Cone snails are predatory gastropods whose neurotoxic venom peptides (conotoxins) have been extensively studied for pharmacological probes, venom evolution mechanisms and potential therapeutics. Conotoxins have a wide range of structural and functional classes that continue to undergo accelerated evolution that underlies the rapid expansion of the genus over their short evolutionary history. A number of pharmacological classes, driven by separately evolved defensive and predatory venoms, have been hypothesised to facilitate shifts in prey that exemplify the adaptability of cone snails. Here we provide an overview of these pharmacological families and discuss their ecological roles and evolutionary impact.

Protein phosphorylation plays a potential role in regulating protein conformation and functions. As a result, identifying an uncharacterized protein sequence as a phosphorylated protein is a very meaningful problem and an urgent issue for both basic research and drug development. Although various types of computational methods have been developed to identify the phosphorylation sites for a recognized phosphorylated protein, very few computational methods have been developed to identify whether an uncharacterized protein can be phosphorylated or not. Therefore, there exists some scope for further improvement to characterize a protein as phosphorylated or not. Among all the residues of protein molecules, three types of amino acid residues, namely serine, threonine, and tyrosine, have been found to be susceptible to phosphorylation, which leads to the requirement of multi-label phosphorylated protein identification. Therefore, in this study, a novel computational tool termed iMulti-HumPhos has been developed to predict multi-label phosphorylated proteins by (1) extracting three different sets of features from protein sequences, (2) defining an individual kernel for each set of features and combining them into a single kernel using multiple kernel learning, and (3) constructing a multi-label predictor using a combination of support vector machines (SVMs) where each SVM has been trained with the combined kernel. In addition, we have balanced the effect of the skewed training dataset by the Different Error Costs method for the development of our system. The experimental results show that the iMulti-HumPhos predictor provides significantly better performance than the existing predictor Multi-iPPseEvo. A user-friendly web-server of iMulti-HumPhos is available at .

Expression level provides important clues about gene function. Previously, various efforts have been undertaken to profile human genes according to their expression level. Intrinsically disordered proteins (IDPs) do not adopt any rigid conformation under physiological conditions, however, are considered as an important functional class in all domains of life. Based on a human tissue-averaged gene expression level, previous studies showed that IDPs are expressed at a lower level than ordered globular proteins. Here, we examined the gene expression pattern of human ordered and disordered proteins in 32 normal tissues. We noticed that in most of the tissues, ordered and disordered proteins are expressed at a similar level. Moreover, in a number of tissues IDPs were found to be expressed at a higher level than ordered proteins. Rigorous statistical analyses suggested that the lower tissue-averaged gene expression level of IDPs (reported earlier) may be the consequence of their biased gene expression in some specific tissues and higher protein length. When we considered the gene repertory of each tissue we noticed that a number of human tissues (brain, testes, etc.) selectively express a higher fraction of disordered proteins, which help them to maintain higher protein connectivity by forming disordered binding motifs and to sustain their functional specificities. Our results demonstrated that the disordered proteins are indispensable in these tissues for their functional advantages.

Amyloid beta peptide (Aβ) is the major protein component of the amyloid plaques that are present in the brains of Alzheimer's disease (AD) patients. Aβ42 peptide is a known neurotoxic agent that binds to neurons and, under specific aggregation conditions, triggers cell death. Aβ peptide can undergo specific amino acid posttranslational modifications, such as phosphorylation, that are important for modulating its proteolytic degradation, aggregation, binding to lipid membranes and neurotoxic functions. Peptides phosphorylated at serine 8 in full-length Aβ42 (pAβ42) were synthesised and compared to native Aβ42 peptide. Their secondary structures, aggregation properties and interactions with plasma membranes of primary cortical neurons were investigated. The results revealed that pAβ42 has increased β-sheet formation with rapid amyloid formation in a synthetic lipid environment, which was associated with increased cellular binding but concomitant diminished neurotoxicity. Our data support the notion that phosphorylation of Aβ42 promotes the formation of amyloid plaques in the brain, which lack the neurotoxic properties associated with oligomeric species causing pathogenesis in AD.

Calcium homeostasis is a key factor in the regulation of cardiac excitation-contraction coupling. Calcium dynamics in cardiomyocytes is governed by ATP which depends on insulin dependent glucose concentration, via the glucose transporter type 4 (GLUT4) transporter. It would therefore be interesting to see how calcium dynamics changes in a cardiomyocyte under diabetic conditions. We proposed and analysed a four dimensional ordinary differential equation (ODE) model to capture the interdependency of calcium dynamics on glucose uptake and ATP generation. More specifically, we looked for the role of GLUT4, energy metabolism, L-type channels, RyR2 channels, SERCA2a pumps and leakage rate in the normal functioning of cardiomyocytes. To understand the system dynamics, we first obtained the stability and Hopf-bifurcation criteria of steady state and then through parameter perturbation we captured the role of different parameters in maintaining normal calcium oscillation (frequency 40 to 180 beats per minute and amplitude ≥0.4 μM) and hence normal cardiac function. We observed that any divergence in the GLUT4 activity (especially a decrease in the glucose uptake rate) might cause abnormal calcium oscillation, leading to cardiac dysfunction (CD). Our study finally hypothesizes that a regulated sarcoplasmic reticulum (SR) calcium flux could be a possible therapeutic strategy to maintain normal calcium dynamics in diabetic heart and to prevent possible CD.

Stem cells offer great promise within the field of regenerative medicine but despite encouraging results, the large scale use of stem cells for therapeutic applications still faces challenges when it comes to controlling signaling pathway responses with respect to environmental perturbations. This step is critical for the elaboration of stable and reproducible differentiation protocols, and computational modeling can be helpful to overcome these difficulties. This article is a comparative review of the mechanism-based methods used for hypothesis-driven approaches and data-driven methods which are two types of computational approaches commonly used for analysing the dynamics of pathways involved in stem cell regulation. We firstly review works based on kinetic modelling. We emphasize the relationships between the dynamics of these pathways and their topological features, and illustrative examples are described to show how the analysis of these relationships can contribute to a more detailed and formal understanding of the signaling dynamics. This discussion is followed by a review of the recent data-driven pathway analysis methods. Based on a simplified description of the pathways, these methods are able to handle high dimensionality data, and topological features of the pathways taken into account in the latest methods improve both accuracy and predictive power. Nevertheless, progress is still needed to clarify the biological meaning of the topological decompositions used by these methods.

Cervical intra-epithelial neoplasia 1 (CIN1) is a pre-cancerous cervical cancer condition characterized by changes in the squamous cells of the cervix. Early detection may facilitate treatment as CIN1 may increase the risk of developing cervical cancer. An extensive system level analysis has been carried out to detect crucial network components of CIN1. Differential expression analysis was performed to identify genes that are differentially expressed in CIN1 in comparison to normal cells. Gene Ontology (GO) and pathway enrichment analyses of DEGs were followed by the construction of a protein-protein interaction (PPI) network. Hubs were identified and module enrichment analysis was performed. A gene regulatory network incorporating DEGs, microRNAs (miRNAs) and transcription factors (TFs) was constructed. To integrate these DEGs with the pathophysiology of CIN1, assessment of interaction of these gene/proteins and crucial motifs with statistical parameters such as z-score and p-value was carried out. Microarray analysis revealed 71 differentially expressed genes including 39 upregulated and 32 downregulated genes. 4 genes, namely PABPC1, RPS27, RPL13A and RPL21, were found to be overlapping among hubs and module genes of the PPI network and also part of the significant motifs of the regulatory network. Gene regulation of the DEGs also revealed important TFs and miRNAs such as ELF1, SRF, has-mir-125b-5p and has-mir-644a. PABPC1, RPS27, RPL13A and RPL21 may serve as potential biomarkers for CIN1 and as prospective targets for therapeutic approaches. Though, further experimental studies are required to confirm our findings.

The lifetimes of protein-DNA adducts are strictly related to the various protein functions. This feature must be encoded by the amino acids located at the protein-DNA interface. The large number of structurally characterized protein-DNA complexes now available from the Protein Data Bank (PDB) allows extensive structural bioinformatics investigations on protein-DNA interfaces. The modes of protein binding to DNA have been explored by dividing 629 non-redundant PDB files of protein-DNA complexes into separate classes for structural proteins, transcription factors and DNA-related enzymes. From the selected PDB structures, we could define 2953 protein-DNA contact regions. A systematic analysis of amino acid occurrences at these protein-DNA contact regions yielded composition profiles, which are typical for each of the three protein classes. The critical role of some amino acids to influence intermolecular contact lifetimes is discussed here. The occurrence of arginine at the protein-DNA interface, by far the most abundant amino acid in this protein moiety, is found to be the main feature that differentiates proteins from the three classes. Structural proteins and, to a lesser extent, transcription factors exhibit the highest Arg occurrence at protein-DNA contact regions. Reduced Arg/Lys ratios together with increased contents of Asp and Glu are observed in all the DNA-interacting enzymes. The amount of negatively charged side chains, highly conserved among homologous DNA-related enzymes at protein-DNA interfaces, is suggested as a tool to modulate protein mobility along DNA chains. Arg/Lys, Asp/Asn and Glu/Gln substitutions at protein-DNA interfaces may represent a very feasible way to control protein motion on DNA rails.

Correction for 'Dynamic properties of dipeptidyl peptidase III from Bacteroides thetaiotaomicron and the structural basis for its substrate specificity - a computational study' by M. Tomin et al., Mol. BioSyst., 2017, 13, 2407-2417.

Shikonin and its enantiomer alkannin, which are natural products, have been extensively studied in vitro and in vivo for, among others, their antitumor activity. The investigation of the molecular pathways involved in their action is of interest, since they are not yet clearly defined. Metabolic profiling in cells can provide a picture of a cell's phenotype upon intervention, assisting in the elucidation of the mechanism of action. In this study, the cytotoxic effect of shikonin on a human hepatocarcinoma cell line was studied. Huh7 cells were treated with shikonin at 5 μM, and it was found that shikonin markedly inhibited cell growth. Metabolic profiling indicated alterations in the metabolic content of the cells and the culture media upon treatment, detecting the metabolic response of the cells. This study demonstrates the potential of metabolomics to improve knowledge on the mechanisms involved in shikonin's antitumor action.