Modares Journal of Biotechnology

Abstract Mnemiopsin 2 is a Ca2+ regulated photo protein with 207 residues and a molecular weight of 24722 Daltons. In the structure of this photoprotein, the EF-hand I-III-IV motifs have retained their function in binding to Ca2+, while the EF-hand II has lost its activity during evolution. Each EF-hand has a helix-loop-helix (HLH) structure. Loops with length of 12 residues are responsible for Ca2+ binding. In this study, in order to recovery the structural and functional of the EF-hand loop II, a Photoprotein Mnemiopsin Chimeric (PMC) was designed using direct evolution and rational design. The mutant structure were modeled by Modeller v2.10 software. Then the best model was evaluated using the Chimera x.8.1 software and Modeval and SAVES and ModEval server for RMSD, RRDistance, Z-Dope, Errat and Verify 3D parameters were investigated. Also, the secondary structure, free energy of folding and accessible surface of the models were investigated by the VADAR server. The hydrophobicity and instability index were evaluated by Protscale and ProtParam servers. The Prosite results indicate the formation of EF-hand II loop in PMC. It is worth mentioning that Changes in the surface hydrophobicity of the recovered EF-hand II motif may affect the interaction with Ca2+. That means, due to the increase the Ca2+ binding sites, sensitivity to Ca2+ and activity were predicted to change.
 

Abstract Epidermal growth factor receptor (EGFR) is one of the most important tyrosine kinase receptors that plays a key role in regulating cellular processes and the progression of many cancers, including lung cancer. In this study, the effects of second-generation EGFR inhibitors, including Afatinib, Dacomitinib, and Neratinib, as well as the candidate drugs Canertinib and Poziotinib, on wild-type EGFR were investigated using molecular dynamics (MD) simulations. For this purpose, structural data were collected and analyzed from reliable databases. Molecular docking studies led to the identification of drug binding sites, and molecular dynamics (MD) simulations under physiological conditions investigated stability and ligand-protein interactions. The parameters such as RMSD, radius of gyration (Rg), SASA, and hydrogen bonds were calculated to evaluate the stability of the protein-ligand complex. The results of the MMPBSA analysis showed that Neratinib, with the lowest free energy of binding (ΔG), has a higher binding affinity to EGFR and demonstrated greater stability during the simulation. Also, the principal component analysis (PCA) showed that the EGFR-Neratinib complex has less dynamics and occupies less phase space, which indicates more stability of this complex.
These results show that of all the compounds studied, Neratinib may be the most potent and promising candidate in advancing combination therapies against EGFR.

Abstract Anaplastic lymphoma kinase (ALK) is an important target in the treatment of cancer, especially non-small cell lung cancer (NSCLC) with gene rearrangements. In this study, the effects of three tyrosine kinase inhibitors (TKIs) including Crizotinib, Ceritinib, and Alectinib on the ALK tyrosine kinase domain were investigated and compared using molecular dynamics (MD) simulations. Various analyses such as RMSD, radius of gyration (RG), solvent accessible surface area (SASA), hydrogen bond number (Hbond), principal component analysis (PCA), and MMPBSA were used in this study.
Molecular dynamics simulations for 100 ns showed that Alectinib resulted in structural stability, reduced solvent accessible surface area (SASA) compared to Crizotinib and Ceritinib, and greater protein compaction. MMPBSA results also indicated a lower binding free energy and higher binding affinity of Alectinib to ALK, which enhances its efficacy. The use of Alectinib, due to its high ability to cross the blood-brain barrier and its effect on brain metastases, can improve treatment efficacy and reduce drug resistance. These results and findings indicate that Alectinib can be an option for first-line treatment.

Abstract Phosphorylation is one of the most important types of post-translational modification (PTM) that plays an important role in protein function studies and experimental design. Considering the importance of phosphorylation in proteins and the increasing number of protein sequences in the database, the need to improve computational methods for predicting phosphorylation sites becomes more important day by day in terms of speed and accuracy. Although many predictive tools have been introduced to predict phosphorylation sites using different machine learning methods, there is still a long way to go to a very efficient tool and efforts to achieve such a tool continue. Recent studies have shown that deep learning-based methods are the best approach for predicting phosphorylation sites. This is because deep learning, as an advanced machine learning method, can automatically recognize complex representations of phosphorylation patterns from raw sequences, therefore providing a powerful tool for improved phosphorylation site prediction.
In this study, a hybrid structure based on the convolutional deep learning method named ConvoPhos has been introduced for predicting phosphorylation sites. In such a way that the CkSAApair feature vector obtained from sequences is used as the input for part of the classifier and the conversion of sequences to images, as the input for another part of the convolutional networks.The results of 10-fold cross-validation show an accuracy value of 94% for phosphosite data and an AUC of 90%, which is the highest performance compared to the other methods.

Abstract Ferroptosis is a newly identified form of cell death associated with lipid peroxidation. This process is dependent on iron and polyunsaturated fatty acids (PUFAs). Despite the importance of ferroptosis, the molecular details of this process, particularly its impact on cellular membrane properties, remain unknown. In this study, structural and permeability changes in the plasma membrane resulting from lipid peroxidation during ferroptosis were investigated using molecular dynamics simulations. Initially, a model of the human red blood cell membrane was constructed based on experimental data. To simulate ferroptosis, the PUFA lipid chains in the red blood cell membrane were replaced with their hydroperoxide derivatives. Both systems (normal and ferroptotic membranes) were examined in All-Atom molecular dynamics simulations for 300 nanoseconds (with three replicates). The results showed that in the ferroptotic membrane, the thickness decreased, and the surface area increased. Additionally, the hydroperoxide groups in the fatty acid chains moved toward the polar head groups of the phospholipids. Besides these structural changes, the function of the membrane, which typically acts as an impermeable barrier to polar molecules such as water, was disrupted due to lipid peroxidation, while the overall membrane integrity remained intact. In summary, lipid peroxidation in ferroptosis induces significant changes in membrane structure and function, which could be utilized in the development of new treatments for severe diseases such as cancer and neurodegenerative disorders.

Abstract Today, the engineering of bone tissue has created special solutions to restore bone tissue by combining biological materials with a scaffold to provide cells suitable for bone formation and growth factors. In this research, a fusion peptide was designed with bioinformatics methods that can bind to the growth factors involved in bone tissue repair and lead to the trapping of these factors in the lesion site. In this study, heparin-binding domain was placed in the designed peptide and this peptide was complexed with growth factor in monomer and dimer forms with the help of docking. The structures of the complex were selected based on the lowest scores obtained, which included -912.5 and -1117, respectively. According to the results of docking and molecular dynamics simulation, this fusion peptide was able to bind to the growth factor of bone morphogenetic proteins. Based on the results of the simulation, unlike the peptide in the monomer state, the changes in the RMSD diagram of the peptide complex in the dimer state became stable after 10 nanoseconds from the simulation time and remained stable until the end of the simulation. These results show that the resulting complex in the dimer state has a better pattern of stability compared to the monomer state according to the investigation of the RMSD factor.

Abstract Viral infections represent pathological conditions arising from the intrusion of viruses into host cells and their replication. The onset of infection is intricately tied to the interplay between viral and host cell proteins. Thus, elucidating these protein-protein interactions assumes a pivotal role in the encompassing prevention, treatment, and control of viral infections. Given traditional laboratory experimentation's prohibitively high costs and time-intensive nature, researchers have increasingly turned to computational approaches for predicting human-virus protein-protein interactions. Despite the performance of these computational approaches, a challenge persists in the need for an effective protein representation that adequately captures their structural intricacies.
In this paper, we present PBS, a novel model for the prediction of protein-protein interactions between viruses and humans. PBS leverages the transformers to effectively represent proteins. The model unified the latent space for human and virus proteins through the implementation of heterogeneous siamese neural networks.The model achieves an accuracy score of 81.41%, an area under the ROC curve score of 87.35%, an area under the precision-recall curve score of 87.78%, an F1 score of 81.58%, and a precision score of 80.84%. These metrics collectively underscore the satisfactory performance of the PBS model.
Furthermore, we assess the model's predictive capabilities in discerning interactions between proteins associated with the H1N1 influenza virus and human proteins.

Abstract Blood pressure monitoring is a vital component of maintaining overall health. High blood pressure values, as a risk factor, can lead to heart attacks, strokes, and heart and kidney failures. Similarly, low blood pressure values can also be dangerous, causing dizziness, weakness, fainting, and impaired oxygen delivery to organs, resulting in brain and heart damage. Consequently, continuous monitoring of blood pressure levels in high-risk individuals is very important. A Holter blood pressure monitoring device is prescribed for many patients due to its ability to provide long-term and valuable blood pressure data. The pursuit of software techniques and the development of cuffless blood pressure measurement devices, while ensuring patient comfort and convenience, are among the significant challenges that researchers are focusing on. In this study, a deep learning framework based on the UNet network is proposed for continuous blood pressure estimation from photoplethysmography signals. The proposed model was evaluated on the UCI database, involving 942 patients under intensive care, and achieved mean absolute errors of 8.88, 4.43, and 3.32, with standard deviations of 11.01, 6.18, and 4.15, respectively, for systolic, diastolic, and mean arterial blood pressure values. According to the international BHS standard, the proposed method meets grade A criteria for diastolic and mean blood pressure estimations and grade C for systolic blood pressure estimation. The results of this study demonstrate that the suggested deep learning framework has the necessary potential for blood pressure estimation from PPG signals in real-world applications.

Abstract Newcastle disease virus (NDV) causes one of the most dangerous infections in birds. High economic losses and high mortality are outcomes of this virus, which does not have any immediate cure. The natural reservoir of this virus can remain among bird and non-bird animals like farm animals. In Iran, this virus has reached a steady situation. Also, it should be mentioned that migrating birds can transfer the virus. The F protein of the virus is essential in pathogenicity and determination of pathogenic strain of NDVs, which has the regions that are essential in pathogenicity, immunogenicity, cell fusibility, and tissue necrosis. In this study, with computational analysis of this protein, some features related to this protein such as protein cleavage site, the conserved region in immunogenicity, infected species in Middle Eastern countries, and physicochemical properties of protein were determined. Results showed that the F protein of NDV consists of highly conserved regions that show a high rate of similarity and identity. Despite the majority of strains characterized as pathogenic, there were still non-pathogenic strains circulating in the Middle East. In this comprehensive study, protein regions essential in immunogenicity and epitope formation were identified, which may be used in the development of recombinant vaccines against this virus.

Abstract Breast cancer is the most common cancer in women, and despite many scientific advances, it remains the leading cause of cancer-related death in women. To solve this global problem, deeper molecular studies in the field of breast cancer are needed. Nowadays, the role of piRNAs in various cancers is of great interest. In this study, we aim to identify important piRNAs involved in breast cancer. For this purpose, raw small RNA seq data related to cancerous and normal breast tissue samples were selected and extracted from the GEO database, and the Galaxy platform was used for their bioinformatic analysis. The differential expression of 372 piRNAs was obtained based on Log2 FC ≥ 2, p-value ≤ 0.05, of which 191 showed increased expression and 181 showed decreased expression. The highest increase is related to hsa-piR-33125, whose target is GATAD2A and plays a role in carcinogenesis processes such as blood vessel development, apoptosis, regulation of gene expression at the transcriptional level, etc. The largest decrease is related to hsa-piR-33073 with Log2 FC= -4.20. To find a list of important piRNAs that have a significant difference in expression in breast cancer compared to normal tissue, as well as to determine the increase or decrease of their expression in cancer tissue and to identify the target genes and investigate their role in the biological pathways involved in the development and progression of cancer. This can be the beginning of studies that will ultimately lead to advances in breast cancer research and treatment methods.

Abstract PFU DNA polymerse shows the lowest error rate in Polymerase Chain Reaction (PCR) but it seperates from DNA after about 20 nucleotides add to the end of primer strand. The research purpus is processivity improvement of PFU DNA polymerase by means of rational design and point mutation due to lowest enthropy and enthalpy costs. so DNA polymerases in B family with high processivity were selected and their structures and sequences were compared with PFU DNA polymerase then an optimized mutation was induced . Native form and mutant were simulated for 100 ns and the trajectories were analyzed. ΔG_binding was calculated by g_mmpbsa tool and proved that the mutant shows a robust affinity .

Abstract The countries’ social and economic conditions have been threatened by corona epidemic and a large number of people’s death in the world. SARS-CoV-2 virus, a form of corona virus family, is responsible for corona disease and its spread in the present century. The study of the receptor binding region (RBD) in the spike protein is very important for scientists because the new corona virus uses its surface spike protein for binding to the ACE2 surface protein and entering its genetic material to the host cells. By this protein and its receptor binding region inhibition, the prevention of virus entrance to the cell is possible. The virus’s genes can be multiplied by cloning and its protein can be purified. The usage of antiviral peptides as the most practical methods and binding inhibitory peptides of RBD to the ACE2 receptor for SARS-CoV-2 treatment, are of great interest to scientists. In the present research, RBD cloning in PET28a expression vector, RBD protein expression and GFP/RBD fusion protein were performed in prokaryotic host. Due to this protein’s insolubility in the prokaryotic host, column refolding was performed with urea gradient with a nickel-agarose column and the synthesized protein was confirmed through western blot technique. Three nominated peptides from articles used to compare their binding to RBD using bioinformatics and their tendency to bind to each other was investigated by molecular docking. The mentioned peptides can be used in this virus infection treatment due to their binding potential to RBD, if their interaction is proven.

Abstract Aspergillus has many species that are important in medicine, agriculture, and various industries. The genus has 446 identified species, which are difficult to distinguish from each other with the use of morphological characteristics. Xylan 1,4-beta-xylosidase is an enzyme that catalyzes the hydrolysis process of xylose in xylooligosaccharides and is produced by different species of Aspergillus. This research was conducted with the aim of a bioinformatics study of the gene region related to this enzyme and to evaluate its similarities and differences in some Aspergillus species. The results showed that this gene region, due to the presence of conserved motifs, was able to distinguish some species studied in this research.

Abstract Cancer is one of the leading causes of death worldwide. Breast cancer is the most common cancer among women and causes a high number of annual deaths. The most reliable method for successful cancer management is accurate and early diagnosis. On the other hand, the lack of timely diagnosis leads to the spread of cancer in the body, making it difficult to treat and control. The gold standard method for breast cancer diagnosis is biopsy. Usually, visual inspection and manual assesement are used to diagnose cancer, where the pathologist examines the histopathology slides under a microscope which is error-prone and time- consuming procedure and requires years of expertise. Therefore, computer-aided diagnosis is essential to help physicians improve the efficiency of interpreting medical images. In this study, we use deep learning models, especially convolutional neural networks (CNNs) to detect whether or not histopathological images are cancerous. The AUC, Precision and F1-score obtained using the pre-trained Incetion-V3 deep neural network are 98.36%, 95.28% and,97.25% respectively, and the same parameters for the pre-trained ResNet-18 deep neural network are equal to 97.90 %, 97.46% and 98.22%. The presented models are able to provide reliable diagnosis results for different morphologies of breast tissues.

Abstract The COVID-19 pandemic has created a global health crisis, and developing effective treatments is essential to prevent the spread of the disease and save millions of lives. One of the key proteins involved in the replication cycle of SARS-CoV-2, the virus that causes COVID-19, is the main protease enzyme, 3CL^pro. Due to its high importance, this enzyme is the subject of molecular, structural, and clinical investigations, and efforts have been made to develop drugs that can inhibit its activity. One such drug is the chemical compound N3, which has been found to have a high inhibitory effect against 3CL^pro. However, traditional medicine perspectives on this issue have been less explored. In this research, molecular docking interaction simulation and all-atom molecular dynamics (MD) simulation were conducted to study the potential inhibitory capability of generally available 21 plant-extracted compounds against the 3CL^pro enzyme. Three compounds with the highest inhibition probability were selected from the molecular docking results and subjected to 100 ns of MD simulation to investigate their stability and structural-dynamic-energetic features. Beside the complexes stability, the results from the simulation demonstrated that, all our selected three compounds induce N3 comparable structural-dynamics characteristics to 3CL^pro and, therefore, are expected to have a similar inhibitory ability against this enzyme. Compound number 5 was found to have the most favorable binding energy and was proposed as the best plant substitute for N3. The results from this research can be directly used to design experimental research for 3CLpro enzyme inhibition, saving the time-financial cost.

Abstract Hepatitis C virus (HCV) NS3/4A Serine protease is an important drug target for treating patients with hepatitis C virus. However, its amino acid mutations, particularly A156G, commonly lead to the rapid emergence of drug resistance. Bosiprevir, simiprevir, and viniprevir drugs approved by the FDA show distinct resistance profiles against the HCV NS3/4A protease. In order to show the behavior of each of these drugs in the interaction with the protease in the wild type and A156G mutant, molecular dynamics simulation and binding free energy calculations were performed. MMPBSA-based binding free energy calculations showed that the binding affinity of each of the drugs in the interaction with NS3/4A protease in the wild type is significantly more than the interaction with the protease in the A156G mutant state. Free energy landscape (FEL) calculations revealed that in the presence of each of the drugs, more basins of conformations are formed. We hope that our data can provide useful insights for the design of a new effective inhibitory drug for the treatment of patients with the hepatitis C virus.

Abstract Aim: Follistatin-like protein 1 (FSTL1) is a secreted glycoprotein that plays an important role in regulating cell survival, proliferation, differentiation, migration, inflammation, and modulating the immune system. The FK domain in FSTL1 has 10 conserved cysteine residues that form 5 disulfide bonds. Despite extensive studies on the function of FSTL1, limited structural information is available about this biologically important molecule.
Materials and Methods:Using the SWISS-MODEL server and using the crystal structure of the FK domain of the mouse FSTL1 protein with the code (PDB: 6jzw) as a template, structural models of the FK domain of the human FSTL1 protein were prepared. In the next step, the resulting structures were checked using Swiss-PDB Viewer 4.10, Chimera 1.12 software, Ramachandaran diagram and PDBSUM server, in terms of the distance between two cysteine residues, the modeling error range, and the formation of disulfide bonds. Molecular dynamics simulations were performed using the AMBER software package with the ff14SB force field.
Results: The results showed that the FK domain without disulfide bond has root mean square deviations (RMSD) and root mean square fluctuations (RMSF), higher than the native FK domain. In addition, the radius of gyration in domain without disulfide bonds is significantly lower than that of native FK domain. The results show that the disulfide bonds of the FK domain play a role in the stability of the structural folding of the FK domain and the removal of these bonds increases the structural flexibility of this domain.

Abstract Nowadays, the peptides and proteins possessing anti-cancer, anti-allergic and anti-inflammatory properties are used for disease treatment. Brazzein is a sweet protein containing 54 amino acids and according to reports, it has anti-cancer properties based on sequence and structurehas sequence. In this study, the role of position 40 aspartate in the structure and function of wild brazzein protein and mutants as well as the anti-cancer properties of the peptides obtained on the TLR5 receptor were investigated. For this, several models of mutated forms were designed and constructed using Modeller.v.9.20 software. Then, the accuracy of the models and the physico-chemical properties of wild type (WT) and mutants of D40N, D40R and D40Deletion were evaluated using various bioinformatics servers and softwares including ProtParam, ProtScale, SAVES, PIC, ModEval, and PredyFlexy. For predicting anticancer properties, the sequence of WT protein and mutants was examined and compared using ACPred and iACP servers. The quality and analysis of WT protein and mutants binding as a ligand with TLR5 receptor, triggering an anti-cancer signaling pathway, were investigated through molecular docking using HADDOCK software.The results of bioinformatics parameters analysis indicated the possibility of improving the stability of brazzein structure and function, and the probability of increasing the available surface to bind to the receptor. Moreover, based on the results of molecular docking analyses, the ability binding TLR5 receptor was higher in D40R than the other proteins indicating an increased probability in anti-cancer properties of the mutant.

Abstract Leguminosae is one of the most important plant families. In this study, we searched Arachis hypogaea, Glycine max, G. soja, Lotus japonicas, Medicago truncatula, Phaseolus vulgaris, and Vigna unguiculata conserved miRNAs through Expressed Sequence Tag (EST) based-homology method. All candidate sequences with appropriate fold-back structures were screened and characterized according to several filtering criteria. Chromosomal MIR locus and their distributions determined. The Dollo maximum parsimony was employed to construct the species relationship based on the MIR birth and death. Also, the number of MIRs gains and losses in the evolutionary process. In addition, we estimated the numbers of MIRs in their ancestral species using Dollo maximum parsimony in their phylogenetic tree. We found 414 novel miRNAs from 130 MIR families meeting the restricted filtering criteria. Either evolutionary time or the number of miRNAs gains and losses are estimated and characterized in the ancestral species based on the taxon-based phylogenetic tree. It speculates that gains of miRNA gene families within Leguminosae have accelerated during its evolutionary time. In addition, several taxon-specific MIR families find to assign diverse taxon and their species. Our thorough analyses resulted in the definition of some miRNA families as being lineage-specific. Therefore, they can use as markers in future systematic studies.

Abstract Resistance to chemotherapy drugs always has been an obstacle in the definitive treatment of cancers. Therefore, the discovery of molecular events leading to drug resistance improves therapeutic methods. Non-coding RNAs (ncRNAs) are a group of molecules that regulate intracellular events, including carcinogenesis and drug resistance pathways. For example, the competitive network of endogenous ncRNAs (ceRNA) regulates the mRNA expression of target genes by binding to miRNAs and limiting their regulatory effect. So far, limited studies have been reported on the role of ceRNA in drug resistance in ovarian cancer. In this study, large-scale RNAseq sequencing data obtained from cisplatin-resistant and sensitive cells were used to search for ceRNAs that are possible regulators of drug resistance in ovarian cancer. For this purpose, the A2780 sensitive and resistant cisplatin ovarian cancer cell line was selected, and the SRA data prepared by RNAseq method was screened. During this process, lncRNAs, microRNAs and mRNAs with expression changes were separated and classified. In the bioinformatic analysis of resistant and sensitive cells, 16 mRNAs, 10 lncRNAs, and 149 miRNAs were overexpressed, and 622 mRNAs, 263 lncRNAs, and 177 miRNAs were underexpressed. These genes were involved in 57 cellular pathways, and by mapping the regulatory ceRNA network, ZNRF3-AS1-miR-33-DUSP1 and ZNRF3-AS1-miR33-HSPA2 axes were identified as potential ceRNA networks involved in cisplatin-resistant ovarian cancer.

Abstract Background:
HIV has at least six regulatory genes among which the Vif protein can control HIV replication. This study, as the first report, investigated the important mutations in VIF protein in sequences from Iranian patients and using immunoinformatics, conserved regions of this protein and B-Cell, T-Cell and CTL epitopes to stimulate the immune system, were determined.
Methods:
VIF sequences were obtained from NCBI GenBank, and tertiary structures, B-Cell, T-Cell and CTL epitopes were predicted by bioinformatics tools; besides, their antigenic and allergenic properties were studied.
Results:
The most prevalent mutations in Vif protein were related to S 49 P (90%), S 140 N and N 186 S (80%). Two substitutions at positions 41 and 42 were introduced which have effect on Vif binding to host factor. In addition, three regions were identified as the best epitope sequences with high potential to induce immune system and the lowest allergic properties, among which 5-32 region was suggested as the best vaccine candidate regions.
Conclusion:
This study as the first study from Iran using immunoinformatics tools to introduced a region with the high potential to induce humoral and cellular immune systems and lowest allergenic properties which can be used for further studies on HIV vaccines.

Abstract Vitamins D and E are two common medicines for diabetes treatment. Among the main issues in this field is the release of insulin into the circulatory system. Increasing the stability of insulin hexamer is an evolving strategy in improving insulin secretion efficiency. Insulin protein is commonly found in three forms: monomer, dimer, and hexamer. In this study, for the first time, computational approaches were used to investigate the effect of vitamins D3 and E on the stability of insulin hexamer. The molecular docking results indicate six specific binding sites for these vitamins. These bind to the hydrophobic sites of insulin subunits due to their structural rings and hydrophobic properties. The G-mmpbsa analysis indicates the stabilizing role of both vitamins. The binding of these vitamins to the hexamer has significantly increased the binding energy between insulin subunits. Also, the number of hydrogen bonds between monomeric subunits of each insulin homodimer increased in the presence of the vitamins. It also significantly increases the number of internal hydrogen bonds of hexamer protein. Accordingly, vitamins D3 and E bind to and stabilize the insulin hexamer, resulting in a slower and more balanced insulin release as well as a longer half-life for the dimer in the bloodstream. These findings will pave the way to design a new strategy to regulate insulin release and increase its half-life in the blood for type II diabetes treatment. Besides, hexamer stabilization can be an effective treatment strategy for type I diabetes through slow release from an implanted biosensor system.

Abstract Moonlight proteins are a subset of multifunctional proteins in which more than one independent or usually distinct function occurs in a single polypeptide chain. Analyzing the interactive networks of proteins in the cell makes it possible to understand how complex processes cause disease. With the help of systems biology, larger and more complex systems can be studied, and the molecular basis of several diseases can be considered. The proteins of the human organism that are moonlight are mostly involved in cancer, anemia, and neurodegeneration. In this work, we created a subnet according to the human PPI network, in which the nodes, the proteins that cause the three selected diseases, and the edges, are the connection of these proteins with each other. We measured the power of the indirect effects of non-disease mediators between the three disease groups and identified key disease-binding intermediate proteins. The results show the relationship between mediator role and centrality and between mediator role and functional properties of these proteins. We have shown that a protein that plays a key indirect mediator between two diseases is not necessarily a hub in the PPI network. Therefore, as hub proteins are considered, intermediate proteins should be considered. We have observed that the mediators between anemia and neurodegeneration diseases are functionally important in the cell. The mediator proteins suggested herein should be experimentally tested as hypothetical disease-related proteins.

Abstract Ras signaling is an important intracellular signaling pathway that is key regulator of several aspects of normal cell growth and malignant transformation. The RAS gene family consists of three small G proteins; H-Ras, N-Ras, and K-Ras that play a central role in cell signaling for growth, proliferation, and migration. Mutation of the Ras oncogenes creates the malignant properties that are needed for cancer to grow and spread. MicroRNAs (miRNAs) that are encoded within the Ras genes might also have roles in cancer development. Here, novel microRNAs located in the human N-Ras gene were bioinformatically predicted. SSC profiler program was utilized to predict the stem-loop structures within the genomic area of interest. UCSC genome browser database was useed to analyze the conservation status of the putative miRNA and its precursor sequence. Furthermore, the N-Ras-miRs prediction was also performed by using MatureBayes online tool. In addition, RNAFOLD online software which applies the minimum-free energy (MFE) RNA structure prediction algorithm, was used for approximate prediction of the stem-loop structure. Our results demonstrate that N-Ras with about 5Kb length has some predicted miRNA stem-loop-like structures that have relatively conserved sequences. Overall, accumulative pieces of evidence indicated the presence of novel miRNAs encoded within the N-Ras oncogene.

Abstract Abstract:

Aptamers are single-stranded sequences of RNA, DNA, or highly specific proteins that tend to bind to a wide range of target molecules. Aptamers are widely used in various fields, especially medicine and diagnostics, and are similar in their application to antibodies. There are many benefits to using aptamer instead of antibodies, such as low cost, longer life, increased tissue permeability, and more. There are several methods for producing aptamer that in silico methods can shorten and simplify the steps of aptamer production. With aptamer modeling, a set of in silico methods such as modeling, docking, and molecular dynamics can be used to screen for the best aptamer sequence. In this article, a review of the types of aptamers, their structure and design methods in silico is briefly stated.

Keywords: Aptamer, DNA, RNA, Protein, insilico



Keywords: Aptamer, DNA, RNA, Protein, insilico