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Exosomes, natural nanovesicles that contain a cargo of biologically active molecules such as lipids, proteins, and nucleic acids, are released from cells to the extracellular environment. They then act as autocrine, paracrine, or endocrine mediators of communication between cells by delivering their cargo into recipient cells and causing downstream effects. Exosomes are greatly enriched in miRNAs, which are small non-coding RNAs that act both as cytoplasmic post-transcriptional repression agents, modulating the translation of mRNAs into proteins, as well as nuclear transcriptional gene activators. Neuronal exosomal miRNAs have important physiologic functions in the central nervous system (CNS), including cell-to-cell communication, synaptic plasticity, and neurogenesis, as well as modulating stress and inflammatory responses. Stress-induced changes in exosomal functions include effects on neurogenesis and neuroinflammation, which can lead to the appearance of various neuropsychiatric disorders such as schizophrenia, major depression, bipolar disorder, and Alzheimer’s and Huntington’s diseases. The current knowledge regarding the roles of exosomes in the pathophysiology of common mental disorders is discussed in this review.

Milk is a biological fluid with a dynamic composition of micronutrients and bioactive molecules that serves as a vital nutrient source for infants. Milk composition is affected by multiple factors, including genetics, geographical location, environmental conditions, lactation phase, and maternal nutrition, and plays a key role in dictating its microbiome. This study addresses a less-explored aspect, comparing the microbial communities in human breast milk with those in mature milk from species that are used for milk consumption. Since mature animal milk is used as a supplement for both the infant (formula) and the child/adolescent, our main aim was to identify shared microbial communities in colostrum and mature human milk. Using 16S rRNA metagenomic sequencing, we focused on characterizing the milk microbiota in the Northern Greek population by identifying shared microbial communities across samples and comparing the relative abundance of prevalent genera. We analyzed ten human milk samples (from five mothers), with five collected three days postpartum (colostrum) and five collected thirty to forty days postpartum (mature milk) from corresponding mothers. To perform an interspecies comparison of human milk microbiota, we analyzed five goat and five bovine milk samples from a local dairy industry, collected fifty to seventy days after birth. Alpha diversity analysis indicated moderate diversity and stability in bovine milk, high richness in goat milk, and constrained diversity in breast milk. Beta diversity analysis revealed significant distinctions among mammalian species, emphasizing both presence/absence and abundance-based clustering. Despite noticeable differences, shared microbial components underscore fundamental aspects across all mammalian species, highlighting the presence of a core microbiota predominantly comprising the Proteobacteria, Firmicutes, and Actinobacteriota phyla. At the genus level, Acinetobacter, Gemella, and Sphingobium exhibit significant higher abundance in human milk compared to bovine and goat milk, while Pseudomonas and Atopostipes are more prevalent in animal milk. Our comparative analysis revealed differences and commonalities in the microbial communities of various mammalian milks and unraveled the existence of a common fundamental milk core microbiome. We thus revealed both species-specific and conserved microbial communities in human, bovine, and goat milk. The existence of a common core microbiome with conserved differences between colostrum and mature human milk underscores fundamental similarities in the microbiota of milk across mammalian species, which could offer valuable implications for optimizing the nutritional quality and safety of dairy products as well as supplements for infant health.

Understanding the structure and function of intermediate filaments (IFs) is necessary in order to explain why more than 70 related IF genes have evolved in vertebrates while maintaining such dramatically tissue-specific expression. Desmin is a member of the large multigene family of IF proteins and is specifically expressed in myocytes. In an effort to elucidate its muscle-specific behavior, we have used a yeast two-hybrid system in order to identify desmin’s head binding partners. We described a mitochondrial and a lysosomal protein, NADH ubiquinone oxidoreductase core subunit S2 (NDUFS2), and saposin D, respectively, as direct desmin binding partners. In silico analysis indicated that both interactions at the atomic level occur in a very similar way, by the formation of a three-helix bundle with hydrophobic interactions in the interdomain space and hydrogen bonds at R16 and S32 of the desmin head domain. The interactions, confirmed also by GST pull-down assays, indicating the necessity of the desmin head domain and, furthermore, point out its role in function of mitochondria and lysosomes, organelles which are disrupted in myopathies due to desmin head domain mutations.

The 24‐h molecular clock is based on the stability of rhythmically expressed transcripts. The shortening of the poly(A) tail of mRNAs is often the first and rate‐limiting step that determines the lifespan of a mRNA and is catalyzed by deadenylases. Herein, we determine the catalytic site of Hesperin, a recently described circadian deadenylase in plants, using a modified site‐directed mutagenesis protocol and a custom vector, pATHRA. To explore the catalytic efficiency of AtHESPERIN, we investigated the effect of AMP and neomycin, and used molecular modeling simulations to propose a catalytic mechanism. Collectively, the biochemical and in silico results classify AtHESPERIN in the exonuclease–endonuclease–phosphatase deadenylase superfamily and contribute to the understanding of the intricate mechanisms of circadian mRNA turnover.

BackgroundCentral precocious puberty (CPP) due to premature activation of GnRH secretion results in early epiphyseal fusion and to a significant compromise in the achieved final adult height. Currently, few genetic determinants of children with CPP have been described. In this translational study, rare sequence variants in MKRN3, DLK1, KISS1, and KISS1R genes were investigated in patients with CPP.MethodsFifty-four index girls and two index boys with CPP were first tested by Sanger sequencing for the MKRN3 gene. All children found negative (n = 44) for the MKRN3 gene were further investigated by whole exome sequencing (WES). In the latter analysis, the status of variants in genes known to be related with pubertal timing was compared with an in-house Cypriot control cohort (n = 43). The identified rare variants were initially examined by in silico computational algorithms and confirmed by Sanger sequencing. Additionally, a genetic network for the MKRN3 gene, mimicking a holistic regulatory depiction of the crosstalk between MKRN3 and other genes was designed.ResultsThree previously described pathogenic MKRN3 variants located in the coding region of the gene were identified in 12 index girls with CPP. The most prevalent pathogenic MKRN3 variant p.Gly312Asp was exclusively found among the Cypriot CPP cohort, indicating a founder effect phenomenon. Seven other CPP girls harbored rare likely pathogenic upstream variants in the MKRN3. Among the 44 CPP patients submitted to WES, nine rare DLK1 variants were identified in 11 girls, two rare KISS1 variants in six girls, and two rare MAGEL2 variants in five girls. Interestingly, the frequent variant rs10407968 (p.Gly8Ter) of the KISS1R gene appeared to be less frequent in the cohort of patients with CPP.ConclusionThe results of the present study confirm the importance of the MKRN3-imprinted gene in genetics of CPP and its key role in pubertal timing. Overall, the results of the present study have emphasized the importance of an approach that aligns genetics and clinical aspects, which is necessary for the management and treatment of CPP.

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

The poly(A) tail at the 3′ end of mRNAs determines their stability, translational efficiency, and fate. The shortening of the poly(A) tail, and its efficient removal, triggers the degradation of mRNAs, thus, regulating gene expression. The process is catalyzed by a family of enzymes, known as deadenylases. As the dysregulation of gene expression is a hallmark of cancer, understanding the role of deadenylases has gained additional interest. Herein, the genetic association network shows that CNOT6 and CNOT7 are the most prevalent and most interconnected nodes in the equilibrated diagram. Subsequent silencing and transcriptomic analysis identifies transcripts possibly regulated by specific deadenylases. Furthermore, several gene ontologies are enriched by common deregulated genes. Given the potential concerted action and overlapping functions of deadenylases, we examined the effect of silencing a deadenylase on the remaining ones. Our results suggest that specific deadenylases target unique subsets of mRNAs, whilst at the same time, multiple deadenylases may affect the same mRNAs with overlapping functions.

Notch family proteins play a key role in a variety of developmental processes by controlling cell fate decisions and operating in a great number of biological processes in several organ systems, such as hematopoiesis, somatogenesis, vasculogenesis, neurogenesis and homeostasis. The Notch signaling pathway is crucial for the majority of developmental programs and regulates multiple pathogenic processes. Notch family receptors’ activation has been largely related to its multiple effects in sustaining oncogenesis. The Notch signaling pathway constitutes an ancient and conserved mechanism for cell to cell communication. Much of what is known about Notch family proteins function comes from studies done in Caenorhabditis Elegans and Drosophila Melanogaster. Although, human Notch homologs had also been identified, the molecular mechanisms which modulate the Notch signaling pathway remained substantially unknown. In this study, an updated evolutionary analysis of the Notch family members among 603 different organisms of all kingdoms, from bacteria to humans, was performed in order to discover key regions that have been conserved throughout evolution and play a major role in the Notch signaling pathway. The major goal of this study is the presentation of a novel updated phylogenetic tree for the Notch family as a reliable phylogeny “map”, in order to correlate information of the closely related members and identify new possible pharmacological targets that can be used in pathogenic cases, including cancer.

Background: Congenital hypogonadotropic hypogonadism (CHH) is a rare genetic disease caused by Gonadotropin-Releasing Hormone (GnRH) deficiency. So far a limited number of variants in several genes have been associated with the pathogenesis of the disease. In this original research and review manuscript the retrospective analysis of known variants in ANOS1 (KAL1), RNF216, WDR11, FGFR1, CHD7, and POLR3A genes is described, along with novel variants identified in patients with CHH by the present study.Methods: Seven GnRH deficient unrelated Cypriot patients underwent whole exome sequencing (WES) by Next Generation Sequencing (NGS). The identified novel variants were initially examined by in silico computational algorithms and structural analysis of their predicted pathogenicity at the protein level was confirmed.Results: In four non-related GnRH males, a novel X-linked pathogenic variant in ANOS1 gene, two novel autosomal dominant (AD) probably pathogenic variants in WDR11 and FGFR1 genes and one rare AD probably pathogenic variant in CHD7 gene were identified. A rare autosomal recessive (AR) variant in the SRA1 gene was identified in homozygosity in a female patient, whilst two other male patients were also, respectively, found to carry novel or previously reported rare pathogenic variants in more than one genes; FGFR1/POLR3A and SRA1/RNF216.Conclusion: This report embraces the description of novel and previously reported rare pathogenic variants in a series of genes known to be implicated in the biological development of CHH. Notably, patients with CHH can harbor pathogenic rare variants in more than one gene which raises the hypothesis of locus-locus interactions providing evidence for digenic inheritance. The identification of such aberrations by NGS can be very informative for the management and future planning of these patients.

Abstract In experimental models, both in vivo and cellular, over-expression of Parkinson’s linked mutant leucine-rich repeat kinase 2 (LRRK2) is sufficient to induce neuronal death. While several cell death associated proteins have been linked to LRRK2, either as protein interactors or as putative substrates, characterization of the neuronal death cascade remains elusive. In this study, we have mapped for the first time the domain within LRRK2 that mediates the interaction with FADD, thereby activating the molecular machinery of the extrinsic death pathway. Using homology modeling and molecular docking approaches, we have identified a critical motif within the N-terminal armadillo repeat region of LRRK2. Moreover, we show that co-expression of fragments of LRRK2 that contain the FADD binding motif, or deletion of this motif itself, blocks the interaction with FADD, and is neuroprotective. We further demonstrate that downstream of FADD, the mitochondrial proteins Bid and Bax are recruited to the death cascade and are necessary for neuronal death. Our work identifies multiple novel points within neuronal death signaling pathways that could potentially be targeted by candidate therapeutic strategies and highlight how the extrinsic pathway can be activated intracellularly in a pathogenic context.

Self-efficacy is perhaps the most important parameter associated with behavioral changes. The main aim of this study was to provide insight into the diet and exercise self-efficacy of Greek adolescents and how they could be modified via a multilevel multicomponent school-based lifestyle intervention. Secondary aims were to study the associations of students’ dietary and exercise self-efficacy indices with their anthropometric and sociodemographic parameters. A representative sample of the adolescent population in Attica, consisting of 1610 adolescents aged 12–17 years, recruited from 23 public high schools in three municipalities of the Attica area in Greece, received a three-component lifestyle educational intervention for health promotion and underwent screening for characteristics of metabolic syndrome with the use of portable telemedicine. All assessments and anthropometric measurements were performed at baseline and after the 6-month intervention. Anthropometric measurements included body mass index, waist circumference (WC), waist-to-height ratio (WHtR) and waist-to-hip ratio (WHR). Assessment tools included the Self-efficacy for Diet and the Self-efficacy for Exercise questionnaires, as well as the Mediterranean Diet Quality Index in Children and Adolescents (KIDMED). Analysis included 1020 adolescent students (421 males and 599 females), who completed the self-efficacy questionnaires pre- and post-intervention. Overall, the dietary (p < 0.001) and exercise (p < 0.001) self-efficacy increased significantly post-intervention. Post-intervention, all adolescents decreased their abdominal obesity indices (WC, WHtR, WHR), and this improvement was even more pronounced and significant (p = 0.019, p = 0.019, p = 0.023 respectively) in the adolescents with overweight/obesity. Post-intervention, the proportion of adolescents with normal weight increased from 73.9% to 78.6%, whereas the proportion of adolescents with overweight and obesity decreased from 20.4% to 15.9% and from 5.7% to 5.5%, respectively. Abdominal obesity also decreased from 10.4% to 9.0%. Female adolescents achieved significantly (p = 0.010) higher changes in diet self-efficacy than males. Other sociodemographic characteristics such as family structure, parental age, parental educational level and family income showed non-significant differences. Adolescents with higher KIDMED scores manifested significantly higher dietary and exercise self-efficacy than those with lower KIDMED scores. Both adolescents with normal weight and overweight/obesity manifested a reciprocal relation between diet and exercise self-efficacy. Multicomponent lifestyle interventions in the school environment may provide a first step in students’ behavior changes and provide grounds for future prevention programs in youth.

Plant growth promoting bacteria (PGPB) are used as biostimulants to improve the growth and yield as well as the quality of crops. In the present study, nine strains of PGPB and one solid mix consisting of two of them were evaluated on the cultivation of industrial tomato under specific soil and climatic conditions. The results showed that Bacillus licheniformis treatment increased dry weight of the tomato plants by 39%, and the photosynthetic rate was increased by Priestia megaterium 9.9%. The application of Bacillus subtilis, Bacillus amyloliquefaciens, Priestia megaterium, and Bacillus licheniformis increased mean fruit weight per plant 26.78–30.70% compared to that of control. Yield per plant was increased 51.94% with the use of Bacillus licheniformis compared to that of control. The quality of the fruits in nearly every bacteria strain was improved. Bacillus pumilus and the mix of Priestia megaterium and Azotobacter chroococcum (1:1) increased the most total soluble solids in the tomato fruits (4.70° Brix), and Priestia megaterium increased content in lycopene and total carotenoids by 52.8% and 25%, respectively; Bacillus pseudomycoides increased Pectin methylesterase (PME) activity (24.94 units/mL), and Bacillusmojavensis, along with the mix of Priestia megaterium and Azotobacter chroococcum, increased Poligalacturonase (PG) activity the most (30.09 and 32.53 units/mL, respectively). Most of the bacteria strains presented an increased antioxidant activity significantly better that that of the control up to 31.25%. The results of this study confirmed that the use of PGPB as biostimulants can improve the yield and the quality of industrial tomato.

Microgreens’ popularity is increasing worldwide, and many efforts are focused on novel techniques that could increase fresh production without affecting the quality and the shelf life of the young plants. Three species of microgreens (kale, durum wheat, and spinach) were cultivated in a greenhouse experiment in November–December 2020. Pulsed electromagnetic field (PEMF) was applied at three different growth stages (seed, newly developed plant, and before harvest) and three different times of exposure at each stage, while untreated seeds were used as control. According to the results, certain PEMF treatments increased fresh weight for all three plant species, while dry weight was higher in the treated plants for wheat and spinach, compared to the control. As for the color parameters L*, a*, and b*, at the harvest and postharvest, PEMF treatments had no negative effects, either at harvest or at green color retention, during storage. Moreover, PEMF treatments improved green color in wheat, and restricted yellow color in spinach. An important finding regarding respiration was that PEMF treatments increased both O2 consumption and CO2 production for durum wheat and CO2 production for spinach.

The treatment of complex and multifactorial diseases constitutes a big challenge in day-to-day clinical practice. As many parameters influence clinical phenotypes, accurate diagnosis and prompt therapeutic management is often difficult. Significant research and investment focuses on state-of-the-art genomic and metagenomic analyses in the burgeoning field of Precision (or Personalized) Medicine with genome-wide-association-studies (GWAS) helping in this direction by linking patient genotypes at specific polymorphic sites (single-nucleotide polymorphisms, SNPs) to the specific phenotype. The generation of polygenic risk scores (PRSs) is a relatively novel statistical method that associates the collective genotypes at many of a person’s SNPs to a trait or disease. As GWAS sample sizes increase, PRSs may become a powerful tool for prevention, early diagnosis and treatment. However, the complexity and multidimensionality of genetic and environmental contributions to phenotypes continue to pose significant challenges for the clinical, broad-scale use of PRSs. To improve the value of PRS measures, we propose a novel pipeline which might better utilize GWAS results and improve the utility of PRS when applied to Alzheimer’s Disease (AD), as a paradigm of multifactorial disease with existing large GWAS datasets that have not yet achieved significant clinical impact. We propose a refined approach for the construction of AD PRS improved by (1), taking into consideration the genetic loci where the SNPs are located, (2) evaluating the post-translational impact of SNPs on coding and non-coding regions by focusing on overlap with open chromatin data and SNPs that are expression quantitative trait loci (QTLs), and (3) scoring and annotating the severity of the associated clinical phenotype into the PRS. Open chromatin and eQTL data need to be carefully selected based on tissue/cell type of origin (e.g., brain, excitatory neurons). Applying such filters to traditional PRS on GWAS studies of complex diseases like AD, can produce a set of SNPs weighted according to our algorithm and a more useful PRS. Our proposed methodology may pave the way for new applications of genomic machine and deep learning pipelines to GWAS datasets in an effort to identify novel clinically useful genetic biomarkers for complex diseases like AD.

Antibodies, also called immunoglobulins, are large Y-shaped proteins which are produced and used by the immune system in order to identify and neutralize foreign substances called antigens. The study and research of antibodies is a critical issue in science and has led in remarkable results with medical applications such as diseases diagnosis, therapies and antibodies related drugs. Due to the importance of antibodies, a comprehensive database of full length protein sequences or crystal structures of immunoglobulin (IG) and T cell receptor (TR) V-like protein sequences from human and other vertebrate species has been created. In this article, we study in detail the unique numbering system for immunoglobulins of IMGT, we correlate the database antibody entries and we provide a more in detailed approach into the data. Finally, due to the deviation of the expected from the real data, a user friendly application has been created. This powerful and flexible tool manipulates the antibodies V-like protein sequences with the approach described below and gives the opportunity to the user to extract and study the results and its physicochemical properties. The need for new therapeutic targets and the development of more potent drugs is enormous, despite the efforts and the investments all these years for the development of novel next generation drugs, the discovery and the characterization of new therapeutic targets using immunoglobulins or antibodies. The final outcome from this project will support the development of novel therapies. Antisoma can be freely downloaded from http://www.dimvl.com/antisoma.

Monocarboxylate transporters (MCTs) are of great research interest for their role in cancer cell metabolism and their potential ability to transport pharmacologically relevant compounds across the membrane. Each member of the MCT family could potentially provide novel therapeutic approaches to various diseases. The major differences among MCTs are related to each of their specific metabolic roles, their relative substrate and inhibitor affinities, the regulation of their expression, their intracellular localization, and their tissue distribution. MCT4 is the main mediator for the efflux of L-lactate produced in the cell. Thus, MCT4 maintains the glycolytic phenotype of the cancer cell by supplying the molecular resources for tumor cell proliferation and promotes the acidification of the extracellular microenvironment from the co-transport of protons. A promising therapeutic strategy in anti-cancer drug design is the selective inhibition of MCT4 for the glycolytic suppression of solid tumors. A small number of studies indicate molecules for dual inhibition of MCT1 and MCT4; however, no selective inhibitor with high-affinity for MCT4 has been identified. In this study, we attempt to approach the structural characteristics of MCT4 through an in silico pipeline for molecular modelling and pharmacophore elucidation towards the identification of specific inhibitors as a novel anti-cancer strategy.

An increase in grain yield remains a major target for all involved in maize cultivation. In this study, five maize genotypes were evaluated in a two-year (2017 and 2018) field experiment that was conducted at six locations. Additive main effects and multiplicative interaction (AMMI) analysis was used to detect the narrow adaptations of genotypes in specific mega-environments and the positive interactions of genotypes (G) and environments (E). According to the results of ANOVA, the G × E interaction effect explained 80.36% of the variation, while G explained only 12.79% of the variation, with the remainder explained by E (6.85%). The AMMI model indicated that GEN2, GEN4, and GEN5 were the most suitable genotypes for the three mega-environments delineated. Furthermore, GEN4 was found to have a large positive interaction with the environments featuring the highest rain precipitation during the cultivation period, while GEN2 had a large positive interaction with environments where the mean monthly high temperature was over 34 °C, according to the climatic data during the cultivation period. Additionally, the four environments with slightly acidic pH had a positive interaction with all genotypes of the research, except GEN2.

During the past decade, with the significant progress of computational power as well as ever-rising data availability, deep learning techniques became increasingly popular due to their excellent performance on computer vision problems. The size of the Protein Data Bank (PDB) has increased more than 15-fold since 1999, which enabled the expansion of models that aim at predicting enzymatic function via their amino acid composition. Amino acid sequence, however, is less conserved in nature than protein structure and therefore considered a less reliable predictor of protein function. This paper presents EnzyNet, a novel 3D convolutional neural networks classifier that predicts the Enzyme Commission number of enzymes based only on their voxel-based spatial structure. The spatial distribution of biochemical properties was also examined as complementary information. The two-layer architecture was investigated on a large dataset of 63,558 enzymes from the PDB and achieved an accuracy of 78.4% by exploiting only the binary representation of the protein shape. Code and datasets are available at https://github.com/shervinea/enzynet.

The number of protein structures in the PDB database has been increasing more than 15-fold since 1999. The creation of computational models predicting enzymatic function is of major importance since such models provide the means to better understand the behavior of newly discovered enzymes when catalyzing chemical reactions. Until now, single-label classification has been widely performed for predicting enzymatic function limiting the application to enzymes performing unique reactions and introducing errors when multi-functional enzymes are examined. Indeed, some enzymes may be performing different reactions and can hence be directly associated with multiple enzymatic functions. In the present work, we propose a multi-label enzymatic function classification scheme that combines structural and amino acid sequence information. We investigate two fusion approaches (in the feature level and decision level) and assess the methodology for general enzymatic function prediction indicated by the first digit of the enzyme commission (EC) code (six main classes) on 40,034 enzymes from the PDB database. The proposed single-label and multi-label models predict correctly the actual functional activities in 97.8% and 95.5% (based on Hamming-loss) of the cases, respectively. Also the multi-label model predicts all possible enzymatic reactions in 85.4% of the multi-labeled enzymes when the number of reactions is unknown. Code and datasets are available at https://figshare.com/s/a63e0bafa9b71fc7cbd7.

Trypanosoma brucei brucei (TBB) belongs to the unicellular parasitic protozoa organisms, specifically to the Trypanosoma genus of the Trypanosomatidae class. A variety of different vertebrate species can be infected by TBB, including humans and animals. Under particular conditions, the TBB can be hosted by wild and domestic animals; therefore, an important reservoir of infection always remains available to transmit through tsetse flies. Although the TBB parasite is one of the leading causes of death in the most underdeveloped countries, to date there is neither vaccination available nor any drug against TBB infection. The subunit RPB1 of the TBB DNA-directed RNA polymerase II (DdRpII) constitutes an ideal target for the design of novel inhibitors, since it is instrumental role is vital for the parasite’s survival, proliferation, and transmission. A major goal of the described study is to provide insights for novel anti-TBB agents via a state-of-the-art drug discovery approach of the TBB DdRpII RPB1. In an attempt to understand the function and action mechanisms of this parasite enzyme related to its molecular structure, an in-depth evolutionary study has been conducted in parallel to the in silico molecular designing of the 3D enzyme model, based on state-of-the-art comparative modelling and molecular dynamics techniques. Based on the evolutionary studies results nine new invariant, first-time reported, highly conserved regions have been identified within the DdRpII family enzymes. Consequently, those patches have been examined both at the sequence and structural level and have been evaluated in regard to their pharmacological targeting appropriateness. Finally, the pharmacophore elucidation study enabled us to virtually in silico screen hundreds of compounds and evaluate their interaction capabilities with the enzyme. It was found that a series of chlorine-rich set of compounds were the optimal inhibitors for the TBB DdRpII RPB1 enzyme. All-in-all, herein we present a series of new sites on the TBB DdRpII RPB1 of high pharmacological interest, alongside the construction of the 3D model of the enzyme and the suggestion of a new in silico pharmacophore model for fast screening of potential inhibiting agents.

Leucine-rich repeat kinase 2 (LRRK2) is a large protein of unclear function. Rare mutations in the LRRK2 gene cause familial Parkinson’s disease (PD) and inflammatory bowel disease. Genome-wide association studies (GWAS) have revealed significant association of the abovementioned diseases at the LRRK2 locus. Cell and systems biology research has led to potential roles that LRRK2 may have in PD pathogenesis, especially the kinase domain (KIN). Previous human expression studies showed evidence of mRNA expression and splicing patterns that may contribute to our understanding of the function of LRRK2. In this work, we investigate and identified significant regional differences in LRRK2 expression at the mRNA level, including a number of splicing events in the Ras of complex protein (Roc) and C-terminal of Roc domain (COR) of LRRK2, in the substantia nigra (SN) and occipital cortex (OCTX). Our findings indicate that the predominant form of LRRK2 mRNA is full length, with shorter isoforms present at a lower copy number. Our molecular modelling study suggests that splicing events in the ROC/COR domains will have major consequences on the enzymatic function and dimer formation of LRRK2. The implications of these are highly relevant to the broader effort to understand the biology and physiological functions of LRRK2, and to better characterize the role(s) of LRRK2 in the underlying mechanism leading to PD.

Gromita is a fully integrated and efficient graphical user interface (GUI) to the recently updated molecular dynamics suite Gromacs, version 4. Gromita is a cross-platform, perl/tcl-tk based, interactive front end designed to break the command line barrier and introduce a new user-friendly environment to run molecular dynamics simulations through Gromacs. Our GUI features a novel workflow interface that guides the user through each logical step of the molecular dynamics setup process, making it accessible to both advanced and novice users. This tool provides a seamless interface to the Gromacs package, while providing enhanced functionality by speeding up and simplifying the task of setting up molecular dynamics simulations of biological systems. Gromita can be freely downloaded from http://bio.demokritos.gr/gromita/.

During the past few years, pharmacophore modeling has become one of the key components in computer-aided drug design and in modern drug discovery. DrugOn is a fully interactive pipeline designed to exploit the advantages of modern programming and overcome the command line barrier with two friendly environments for the user (either novice or experienced in the field of Computer Aided Drug Design) to perform pharmacophore modeling through an efficient combination of the PharmACOphore, Gromacs, Ligbuilder and PDB2PQR suites. Our platform features a novel workflow that guides the user through each logical step of the iterative 3D structural optimization setup and drug design process. For the pharmacophore modeling we are focusing on either the characteristics of the receptor or the full molecular system, including a set of selected ligands. DrugOn can be freely downloaded from our dedicated server system at www.bioacademy.gr/bioinformatics/drugon/.

The Greek Goat Encephalitis virus (GGE) belongs to the Flaviviridae family of the genus Flavivirus. The GGE virus constitutes an important pathogen of livestock that infects the goat’s central nervous system. The viral enzymes of GGE, helicase and RNA-dependent RNA polymerase (RdRP), are ideal targets for inhibitor design, since those enzymes are crucial for the virus’ survival, proliferation and transmission. In an effort to understand the molecular structure underlying the functions of those viral enzymes, the three dimensional structures of GGE NS3 helicase and NS5 RdRP have been modelled. The models were constructed in silico using conventional homology modelling techniques and the known 3D crystal structures of solved proteins from closely related species as templates. The established structural models of the GGE NS3 helicase and NS5 RdRP have been evaluated for their viability using a repertoire of in silico tools. The goal of this study is to present the 3D conformations of the GGE viral enzymes as reliable structural models that could provide the platform for the design of novel anti-GGE agents.

The fission yeast, Schizosaccharomyces pombe, is a well-established model for heterochromatin formation, but the exact sequence of events for initiation remains to be elucidated. The essential factors involved include RNA transcribed from repeated sequences together with the methyltransferase Clr4. In addition, histone deacetylases, like Clr3, found in the SHREC complex are also necessary for transcriptional silencing. Clr2 is another crucial factor required for heterochromatin formation found in the SHREC complex. The function of Clr2 has been difficult to establish due to the lack of conserved domains or homology to proteins of known molecular function. Using a bioinformatics approach, three conserved motifs in Clr2 were identified, which contained amino acids important for transcriptional repression. Analysis of clr2 mutant strains revealed a major role for Clr2 in mating-type and rDNA silencing, and weaker effects on centromeric silencing. The effect on mating-type silencing showed variegation in several of the strains with mutated versions of Clr2 indicating an establishment or maintenance defect. Moreover, the critical amino acids in Clr2 were also necessary for transcriptional repression in a minimal system, by the tethering of Clr4 upstream of a reporter gene, inserted into the euchromatic part of the genome. Finally, in silico modeling suggested that the mutations in Clr2 cause disruption of secondary structures in the Clr2 protein. Identification of these critical amino acids in the protein provides a useful tool to explore the molecular mechanism behind the role of Clr2 in heterochromatin formation.

We developed a novel method based on the Fourier analysis of protein molecular surfaces to speed up the analysis of the vast structural data generated in the post-genomic era. This method computes the power spectrum of surfaces of the molecular electrostatic potential, whose three-dimensional coordinates have been either experimentally or theoretically determined. Thus we achieve a reduction of the initial three-dimensional information on the molecular surface to the one-dimensional information on pairs of points at a fixed scale apart. Consequently, the similarity search in our method is computationally less demanding and significantly faster than shape comparison methods. As proof of principle, we applied our method to a training set of viral proteins that are involved in major diseases such as Hepatitis C, Dengue fever, Yellow fever, Bovine viral diarrhea and West Nile fever. The training set contains proteins of four different protein families, as well as a mammalian representative enzyme. We found that the power spectrum successfully assigns a unique signature to each protein included in our training set, thus providing a direct probe of functional similarity among proteins. The results agree with established biological data from conventional structural biochemistry analyses.

The Classical Swine Fever virus (CSFV) is a major pathogen of livestock and belongs to the flaviviridae viral family. Even though there aren’t any verified zoonosis cases yet, the outcomes of CSFV epidemics have been devastating to local communities. In an effort to shed light to the molecular mechanisms underlying the structural and drug design potential of the viral helicase, the three dimensional structure of CSFV helicase has been modeled using conventional homology modeling techniques and the crystal structure of the Hepatitis C virus (HCV) as a template. The established structure of the CSFV helicase has been in silico evaluated for its viability using a repertoire of in silico tools. The ultimate goal of this study is to introduce the 3D conformation of the CSFV helicase as a reliable structure that may be used as the designing platform for de novo, structure-based drug design experiments. In this direction using the modeled structure of CSVF helicase, a 3D pharmacophore was designed. The pharmacophore comprises of a series of key characteristics that molecular inhibitors must satisfy in order to achieve maximum predicted affinity for the given enzyme. Overall, invaluable insights and conclusions are drawn from this structural study of the CSFV helicase, which may provide the scientific community with the founding plinth in the fight against CSFV infections through the perspective of the CSFV helicase as a potential pharmacological target. Notably, to date no antiviral agent is available against the CSFV nor is expected soon. Subsequently, there is urgent need for new modern and state-of-the-art antiviral strategies to be developed.

Viral RNA helicases are involved in duplex unwinding during the RNA replication of the virus. It is suggested that these helicases represent very promising antiviral targets. Viruses of the flaviviridae family are the causative agents of many common and devastating diseases, including hepatitis, yellow fever and dengue fever. As there is currently no available anti-Flaviviridae therapy, there is urgent need for the development of efficient anti-viral pharmaceutical strategies. Herein, we report the complete phylogenetic analysis across flaviviridae alongside a more in-depth evolutionary study that revealed a series of conserved and invariant amino acids that are predicted to be key to the function of the helicase. Structural molecular modelling analysis revealed the strategic significance of these residues based on their relative positioning on the 3D structures of the helicase enzymes, which may be used as pharmacological targets. We previously reported a novel series of highly potent HCV helicase inhibitors, and we now re-assess their antiviral potential using the 3D structural model of the invariant helicase residues. It was found that the most active compound of the series, compound C4, exhibited an IC50 in the submicromolar range, whereas its stereoisomer (compound C12) was completely inactive. Useful insights were obtained from molecular modelling and conformational search studies via molecular dynamics simulations. C12 tends to bend and lock in an almost “U” shape conformation, failing to establish vital interactions with the active site of HCV. On the contrary, C4 spends most of its conformational time in a straight, more rigid formation that allows it to successfully block the passage of the oligonucleotide in the ssRNA channel of the HCV helicase. This study paves the way and provides the necessary framework for the in-depth analysis required to enable the future design of new and potent anti-viral agents.

Poly(A)-specific ribonuclease (PARN) is an exoribonuclease/deadenylase that degrades 3'-end poly(A) tails in almost all eukaryotic organisms. Much of the biochemical and structural information on PARN comes from the human enzyme. However, the existence of PARN all along the eukaryotic evolutionary ladder requires further and thorough investigation. Although the complete structure of the full-length human PARN, as well as several aspects of the catalytic mechanism still remain elusive, many previous studies indicate that PARN can be used as potent and promising anti-cancer target. In the present study, we attempt to complement the existing structural information on PARN with in-depth bioinformatics analyses, in order to get a hologram of the molecular evolution of PARNs active site. In an effort to draw an outline, which allows specific drug design targeting PARN, an unequivocally specific platform was designed for the development of selective modulators focusing on the unique structural and catalytic features of the enzyme. Extensive phylogenetic analysis based on all the publicly available genomes indicated a broad distribution for PARN across eukaryotic species and revealed structurally important amino acids which could be assigned as potentially strong contributors to the regulation of the catalytic mechanism of PARN. Based on the above, we propose a comprehensive in silico model for the PARN's catalytic mechanism and moreover, we developed a 3D pharmacophore model, which was subsequently used for the introduction of DNP-poly(A) amphipathic substrate analog as a potential inhibitor of PARN. Indeed, biochemical analysis revealed that DNP-poly(A) inhibits PARN competitively. Our approach provides an efficient integrated platform for the rational design of pharmacophore models as well as novel modulators of PARN with therapeutic potential.