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.

A Combination of Microarray-Based Profiling and Biocomputational Analysis Identified miR331-3p and hsa-let-7d-5p as Potential Biomarkers of Ulcerative Colitis Progression to Colorectal Cancer.

Ulcerative colitis (UC), an inflammatory bowel disease (IBD), may increase the risk of colorectal cancer (CRC) by activating chronic proinflammatory pathways. The goal of this study was to find serum prediction biomarkers in UC to CRC development by combining low-density miRNA microarray and biocomputational approaches. The UC and CRC miRNA expression profiles were compared by low-density miRNA microarray, finding five upregulated miRNAs specific to UC progression to CRC (hsa-let-7d-5p, hsa-miR-16-5p, hsa-miR-145-5p, hsa-miR-223-5p, and hsa-miR-331-3p). The circRNA/miRNA/mRNA competitive endogenous RNA (ceRNA) network analysis showed that the candidate miRNAs were connected to well-known colitis-associated CRC ACVR2A, SOCS1, IGF2BP1, FAM126A, and CCDC85C mRNAs, and circ-SHPRH circRNA. SST and SCARA5 genes regulated by hsa-let-7d-5p, hsa-miR-145-5p, and hsa-miR-331-3p were linked to a poor survival prognosis in a CRC patient dataset from The Cancer Genome Atlas (TCGA). Lastly, our mRNA and miRNA candidates were validated by comparing their expression to differentially expressed mRNAs and miRNAs from colitis-associated CRC tissue databases. A high level of hsa-miR-331-3p and a parallel reduction in SOCS1 mRNA were found in tissue and serum. We propose hsa-miR-331-3p and possibly hsa-let-7d-5p as novel serum biomarkers for predicting UC progression to CRC. More clinical sample analysis is required for further validation.

Predicting the stability of mutant proteins by computational approaches: an overview.

A very large number of computational methods to predict the change in thermodynamic stability of proteins due to mutations have been developed during the last 30 years, and many different web servers are currently available. Nevertheless, most of them suffer from severe drawbacks that decrease their general reliability and, consequently, their applicability to different goals such as protein engineering or the predictions of the effects of mutations in genetic diseases. In this review, we have summarized all the main approaches used to develop these tools, with a survey of the web servers currently available. Moreover, we have also reviewed the different assessments made during the years, in order to allow the reader to check directly the different performances of these tools, to select the one that best fits his/her needs, and to help naïve users in finding the best option for their needs.

A review on drug repurposing applicable to COVID-19.

Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug-disease or drug-target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application.

A multiple network-based bioinformatics pipeline for the study of molecular mechanisms in oncological diseases for personalized medicine.

MOTIVATION: Assessment of genetic mutations is an essential element in the modern era of personalized cancer treatment. Our strategy is focused on 'multiple network analysis' in which we try to improve cancer diagnostics by using biological networks. Genetic alterations in some important hubs or in driver genes such as BRAF and TP53 play a critical role in regulating many important molecular processes. Most of the studies are focused on the analysis of the effects of single mutations, while tumors often carry mutations of multiple driver genes. The aim of this work is to define an innovative bioinformatics pipeline focused on the design and analysis of networks (such as biomedical and molecular networks), in order to: (1) improve the disease diagnosis; (2) identify the patients that could better respond to a given drug treatment; and (3) predict what are the primary and secondary effects of gene mutations involved in human diseases. RESULTS: By using our pipeline based on a multiple network approach, it has been possible to demonstrate and validate what are the joint effects and changes of the molecular profile that occur in patients with metastatic colorectal carcinoma (mCRC) carrying mutations in multiple genes. In this way, we can identify the most suitable drugs for the therapy for the individual patient. This information is useful to improve precision medicine in cancer patients. As an application of our pipeline, the clinically significant case studies of a cohort of mCRC patients with the BRAF V600E-TP53 I195N missense combined mutation were considered. AVAILABILITY: The procedures used in this paper are part of the Cytoscape Core, available at (www.cytoscape.org). Data used here on mCRC patients have been published in [55]. SUPPLEMENTARY INFORMATION: A supplementary file containing a more detailed discussion of this case study and other cases is available at the journal site as Supplementary Data.

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.

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.

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.

Bioinformatics methodologies for coeliac disease and its comorbidities.

Coeliac disease (CD) is a complex, multifactorial pathology caused by different factors, such as nutrition, immunological response and genetic factors. Many autoimmune diseases are comorbidities for CD, and a comprehensive and integrated analysis with bioinformatics approaches can help in evaluating the interconnections among all the selected pathologies. We first performed a detailed survey of gene expression data available in public repositories on CD and less commonly considered comorbidities. Then we developed an innovative pipeline that integrates gene expression, cell-type data and online resources (e.g. a list of comorbidities from the literature), using bioinformatics methods such as gene set enrichment analysis and semantic similarity. Our pipeline is written in R language, available at the following link: http://bioinformatica.isa.cnr.it/COELIAC_DISEASE/SCRIPTS/. We found a list of common differential expressed genes, gene ontology terms and pathways among CD and comorbidities and the closeness among the selected pathologies by means of disease ontology terms. Physicians and other researchers, such as molecular biologists, systems biologists and pharmacologists can use it to analyze pathology in detail, from differential expressed genes to ontologies, performing a comparison with the pathology comorbidities or with other diseases.

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.

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.

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.

Performance of Web tools for predicting changes in protein stability caused by mutations.

BACKGROUND: Despite decades on developing dedicated Web tools, it is still difficult to predict correctly the changes of the thermodynamic stability of proteins caused by mutations. Here, we assessed the reliability of five recently developed Web tools, in order to evaluate the progresses in the field. RESULTS: The results show that, although there are improvements in the field, the assessed predictors are still far from ideal. Prevailing problems include the bias towards destabilizing mutations, and, in general, the results are unreliable when the mutation causes a ΔΔG within the interval ± 0.5 kcal/mol. We found that using several predictors and combining their results into a consensus is a rough, but effective way to increase reliability of the predictions. CONCLUSIONS: We suggest all developers to consider in their future tools the usage of balanced data sets for training of predictors, and all users to combine the results of multiple tools to increase the chances of having correct predictions about the effect of mutations on the thermodynamic stability of a protein.

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.

Virtual Screening of Natural Compounds as Potential PI3K-AKT1 Signaling Pathway Inhibitors and Experimental Validation.

A computational screening for natural compounds suitable to bind the AKT protein has been performed after the generation of a pharmacophore model based on the experimental structure of AKT1 complexed with IQO, a well-known inhibitor. The compounds resulted as being most suitable from the screening have been further investigated by molecular docking, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analysis and toxicity profiles. Two compounds selected at the end of the computational analysis, i.e., ZINC2429155 (also named STL1) and ZINC1447881 (also named AC1), have been tested in an experimental assay, together with IQO as a positive control and quercetin as a negative control. Only STL1 clearly inhibited AKT activation negatively modulating the PI3K/AKT pathway.

STL1, a New AKT Inhibitor, Synergizes with Flavonoid Quercetin in Enhancing Cell Death in A Chronic Lymphocytic Leukemia Cell Line.

Using a pharmacophore model based on the experimental structure of AKT-1, we recently identified the compound STL1 (ZINC2429155) as an allosteric inhibitor of AKT-1. STL1, was able to reduce Ser473 phosphorylation, thus inhibiting the PI3K/AKT pathway. Moreover, we demonstrated that the flavonoid quercetin downregulated the phosphorylated and active form of AKT. However, in this case, quercetin inhibited the PI3K/AKT pathway by directly binding the kinases CK2 and PI3K. In the present work, we investigated the antiproliferative effects of the co-treatment quercetin plus STL1 in HG-3 cells, derived from a patient affected by chronic lymphocytic leukemia. Quercetin and STL1 in the mono-treatment maintained the capacity to inhibit AKT phosphorylation on Ser473, but did not significantly reduce cell viability. On the contrary, they activated a protective form of autophagy. When the HG-3 cells were co-treated with quercetin and STL1, their association synergistically (combination index < 1) inhibited cell growth and induced apoptosis. The combined treatment caused the switch from protective to non-protective autophagy. This work demonstrated that cytotoxicity could be enhanced in a drug-resistant cell line by combining the effects of different inhibitors acting in concert on PI3K and AKT kinases.

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.

PAX8 expression in high-grade serous ovarian cancer positively regulates attachment to ECM via Integrin ß3.

BACKGROUND: Ovarian cancer is the third most common cause of death among gynecologic malignancies worldwide. Understanding the biology and molecular pathogenesis of ovarian epithelial tumors is key to developing improved prognostic indicators and effective therapies. We aimed to determine the effects of PAX8 expression on the migrative, adhesive and survival capabilities of high-grade serous carcinoma cells. METHODS: PAX8 depleted Fallopian tube secretory cells and ovarian cancer cells were generated using short interfering siRNA. Anoikis resistance, cell migration and adhesion properties of PAX8 silenced cells were analyzed by means of specific assays. Chromatin immunoprecipitation (ChIP) was carried out using a PAX8 polyclonal antibody to demonstrate that PAX8 is able to bind to the 5'-flanking region of the ITGB3 gene positively regulating its expression. RESULTS: Here, we report that RNAi silencing of PAX8 sensitizes non-adherent cancer cells to anoikis and affects their tumorigenic properties. We show that PAX8 plays a critical role in migration and adhesion of both Fallopian tube secretory epithelial cells and ovarian cancer cells. Inhibition of PAX8 gene expression reduces the ability of ovarian cancer cells to migrate and adhere to the ECM and specifically to fibronectin and/or collagen substrates. Moreover, loss of PAX8 strongly reduces ITGB3 expression and consequently the correct expression of the αvß3 heterodimer on the plasma membrane. CONCLUSIONS: Our results demonstrate that PAX8 modulates the interaction of tumor cells with the extracellular matrix (ECM). Notably, we also highlight a novel pathway downstream this transcription factor. Overall, PAX8 could be a potential therapeutic target for high-grade serous carcinoma.

The evolution of a Web resource: The Galactosemia Proteins Database 2.0.

Galactosemia Proteins Database 2.0 is a Web-accessible resource collecting information about the structural and functional effects of the known variations associated to the three different enzymes of the Leloir pathway encoded by the genes GALT, GALE, and GALK1 and involved in the different forms of the genetic disease globally called "galactosemia." It represents an evolution of two available online resources we previously developed, with new data deriving from new structures, new analysis tools, and new interfaces and filters in order to improve the quality and quantity of information available for different categories of users. We propose this new resource both as a landmark for the entire world community of galactosemia and as a model for the development of similar tools for other proteins object of variations and involved in human diseases.

The genetic code is not an optimal code in a model taking into account both the biosynthetic relationships between amino acids and their physicochemical properties.

We have considered a model for the origin of the genetic code that takes into account the two factors that have determined its evolution: the biosynthetic relationships between amino acids and their physicochemical properties. The model evaluates the biosynthetic relationships between amino acids considering of constraints based on the biosynthetic families of amino acids. These biosynthetic constraints are able to define six subsets of codes - that we have analyzed - in which the evolution of the genetic code might have passed. At the same time, the physicochemical properties of amino acids have been described by means of two scales of polarity, considered solely or combined with the molecular volume of amino acids. Furthermore, we have considered three cost functions. The results maintain the hypothesis that the genetic code organization is not optimal even in these subsets containing a very limited number of elements. That is to say, the genetic code is not a local or absolute minimum. For instance, only a few amino acid exchanges would have rendered the genetic code more optimized or even they would have transformed it into a completely optimized code. This would imply that the evolution of the genetic code, not considering these possibilities, has evolved through different pathways from the one that was oriented to a high optimization. Moreover, the combination of polarity and the molecular volume of amino acids results to have been more significant than when the only polarity is considered, in conditioning the evolution of the genetic code contrary to that reported in the literature. However, this is not such to produce an organization of the genetic code optimized if referred to these two properties. Nevertheless, these two properties being crucial in defining the structure of proteins, they would have affected the origin of the genetic code by means of the selective pressure directed to improve the ancestral enzymatic catalysis. As a whole these observations contradict the predictions of the physicochemical theories of the origin of the genetic code because the non-optimization of the genetic code organization - even in subsets of codes with a very low element number - would deny the absolute importance of the physicochemical properties of amino acids in its structuration, which, on the contrary, is expected from these theories. Conversely, these same observations would be in perfect agreement with the coevolution theory of the origin of the genetic code because they would explain both the not fundamental role of the physicochemical properties in organizing the genetic code and the importance of these properties in the evolution of coded catalysis - that is to say, of the genetic code - both predicted by this last theory.