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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

Oenothera rosea L´Hér. ex Ait attenuates acute colonic inflammation in TNBS-induced colitis model in rats: in vivo and in silico myeloperoxidase role.

Oenothera rosea L´Hér. ex Ait is a species traditionally used in the treatment of inflammation, headache, stomach pain, infections, among others. The aim of this study was evaluating the acute anti-inflammatory activity of the aqueous extract of O. rosea by 2,4,6-trinitrobenzene sulfonic acid (TNBS)-induced colitis. Rats were randomized into six groups: (I) Sham; (II) EtOH; (III) TNBS; and (IV-VI) 250, 500 and 750 mg/Kg, respectively. The colonic injury was induced (groups III-VI) by intrarectal instillation of 0.25 mL of TNBS (10 mg) in 50% ethanol. Groups I and II received an enema (0.25 mL) of physiological saline solution or 50% ethanol, respectively. Treatments were administered by oral gavage 48, 24 and 1 h prior, and 24 h after the induction. The inflammatory response was assessed considering the macroscopic and microscopic damage, the serum nitric oxide (NO), the colonic IL-1ß levels, and the myeloperoxidase (MPO) activity. Moreover, we performed an LC-MS-based metabolite profiling, and a docking on the MPO. Doses of 500 and 750 mg/Kg showed a protective effect in the TNBS-induced colonic damage. This activity was related to the downregulation of evaluated parameters. Also, considering previous reports, 29 metabolites of 91 detected were selected for the docking, of which Isolimonic acid (29) and Kaempferol 3-(2'',4''-diacetylrhamnoside) (10) showed the highest affinity to MPO. The aqueous extract of O. rosea protected the TNBS-induced colonic damage in rats, an effect that could be associated with the presence of polyphenolic compounds, alkaloids, and terpenes; as well as their ability to down-regulate MPO activity.

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.

Selection of a GPER1 Ligand via Ligand-based Virtual Screening Coupled to Molecular Dynamics Simulations and Its Anti-proliferative Effects on Breast Cancer Cells.

BACKGROUND: Recent reports have demonstrated the role of the G Protein-Coupled Estrogen Receptor 1 (GPER1) on the proliferation of breast cancer. The coupling of GPER1 to estrogen triggers cellular signaling pathways related to cell proliferation. OBJECTIVE: Develop new therapeutic strategies against breast cancer. METHOD: We performed in silico studies to explore the binding mechanism of a set of G15 /G1 analogue compounds. We included a carboxyl group instead of the acetyl group from G1 to form amides with several moieties to increase affinity on GPER1. The designed ligands were submitted to ligand-based and structure-based virtual screening to get insights into the binding mechanism of the best designed compound and phenol red on GPER1. RESULTS: According to the in silico studies, the best molecule was named G1-PABA ((3aS,4R,9bR)-4-(6- bromobenzo[d][1,3]dioxol-5-yl)-3a,4,5,9b-tetrahydro-3H-cyclopenta[c]quinoline-8-carboxylic acid). It was synthesized and assayed in vitro in breast cancer (MCF-7 and MDA-MB-231) and normal (MCF-10A) cell lines. Experimental studies showed that the target compound was able to decrease cell proliferation, IC50 values of 15.93 µM, 52.92 µM and 32.45 µM in the MCF-7, MDA-MB-231 and MCF-10A cell lines, respectively, after 72 h of treatment. The compound showed better IC50 values without phenol red, suggesting that phenol red interfere with the G1-PABA action at GPER1, as observed through in silico studies, which is present in MCF-7 cells according to PCR studies and explains the cell proliferation effects. CONCLUSION: Concentration-dependent inhibition of cell proliferation occurred with G1-PABA in the assayed cell lines and could be due to its action on GPER1.

Study of new interactions of glitazone's stereoisomers and the endogenous ligand 15d-PGJ2 on six different PPAR gamma proteins.

Diabetes mellitus is a chronic disease characterized by hyperglycemia, insulin resistance and hyperlipidemia. Glitazones or thiazolidinediones (TZD) are drugs that act as insulin-sensitizing agents whose molecular target is the peroxisome proliferator-activated receptor gamma (PPARγ). The euglycemic action of TZD has been linked with the induction of type 4 glucose transporter. However, it has been shown that the effect of TZD depends on the specific stereoisomer that interacts with PPARγ. Therefore, this work is focused on exploring the interactions and geometry adopted by glitazone's stereoisomers and one endogenous ligand on different conformations of the six crystals of the PPARγ protein using molecular docking and molecular dynamics (MD) simulations accompanied by the MMGBSA approach. Specifically, the 2,4-thiazolidinedione ring, pioglitazone (PIO), rosiglitazone (ROSI) and troglitazone (TRO) stereoisomers (exogenous ligands), as well as the endogenous ligand 15d-PGJ2, were evaluated. The six crystallographic structures of PPARγ are available at Protein Data Bank as the PDB entries 2PRG, 4PRG, 3T03, 1I7I, 1FM6, and 4EMA. According to the results, a boomerang shape and a particular location of ligands were found with low variations according to the protein conformations. The 15d-PGJ2, TZD, PIO, ROSI and (S,S)-TRO enantiomers were mostly stabilized by twenty hydrophobic residues: Phe226, Pro227, Leu228, Ile281, Phe282, Cys285, Ala292, Ile296, Ile326, Tyr327, Met329, Leu330, Leu333, Met334, Val339, Ile341, Met348, Leu353, Phe363 and Met364. Most hydrogen bond interactions were found between the polar groups of ligands with Arg288, Ser289, Lys367, Gln286, His323, Glu343 and His449 residues. An energetic analysis revealed binding free energy trends that supported known experimental findings of other authors describing better binding properties for PIO, ROSI and (S,S)-TRO than for 15d-PGJ2 and the TZD ring.

Ligand recognition properties of the vasopressin V2 receptor studied under QSAR and molecular modeling strategies.

The design of new drugs that target vasopressin 2 receptor (V2R) is of vital importance to develop new therapeutic alternatives to treat diseases such as heart failure, polycystic kidney disease. To get structural insights related to V2R-ligand recognition, we have used a combined approach of docking, molecular dynamics simulations (MD) and quantitative structure-activity relationship (QSAR) to elucidate the detailed interaction of the V2R with 119 of its antagonists. The three-dimensional model of V2R was built by threading methods refining its structure through MD simulations upon which the 119 ligands were subjected to docking studies. The theoretical results show that binding recognition of these ligands on V2R is diverse, but the main pharmacophore (electronic and π-π interactions) is maintained; thus, this information was validated under QSAR results. QSAR studies were performed using MLR analysis followed by ANN analysis to increase the model quality. The final equation was developed by choosing the optimal combination of descriptors after removing the outliers. The applicability domains of the constructed QSAR models were defined using the leverage and standardization approaches. The results suggest that the proposed QSAR models can reliably predict the reproductive toxicity potential of diverse chemicals, and they can be useful tools for screening new chemicals for safety assessment.

Searching the conformational complexity and binding properties of HDAC6 through docking and molecular dynamic simulations.

Histone deacetylases (HDACs) are a family of proteins involved in the deacetylation of histones and other non-histones substrates. HDAC6 belongs to class II and shares similar biological functions with others of its class. Nevertheless, its three-dimensional structure that involves the catalytic site remains unknown for exploring the ligand recognition properties. Therefore, in this contribution, homology modeling, 100-ns-long Molecular Dynamics (MD) simulation and docking calculations were combined to explore the conformational complexity and binding properties of the catalytic domain 2 from HDAC6 (DD2-HDAC6), for which activity and affinity toward five different ligands have been reported. Clustering analysis allowed identifying the most populated conformers present during the MD simulation, which were used as starting models to perform docking calculations with five DD2-HDAC6 inhibitors: Cay10603 (CAY), Rocilinostat (RCT), Tubastatin A (TBA), Tubacin (TBC), and Nexturastat (NXT), and then were also submitted to 100-ns-long MD simulations. Docking calculations revealed that the five inhibitors bind at the DD2-HDAC6 binding site with the lowest binding free energy, the same binding mode is maintained along the 100-ns-long MD simulations. Overall, our results provide structural information about the molecular flexibility of apo and holo DD2-HDAC6 states as well as insight of the map of interactions between DD2-HDAC6 and five well-known DD2-HDAC6 inhibitors allowing structural details to guide the drug design. Finally, we highlight the importance of combining different theoretical approaches to provide suitable structural models for structure-based drug design.