Structural behavior of monomer of SARS-CoV-2 spike protein during initial stage of adsorption on graphene.

Spike glycoprotein of the SARS-CoV-2 virus and its structure play a crucial role in the infections of cells containing angiotensin-converting enzyme 2 (ACE2) as well as in the interactions of this virus with surfaces. Protection against viruses and often even their deactivation is one of the great varieties of graphene applications. The structural changes of the non-glycosylated monomer of the spike glycoprotein trimer (denoted as S-protein in this work) triggered by its adsorption onto graphene at the initial stage are investigated by means of atomistic molecular dynamics simulations. The adsorption of the S-protein happens readily during the first 10 ns. The shape of the S-protein becomes more prolate during the adsorption, but this trend, albeit less pronounced, is observed also for the freely relaxing S-protein in water. The receptor-binding domain (RBD) of the free and adsorbed S-protein manifests itself as the most rigid fragment of the whole S-protein. The adsorption even enhances the rigidity of the whole S-protein as well as its subunits. Only one residue of the RBD involved in the specific interactions with ACE2 during the cell infection is involved in the direct contact of the adsorbed S-protein with the graphene. The new intramolecular hydrogen bonds formed during the S-protein adsorption replace the S-protein-water hydrogen bonds; this trend, although less apparent, is observed also during the relaxation of the free S-protein in water. In the initial phase, the secondary structure of the RBD fragment specifically interacting with ACE2 receptor is not affected during the S-protein adsorption onto the graphene.

Synergic effect of oral contraceptives, GSTP1 polymorphisms, and high-risk HPV infection in development of cervical lesions.

Human papillomavirus (HPV) infection is considered a risk factor for cervical cancer. Even if the high-risk HPV (HR-HPV) infection is necessary, environmental co-factors and genetic susceptibility also play an important role in cervical cancer development. In this study, a possible association of rs1695 GSTP1 polymorphisms, HR-HPV infection, and oral contraceptive use with cancer lesion development in women was investigated. The study population comprised 441 Brazilian women from the Northeast region including 98 HPV-infected women with high-grade squamous intraepithelial lesions, 77 HPV-infected women with low-grade squamous intraepithelial lesions, and 266 HPV-negative women with no lesion, used as a control. Our data did not show a significant association between the GSTP1 polymorphism A/G (rs1695) and any HPV-related cervical abnormalities. However, considering the use of oral contraceptives, the GSTP1 rs1695 polymorphism was associated with higher susceptibility to the development of cervical lesions in HR-HPV-infected women. Our study suggests a synergic effect of oral contraceptive use, GSTP1 polymorphisms, and HR-HPV infection in the development of cervical lesions. Together, these risk factors may induce neoplastic transformation of the cervical squamous epithelium, setting conditions for secondary genetic events leading to cervical cancer.

First Multitarget Chemo-Bioinformatic Model To Enable the Discovery of Antibacterial Peptides against Multiple Gram-Positive Pathogens.

Antimicrobial peptides (AMPs) have emerged as promising therapeutic alternatives to fight against the diverse infections caused by different pathogenic microorganisms. In this context, theoretical approaches in bioinformatics have paved the way toward the creation of several in silico models capable of predicting antimicrobial activities of peptides. All current models have several significant handicaps, which prevent the efficient search for highly active AMPs. Here, we introduce the first multitarget (mt) chemo-bioinformatic model devoted to performing alignment-free prediction of antibacterial activity of peptides against multiple Gram-positive bacterial strains. The model was constructed from a data set containing 2488 cases of AMPs sequences assayed against at least 1 out of 50 Gram-positive bacterial strains. This mt-chemo-bioinformatic model displayed percentages of correct classification higher than 90.00% in both training and prediction (test) sets. For the first time, two computational approaches derived from basic concepts in genetics and molecular biology were applied, allowing the calculations of the relative contributions of any amino acid (in a defined position) to the antibacterial activity of an AMP and depending on the bacterial strain used in the biological assay. The present mt-chemo-bioinformatic model constitutes a powerful tool to enable the discovery of potent and versatile AMPs.

Multi-Target QSAR Approaches for Modeling Protein Inhibitors. Simultaneous Prediction of Activities Against Biomacromolecules Present in Gram-Negative Bacteria.

Drug discovery is aimed at finding therapeutic agents for the treatment of many diverse diseases and infections. However, this is a very slow an expensive process, and for this reason, in silico approaches are needed to rationalize the search for new molecular entities with desired biological profiles. Models focused on quantitative structure-activity relationships (QSAR) have constituted useful complementary tools in medicinal chemistry, allowing the virtual predictions of dissimilar pharmacological activities of compounds. In the last 10 years, multi-target (mt) QSAR models have been reported, representing great advances with respect to those models generated from classical approaches. Thus, mt- QSAR models can simultaneously predict activities against different biological targets (proteins, microorganisms, cell lines, etc.) by using large and heterogeneous datasets of chemicals. The present review is devoted to discuss the most promising mt-QSAR models, particularly those developed for the prediction of protein inhibitors. We also report the first multi-tasking QSAR (mtk-QSAR) model for simultaneous prediction of inhibitors against biomacromolecules (specifically proteins) present in Gram-negative bacteria. This model allowed us to consider both different proteins and multiple experimental conditions under which the inhibitory activities of the chemicals were determined. The mtk-QSAR model exhibited accuracies higher than 98% in both training and prediction sets, also displaying a very good performance in the classification of active and inactive cases that depended on the specific elements of the experimental conditions. The physicochemical interpretations of the molecular descriptors were also analyzed, providing important insights regarding the molecular patterns associated with the appearance/enhancement of the inhibitory potency.

Enabling virtual screening of potent and safer antimicrobial agents against noma: mtk-QSBER model for simultaneous prediction of antibacterial activities and ADMET properties.

Neglected diseases are infections that thrive mainly among underdeveloped countries, particularly those belonging to regions found in Asia, Africa, and America. One of the most complex diseases is noma, a dangerous health condition characterized by a polymicrobial and opportunistic nature. The search for potent and safer antibacterial agents against this disease is therefore a goal of particular interest. Chemoinformatics can be used to rationalize the discovery of drug candidates, diminishing time and financial resources. However, in the case of noma, there is no in silico model available for its use in the discovery of efficacious antibacterial agents. This work is devoted to report the first mtk-QSBER model, which integrates dissimilar kinds of chemical and biological data. The model was generated with the aim of simultaneously predicting activity against bacteria present in noma, and ADMET (absorption, distribution, metabolism, elimination, toxicity) parameters. The mtk-QSBER model was constructed by employing a large and heterogeneous dataset of chemicals and displayed accuracies higher than 90% in both training and prediction sets. We confirmed the practical applicability of the model by predicting multiple profiles of the investigational antibacterial drug delafloxacin, and the predictions converged with the experimental reports. To date, this is the first model focused on the virtual search for desirable anti-noma agents.

Identification of C-type isolectins in the venom of the scorpionfish Scorpaena plumieri.

Chemical analyses of the hemagglutinating fraction from Scorpaena plumieri venom revealed that it contains five components (Sp-CL 1-5) with similar chromatographic elution profiles (35-38% of acetonitrile), molecular masses (16,800-17,000 Da) and N-terminal sequences, suggesting that they are isoforms of the same protein. The amino acid sequence of Sp-CL4 was determined and shown to have homology with fish C-type lectins. These data demonstrate for the first time the presence of C-type isolectins in a scorpionfish venom.

A general ANN-based multitasking model for the discovery of potent and safer antibacterial agents.

Bacteria have been one of the world's most dangerous and deadliest pathogens for mankind, nowadays giving rise to significant public health concerns. Given the prevalence of these microbial pathogens and their increasing resistance to existing antibiotics, there is a pressing need for new antibacterial drugs. However, development of a successful drug is a complex, costly, and time-consuming process. Quantitative Structure-Activity Relationships (QSAR)-based approaches are valuable tools for shortening the time of lead compound identification but also for focusing and limiting time-costly synthetic activities and in vitro/vivo evaluations. QSAR-based approaches, supported by powerful statistical techniques such as artificial neural networks (ANNs), have evolved to the point of integrating dissimilar types of chemical and biological data. This chapter reports an overview of the current research and potential applications of QSAR modeling tools toward the rational design of more efficient antibacterial agents. Particular emphasis is given to the setup of multitasking models along with ANNs aimed at jointly predicting different antibacterial activities and safety profiles of drugs/chemicals under diverse experimental conditions.

Dynamic structure of NGF and proNGF complexed with p75NTR: pro-peptide effect.

Crystallographic structures of NGF/p75NTR and proNGF/p75NTR were previously obtained in 2:1 and 2:2 stoichiometries, respectively. However, evidence shows that both stoichiometries can occur for mature neurotrophins and pro-neurotrophins. We used Molecular Dynamics (MD) simulations to examine the energetic and structural characteristics of these two complete systems as well as the uncomplexed forms of NGF and understand how these could translate in a new view of different biological outcomes. Here, we show that one chain at the 2:2 proNGF complex seems to be preferentially lost creating a 2:1 structure able to interact with sortilin. We also demonstrated that the structure of the neurotrophin dimers is not pre-established and suffers large structural modifications upon p75NTR binding. Moreover, our data suggests an elegant explanation for the dual role of NGF in neuronal cell death and survival, where different stoichiometries induce conformational changes that might be the basis for the different biological outcomes observed with the mature and proforms of neurotrophins.

Simultaneous virtual prediction of anti-Escherichia coli activities and ADMET profiles: A chemoinformatic complementary approach for high-throughput screening.

Escherichia coli remains one of the principal pathogens that cause nosocomial infections, medical conditions that are increasingly common in healthcare facilities. E. coli is intrinsically resistant to many antibiotics, and multidrug-resistant strains have emerged recently. Chemoinformatics has been a great ally of experimental methodologies such as high-throughput screening, playing an important role in the discovery of effective antibacterial agents. However, there is no approach that can design safer anti-E. coli agents, because of the multifactorial nature and complexity of bacterial diseases and the lack of desirable ADMET (absorption, distribution, metabolism, elimination, and toxicity) profiles as a major cause of disapproval of drugs. In this work, we introduce the first multitasking model based on quantitative-structure biological effect relationships (mtk-QSBER) for simultaneous virtual prediction of anti-E. coli activities and ADMET properties of drugs and/or chemicals under many experimental conditions. The mtk-QSBER model was developed from a large and heterogeneous data set of more than 37800 cases, exhibiting overall accuracies of >95% in both training and prediction (validation) sets. The utility of our mtk-QSBER model was demonstrated by performing virtual prediction of properties for the investigational drug avarofloxacin (AVX) under 260 different experimental conditions. Results converged with the experimental evidence, confirming the remarkable anti-E. coli activities and safety of AVX. Predictions also showed that our mtk-QSBER model can be a promising computational tool for virtual screening of desirable anti-E. coli agents, and this chemoinformatic approach could be extended to the search for safer drugs with defined pharmacological activities.

Evolution of graph theory-based QSAR methods and their applications to the search for new antibacterial agents.

Resistance of bacteria to current antibiotics has increased worldwide, being one of the leading unresolved situations in public health. Due to negligence regarding the treatment of community-acquired diseases, even healthcare facilities have been highly impacted by an emerging problem: nosocomial infections. Moreover, infectious diseases, including nosocomial infections, have been found to depend on multiple pathogenicity factors, confirming the need to discover of multi-target antibacterial agents. Drug discovery is a very complex, expensive, and time-consuming process. In this sense, Quantitative Structure-Activity Relationships (QSAR) methods have become complementary tools for medicinal chemistry, permitting the efficient screening of potential drugs, and consequently, rationalizing the organic synthesis as well as the biological evaluation of compounds. In the consolidation of QSAR methods as important components of chemoinformatics, the use of mathematical chemistry, and more specifically, the use of graph-theoretical approaches has played a vital role. Here, we focus our attention on the evolution of QSAR methods, citing the most relevant works devoted to the development of promising graph-theoretical approaches in the last 8 years, and their applications to the prediction of antibacterial activities of chemicals against pathogens causing both community-acquired and nosocomial infections.

Simultaneous modeling of antimycobacterial activities and ADMET profiles: a chemoinformatic approach to medicinal chemistry.

Mycobacteria represent a group of pathogens which cause serious diseases in mammals, including the lethal tuberculosis (Mycobacterium tuberculosis). Despite the mortality of this community-acquired and nosocomial disease mentioned above, other mycobacteria may cause similar infections, acting as dangerous opportunistic pathogens. Additionally, resistant strains belonging to Mycobacterium spp. have emerged. Thus, the design of novel antimycobacterial agents is a challenge for the scientific community. In this sense, chemoinformatics has played a vital role in drug discovery, helping to rationalize chemical synthesis, as well as the evaluation of pharmacological and ADMET (absorption, distribution, metabolism, excretion, toxicity) profiles in both medicinal and pharmaceutical chemistry. Until now, there is no in silico methodology able to assess antimycobacterial activity and ADMET properties at the same time. This work introduces the first multitasking model based on quantitative-structure biological effect relationships (mtk-QSBER) for simultaneous prediction of antimycobacterial activities and ADMET profiles of drugs/chemicals under diverse experimental conditions. The mtk-QSBER model was constructed by using a large and heterogeneous dataset of compounds (more than 34600 cases), displaying accuracies higher than 90% in both, training and prediction sets. To illustrate the utility of the present model, several molecular fragments were selected and their contributions to different biological effects were calculated and analyzed. Also, many properties of the investigational drug TMC-207 were predicted. Results confirmed that, from one side, TMC-207 can be a promising antimycobacterial drug, and on the other hand, this study demonstrates that the present mtk-QSBER model can be used for virtual screening of safer antimycobacterial agents.

Chemoinformatics for rational discovery of safe antibacterial drugs: simultaneous predictions of biological activity against streptococci and toxicological profiles in laboratory animals.

Streptococci are a group of Gram-positive bacteria which are responsible for causing many diverse diseases in humans and other animals worldwide. The high prevalence of resistance of these bacteria to current antibacterial drugs is an alarming problem for the scientific community. The battle against streptococci by using antimicrobial chemotherapies will depend on the design of new chemicals with high inhibitory activity, having also as low toxicity as possible. Multi-target approaches based on quantitative-structure activity relationships (mt-QSAR) have played a very important role, providing a better knowledge about the molecular patterns related with the appearance of different pharmacological profiles including antimicrobial activity. Until now, almost all mt-QSAR models have considered the study of biological activity or toxicity separately. In the present study, we develop by the first time, a unified multitasking (mtk) QSAR model for the simultaneous prediction of anti-streptococci activity and toxic effects against biological models like Mus musculus and Rattus norvegicus. The mtk-QSAR model was created by using artificial neural networks (ANN) analysis for the classification of compounds as positive (high biological activity and/or low toxicity) or negative (otherwise) under diverse sets of experimental conditions. Our mtk-QSAR model, correctly classified more than 97% of the cases in the whole database (more than 11,500 cases), serving as a promising tool for the virtual screening of potent and safe anti-streptococci drugs.

Characterization and cytotoxic activity of sulfated derivatives of polysaccharides from Agaricus brasiliensis.

Agaricus brasiliensis cell-wall polysaccharides isolated from fruiting body (FR) and mycelium (MI) and their respective sulfated derivatives (FR-S and MI-S) were chemically characterized using elemental analysis, TLC, FT-IR, NMR, HPLC, and thermal analysis. Cytotoxic activity was evaluated against A549 tumor cells by MTT and sulforhodamine assays. The average molecular weight (Mw) of FR and MI was estimated to be 609 and 310 kDa, respectively. FR-S (127 kDa) and MI-S (86 kDa) had lower Mw, probably due to hydrolysis occurring during the sulfation reaction. FR-S and MI-S presented ~14% sulfur content in elemental analysis. Sulfation of samples was characterized by the appearance of two new absorption bands at 1253 and 810 cm(-1) in the infrared spectra, related to S=O and C-S-O sulfate groups, respectively. Through (1)H and (13)C NMR analysis FR-S was characterized as a (1→6)-(1→3)-ß-D-glucan fully sulfated at C-4 and C-6 terminal and partially sulfated at C-6 of (1→3)-ß-D-glucan moiety. MI-S was shown to be a (1→3)-ß-D-gluco-(1→2)-ß-D-mannan, partially sulfated at C-2, C-3, C-4, and C-6, and fully sulfated at C-6 of the terminal residues. The combination of high degree of sulfation and low molecular weight was correlated with the increased cytotoxic activity (48 h of treatment) of both FR-S (EC50=605.6 µg/mL) and MI-S (EC50=342.1 µg/mL) compared to the non-sulfated polysaccharides FR and MI (EC50>1500 µg/mL).

Unified multi-target approach for the rational in silico design of anti-bladder cancer agents.

Bladder cancer (BLC) is a very dangerous and common disease which is characterized by an uncontrolled growth of the urinary bladder cells. In the field of chemotherapy, many compounds have been synthesized and evaluated as anti-BLC agents. The future design of more potent anti-BLC drugs depends on a rigorous and rational discovery, where the computer-aided design (CADD) methodologies should play a very important role. However, until now, there is no CADD methodology able to predict anti-BLC activity of compounds versus different BLC cell lines. We report in this work the first unified approach by exploring Quantitative- Structure Activity Relationship (QSAR) studies using a large and heterogeneous database of compounds. Here, we constructed two multi-target (mt) QSAR models for the classification of compounds as anti-BLC agents against four BLC cell lines. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors while the second model was obtained using artificial neural networks (mt-QSAR-ANN) with global 2D descriptors. Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets. We also extracted different substructural patterns which could be responsible for the activity/inactivity of molecules against BLC and we suggested new molecular entities as possible potent and versatile anti-BLC agents.

Expression of the bovine papillomavirus type 1, 2 and 4 L1 genes in the yeast Pichia pastoris.

Papillomaviruses are known to cause benign or malignant lesions in various animals. In cattle, bovine papillomavirus (BPV) is the etiologic agent of papillomatosis and neoplasia of the upper gastrointestinal tract and urinary bladder. Currently, there are no standard diagnostic tests or prophylactic vaccines. Protection against papillomavirus infection is conferred by neutralizing antibodies directed towards the major structural protein L1. These antibodies can be efficiently induced by immunization with virus-like particles that are formed spontaneously after L1 gene expression in recombinant systems. The yeast Pichia pastoris is known to provide an efficient system for expression of proteins due to reduced cost and high levels of protein production. We evaluated P. pastoris for expression of the L1 gene from BPV1, BPV2 and BPV4. After methanol induction, the recombinants were able to produce L1 proteins of the three different BPV types. To increase heterologous L1 protein levels, a codon optimization strategy was used for production under bioreactor conditions. The BPV1 L1 protein was identified by monoclonal antibody anti-6xHis. This is the first report of BPV L1 expression in yeast.

QSAR studies of PTP1B inhibitors: recent advances and perspectives.

Diabetes mellitus, a chronic condition caused by defects in insulin secretion, or action, or both, is a group of metabolic disorders, complications of which can contribute significantly to ill health, disability, poor quality of life and premature death. From the three main types of diabetes, Type 2 is by far the most common, accounting for about 90% of cases worldwide. Studies on the role of protein tyrosine phosphatase 1B (PTP1B) have clearly shown that it serves as a key negative regulator of insulin signaling and is involved in the insulin resistance associated with Type 2 diabetes. The present work aims to survey information related to PTP1B research published in the last decade. Emphasis is laid particularly on Quantitative Structure-Activity Relationships (QSAR) based studies that supported so far the design of new, potent and selective PTP1B inhibitors. Finally, the challenges and perspectives of QSAR studies in this field are discussed to show how these method can be used to design new chemical entities with enhanced PTP1B inhibition activity.

Expression of a recombinant Phoneutria toxin active in calcium channels.

PnTx3-4 is a toxin isolated from the venom of the spider Phoneutria nigriventer that blocks N-, P/Q-, and R-type voltage-gated calcium channels and has great potential for clinical applications. In this report we used the SUMO system to express large amounts of recombinant PnTx3-4 peptide, which was found in both soluble and insoluble fractions of bacterial extracts. We purified the recombinant toxin from both fractions and showed that the recombinant peptide showed biological activity similar to the native PnTx3-4. In silico analysis of the primary sequence of PnTx3-4 indicated that the peptide conforms to all the criteria of a knottin scaffold. Additionally, circular dichroism spectrum analysis of the recombinant PnTx3-4 predicted that the toxin structure is composed of approximately 53% turns/unordered, 31% α-helix and 16% ß-strand, which is consistent with predicted model of the PnTx3-4 knottin scaffold available at the knottin database (http://knottin.cbs.cnrs.fr). These studies provide the basis for future large scale production and structure-function investigation of PnTx3-4.