg/ml of inactivated bacteria were prepared to evaluate the effects of heat-inactivated bacterial solutions on MCF7 and HT-29 cell lines. MTT assay was used to measure the cell viability of cancer cells treated with different concentration of inactivated bacterial solutions.The MTT assay results after 48 hours showed that the heat-killed bacterial solutions were able to induce the proliferation of both cancer cell lines. In addition, the most cell viability in MCF-7 cell line was seen in samples treated with S. epidermidis, while in HT29 cells, the most one was seen in S. typhi treated samples. It was concluded that bacterial infections are cancer-deteriorating agents, and any species of bacteria is specific to certain cancerous tissue.

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.

Predicting Promiscuous T Cell Epitopes for Designing a Vaccine Against Streptococcus pyogenes.

One of the most dangerous human pathogens with high prevalence worldwide is Streptococcus pyogenes, which has major impacts on global morbidity and mortality. A major challenge for S. pyogenes vaccine development is the detection of epitopes that confer protection from infection by multiple S. pyogenes types. Our aim was to identify the most conserved and immunogenic antigens of S. pyogenes, which can be a potential candidate for vaccine design in the future. Eight important surface proteins were analyzed. Using different prediction servers, strongest epitopes were selected. They had the ability to stimulate the humoral and cell-mediated immune system. Molecular docking was performed for measuring free-binding energy of selected epitopes. Seven epitopes from three surface proteins were selected as potential candidates for vaccine development. Conservation of selected epitopes among different Streptococcus types was checked. Further in vitro and in vivo tests are required to validate the suitability of the epitopes for vaccine design.

Computational prediction of antifungal peptides via Chou's PseAAC and SVM.

With the increase in immunocompromised patients in the recent years, fungal infections have emerged as new and serious threat in hospitals. This, and the insufficiency of current antifungal therapies alongside their toxic effects on patients, has led to the increased interest in seeking new antifungal peptides. In the present study, we have developed a prediction method for screening of antifungal peptides. For this, we have chosen Chou's pseudo amino acid composition (PseAAC) to translate peptide sequences into numeric values. Thus, the SVM classifier was performed for binomial classification of antifungal peptides. The performance of the classifier was evaluated via ten-fold cross-validation and an independent dataset. For further validation of the model developed, 22 P24-derived peptides were predicted using the classifier and in vitro assays were performed on the three peptides with the highest prediction score. The results showed that the PseAAC + SVM method is able to predict AFPs with ACC of 94.76%. In vitro results also validate the SEN and SPC of the classifier. The results suggest that the computational approach used in this study is highly efficient for prediction of antifungal peptides, which can save time and money in AFP screening and synthesis of novel peptides.

Prediction of T-cell epitopes for designing a reverse vaccine against streptococcal bacteria.

Streptococcal bacteria are among dangerous human pathogens with major prevalence worldwide. A good vaccine against streptococcal bacteria should have epitopes that confer protection from infection by different streptococcal bacteria types. we aimed was to recognize the most immunogenic and conserved epitopes of streptococcal bacteria, which could be a potential candidate for vaccine development. Nineteen different M proteins of different streptococcal bacteria were chosen and analyzed. Nine-mer epitopes able to simulate both cells mediate and humoral immunity were predicted. Molecular docking was applied in order to measure free binding energy of selected epitopes. Final epitopes were analyzed if they were conserved among different streptococcal bacteria. The identified epitopes require experimental validation for their potential use in peptide vaccines.

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.