Structural and Functional Characterization of Lipoxygenases from Diatoms by Bioinformatics and Modelling Studies.

Lipoxygenases make several biological functions in cells, based on the products of the catalyzed reactions. In diatoms, microalgae ubiquitous in aquatic ecosystems, lipoxygenases have been noted for the oxygenation of fatty acids with the production of oxylipins, which are involved in many physiological and pathological processes in marine organisms. The interest in diatoms' lipoxygenases and oxylipins has increased due to their possible biotechnological applications, ranging from ecology to medicine. We investigated using bioinformatics and molecular docking tools the lipoxygenases of diatoms and the possible interaction with substrates. A large-scale analysis of sequence resources allowed us to retrieve 45 sequences of lipoxygenases from diatoms. We compared and analyzed the sequences by multiple alignments and phylogenetic trees, suggesting the possible clustering in phylogenetic groups. Then, we modelled the 3D structure of representative enzymes from the different groups and investigated in detail the structural and functional properties by docking simulations with possible substrates. The results allowed us to propose a classification of the lipoxygenases from diatoms based on their sequence features, which may be reflected in specific structural differences and possible substrate specificity.

Phenolic Compounds and Capsaicinoids in Three Capsicum annuum Varieties: From Analytical Characterization to In Silico Hypotheses on Biological Activity.

The affinity of specific phenolic compounds (PCs) and capsaicinoids (CAPs) present in three Capsicum annuum varieties (Friariello, Cayenne and Dzuljunska Sipka) to the transient receptor potential vanilloid member 1 (TRPV1) was investigated by integrating an analytic approach for the simultaneous extraction and analysis through high-performance liquid chromatography coupled with ion trap mass spectrometry (HPLC/ITMS) and UV detection (HPLC-UV) of PCs and CAPs and structural bioinformatics based on the protein modelling and molecular simulations of protein-ligand docking. Overall, a total of 35 compounds were identified in the different samples and CAPs were quantified. The highest content of total polyphenols was recorded in the pungent Dzuljunska Sipka variety (8.91 ± 0.05 gGAE/Kg DW) while the lowest was found in the non-pungent variety Friariello (3.58 ± 0.02 gGAE/Kg DW). Protein modelling generated for the first time a complete model of the homotetrameric human TRPV1, and it was used for docking simulations with the compounds detected via the analytic approach, as well as with other compounds, as an inhibitor reference. The simulations indicate that different capsaicinoids can interact with the receptor, providing details on the molecular interaction, with similar predicted binding energy values. These results offer new insights into the interaction of capsaicinoids with TRPV1 and their possible actions.

Sirtuin Inhibitor Cambinol Induces Cell Differentiation and Differently Interferes with SIRT1 and 2 at the Substrate Binding Site.

Epigenetic mechanisms finely regulate gene expression and represent potential therapeutic targets. Cambinol is a synthetic heterocyclic compound that inhibits class III histone deacetylases known as sirtuins (SIRTs). The acetylating action that results could be crucial in modulating cellular functions via epigenetic regulations. The main aim of this research was to investigate the effects of cambinol, and its underlying mechanisms, on cell differentiation by combining wet experiments with bioinformatics analyses and molecular docking simulations. Our in vitro study evidenced the ability of cambinol to induce the differentiation in MCF-7, NB4, and 3T3-L1 cell lines. Interestingly, focusing on the latter that accumulated cytoplasmic lipid droplets, the first promising results related to the action mechanisms of cambinol have shown the induction of cell cycle-related proteins (such as p16 and p27) and modulation of the expression of Rb protein and nuclear receptors related to cell differentiation. Moreover, we explored the inhibitory mechanism of cambinol on human SIRT1 and 2 performing in silico molecular simulations by protein-ligand docking. Cambinol, unlike from other sirtuin inhibitors, is able to better interact with the substrate binding site of SIRT1 than with the inhibition site. Additionally, for SIRT2, cambinol partially interacts with the substrate binding site, although the inhibition site is preferred. Overall, our findings suggest that cambinol might contribute to the development of an alternative to the existing epigenetic therapies that modulate SIRTs.

Resources and tools for rare disease variant interpretation.

Collectively, rare genetic disorders affect a substantial portion of the world's population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.

Molecular Docking of Natural Compounds for Potential Inhibition of AhR.

The aryl hydrocarbon receptor (AhR) is a highly conserved environmental sensor, historically known for mediating the toxicity of xenobiotics. It is involved in numerous cellular processes such as differentiation, proliferation, immunity, inflammation, homeostasis, and metabolism. It exerts a central role in several conditions such as cancer, inflammation, and aging, acting as a transcription factor belonging to the basic helix-loop-helix/Per-ARNT-Sim (bHLH-PAS) protein family. A key step in the canonical AhR activation is AhR-ARNT heterodimerization followed by the binding to the xenobiotic-responsive elements (XREs). The present work aims to investigate the potential AhR inhibitory activity of selected natural compounds. Due to the absence of a complete structure of human AhRs, a model consisting of the bHLH, the PAS A, and the PAS B domains was constructed. Blind and focused docking simulations revealed the presence of further binding pockets, different from the canonical one presented in the PAS B domain, which could be important for AhR inhibition due to the possibility to impede AhR:ARNT heterodimerization, either preventing conformational changes or masking crucial sites necessary for protein-protein interaction. Two of the compounds retrieved from the docking simulations, i.e., ß-carotene and ellagic acid, confirmed their capacity of inhibiting benzo[a]pyrene (BaP)-induced AhR activation in in vitro tests on the human hepatoma cell line HepG2, validating the efficacy of the computational approach.

Machine Learning as a Support for the Diagnosis of Type 2 Diabetes.

Diabetes is a chronic, metabolic disease characterized by high blood sugar levels. Among the main types of diabetes, type 2 is the most common. Early diagnosis and treatment can prevent or delay the onset of complications. Previous studies examined the application of machine learning techniques for prediction of the pathology, and here an artificial neural network shows very promising results as a possible valuable aid in the management and prevention of diabetes. Additionally, its superior ability for long-term predictions makes it an ideal choice for this field of study. We utilized machine learning methods to uncover previously undiscovered associations between an individual's health status and the development of type 2 diabetes, with the goal of accurately predicting its onset or determining the individual's risk level. Our study employed a binary classifier, trained on scratch, to identify potential nonlinear relationships between the onset of type 2 diabetes and a set of parameters obtained from patient measurements. Three datasets were utilized, i.e., the National Center for Health Statistics' (NHANES) biennial survey, MIMIC-III and MIMIC-IV. These datasets were then combined to create a single dataset with the same number of individuals with and without type 2 diabetes. Since the dataset was balanced, the primary evaluation metric for the model was accuracy. The outcomes of this study were encouraging, with the model achieving accuracy levels of up to 86% and a ROC AUC value of 0.934. Further investigation is needed to improve the reliability of the model by considering multiple measurements from the same patient over time.

Standardizing macromolecular structure files: further efforts are needed.

Investigating large datasets of biological information by automatic procedures may offer chances of progress in knowledge. Recently, tremendous improvements in structural biology have allowed the number of structures in the Protein Data Bank (PDB) archive to increase rapidly, in particular those for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-associated proteins. However, their automatic analysis can be hampered by the nonuniform descriptors used by authors in some records of the PDB and PDBx/mmCIF files. In this opinion article we highlight the difficulties encountered in automating the analysis of hundreds of structures, suggesting that further standardization of the description of these molecular entities and of their attributes, generalized to the macromolecular structures contained in the PDB, might generate files more suitable for automatized analyses of a large number of structures.

Food Plant Secondary Metabolites Antiviral Activity and Their Possible Roles in SARS-CoV-2 Treatment: An Overview.

Natural products and plant extracts exhibit many biological activities, including that related to the defense mechanisms against parasites. Many studies have investigated the biological functions of secondary metabolites and reported evidence of antiviral activities. The pandemic emergencies have further increased the interest in finding antiviral agents, and efforts are oriented to investigate possible activities of secondary plant metabolites against human viruses and their potential application in treating or preventing SARS-CoV-2 infection. In this review, we performed a comprehensive analysis of studies through in silico and in vitro investigations, also including in vivo applications and clinical trials, to evaluate the state of knowledge on the antiviral activities of secondary metabolites against human viruses and their potential application in treating or preventing SARS-CoV-2 infection, with a particular focus on natural compounds present in food plants. Although some of the food plant secondary metabolites seem to be useful in the prevention and as a possible therapeutic management against SARS-CoV-2, up to now, no molecules can be used as a potential treatment for COVID-19; however, more research is needed.

Identification of Dihydrolipoamide Dehydrogenase as Potential Target of Vemurafenib-Resistant Melanoma Cells.

BACKGROUND: Despite recent improvements in therapy, the five-year survival rate for patients with advanced melanoma is poor, mainly due to the development of drug resistance. The aim of the present study was to investigate the mechanisms underlying this phenomenon, applying proteomics and structural approaches to models of melanoma cells. METHODS: Sublines from two human (A375 and SK-MEL-28) cells with acquired vemurafenib resistance were established, and their proteomic profiles when exposed to denaturation were identified through LC-MS/MS analysis. The pathways derived from bioinformatics analyses were validated by in silico and functional studies. RESULTS: The proteomic profiles of resistant melanoma cells were compared to parental counterparts by taking into account protein folding/unfolding behaviors. Several proteins were found to be involved, with dihydrolipoamide dehydrogenase (DLD) being the only one similarly affected by denaturation in all resistant cell sublines compared to parental ones. DLD expression was observed to be increased in resistant cells by Western blot analysis. Protein modeling analyses of DLD's catalytic site coupled to in vitro assays with CPI-613, a specific DLD inhibitor, highlighted the role of DLD enzymatic functions in the molecular mechanisms of BRAFi resistance. CONCLUSIONS: Our proteomic and structural investigations on resistant sublines indicate that DLD may represent a novel and potent target for overcoming vemurafenib resistance in melanoma cells.

Molecular dynamics analysis of the structural properties of the transglutaminases of Kutzneria albida and Streptomyces mobaraensis.

The microbial transglutaminase (TGase) from Streptomyces mobaraensis (MTGase) is widely used for industrial applications. However, in the last decades, TGases from other bacteria have been described. We focused our attention on TGase, from Kutzneria albida (KalbTGase), recently characterized as more selective than MTGase and proposed for applications in drug delivery. By comparison of the crystallographic structures, the volume of the catalytic site results smaller in KalbTGase. We compared KalbTGase and MTGase structural flexibility by molecular dynamics (MD) simulations at different conditions. KalbTGase is more rigid than MTGase at 300 K, but the catalytic site has a preserved conformation in both structures. Preliminary studies at higher temperatures suggest that KalbTGase acquires enhanced conformational flexibility far from the active site region. The volume of the catalytic active site pocket of KalbTGase at room temperature is smaller than that of MTGase, and decreases at 335 K, remaining stable after further temperature increase. On the contrary, in MTGase the pocket volume continues to decrease as the temperature increases. Overall, the results of our study suggest that at room temperature the enhanced specificity of KalbTGase could be related to a more closed catalytic pocket and lower flexibility than MTGase. Moreover, by preliminary results at higher temperature, KalbTGase structural flexibility suggests an adaptability to different substrates not recognized at room temperature. Lower adaptability of MTGase at higher temperature with a reduction of the catalytic pocket, instead, suggests a reduction of its activity.

In Silico Analysis of the Effects of Omicron Spike Amino Acid Changes on the Interactions with Human Proteins.

The SARS-CoV-2 variant Omicron is characterized, among others, by more than 30 amino acid changes occurring on the spike glycoprotein with respect to the original SARS-CoV-2 spike protein. We report a comprehensive analysis of the effects of the Omicron spike amino acid changes in the interaction with human antibodies, obtained by modeling them into selected publicly available resolved 3D structures of spike-antibody complexes and investigating the effects of these mutations at structural level. We predict that the interactions of Omicron spike with human antibodies can be either negatively or positively affected by amino acid changes, with a predicted total loss of interactions only in a few complexes. Moreover, our analysis applied also to the spike-ACE2 interaction predicts that these amino acid changes may increase Omicron transmissibility. Our approach can be used to better understand SARS-CoV-2 transmissibility, detectability, and epidemiology and represents a model to be adopted also in the case of other variants.

Drug Design by Pharmacophore and Virtual Screening Approach.

Computer-aided drug discovery techniques reduce the time and the costs needed to develop novel drugs. Their relevance becomes more and more evident with the needs due to health emergencies as well as to the diffusion of personalized medicine. Pharmacophore approaches represent one of the most interesting tools developed, by defining the molecular functional features needed for the binding of a molecule to a given receptor, and then directing the virtual screening of large collections of compounds for the selection of optimal candidates. Computational tools to create the pharmacophore model and to perform virtual screening are available and generated successful studies. This article describes the procedure of pharmacophore modelling followed by virtual screening, the most used software, possible limitations of the approach, and some applications reported in the literature.

Investigating the Effects of Amino Acid Variations in Human Menin.

Human menin is a nuclear protein that participates in many cellular processes, as transcriptional regulation, DNA damage repair, cell signaling, cell division, proliferation, and migration, by interacting with many other proteins. Mutations of the gene encoding menin cause multiple endocrine neoplasia type 1 (MEN1), a rare autosomal dominant disorder associated with tumors of the endocrine glands. In order to characterize the structural and functional effects at protein level of the hundreds of missense variations, we investigated by computational methods the wild-type menin and more than 200 variants, predicting the amino acid variations that change secondary structure, solvent accessibility, salt-bridge and H-bond interactions, protein thermostability, and altering the capability to bind known protein interactors. The structural analyses are freely accessible online by means of a web interface that integrates also a 3D visualization of the structure of the wild-type and variant proteins. The results of the study offer insight into the effects of the amino acid variations in view of a more complete understanding of their pathological role.

Structural Dissection of Viral Spike-Protein Binding of SARS-CoV-2 and SARS-CoV-1 to the Human Angiotensin-Converting Enzyme 2 (ACE2) as Cellular Receptor.

An outbreak by a new severe acute respiratory syndrome betacoronavirus (SARS-CoV-2) has spread CoronaVirus Disease 2019 (COVID-19) all over the world. Immediately, following studies have confirmed the human Angiotensin-Converting Enzyme 2 (ACE2) as a cellular receptor of viral Spike-Protein (Sp) that mediates the CoV-2 invasion into the pulmonary host cells. Here, we compared the molecular interactions of the viral Sp from previous SARS-CoV-1 of 2002 and SARS-CoV-2 with the host ACE2 protein by in silico analysis of the available experimental structures of Sp-ACE2 complexes. The K417 amino acid residue, located in the region of Sp Receptor-Binding Domain (RBD) of the new coronavirus SARS-CoV-2, showed to have a key role for the binding to the ACE2 N-terminal region. The R426 residue of SARS-CoV-1 Sp-RBD also plays a key role, although by interacting with the central region of the ACE2 sequence. Therefore, our study evidenced peculiarities in the interactions of the two Sp-ACE2 complexes. Our outcomes were consistent with previously reported mutagenesis studies on SARS-CoV-1 and support the idea that a new and different RBD was acquired by SARS-CoV-2. These results have interesting implications and suggest further investigations.

Transamidation Down-Regulates Intestinal Immunity of Recombinant α-Gliadin in HLA-DQ8 Transgenic Mice.

Enzymatic transamidation of gliadins by microbial transglutaminase (mTG) inhibits interferon-γ (IFN-γ) secretion by intestinal T cell lines in patients with celiac disease (CD). To gain insight into the cellular mechanisms underlying the down-regulatory effects of transamidation, we tested a single recombinant α-gliadin (r-gliadin) harbouring two immunodominant peptides, p13 (aa. 120-139) and p23 (aa. 220-239), in HLA-DQ8 transgenic mice, a model of gluten sensitivity. Mice were intranasally immunised with r-gliadin or r-gliadin transamidated by mTG (K-r-gliadin) along with cholera toxin, and the response of mesenteric lymph node cells was analysed by cytokine multiplex assay. An in vitro challenge with r-gliadin was characterised by secretion of specific cytokines featuring both innate immunity and the Th1/Th2/Th17 pattern of the adaptive response. Notably, transamidation specifically down-regulated the Th1 response. Structural studies performed on K-r-gliadin confirmed that specific glutamine residues in p13 and p23, previously found to be deamidated by tissue transglutaminase, were also transamidated by mTG. In silico analysis, simulating p13 and p23 peptide binding to HLA-DQ8 showed that these glutamines, in the form of glutamate, could interact by means of salt bridges with peculiar amino acids of the alpha chain of HLA-DQ8, suggesting that their transamidation may influence the HLA-restricted recognition of these peptides. Thus, the structural findings provided a rationale to explain the down-regulation of the r-gliadin-specific Th1 response following transamidation.

A hypothesis on the capacity of plant odorant-binding proteins to bind volatile isoprenoids based on in silico evidences.

Volatile organic compounds (VOCs) from 'emitting' plants inform the 'receiving' (listening) plants of impending stresses or simply of their presence. However, the receptors that allow receivers to detect the volatile cue are elusive. Most likely, plants (as animals) have odorant-binding proteins (OBPs), and in fact, a few OBPs are known to bind 'stress-induced' plant VOCs. We investigated whether these and other putative OBPs may bind volatile constitutive and stress-induced isoprenoids, the most emitted plant VOCs, with well-established roles in plant communication and defense. Molecular docking simulation experiments suggest that structural features of a few plant proteins screened in databases could allow VOC binding. In particular, our results show that monoterpenes may bind the same plant proteins that were described to bind other stress-induced VOCs, while the constitutive hemiterpene isoprene is unlikely to bind any investigated putative OBP and may not have an info-chemical role. We conclude that, as for animal, there may be plant OBPs that bind multiple VOCs. Plant OBPs may play an important role in allowing plants to eavesdrop messages by neighboring plants, triggering defensive responses and communication with other organisms.

Cytotoxic, Antioxidant, and Enzyme Inhibitory Properties of the Traditional Medicinal Plant Matthiola incana (L.) R. Br.

Matthiola incana (L.) R. Br. (Brassicaceae) is widely cultivated for ornamental purposes and utilized as a medicinal plant. In the present work, the hydroalcoholic extract from the aerial parts of this species has been evaluated in different bioassays in order to detect potential pharmacological applications. The cytotoxic capacity against the human colorectal adenocarcinoma (CaCo-2) and breast cancer (MCF-7) cell lines was tested using the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. The extract was investigated as a neuroprotective inhibitor of central nervous system (CNS) enzymes such as monoamine oxidase A, tyrosinase, acetylcholinesterase, and as a natural enzyme inhibitor of α-glucosidase and lipase involved in some metabolic disorders such as obesity or type 2 diabetes. The antioxidant ability was also evaluated in an enzymatic system (xanthine/xanthine oxidase assay). Results showed that the M. incana extract displayed moderate to low cytotoxicity vs. CaCo-2 cells. The extract acted as a superoxide radical scavenger and enzymatic inhibitor of monoamine oxidase A, tyrosinase, α-glucosidase, and lipase. The best results were found in the α-glucosidase assay, as M. incana hydroalcoholic extract was able to inhibit the enzyme α-glucosidase up to 100% without significant differences, compared to the antidiabetic drug acarbose. Matthiola incana has been demonstrated to exert different biological properties. These are important in order to consider this species as a source of bioactive compounds.

New compounds for a good old class: Synthesis of two Β-lactam bearing cephalosporins and their evaluation with a multidisciplinary approach.

Antimicrobial resistance is spreading massively in the world and is becoming one of the main health threats of the 21st century. One of the possible strategies to overcome this problem is to modify the known classes of antibiotics in a rational way, with the aim of tuning their efficacy. In this paper, we present the synthesis and the evaluation of the biological activity of a series of two ß-lactam bearing cephalosporin derivatives, in which an additional isolated azetidinone ring, bearing different substituents, is joined to the classical cephalosporanic nucleus by a chain of variable length. A computational approach has been also applied in order to predict the molecular interactions between some representative derivatives and selected penicillin-binding proteins, the natural targets of ß-lactam antibiotics. All these derivatives are active against Gram-positive bacteria, with MIC100 comparable or even better than that of the reference antibiotic ceftriaxone, and show no or very low cytotoxic activity on different cell lines. Overall, these molecules appear to be able to exert their activity in particular against microorganisms belonging to some of the species more involved in the development of multidrug resistance.

Classification of microbial transglutaminases by evaluation of evolution trees, sequence motifs, secondary structure topology and conservation of potential catalytic residues.

Despite the growing interest for microbial transglutaminases (TGases), and the large number of genome sequencing data, there is no deep investigation about structural properties within this family of enzymes in bacteria. We performed a classification of microbial TGases, starting from large-scale analysis of all protein sequences annotated as TGase (more than 8000) in database PFAM. We developed a reiterative procedure based on the construction of several phylogenetic trees and manual selection, and detected five main groups of microbial TGases. Searches for sequence motifs evidenced strong conservation in regions containing potential catalytic residues for some groups. Protein structure modelling has been possible for three of the five groups. Analyses of motifs, structural topologies and potential catalytic sites suggest possible interpretations for function similarities and divergences among groups.