In silico study suggests potential drugs that target CD151 to treat breast cancer and lioblastoma.

Recently tetraspanin CD151 has been identified as an important biological target involved in metastatic processes which include cell adhesion, tumor progression processes, and so forth in different types of cancers, such as breast cancer and glioblastoma. This in Silico study considered 1603 compounds from the Food and Drug Administration database, after performing an ADMET analysis; we selected 853 ligands, which were used for docking analysis. The most promising ligands were selected from docking studies, based on two criteria: (a) showed lowest affinity to the CD151 protein and (b) they interact with the QRD motif, located in the second extracellular loop. Furthermore, we investigate the stability of the protein-ligand complexes through MD simulations as well as free energy MM-PBSA calculations. From these results, loperamide and glipizide were identified as the best evaluated drugs. We suggest an in vitro analysis is needed to confirm our in silico prediction studies.

Atomistic Molecular Insights into Angiotensin-(1-7) Interpeptide Interactions.

Angiotensin-(1-7) is an endogenous peptide known for its vasoprotective, antioxidant, and anti-inflammatory effects, making it a promising therapeutic candidate for various clinical conditions. However, the peptide exhibits pH-dependent physical instability in aqueous solutions, and a comprehensive atomistic study elucidating this behavior and its implications is currently lacking. Therefore, we performed all-atom molecular dynamics simulations to investigate the early formation of angiotensin-(1-7) oligomeric aggregates under different conditions: acidic and neutral pH-like conditions, physiological and high ionic strength, and high and low peptide concentrations. Our results are as follows: (1) under acidic pH-like conditions, angiotensin-(1-7) showed minimal clustering, (2) under neutral pH-like conditions, the peptides aggregated into a single cluster, consistent with the reported physical instability, and (3) increasing salt concentration under acidic pH-like conditions resulted in aggregation similar to that observed under neutral pH-like conditions. These results suggest that a combination of salt concentration and pH conditions can modulate angiotensin-(1-7) aggregation. Our protocol (molecular dynamics + cluster analysis + amino acid interaction map analysis) is general and could be applied to other peptides to study interpeptide interaction mechanisms.

Unveiling the G4-PAMAM capacity to bind and protect Ang-(1-7) bioactive peptide by molecular dynamics simulations.

Angiotensin-(1-7) re-balance the Renin-Angiotensin system affected during several pathologies, including the new COVID-19; cardiovascular diseases; and cancer. However, one of the limiting factors for its therapeutic use is its short half-life, which might be overcome with the use of dendrimers as nanoprotectors. In this work, we addressed the following issues: (1) the capacity of our computational protocol to reproduce the experimental structural features of the (hydroxyl/amino)-terminated PAMAM dendrimers as well as the Angiotensin-(1-7) peptide; (2) the coupling of Angiotensin-(1-7) to (hydroxyl/amino)-terminated PAMAM dendrimers in order to gain insight into the structural basis of its molecular binding; (3) the capacity of the dendrimers to protect Angiotensin-(1-7); and (4) the effect of pH changes on the peptide binding and covering. Our Molecular-Dynamics/Metadynamics-based computational protocol well modeled the structural experimental features reported in the literature and our double-docking approach was able to provide reasonable initial structures for stable complexes. At neutral pH, PAMAM dendrimers with both terminal types were able to interact stably with 3 Angiotensin-(1-7) peptides through ASP1, TYR4 and PRO7 key amino acids. In general, they bind on the surface in the case of the hydroxyl-terminated compact dendrimer and in the internal zone in the case of the amino-terminated open dendrimer. At acidic pH, PAMAM dendrimers with both terminal groups are still able to interact with peptides either internalized or in its periphery, however, the number of contacts, the percentage of coverage and the number of hydrogen bonds are lesser than at neutral pH, suggesting a state for peptide release. In summary, amino-terminated PAMAM dendrimer showed slightly better features to bind, load and protect Angiotensin-(1-7) peptides.

The ß2-Subunit (AMOG) of Human Na+, K+-ATPase Is a Homophilic Adhesion Molecule.

The ß2 subunit of Na+, K+-ATPase was originally identified as the adhesion molecule on glia (AMOG) that mediates the adhesion of astrocytes to neurons in the central nervous system and that is implicated in the regulation of neurite outgrowth and neuronal migration. While ß1 isoform have been shown to trans-interact in a species-specific mode with the ß1 subunit on the epithelial neighboring cell, the ß2 subunit has been shown to act as a recognition molecule on the glia. Nevertheless, none of the works have identified the binding partner of ß2 or described its adhesion mechanism. Until now, the interactions pronounced for ß2/AMOG are heterophilic cis-interactions. In the present report we designed experiments that would clarify whether ß2 is a cell-cell homophilic adhesion molecule. For this purpose, we performed protein docking analysis, cell-cell aggregation, and protein-protein interaction assays. We observed that the glycosylated extracellular domain of ß2/AMOG can make an energetically stable trans-interacting dimer. We show that CHO (Chinese Hamster Ovary) fibroblasts transfected with the human ß2 subunit become more adhesive and make large aggregates. The treatment with Tunicamycin in vivo reduced cell aggregation, suggesting the participation of N-glycans in that process. Protein-protein interaction assay in vivo with MDCK (Madin-Darby canine kidney) or CHO cells expressing a recombinant ß2 subunit show that the ß2 subunits on the cell surface of the transfected cell lines interact with each other. Overall, our results suggest that the human ß2 subunit can form trans-dimers between neighboring cells when expressed in non-astrocytic cells, such as fibroblasts (CHO) and epithelial cells (MDCK).

Drug repositioning to target NSP15 protein on SARS-CoV-2 as possible COVID-19 treatment.

SARS-CoV and SARS-CoV-2 belong to the subfamily Coronaviridae and infect humans, they are constituted by four structural proteins: Spike glycoprotein (S), membrane (M), envelope (E) and nucleocapsid (N), and nonstructural proteins, such as Nsp15 protein which is exclusively present on nidoviruses and is absent in other RNA viruses, making it an ideal target in the field of drug design. A virtual screening strategy to search for potential drugs was proposed, using molecular docking to explore a library of approved drugs available in the DrugBank database in order to identify possible NSP15 inhibitors to treat Covid19 disease. We found from the docking analysis that the antiviral drugs: Paritaprevir and Elbasvir, currently both approved for hepatitis C treatment which showed some of the lowest free binding energy values were considered as repositioning drugs to combat SARS-CoV-2. Furthermore, molecular dynamics simulations of the Apo and Holo-Nsp15 systems were performed in order to get insights about the stability of these protein-ligand complexes.

Identification of two arylimides as cholinesterase inhibitors and testing of propranolol addition on impaired rat memory.

Alzheimer's disease (AD) is clearly linked to the decline of acetylcholine (ACh) effects in the brain. These effects are regulated by the hydrolytic action of acetylcholinesterase (AChE). Therefore, a central palliative treatment of AD is the administration of AChE inhibitors although additional mechanisms are currently described and tested for generating advantageous therapeutic strategies. In this work, we tested new arylamides and arylimides as potential inhibitors of AChE using in silico tools. Then, these compounds were tested in vitro, and two selected compounds, C7 and C8, as well as propranolol showed inhibition of AChE. In addition, they demonstrated an advantageous acute toxicity profile compared to that of galantamine as a reference AChE inhibitor. in vivo evaluation of memory performance enhancement was performed in an animal model of cognitive disturbance with each of these compounds and propranolol individually as well as each compound combined with propranolol. Memory improvement was observed in each case, but without a significant additive effect with the combinations.

Repositioning of Ligands That Target the Spike Glycoprotein as Potential Drugs for SARS-CoV-2 in an In Silico Study.

The worldwide health emergency of the SARS-CoV-2 pandemic and the absence of a specific treatment for this new coronavirus have led to the use of computational strategies (drug repositioning) to search for treatments. The aim of this work is to identify FDA (Food and Drug Administration)-approved drugs with the potential for binding to the spike structural glycoprotein at the hinge site, receptor binding motif (RBM), and fusion peptide (FP) using molecular docking simulations. Drugs that bind to amino acids are crucial for conformational changes, receptor recognition, and fusion of the viral membrane with the cell membrane. The results revealed some drugs that bind to hinge site amino acids (varenicline, or steroids such as betamethasone while other drugs bind to crucial amino acids in the RBM (naldemedine, atovaquone, cefotetan) or FP (azilsartan, maraviroc, and difluprednate); saquinavir binds both the RBM and the FP. Therefore, these drugs could inhibit spike glycoprotein and prevent viral entry as possible anti-COVID-19 drugs. Several drugs are in clinical studies; by focusing on other pharmacological agents (candesartan, atovaquone, losartan, maviroc and ritonavir) in this work we propose an additional target: the spike glycoprotein. These results can impact the proposed use of treatments that inhibit the first steps of the virus replication cycle.

Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection.

BACKGROUND: MicroRNAs (miRNAs) are noncoding RNA molecules heavily involved in human tumors, in which few of them circulating the human body. Finding a tumor-associated signature of miRNA, that is, the minimum miRNA entities to be measured for discriminating both different types of cancer and normal tissues, is of utmost importance. Feature selection techniques applied in machine learning can help however they often provide naive or biased results. RESULTS: An ensemble feature selection strategy for miRNA signatures is proposed. miRNAs are chosen based on consensus on feature relevance from high-accuracy classifiers of different typologies. This methodology aims to identify signatures that are considerably more robust and reliable when used in clinically relevant prediction tasks. Using the proposed method, a 100-miRNA signature is identified in a dataset of 8023 samples, extracted from TCGA. When running eight-state-of-the-art classifiers along with the 100-miRNA signature against the original 1046 features, it could be detected that global accuracy differs only by 1.4%. Importantly, this 100-miRNA signature is sufficient to distinguish between tumor and normal tissues. The approach is then compared against other feature selection methods, such as UFS, RFE, EN, LASSO, Genetic Algorithms, and EFS-CLA. The proposed approach provides better accuracy when tested on a 10-fold cross-validation with different classifiers and it is applied to several GEO datasets across different platforms with some classifiers showing more than 90% classification accuracy, which proves its cross-platform applicability. CONCLUSIONS: The 100-miRNA signature is sufficiently stable to provide almost the same classification accuracy as the complete TCGA dataset, and it is further validated on several GEO datasets, across different types of cancer and platforms. Furthermore, a bibliographic analysis confirms that 77 out of the 100 miRNAs in the signature appear in lists of circulating miRNAs used in cancer studies, in stem-loop or mature-sequence form. The remaining 23 miRNAs offer potentially promising avenues for future research.

A Model for the Homotypic Interaction between Na+,K+-ATPase ß1 Subunits Reveals the Role of Extracellular Residues 221-229 in Its Ig-Like Domain.

The Na+, K+-ATPase transports Na+ and K+ across the membrane of all animal cells. In addition to its ion transporting function, the Na+, K+-ATPase acts as a homotypic epithelial cell adhesion molecule via its ß1 subunit. The extracellular region of the Na+, K+-ATPase ß1 subunit includes a single globular immunoglobulin-like domain. We performed Molecular Dynamics simulations of the ectodomain of the ß1 subunit and a refined protein-protein docking prediction. Our results show that the ß1 subunit Ig-like domain maintains an independent structure and dimerizes in an antiparallel fashion. Analysis of the putative interface identified segment Lys221-Tyr229. We generated triple mutations on YFP-ß1 subunit fusion proteins to assess the contribution of these residues. CHO fibroblasts transfected with mutant ß1 subunits showed a significantly decreased cell-cell adhesion. Association of ß1 subunits in vitro was also reduced, as determined by pull-down assays. Altogether, we conclude that two Na+, K+-ATPase molecules recognize each other by a large interface spanning residues 221-229 and 198-207 on their ß1 subunits.

The Importance of Structured Water in HDAC8 for Correct Binding Pose Applied for Drug Design of Anticancer Molecules.

AIMS: Validating the docking procedure and maintaining the structural water molecules at HDAC8 catalytic site. BACKGROUND: Molecular docking simulations play a significant role in Computer-Aided Drug Design, contributing to the development of new molecules. To ensure the reliability of these simulations, a validation process called "Re-docking" is employed, focusing on the binding mode of a ligand co-crystallized with the protein of interest. OBJECTIVE: In this study, several molecular docking studies were conducted using five X-ray structures of HDAC8-ligand complexes from the PDB. METHOD: Ligands initially complexed with HDAC8 were removed and re-docked onto the free protein, revealing a poor reproduction of the expected binding mode. In response to this, we observed that most HDAC8-ligand complexes contained one to two water molecules in the catalytic site, which were crucial for maintaining the cocrystallized ligand. RESULT: These water molecules enhance the binding mode of the co-crystallized ligand by stabilizing the proteinligand complex through hydrogen bond interactions between ligand and water molecules. Notably, these interactions are lost if water molecules are removed, as is often done in classical docking methodologies. Considering this, molecular docking simulations were repeated, both with and without one or two conserved water molecules near Zn+2 in the catalytic cavity. Simulations indicated that replicating the native binding pose of co-crystallized ligands on free HDAC8 without these water molecules was challenging, showing greater coordinate displacements (RMSD) compared to those including conserved water molecules from crystals. CONCLUSION: The study highlighted the importance of conserved water molecules within the active site, as their presence significantly influenced the successful reproduction of the ligands' native binding modes. The results suggest an optimal molecular docking procedure for validating methods suitable for filtering new HDAC8 inhibitors for future experimental assays.

Morphological changes, chitinolytic enzymes and hydrophobin-like proteins as responses of Lecanicillium lecanii during growth with hydrocarbon.

Lecanicillium lecanii, Verticillium chlamydosporium, V. fungicola var flavidum and Beauveria bassiana were evaluated on their growth with pure n-hexane, toluene and n-hexane:toluene 17:83 (v:v) mixture. Another set of treatments were conducted with colloidal chitin as additional carbon source. All the strains of Lecanicillium were able to grow using hydrocarbons with or without the addition of chitin, although the presence of hydrocarbons showed significant inhibition evidenced by measured biomass, radial growth and microscopic analyses. Degradation of n-hexane ranged within 43 and 62 % and it was higher than that with toluene. The strains L460, L157 and L2149, which presented the highest growth, were further selected for determinations of hydrocarbon consumptions in microcosms. Strain L157 showed the highest consumption of n-hexane (55.6 %) and toluene (52.9 %) as sole carbon source and it also displayed activities of endochitinases, N-acetylhexosaminidase and production of hydrophobins class I and II.

In Silico Screening of Drugs That Target Different Forms of E Protein for Potential Treatment of COVID-19.

Recently the E protein of SARS-CoV-2 has become a very important target in the potential treatment of COVID-19 since it is known to regulate different stages of the viral cycle. There is biochemical evidence that E protein exists in two forms, as monomer and homopentamer. An in silico screening analysis was carried out employing 5852 ligands (from Zinc databases), and performing an ADMET analysis, remaining a set of 2155 compounds. Furthermore, docking analysis was performed on specific sites and different forms of the E protein. From this study we could identify that the following ligands showed the highest binding affinity: nilotinib, dutasteride, irinotecan, saquinavir and alectinib. We carried out some molecular dynamics simulations and free energy MM-PBSA calculations of the protein-ligand complexes (with the mentioned ligands). Of worthy interest is that saquinavir, nilotinib and alectinib are also considered as a promising multitarget ligand because it seems to inhibit three targets, which play an important role in the viral cycle. On the other side, saquinavir was shown to be able to bind to E protein both in its monomeric as well as pentameric forms. Finally, further experimental assays are needed to probe our hypothesis derived from in silico studies.

Molecular modeling of the M3 acetylcholine muscarinic receptor and its binding site.

The present study reports the results of a combined computational and site mutagenesis study designed to provide new insights into the orthosteric binding site of the human M3 muscarinic acetylcholine receptor. For this purpose a three-dimensional structure of the receptor at atomic resolution was built by homology modeling, using the crystallographic structure of bovine rhodopsin as a template. Then, the antagonist N-methylscopolamine was docked in the model and subsequently embedded in a lipid bilayer for its refinement using molecular dynamics simulations. Two different lipid bilayer compositions were studied: one component palmitoyl-oleyl phosphatidylcholine (POPC) and two-component palmitoyl-oleyl phosphatidylcholine/palmitoyl-oleyl phosphatidylserine (POPC-POPS). Analysis of the results suggested that residues F222 and T235 may contribute to the ligand-receptor recognition. Accordingly, alanine mutants at positions 222 and 235 were constructed, expressed, and their binding properties determined. The results confirmed the role of these residues in modulating the binding affinity of the ligand.

The Advantage of Using Immunoinformatic Tools on Vaccine Design and Development for Coronavirus.

After the outbreak of SARS-CoV-2 by the end of 2019, the vaccine development strategies became a worldwide priority. Furthermore, the appearances of novel SARS-CoV-2 variants challenge researchers to develop new pharmacological or preventive strategies. However, vaccines still represent an efficient way to control the SARS-CoV-2 pandemic worldwide. This review describes the importance of bioinformatic and immunoinformatic tools (in silico) for guide vaccine design. In silico strategies permit the identification of epitopes (immunogenic peptides) which could be used as potential vaccines, as well as nonacarriers such as: vector viral based vaccines, RNA-based vaccines and dendrimers through immunoinformatics. Currently, nucleic acid and protein sequential as well structural analyses through bioinformatic tools allow us to get immunogenic epitopes which can induce immune response alone or in complex with nanocarriers. One of the advantages of in silico techniques is that they facilitate the identification of epitopes, while accelerating the process and helping to economize some stages of the development of safe vaccines.

Searching Epitope-Based Vaccines Using Bioinformatics Studies.

Epitope-based vaccines is one of the most recent methodologies applied in bioinformatics studies. This strategy consists of identifying regions of the protein (peptides or epitopes) which show antigen properties capable of stimulating the immune system against proteins from virus, bacteria, fungi, etc. This chapter describes a general procedure to identify epitopes to be used as epitope vaccine using bioinformatics methods including primary protein sequence analyses, epitope predictor, docking, and molecular dynamics simulations for the selection of T- and B-cell epitopes.

Salt effects on the conformational stability of the visual G-protein-coupled receptor rhodopsin.

Membrane protein stability is a key parameter with important physiological and practical implications. Inorganic salts affect protein stability, but the mechanisms of their interactions with membrane proteins are not completely understood. We have undertaken the study of a prototypical G-protein-coupled receptor, the α-helical membrane protein rhodopsin from vertebrate retina, and explored the effects of inorganic salts on the thermal decay properties of both its inactive and photoactivated states. Under high salt concentrations, rhodopsin significantly increased its activation enthalpy change for thermal bleaching, whereas acid denaturation affected the formation of a denatured loose-bundle state for both the active and inactive conformations. This behavior seems to correlate with changes in protonated Schiff-base hydrolysis. However, chromophore regeneration with the 11-cis-retinal chromophore and MetarhodopsinII decay kinetics were slower only in the presence of sodium chloride, suggesting that in this case, the underlying phenomenon may be linked to the activation of rhodopsin and the retinal release processes. Furthermore, the melting temperature, determined by means of circular dichroism and differential scanning calorimetry measurements, was increased in the presence of high salt concentrations. The observed effects on rhodopsin could indicate that salts favor electrostatic interactions in the retinal binding pocket and indirectly favor hydrophobic interactions at the membrane protein receptor core. These effects can be exploited in applications where the stability of membrane proteins in solution is highly desirable.

Liver disorders in COVID-19, nutritional approaches and the use of phytochemicals.

Coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), has affected millions of people globally. It was declared a pandemic by the World Health Organization in March 2020. The hyperinflammatory response to the entry of SARS-CoV-2 into the host through angiotensin-converting enzyme 2 is the result of a "cytokine storm" and the high oxidative stress responsible for the associated symptomatology. Not only respiratory symptoms are reported, but gastrointestinal symptoms (diarrhea, vomiting, and nausea) and liver abnormalities (high levels of aspartate aminotransferase, alanine aminotransferase transaminases, and bilirubin) are observed in at least 30% of patients. Reduced food intake and a delay in medical services may lead to malnutrition, which increases mortality and poor outcomes. This review provides some strategies to identify malnutrition and establishes nutritional approaches for the management of COVID-19 and liver injury, taking energy and nutrient requirements and their impact on the immune response into account. The roles of certain phytochemicals in the prevention of the disease or as promising target drugs in the treatment of this disease are also considered.

Machine Learning-Based Ensemble Recursive Feature Selection of Circulating miRNAs for Cancer Tumor Classification.

Circulating microRNAs (miRNA) are small noncoding RNA molecules that can be detected in bodily fluids without the need for major invasive procedures on patients. miRNAs have shown great promise as biomarkers for tumors to both assess their presence and to predict their type and subtype. Recently, thanks to the availability of miRNAs datasets, machine learning techniques have been successfully applied to tumor classification. The results, however, are difficult to assess and interpret by medical experts because the algorithms exploit information from thousands of miRNAs. In this work, we propose a novel technique that aims at reducing the necessary information to the smallest possible set of circulating miRNAs. The dimensionality reduction achieved reflects a very important first step in a potential, clinically actionable, circulating miRNA-based precision medicine pipeline. While it is currently under discussion whether this first step can be taken, we demonstrate here that it is possible to perform classification tasks by exploiting a recursive feature elimination procedure that integrates a heterogeneous ensemble of high-quality, state-of-the-art classifiers on circulating miRNAs. Heterogeneous ensembles can compensate inherent biases of classifiers by using different classification algorithms. Selecting features then further eliminates biases emerging from using data from different studies or batches, yielding more robust and reliable outcomes. The proposed approach is first tested on a tumor classification problem in order to separate 10 different types of cancer, with samples collected over 10 different clinical trials, and later is assessed on a cancer subtype classification task, with the aim to distinguish triple negative breast cancer from other subtypes of breast cancer. Overall, the presented methodology proves to be effective and compares favorably to other state-of-the-art feature selection methods.

Studying the collective motions of the adenosine A2A receptor as a result of ligand binding using principal component analysis.

Adenosine receptors (ARs) belong to family A of GPCRs that are involved in many diseases, including cerebral and cardiac ischemic diseases, immune and inflammatory disorders, etc. Thus, they represent important therapeutic targets to treat these conditions. Computational techniques such as molecular dynamics (MD) simulations permit researchers to obtain structural information about these proteins, and principal component analysis (PCA) allows for the identification of collective motions. There are available structures for the active form (3QAK) and the inactive form (3EML) of A2AR which permit us to gain insight about their activation/inactivation mechanism. In this work, we have proposed an inverse strategy using MD simulations where the active form was coupled to the antagonist caffeine and the inactive form was coupled to adenosine agonist. Moreover, we have included four reported thermostabilizing mutations in the inactive form to study A2AR structural differences under different conditions. Some observations stand out from the PCA studies. For instance, the apo structures showed remarkable similarities, and the principal components (PCs) were rearranged in a ligand-dependent manner. Additionally, the active conformation was less stable compared to the inactive one. Some PCs inverted their direction in the presence of a ligand, and comparison of the PCs between 3EML and 3EML_ADN showed that adenosine induced major changes in the structure of A2AR. Rearrangement of PCs precedes and drives conformational changes that occur after ligand binding. Knowledge about these conformational changes provides important insights about the activity of A2AR.

Design of Drugs by Filtering Through ADMET, Physicochemical and Ligand-Target Flexibility Properties.

There is a synergistic interaction between medicinal chemistry, chemoinformatics, and bioinformatics. The last one includes analyses of sequences as well as structural analysis which employ computational techniques such as docking studies and molecular dynamics (MD) simulations. Over the last years these techniques have allowed the development of new accurate computational tools for drug design. As a result, there have been an increased number of publications where computational methods such as pharmacophore modeling, de novo drug design, evaluation of physicochemical properties, and analysis of ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties have been quite useful for eliminating the compounds with poor physicochemical or toxicological properties. Furthermore, using MD simulations and docking analysis, it is possible to estimate the binding energy of the protein-ligand complexes by using scoring functions, as well as to structurally depict the binding pose of the compounds on proteins, in order to select the best evaluated compounds for subsequent synthetizing and evaluation through biological assays. In this work, we describe some computational tools that have been used for structure-based drug design of new compounds that target histone deacetylases (HDACs), which are known to be potential targets in cancer and parasitic diseases.