Amino acid size, charge, hydropathy indices and matrices for protein structure analysis

From Fundamental Amyloid Protein Self-Assembly to Development of Bioplastics

Proteins can self-assemble into a range of nanostructures as a result of molecular interactions. Amyloid nanofibrils, as one of them, were first discovered with regard to the relevance of neurodegenerative diseases but now have been exploited as building blocks to generate multiscale materials with designed functions for versatile applications. This review interconnects the mechanism of amyloid fibrillation, the current approaches to synthesizing amyloid protein-based materials, and the application in bioplastic development. We focus on the fundamental structures of self-assembled amyloid fibrils and how external factors can affect protein aggregation to optimize the process. Protein self-assembly is essentially the autonomous congregation of smaller protein units into larger, organized structures. Since the properties of the self-assembly can be manipulated by changing intrinsic factors and external conditions, protein self-assembly serves as an excellent building block for bioplastic development. Building on these principles, general processing methods and pathways from raw protein sources to mature state materials are proposed, providing a guide for the development of large-scale production. Additionally, this review discusses the diverse properties of protein-based amyloid nanofibrils and how they can be utilized as bioplastics. The economic feasibility of the protein bioplastics is also compared to conventional plastics in large-scale production scenarios, supporting their potential as sustainable bioplastics for future applications.

Antimicrobial Peptides as Immunomodulators and Antimycobacterial Agents to Combat Mycobacterium tuberculosis: a Critical Review

Tuberculosis (TB) is a devastating disease foisting a significantly high morbidity, prepotent in low- and middle-income developing countries. Evolution of drug resistance among Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, has made the TB treatment more complicated. The protracted nature of present TB treatment, persistent and tolerant Mtb populations, interaction with antiretroviral therapy and existing toxicity concerned with conventional anti-TB drugs are the four major challenges inflicted with emergence of drug-resistant mycobacterial strains, and the standard medications are unable to combat these strains. These factors emphasize an exigency to develop new drugs to overcome these barriers in current TB therapy. With this regard, antimycobacterial peptides derived from various sources such as human cells, bacterial sources, mycobacteriophages, fungal, plant and animal sources could be considered as antituberculosis leads as most of these peptides are associated with dual advantages of having both bactericidal activity towards Mtb as well as immuno-regulatory property. Some of the peptides possess the additional advantage of interacting synergistically with antituberculosis medications too, thereby increasing their efficiency, underscoring the vigour of antimicrobial peptides (AMPs) as best possible alternative therapeutic candidates or adjuvants in TB treatment. Albeit the beneficiary features of these peptides, few obstacles allied with them like cytotoxicity and proteolytic degradation are matter of concerns too. In this review, we have focused on structural hallmarks, targeting mechanisms and specific structural aspects contributing to antimycobacterial activity and discovered natural and synthetic antimycobacterial peptides along with their sources, anti-TB, immuno-regulatory properties, merits and demerits and possible delivery methods of AMPs.

Molecular dynamics articulated multilevel virtual screening protocol to discover novel dual PPAR α/γ agonists for anti-diabetic and metabolic applications

PPARα and PPARγ are isoforms of the nuclear receptor superfamily which regulate glucose and lipid metabolism. Activation of PPARα and PPARγ receptors by exogenous ligands could transactivate the expression of PPARα and PPARγ-dependent genes, and thereby, metabolic pathways get triggered, which are helpful to ameliorate treatment for the type 2 diabetes mellitus, and related metabolic complications. Herein, by understanding the structural requirements for ligands to activate PPARα and PPARγ proteins, we developed a multilevel in silico-based virtual screening protocol to identify novel chemical scaffolds and further design and synthesize two distinct series of glitazone derivatives with advantages over the classical PPARα and PPARγ agonists. Moreover, the synthesized compounds were biologically evaluated for PPARα and PPARγ transactivation potency from nuclear extracts of 3T3-L1 cell. Furthermore, glucose uptake assay on L6 cells confirmed the potency of the synthesized compounds toward glucose regulation. Percentage lipid-lowering potency was also assessed through triglyceride estimate from 3T3-L1 cell extracts. Results suggested the ligand binding mode was in orthosteric fashion as similar to classical agonists. Thus molecular docking and molecular dynamics (MD) simulation experiments were executed to validate our hypothesis on mode of ligands binding and protein complex stability. Altogether, the present study developed a newer protocol for virtual screening and enables to design of novel glitazones for activation of PPARα and PPARγ-mediated pathways. Accordingly, present approach will offer benefit as a therapeutic strategy against type 2 diabetes mellitus and associated metabolic complications.

Modulating the folding and binding of peptides using a stimuli-responsive molecular tweezer

This study presents the development of a β-hairpin (tryptophan zipper, Trpzip)-based molecular tweezer (MT) that can control the folding and binding of α-helical peptides. When an α-helix isolated from the p53 protein was conjugated with Trpzip in an optimized macrocyclic structure, the folded β-hairpin stabilized the helix conformation through the side chain-to-side chain stapling strategy, which notably enhanced target (hDM2) affinity of the peptide. On the other hand, the helicity and binding affinity were significantly reduced when the hairpin was unfolded by a redox stimulus. This stimulus-responsive property was translated into the effective capture and release of model multivalent biomaterials, hDM2-gold nanoparticle conjugates. Since numerous protein interactions are mediated by α-helical peptides, these results suggest that the β-hairpin-based MT holds great potential to be utilized in various biomedical applications, such as protein interaction inhibition and cancer biomarker (e.g., circulating tumor cells and exosomes) detection.

Brain Hydrophobic Peptides Antagonists of Neurotoxic Amyloid β Peptide Monomers/Oligomers-Protein Interactions

Amyloid β (Aβ) oligomers have been linked to Alzheimer's disease (AD) pathogenesis and are the main neurotoxic forms of Aβ. This review focuses on the following: (i) the Aβ(1-42):calmodulin interface as a model for the design of antagonist Aβ peptides and its limitations; (ii) proteolytic degradation as the major source of highly hydrophobic peptides in brain cells; and (iii) brain peptides that have been experimentally demonstrated to bind to Aβ monomers or oligomers, Aβ fibrils, or Aβ plaques. It is highlighted that the hydrophobic amino acid residues of the COOH-terminal segment of Aβ(1-42) play a key role in its interaction with intracellular protein partners linked to its neurotoxicity. The major source of highly hydrophobic endogenous peptides of 8-10 amino acids in neurons is the proteasome activity. Many canonical antigen peptides bound to the major histocompatibility complex class 1 are of this type. These highly hydrophobic peptides bind to Aβ and are likely to be efficient antagonists of the binding of Aβ monomers/oligomers concentrations in the nanomolar range with intracellular proteins. Also, their complexation with Aβ will protect them against endopeptidases, suggesting a putative chaperon-like physiological function for Aβ that has been overlooked until now. Remarkably, the hydrophobic amino acid residues of Aβ responsible for the binding of several neuropeptides partially overlap with those playing a key role in its interaction with intracellular protein partners that mediates its neurotoxicity. Therefore, these latter neuropeptides are also potential candidates to antagonize Aβ peptides binding to target proteins. In conclusion, the analysis performed in this review points out that hydrophobic endogenous brain neuropeptides could be valuable biomarkers to evaluate the risk of the onset of sporadic AD, as well as for the prognosis of AD.

ProtInteract: A deep learning framework for predicting protein-protein interactions

Proteins mainly perform their functions by interacting with other proteins. Protein-protein interactions underpin various biological activities such as metabolic cycles, signal transduction, and immune response. However, due to the sheer number of proteins, experimental methods for finding interacting and non-interacting protein pairs are time-consuming and costly. We therefore developed the ProtInteract framework to predict protein-protein interaction. ProtInteract comprises two components: first, a novel autoencoder architecture that encodes each protein's primary structure to a lower-dimensional vector while preserving its underlying sequence attributes. This leads to faster training of the second network, a deep convolutional neural network (CNN) that receives encoded proteins and predicts their interaction under three different scenarios. In each scenario, the deep CNN predicts the class of a given encoded protein pair. Each class indicates different ranges of confidence scores corresponding to the probability of whether a predicted interaction occurs or not. The proposed framework features significantly low computational complexity and relatively fast response. The contributions of this work are twofold. First, ProtInteract assimilates the protein's primary structure into a pseudo-time series. Therefore, we leverage the nature of the time series of proteins and their physicochemical properties to encode a protein's amino acid sequence into a lower-dimensional vector space. This approach enables extracting highly informative sequence attributes while reducing computational complexity. Second, the ProtInteract framework utilises this information to identify protein interactions with other proteins based on its amino acid configuration. Our results suggest that the proposed framework performs with high accuracy and efficiency in predicting protein-protein interactions.

Analyzing Effect of Multi-Modality in Predicting Protein-Protein Interactions

Nowadays, multiple sources of information about proteins are available such as protein sequences, 3D structures, Gene Ontology (GO), etc. Most of the works on protein-protein interaction (PPI) identification had utilized these information about proteins, mainly sequence-based, but individually. The new advances in deep learning techniques allow us to leverage multiple sources/modalities of proteins, which complement each other. Some recent works have shown that multi-modal PPI models perform better than uni-modal approaches. This paper aims to investigate whether the performance of multi-modal PPI models is always consistent or depends on other factors such as dataset distribution, algorithms used to learn features, etc. We have used three modalities for this study: Protein sequence, 3D structure, and GO. Various techniques, including deep learning algorithms, are employed to extract features from multiple sources of proteins. These feature vectors from different modalities are then integrated in several combinations (bi-modal and tri-modal) to predict PPI. To conduct this study, we have used Human and S. cerevisiae PPI datasets. The obtained results demonstrate the potentiality of a multi-modal approach and deep learning techniques in predicting protein interactions. However, the predictive capability of a model for PPI depends on feature extraction methods as well. Also, increasing the modality does not always ensure performance improvement. In this study, the PPI model integrating two modalities outperforms the designed uni-modal and tri-modal PPI models.

Protein-protein interaction prediction with deep learning: A comprehensive review

Most proteins perform their biological function by interacting with themselves or other molecules. Thus, one may obtain biological insights into protein functions, disease prevalence, and therapy development by identifying protein-protein interactions (PPI). However, finding the interacting and non-interacting protein pairs through experimental approaches is labour-intensive and time-consuming, owing to the variety of proteins. Hence, protein-protein interaction and protein-ligand binding problems have drawn attention in the fields of bioinformatics and computer-aided drug discovery. Deep learning methods paved the way for scientists to predict the 3-D structure of proteins from genomes, predict the functions and attributes of a protein, and modify and design new proteins to provide desired functions. This review focuses on recent deep learning methods applied to problems including predicting protein functions, protein-protein interaction and their sites, protein-ligand binding, and protein design.

Analytical Studies of Antimicrobial Peptides as Diagnostic Biomarkers for the Detection of Bacterial and Viral Pneumonia

Pneumonia remains one of the leading causes of infectious mortality and significant economic losses among our growing population. The lack of specific biomarkers for correct and timely diagnosis to detect patients’ status is a bane towards initiating a proper treatment plan for the disease; thus, current biomarkers cannot distinguish between pneumonia and other associated conditions such as atherosclerotic plaques and human immunodeficiency virus (HIV). Antimicrobial peptides (AMPs) are potential candidates for detecting numerous illnesses due to their compensatory roles as theranostic molecules. This research sought to generate specific data for parental AMPs to identify viral and bacterial pneumonia pathogens using in silico technology. The parental antimicrobial peptides (AMPs) used in this work were AMPs discovered in our previous in silico analyses using the HMMER algorithm, which were used to generate derivative (mutated) AMPs that would bind with greater affinity, in order to detect the bacterial and viral receptors using an in silico site-directed mutagenesis approach. These AMPs’ 3D structures were subsequently predicted and docked against receptor proteins. The result shows putative AMPs with the potential capacity to detect pneumonia caused by these pathogens through their binding precision with high sensitivity, accuracy, and specificity for possible use in point-of-care diagnosis. These peptides’ tendency to detect receptor proteins of viral and bacterial pneumonia with precision justifies their use for differential diagnostics, in an attempt to reduce the problems of indiscriminate overuse, toxicity due to the wrong prescription, bacterial resistance, and the scarcity and high cost of existing pneumonia antibiotics.

Unravelling the Structural Mechanism of Action of 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl)phenyl]imidazolidine-2,4-dione in Dual-Targeting Tankyrase 1 and 2: A Novel Avenue in Cancer Therapy

Tankyrase (TNKS) belonging to the poly(ADPribose) polymerase family, are known for their multi-functioning capabilities, and play an essential role in the Wnt β-catenin pathway and various other cellular processes. Although showing inhibitory potential at a nanomolar level, the structural dual-inhibitory mechanism of the novel TNKS inhibitor, 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl)phenyl]imidazolidine-2,4-dione, remains unexplored. By employing advanced molecular modeling, this study provides these insights. Results of sequence alignments of binding site residues identified conserved residues; GLY1185 and ILE1224 in TNKS-1 and PHE1035 and PRO1034 in TNKS-2 as crucial mediators of the dual binding mechanism of 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl)phenyl]imidazolidine-2,4-dione, corroborated by high per-residue energy contributions and consistent high-affinity interactions of these residues. Estimation of the binding free energy of 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl)phenyl]imidazolidine-2,4-dione showed estimated total energy of -43.88 kcal/mol and -30.79 kcal/mol towards TNKS-1 and 2, respectively, indicating favorable analogous dual binding as previously reported. Assessment of the conformational dynamics of TNKS-1 and 2 upon the binding of 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl)phenyl]imidazolidine-2,4-dione revealed similar structural changes characterized by increased flexibility and solvent assessible surface area of the residues inferring an analogous structural binding mechanism. Insights from this study show that peculiar, conserved residues are the driving force behind the dual inhibitory mechanism of 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl)phenyl]imidazolidine-2,4-dione and could aid in the design of novel dual inhibitors of TNKS-1 and 2 with improved therapeutic properties.

Prion protein with a mutant N-terminal octarepeat region undergoes cobalamin-dependent assembly into high-molecular weight complexes

The cellular prion protein (PrP^C) has a C-terminal globular domain and a disordered N-terminal region encompassing five octarepeats (ORs). Encounters between Cu(II) ions and four OR sites produce interchangeable binding geometries; however, the significance of Cu(II) binding to ORs in different combinations is unclear. To understand the impact of specific binding geometries, OR variants were designed that interact with multiple or single Cu(II) ions in specific locked coordinations. Unexpectedly, we found that one mutant produced detergent-insoluble, protease-resistant species in cells in the absence of exposure to the infectious prion protein isoform, scrapie-associated prion protein (PrP^Sc). Formation of these assemblies, visible as puncta, was reversible and dependent upon medium formulation. Cobalamin (Cbl), a dietary cofactor containing a corrin ring that coordinates a Co³⁺ ion, was identified as a key medium component, and its effect was validated by reconstitution experiments. Although we failed to find evidence that Cbl interacts with Cu-binding OR regions, we instead noted interactions of Cbl with the PrP^C C-terminal domain. We found that some interactions occurred at a binding site of planar tetrapyrrole compounds on the isolated globular domain, but others did not, and N-terminal sequences additionally had a marked effect on their presence and position. Our studies define a conditional effect of Cbl wherein a mutant OR region can act in cis to destabilize a globular domain with a wild type sequence. The unexpected intersection between the properties of PrP^Sc's disordered region, Cbl, and conformational remodeling events may have implications for understanding sporadic prion disease that does not involve exposure to PrP^Sc.

In silico characterization of the GH5-cellulase family from uncultured microorganisms: physicochemical and structural studies

BACKGROUND

Hydrolysis of cellulose-based biomass by cellulases produce fermented sugar for making biofuels, such as bioethanol. Cellulases hydrolyze the β-1,4-glycosidic linkage of cellulose and can be obtained from cultured and uncultured microorganisms. Uncultured microorganisms are a source for exploring novel cellulase genes through the metagenomic approach. Metagenomics concerns the extraction, cloning, and analysis of the entire genetic complement of a habitat without cultivating microbes. The glycoside hydrolase 5 family (GH5) is a cellulase family, as the largest group of glycoside hydrolases. Numerous variants of GH5-cellulase family have been identified through the metagenomic approach, including CelGH5 in this study. University-CoE-Research Center for Biomolecule Engineering, Universitas Airlangga successfully isolated CelGH5 from waste decomposition of oil palm empty fruit bunches (OPEFB) soil by metagenomics approach. The properties and structural characteristics of GH5-cellulases from uncultured microorganisms can be studied using computational tools and software.

RESULTS

The GH5-cellulase family from uncultured microorganisms was characterized using standard computational-based tools. The amino acid sequences and 3D-protein structures were retrieved from the GenBank Database and Protein Data Bank. The physicochemical analysis revealed the sequence length was roughly 332-751 amino acids, with the molecular weight range around 37-83 kDa, dominantly negative charges with pI values below 7. Alanine was the most abundant amino acid making up the GH5-cellulase family and the percentage of hydrophobic amino acids was more than hydrophilic. Interestingly, ten endopeptidases with the highest average number of cleavage sites were found. Another uniqueness demonstrated that there was also a difference in stability between in silico and wet lab. The II values indicated CelGH5 and ACA61162.1 as unstable enzymes, while the wet lab showed they were stable at broad pH range. The program of SOPMA, PDBsum, ProSA, and SAVES provided the secondary and tertiary structure analysis. The predominant secondary structure was the random coil, and tertiary structure has fulfilled the structure quality of QMEAN4, ERRAT, Ramachandran plot, and Z score.

CONCLUSION

This study can afford the new insights about the physicochemical and structural properties of the GH5-cellulase family from uncultured microorganisms. Furthermore, in silico analysis could be valuable in selecting a highly efficient cellulases for enhanced enzyme production.

Genes Common in Primary Immunodeficiencies and Cancer Display Overrepresentation of Codon CTG and Dominant Role of Selection Pressure in Shaping Codon Usage

Primary immunodeficiencies (PIDs) are disorders of the immune system that involve faulty cellular, humoral, or both cellular and humoral functions. PIDs are present at the crossroad between infections, immune dysregulation, and cancers. A panel encompassing 42 genes involved in both PIDs and cancer has been investigated for the genes’ compositional properties, codon usage patterns, various forces affecting codon choice, protein properties, and gene expression profiles. In the present study, the codon choice of genes was found to be dependent upon the richness of the nucleotide; the viz AT nucleotide rich genome preferred AT ending codons. The dinucleotide TpA adversely affected protein expression, while CpG did not. The CTG codon was the most overrepresented codon in 80.95% of genes. Analysis of various protein properties, including GRAVY, AROMA, isoelectric point, aliphatic index, hydrophobicity, instability index, and numbers of acidic, basic, and neutral amino acid residues revealed that the hydrophobicity index, instability index, and numbers of acidic and basic amino acid residues are the factors affecting gene expression. Based on neutrality analysis, parity analysis, ENc-GC3 analysis, and regression analysis of nucleotides present at the first and third positions of the codon, it was determined that selection pressure, mutation pressure, and compositional constraints all participated in shaping codon usage. The study will help determine the various evolutionary forces acting on genes common to both PIDs and cancer. Codon usage analysis might be helpful in the future to augment both diseases simultaneously. The research also indicates a peculiar pattern adapted by a set of genes involved in any disease.

Structure-sequence features based prediction of phosphosites of serine/threonine protein kinases of Mycobacterium tuberculosis

Elucidation of signaling events in a pathogen is potentially important to tackle the infection caused by it. Such events mediated by protein phosphorylation play important roles in infection, and therefore, to predict the phosphosites and substrates of the serine/threonine protein kinases, we have developed a Machine learning-based approach for Mycobacterium tuberculosis serine/threonine protein kinases using kinase-peptide structure-sequence data. This approach utilizes features derived from kinase three-dimensional-structure environment and known phosphosite sequences to generate support vector machine (SVM)-based kinase-specific predictions of phosphosites of serine/threonine protein kinases (STPKs) with no or scarce data of their substrates. SVM outperformed the four machine learning algorithms we tried (random forest, logistic regression, SVM, and k-nearest neighbors) with an area under the curve receiver-operating characteristic value of 0.88 on the independent testing dataset and a 10-fold cross-validation accuracy of ~81.6% for the final model. Our predicted phosphosites of M. tuberculosis STPKs form a useful resource for experimental biologists enabling elucidation of STPK mediated posttranslational regulation of important cellular processes.

Dual-Knockout of Mutant Isocitrate Dehydrogenase 1 and 2 Subtypes Towards Glioma Therapy: Structural Mechanistic Insights on the Role of Vorasidenib

Recently, Vorasidenib (AG-881) has been reported as a therapeutic alternative that exerts potent dual inhibitory activity against mIDH1/2 towards the treatment of low-grade glioma. However, structural and dynamic events associated with its dual inhibition mechanism remain unclear. As such, we employ integrative computer-assisted atomistic techniques to provide thorough structural and dynamic insights. Our analysis proved that the dual-targeting ability of AG-881 is mediated by Val255/Val294 within the binding pockets of both mIDH1 and mIDH2 which are shown to elicit a strong intermolecular interaction, thus favoring binding affinity. The structural orientations of AG-881 within the respective hydrophobic pockets allowed favorable interactions with binding site residues which accounted for its high binding free energy of -28.69 kcal/mol and -19.89 kcal/mol towards mIDH1 and mIDH2, respectively. Interestingly, upon binding, AG-881 was found to trigger systemic alterations of mIDH1 and mIDH2 characterized by restricted residue flexibility and a reduction in exposure of residues to the solvent surface area. As a result of these structural alterations, crucial interactions of the mutant enzymes were inhibited, a phenomenon that results in a suppression of the production of oncogenic stimulator 2-HG. Findings therefore provide thorough structural and dynamic insights associated with the dual inhibitory activity of AG-881 towards glioma therapy.

Discovery of novel JAK2 and EGFR inhibitors from a series of thiazole-based chalcone derivatives

The Janus kinase (JAK) and epidermal growth factor receptor (EGFR) have been considered as potential targets for cancer therapy due to their role in regulating proliferation and survival of cancer cells. In the present study, the aromatic alkyl-amino analogs of thiazole-based chalcone were selected to experimentally and theoretically investigate their inhibitory activity against JAK2 and EGFR proteins as well as their anti-cancer effects on human cancer cell lines expressing JAK2 (TF1 and HEL) and EGFR (A549 and A431). In vitro cytotoxicity screening results demonstrated that the HEL erythroleukemia cell line was susceptible to compounds 11 and 12, whereas the A431 lung cancer cell line was vulnerable to compound 25. However, TF1 and A549 cells were not sensitive to our thiazole derivatives. From kinase inhibition assay results, compound 25 was found to be a dual inhibitor against JAK2 and EGFR, whereas compounds 11 and 12 selectively inhibited the JAK2 protein. According to the molecular docking analysis, compounds 11, 12 and 25 formed hydrogen bonds with the hinge region residues Lys857, Leu932 and Glu930 and hydrophobically came into contact with Leu983 at the catalytic site of JAK2, while compound 25 formed a hydrogen bond with Met769 at the hinge region, Lys721 near a glycine loop, and Asp831 at the activation loop of EGFR. Altogether, these potent thiazole derivatives, following Lipinski's rule of five, could likely be developed as a promising JAK2/EGFR targeted drug(s) for cancer therapy.

Oxytocin amino acid variation within Neotropical primates: new genetic variants in hormone and receptor sequences and evidence for evolutionary forces driving this unexpected diversity

Oxytocin is a mammalian neuropeptide hormone that mediates behaviours important to reproduction. Despite almost universal amino acid sequence conservation across most groups of mammals, several unique forms have been reported across Neotropical primates. To explore sequence diversity, we investigated the genes encoding oxytocin and its receptor across the Atelidae, which was known to contain at least three unique oxytocin sequences. Additionally, we included the genus Cebus, within the Cebidae, to further explore the ubiquity of the Pro8 variant in this family. We found a novel amino acid variant (Val3) within the Atelidae radiation, bringing the total number of oxytocin sequences within Neotropical primates to seven. Analyses of physicochemical properties revealed conservative substitutions that are likely tolerated within the selective constraints imposed by receptor binding. Furthermore, we report radical substitutions at the eighth codon and evidence for co-evolution between Pro8 and a ligand-binding region of the oxytocin receptor in the Atelidae, supporting the notion that this variant may affect binding specificity. Overall, we suggest that selective constraint on binding specificity may maintain proper oxytocin function and that the diversification of amino acid sequence is likely due to a variety of processes such as relaxed constraint, neutral mutation, positive selection and coevolution.

dagLogo: An R/Bioconductor package for identifying and visualizing differential amino acid group usage in proteomics data

Sequence logos have been widely used as graphical representations of conserved nucleic acid and protein motifs. Due to the complexity of the amino acid (AA) alphabet, rich post-translational modification, and diverse subcellular localization of proteins, few versatile tools are available for effective identification and visualization of protein motifs. In addition, various reduced AA alphabets based on physicochemical, structural, or functional properties have been valuable in the study of protein alignment, folding, structure prediction, and evolution. However, there is lack of tools for applying reduced AA alphabets to the identification and visualization of statistically significant motifs. To fill this gap, we developed an R/Bioconductor package dagLogo, which has several advantages over existing tools. First, dagLogo allows various formats for input sets and provides comprehensive options to build optimal background models. It implements different reduced AA alphabets to group AAs of similar properties. Furthermore, dagLogo provides statistical and visual solutions for differential AA (or AA group) usage analysis of both large and small data sets. Case studies showed that dagLogo can better identify and visualize conserved protein sequence patterns from different types of inputs and can potentially reveal the biological patterns that could be missed by other logo generators.

Amalgamation of 3D structure and sequence information for protein-protein interaction prediction

Protein is the primary building block of living organisms. It interacts with other proteins and is then involved in various biological processes. Protein-protein interactions (PPIs) help in predicting and hence help in understanding the functionality of the proteins, causes and growth of diseases, and designing new drugs. However, there is a vast gap between the available protein sequences and the identification of protein-protein interactions. To bridge this gap, researchers proposed several computational methods to reveal the interactions between proteins. These methods merely depend on sequence-based information of proteins. With the advancement of technology, different types of information related to proteins are available such as 3D structure information. Nowadays, deep learning techniques are adopted successfully in various domains, including bioinformatics. So, current work focuses on the utilization of different modalities, such as 3D structures and sequence-based information of proteins, and deep learning algorithms to predict PPIs. The proposed approach is divided into several phases. We first get several illustrations of proteins using their 3D coordinates information, and three attributes, such as hydropathy index, isoelectric point, and charge of amino acids. Amino acids are the building blocks of proteins. A pre-trained ResNet50 model, a subclass of a convolutional neural network, is utilized to extract features from these representations of proteins. Autocovariance and conjoint triad are two widely used sequence-based methods to encode proteins, which are used here as another modality of protein sequences. A stacked autoencoder is utilized to get the compact form of sequence-based information. Finally, the features obtained from different modalities are concatenated in pairs and fed into the classifier to predict labels for protein pairs. We have experimented on the human PPIs dataset and Saccharomyces cerevisiae PPIs dataset and compared our results with the state-of-the-art deep-learning-based classifiers. The results achieved by the proposed method are superior to those obtained by the existing methods. Extensive experimentations on different datasets indicate that our approach to learning and combining features from two different modalities is useful in PPI prediction.

CoRNeA: A Pipeline to Decrypt the Inter-Protein Interfaces from Amino Acid Sequence Information

Decrypting the interface residues of the protein complexes provides insight into the functions of the proteins and, hence, the overall cellular machinery. Computational methods have been devised in the past to predict the interface residues using amino acid sequence information, but all these methods have been majorly applied to predict for prokaryotic protein complexes. Since the composition and rate of evolution of the primary sequence is different between prokaryotes and eukaryotes, it is important to develop a method specifically for eukaryotic complexes. Here, we report a new hybrid pipeline for predicting the protein-protein interaction interfaces in a pairwise manner from the amino acid sequence information of the interacting proteins. It is based on the framework of Co-evolution, machine learning (Random Forest), and Network Analysis named CoRNeA trained specifically on eukaryotic protein complexes. We use Co-evolution, physicochemical properties, and contact potential as major group of features to train the Random Forest classifier. We also incorporate the intra-contact information of the individual proteins to eliminate false positives from the predictions keeping in mind that the amino acid sequence of a protein also holds information for its own folding and not only the interface propensities. Our prediction on example datasets shows that CoRNeA not only enhances the prediction of true interface residues but also reduces false positive rates significantly.

Fine Tuning of the Copper Active Site in Polysaccharide Monooxygenases

Type 2 copper active sites, one of the several important copper active sites in biology, were recently found in the novel superfamily of polysaccharide monooxygenases (PMOs) that cleave recalcitrant polysaccharides via an unprecedented oxidative mechanism. The copper center in PMOs is ligated by the bidentate N-terminal histidine residue and another conserved histidine residue, forming a unique T-shaped core termed as Histidine brace. This core serves as the foundation for diverse structures and electronic properties among PMO families and subfamilies. Understanding of the copper active site in PMOs is limited to the static solid structures obtained with X-ray diffraction (XRD), whereas in several families, the copper center exists as a mixture of species in solution as indicated by electron paramagnetic resonance (EPR) spectroscopy. To obtain further details on the copper active sites in PMOs, we carried out density functional theory calculations and molecular dynamics simulations on MtPMO3* that were previously studied with XRD, EPR, mutagenesis, and activity assays. The results reveal the fine-tuning of the binding of the distal ligands by both proximal and distal H-bond-forming residues. Q167 forms H bonds with the proximal O_Tyr ligand of Y167 and the equatorial aqueous ligand (O_eq). T74 forms a H bond with the distal aqueous ligand (O_dis). Removing these H bonds by mutating Q167 or T74 to alanine results in great fluctuations of the axial ligands. Strengthening the proximal H bonds by mutating Q167 to glutamate confines Y167 to the copper centers. In all mutants, the residence time of O_dis is significantly reduced. Q167A, Q167E, and T74A mutants were previously shown to have a significantly reduced activity. Our results indicate that well-tuned H bonds are required for the activity of PMOs.

A Novel Mutation p.S93R in CRYBB1 Associated with Dominant Congenital Cataract and Microphthalmia

Purpose: To identify the pathogenetic mutations in a four-generation Chinese family with dominant congenital cataracts and microphthalmia.Methods: A four-generation Chinese family with dominant congenital cataracts were recruited. Genomic DNAs were collected from their peripheral blood leukocytes and subjected to whole exome sequencing. The genetic mutations were identified by bioinformatic analyses and verified by Sanger sequencing.Results: Whole exome sequencing revealed a c.279C>G point mutation in the CRYBB1 gene which was further verified by Sanger sequencing. The nucleotide replacement results in a novel mutation p.S93R in a conserved residue of βB1 crystallin which is predicted to disrupt normal βB1 structure and function.Conclusions: We identified a novel missense mutation p.S93R in CRYBB1 in a Chinese family with autosomal dominant congenital cataracts and microphthalmia. This serine residue is extremely conserved evolutionarily in more than 50 βγ-crystallins of many species. These data will be very helpful to further understand the structural and functional features of crystallins.

Computational analysis of functional SNPs in Alzheimer's disease-associated endocytosis genes

Background

From genome wide association studies on Alzheimer’s disease (AD), it has been shown that many single nucleotide polymorphisms (SNPs) of genes of different pathways affect the disease risk. One of the pathways is endocytosis, and variants in these genes may affect their functions in amyloid precursor protein (APP) trafficking, amyloid-beta (Aβ) production as well as its clearance in the brain. This study uses computational methods to predict the effect of novel SNPs, including untranslated region (UTR) variants, splice site variants, synonymous SNPs (sSNPs) and non-synonymous SNPs (nsSNPs) in three endocytosis genes associated with AD, namely PICALM, SYNJ1 and SH3KBP1.

Materials and Methods

All the variants’ information was retrieved from the Ensembl genome database, and then different variation prediction analyses were performed. UTRScan was used to predict UTR variants while MaxEntScan was used to predict splice site variants. Meta-analysis by PredictSNP2 was used to predict sSNPs. Parallel prediction analyses by five different software packages including SIFT, PolyPhen-2, Mutation Assessor, I-Mutant2.0 and SNPs&GO were used to predict the effects of nsSNPs. The level of evolutionary conservation of deleterious nsSNPs was further analyzed using ConSurf server. Mutant protein structures of deleterious nsSNPs were modelled and refined using SPARKS-X and ModRefiner for structural comparison.

Results

A total of 56 deleterious variants were identified in this study, including 12 UTR variants, 18 splice site variants, eight sSNPs and 18 nsSNPs. Among these 56 deleterious variants, seven variants were also identified in the Alzheimer’s Disease Sequencing Project (ADSP), Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Mount Sinai Brain Bank (MSBB) studies.

Discussion

The 56 deleterious variants were predicted to affect the regulation of gene expression, or have functional impacts on these three endocytosis genes and their gene products. The deleterious variants in these genes are expected to affect their cellular function in endocytosis and may be implicated in the pathogenesis of AD as well. The biological consequences of these deleterious variants and their potential impacts on the disease risks could be further validated experimentally and may be useful for gene-disease association study.

Revelation of enzyme activity of mutant pyrazinamidases from Mycobacterium tuberculosis upon binding with various metals using quantum mechanical approach

Pyrazinamide (PZA) is one of the most potent bacteriostatic drug against tuberculosis, a deadliest disease with high mortality and morbidity rate. PZA metabolizes into its active form pyrazinoic acid (POA) with the help of a metalloenzyme, pyrazinamidase (PZase). Mutagenicity and metal substitution in PZase weakens the binding of PZA with PZase and increases the drug resistance in Mycobacterium tuberculosis. The present study aims at the quantum mechanistic analysis of mutant-metal substituted PZase complexes by studying the mechanics of metals and PZA binding at MCS and catalytic site, respectively. A total of 66 complexes are scrutinised in this study to elucidate the effect of mutations on the enzymatic function of PZase. Among the 10 mutations considered in this study, 7 different mutations i.e. Asp₄₉ → Asn, His₅₁ → Arg, Gly₇₈ → Cys, Asp₁₂ → Gly, Asp₁₂ → Ala, Thr₁₃₅ → Pro and Asp₁₃₆ → Gly cause a detrimental effect on the activity of PZase. In addition to this, the substitution of iron with cobalt enhances the enzymatic activity of both wild type and mutant PZase while zinc, magnesium and copper reduce it. Based on these results, it is concluded that upon substitution of iron with zinc, magnesium and copper, PZase cannot function properly. Due to mutations, the reactivity of the drug also reduces as its binding with PZase weakens and this phenomenon enhances the resistance of Mycobacterium tuberculosis against drug.

Amino Acid Loss during Continuous Venovenous Hemofiltration in Critically Ill Patients

BACKGROUND/OBJECTIVES

During continuous venovenous hemofiltration (CVVH), there is unwanted loss of amino acids (AA) in the ultrafiltrate (UF). Solutes may also be removed by adsorption to the filter membrane. The aim was to quantify the total loss of AA via the CVVH circuit using a high-flux polysulfone membrane and to differentiate between the loss by ultrafiltration and adsorption.

METHODS

Prospective observational study in ten critically ill patients, receiving predilution CVVH with a new filter, blood flow 180 mL/min, and predilution flow 2,400 mL/h. Arterial blood, postfilter blood, and UF samples were taken at baseline, and 1, 8, and 24-h after CVVH initiation, to determine AA concentrations and hematocrit. Mass transfer calculations were used to determine AA loss in the filter and by UF, and the difference between these 2.

RESULTS

The median AA loss in the filter was 10.4 g/day, the median AA loss by UF was 13.4 g/day, and the median difference was -2.9 g/day (IQR -5.9 to -1.4 g/day). For the individual AA, the difference ranged from -1 g/day to +0.4 g/day, suggesting that some AA were consumed or adsorbed and others were generated. AA losses did not significantly change over the 24-h study period.

CONCLUSION

During CVVH with a modern polysulfone membrane, the estimated AA loss was 13.4 g/day, which corresponds to a loss of about 11.2 g of protein per day. Adsorption did not play a major role. However, individual AA behaved differently, suggesting complex interactions and processes at the filter membrane or peripheral AA production.

Structural and quantum mechanical computations to elucidate the altered binding mechanism of metal and drug with pyrazinamidase from Mycobacterium tuberculosis due to mutagenicity

Pyrazinamide is known to be the most effective treatment against tuberculosis disease and is known to have bacteriostatic action. By targeting the bacterial spores, this drug reduces the chances for the progression of the infection in organisms. In recent years, increased instances of the drug resistance of bacterial strains are reported. Pyrazinamidase, activator for pyrazinamide, leads to resistance against the drug due to mutagenicity across the world. The present study aimed at the quantum mechanistic analysis of mutations in pyrazinamidase to gain insights into the mechanism of this enzyme. Quantum mechanical calculations were performed to analyse the effect of mutations at the metal coordination site using ORCA software program. Moreover, conformational changes in PZase binding cavity has also been analysed due to mutations of binding pocket residues using CASTp server. In order to elucidate the behaviour of the mutant pyrazinamidase, docking of PZA in the binding pocket of PZase was performed using AutoDock Vina. Analysis of results revealed that iron showed weak binding with the metal coordination site of the mutant proteins due to alteration in electron transfer mechanism. The binding cavity of the mutant PZase has undergone major conformational changes as the volume of pocket increased due to bulky R-chains of mutated amino acids. These conformational changes lead to weak binding of the drug at binding cavity of PZase and reduce the drug activation mechanism leading to increased drug resistance in the bacterial strains.

Amyloid-like aggregation of designer bolaamphiphilic peptides: Effect of hydrophobic section and hydrophilic heads

Amyloid-like aggregation of natural proteins or polypeptides is an important process involved in many human diseases as well as some normal biological functions. Plenty of works have been done on this ubiquitous phenomenon, but the molecular mechanism of amyloid-like aggregation has not been fully understood yet. In this study, we showed that a series of designer bolaamphiphilic peptides could undergo amyloid-like aggregation even though they didn't possess typical β-sheet secondary structure. Through systematic amino acid substitution, we found that for the self-assembling ability, the number and species of amino acid in hydrophobic section could be variable as long as enough hydrophobic interaction is provided, while different polar amino acids as the hydrophilic heads could change the self-assembling nanostructures with their aggregating behaviors affected by pH value change. Based on these results, novel self-assembling models and aggregating mechanisms were proposed, which might provide new insight into the molecular basis of amyloid-like aggregation.

Conserved Binding Regions Provide the Clue for Peptide-Based Vaccine Development: A Chemical Perspective

Synthetic peptides have become invaluable biomedical research and medicinal chemistry tools for studying functional roles, i.e., binding or proteolytic activity, naturally-occurring regions’ immunogenicity in proteins and developing therapeutic agents and vaccines. Synthetic peptides can mimic protein sites; their structure and function can be easily modulated by specific amino acid replacement. They have major advantages, i.e., they are cheap, easily-produced and chemically stable, lack infectious and secondary adverse reactions and can induce immune responses via T- and B-cell epitopes. Our group has previously shown that using synthetic peptides and adopting a functional approach has led to identifying Plasmodium falciparumconserved regions binding to host cells. Conserved high activity binding peptides’ (cHABPs) physicochemical, structural and immunological characteristics have been taken into account for properly modifying and converting them into highly immunogenic, protection-inducing peptides (mHABPs) in the experimental Aotus monkey model. This article describes stereo–electron and topochemical characteristics regarding major histocompatibility complex (MHC)-mHABP-T-cell receptor (TCR) complex formation. Some mHABPs in this complex inducing long-lasting, protective immunity have been named immune protection-inducing protein structures (IMPIPS), forming the subunit components in chemically synthesized vaccines. This manuscript summarizes this particular field and adds our recent findings concerning intramolecular interactions (H-bonds or π-interactions) enabling proper IMPIPS structure as well as the peripheral flanking residues (PFR) to stabilize the MHCII-IMPIPS-TCR interaction, aimed at inducing long-lasting, protective immunological memory.

SubVis: an interactive R package for exploring the effects of multiple substitution matrices on pairwise sequence alignment

Understanding how proteins mutate is critical to solving a host of biological problems. Mutations occur when an amino acid is substituted for another in a protein sequence. The set of likelihoods for amino acid substitutions is stored in a matrix and input to alignment algorithms. The quality of the resulting alignment is used to assess the similarity of two or more sequences and can vary according to assumptions modeled by the substitution matrix. Substitution strategies with minor parameter variations are often grouped together in families. For example, the BLOSUM and PAM matrix families are commonly used because they provide a standard, predefined way of modeling substitutions. However, researchers often do not know if a given matrix family or any individual matrix within a family is the most suitable. Furthermore, predefined matrix families may inaccurately reflect a particular hypothesis that a researcher wishes to model or otherwise result in unsatisfactory alignments. In these cases, the ability to compare the effects of one or more custom matrices may be needed. This laborious process is often performed manually because the ability to simultaneously load multiple matrices and then compare their effects on alignments is not readily available in current software tools. This paper presents SubVis, an interactive R package for loading and applying multiple substitution matrices to pairwise alignments. Users can simultaneously explore alignments resulting from multiple predefined and custom substitution matrices. SubVis utilizes several of the alignment functions found in R, a common language among protein scientists. Functions are tied together with the Shiny platform which allows the modification of input parameters. Information regarding alignment quality and individual amino acid substitutions is displayed with the JavaScript language which provides interactive visualizations for revealing both high-level and low-level alignment information.

A comprehensive computational study on pathogenic mis-sense mutations spanning the RING2 and REP domains of Parkin protein

Various mutations in PARK2 gene, which encodes the protein parkin, are significantly associated with the onset of autosomal recessive juvenile Parkinson (ARJP) in neuronal cells. Parkin is a multi domain protein, the N-terminal part contains the Ubl and the C-terminal part consists of four zinc coordinating domains, viz., RING0, RING1, in between ring (IBR) and RING2. Disease mutations are spread over all the domains of Parkin, although mutations in some regions may affect the functionality of Parkin more adversely. The mutations in the RING2 domain are seen to abolish the neuroprotective E3 ligase activity of Parkin. In this current work, we carried out detailed in silico analysis to study the extent of pathogenicity of mutations spanning the Parkin RING2 domain and the adjoining REP region by SIFT, Mutation Accessor, PolyPhen2, SNPs and GO, GV/GD and I-mutant. To study the structural and functional implications of these mutations on RING2-REP domain of Parkin, we studied the solvent accessibility (SASA/RSA), hydrophobicity, intra-molecular hydrogen bonding profile and domain analysis by various computational tools. Finally, we analysed the interaction energy profiles of the mutants and compared them to the wild type protein using Discovery studio 2.5. By comparing the various analyses it could be safely concluded that except P437L and A379V mutations, all other mutations were potentially deleterious affecting various structural aspects of RING2 domain architecture. This study is based purely on computational approach which has the potential to identify disease mutations and the information could further be used in treatment of diseases and prognosis.

F420H2 Is Required for Phthiocerol Dimycocerosate Synthesis in Mycobacteria

Phthiocerol dimycocerosates (PDIM) are a group of cell surface-associated apolar lipids of Mycobacterium tuberculosis and closely related mycobacteria, such as Mycobacterium bovis and Mycobacterium leprae A characteristic methoxy group of these lipids is generated from the methylation of a hydroxyl group of the direct precursors, the phthiotriols. The precursors arise from the reduction of phthiodiolones, the keto intermediates, by a ketoreductase. The putative phthiodiolone ketoreductase (PKR) is encoded by Rv2951c in M. tuberculosis and BCG_2972c in M. bovis BCG, and these open reading frames (ORFs) encode identical amino acid sequences. We investigated the cofactor requirement of the BCG_2972c protein. A comparative analysis based on the crystallographic structures of similar enzymes identified structural elements for binding of coenzyme F420 and hydrophobic phthiodiolones in PKR. Coenzyme F420 is a deazaflavin coenzyme that serves several key functions in pathogenic and nonpathogenic mycobacteria. We found that an M. bovis BCG mutant lacking F420-dependent glucose-6-phosphate dehydrogenase (Fgd), which generates F420H2 (glucose-6-phosphate + F420 → 6-phosphogluconate + F420H2), was devoid of phthiocerols and accumulated phthiodiolones. When the mutant was provided with F420H2, a broken-cell slurry of the mutant converted accumulated phthiodiolones to phthiocerols; F420H2 was generated in situ from F420 and glucose-6-phosphate by the action of Fgd. Thus, the reaction mixture was competent in reducing phthiodiolones to phthiotriols (phthiodiolones + F420H2 → phthiotriols + F420), which were then methylated to phthiocerols. These results established the mycobacterial phthiodiolone ketoreductase as an F420H2-dependent enzyme (fPKR). A phylogenetic analysis of close homologs of fPKR revealed potential F420-dependent lipid-modifying enzymes in a broad range of mycobacteria.

IMPORTANCE

Mycobacterium tuberculosis is the causative agent of tuberculosis, and phthiocerol dimycocerosates (PDIM) protect this pathogen from the early innate immune response of an infected host. Thus, the PDIM synthesis system is a potential target for the development of effective treatments for tuberculosis. The current study shows that a PDIM synthesis enzyme is dependent on the coenzyme F420 F420 is universally present in mycobacteria and absent in humans. This finding expands the number of experimentally validated F420-dependent enzymes in M. tuberculosis to six, each of which helps the pathogen to evade killing by the host immune system, and one of which activates an antituberculosis drug, PA-824. This work also has relevance to leprosy, since similar waxy lipids are found in Mycobacterium leprae.

CCProf: exploring conformational change profile of proteins

In many biological processes, proteins have important interactions with various molecules such as proteins, ions or ligands. Many proteins undergo conformational changes upon these interactions, where regions with large conformational changes are critical to the interactions. This work presents the CCProf platform, which provides conformational changes of entire proteins, named conformational change profile (CCP) in the context. CCProf aims to be a platform where users can study potential causes of novel conformational changes. It provides 10 biological features, including conformational change, potential binding target site, secondary structure, conservation, disorder propensity, hydropathy propensity, sequence domain, structural domain, phosphorylation site and catalytic site. All these information are integrated into a well-aligned view, so that researchers can capture important relevance between different biological features visually. The CCProf contains 986 187 protein structure pairs for 3123 proteins. In addition, CCProf provides a 3D view in which users can see the protein structures before and after conformational changes as well as binding targets that induce conformational changes. All information (e.g. CCP, binding targets and protein structures) shown in CCProf, including intermediate data are available for download to expedite further analyses.

Database URL: http://zoro.ee.ncku.edu.tw/ccprof/

Systematic exploration of guide-tree topology effects for small protein alignments

Background

Guide-trees are used as part of an essential heuristic to enable the calculation of multiple sequence alignments. They have been the focus of much method development but there has been little effort at determining systematically, which guide-trees, if any, give the best alignments. Some guide-tree construction schemes are based on pair-wise distances amongst unaligned sequences. Others try to emulate an underlying evolutionary tree and involve various iteration methods.

Results

We explore all possible guide-trees for a set of protein alignments of up to eight sequences. We find that pairwise distance based default guide-trees sometimes outperform evolutionary guide-trees, as measured by structure derived reference alignments. However, default guide-trees fall way short of the optimum attainable scores. On average chained guide-trees perform better than balanced ones but are not better than default guide-trees for small alignments.

Conclusions

Alignment methods that use Consistency or hidden Markov models to make alignments are less susceptible to sub-optimal guide-trees than simpler methods, that basically use conventional sequence alignment between profiles. The latter appear to be affected positively by evolutionary based guide-trees for difficult alignments and negatively for easy alignments. One phylogeny aware alignment program can strongly discriminate between good and bad guide-trees. The results for randomly chained guide-trees improve with the number of sequences.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-338) contains supplementary material, which is available to authorized users.

Rv0132c of Mycobacterium tuberculosis encodes a coenzyme F420-dependent hydroxymycolic acid dehydrogenase

The ability of Mycobacterium tuberculosis to manipulate and evade human immune system is in part due to its extraordinarily complex cell wall. One of the key components of this cell wall is a family of lipids called mycolic acids. Oxygenation of mycolic acids generating methoxy- and ketomycolic acids enhances the pathogenic attributes of M. tuberculosis. Thus, the respective enzymes are of interest in the research on mycobacteria. The generation of methoxy- and ketomycolic acids proceeds through intermediary formation of hydroxymycolic acids. While the methyl transferase that generates methoxymycolic acids from hydroxymycolic acids is known, hydroxymycolic acids dehydrogenase that oxidizes hydroxymycolic acids to ketomycolic acids has been elusive. We found that hydroxymycolic acid dehydrogenase is encoded by the rv0132c gene and the enzyme utilizes F₄₂₀, a deazaflavin coenzyme, as electron carrier, and accordingly we called it F₄₂₀-dependent hydroxymycolic acid dehydrogenase. This is the first report on the involvement of F₄₂₀ in the synthesis of a mycobacterial cell envelope. Also, F₄₂₀-dependent hydroxymycolic acid dehydrogenase was inhibited by PA-824, and therefore, it is a previously unknown target for this new tuberculosis drug.

Inhibition of dengue virus entry into target cells using synthetic antiviral peptides

Despite the importance of DENV as a human pathogen, there is no specific treatment or protective vaccine. Successful entry into the host cells is necessary for establishing the infection. Recently, the virus entry step has become an attractive therapeutic strategy because it represents a barrier to suppress the onset of the infection. Four putative antiviral peptides were designed to target domain III of DENV-2 E protein using BioMoDroid algorithm. Two peptides showed significant inhibition of DENV when simultaneously incubated as shown by plaque formation assay, RT-qPCR, and Western blot analysis. Both DET4 and DET2 showed significant inhibition of virus entry (84.6% and 40.6% respectively) using micromolar concentrations. Furthermore, the TEM images showed that the inhibitory peptides caused structural abnormalities and alteration of the arrangement of the viral E protein, which interferes with virus binding and entry. Inhibition of DENV entry during the initial stages of infection can potentially reduce the viremia in infected humans resulting in prevention of the progression of dengue fever to the severe life-threatening infection, reduce the infected vector numbers, and thus break the transmission cycle. Moreover these peptides though designed against the conserved region in DENV-2 would have the potential to be active against all the serotypes of dengue and might be considered as Hits to begin designing and developing of more potent analogous peptides that could constitute as promising therapeutic agents for attenuating dengue infection.

Attributes of short linear motifs

Traditionally, protein-protein interactions were thought to be mediated by large, structured domains. However, it has become clear that the interactome comprises a wide range of binding interfaces with varying degrees of flexibility, ranging from rigid globular domains to disordered regions that natively lack structure. Enrichment for disorder in highly connected hub proteins and its correlation with organism complexity hint at the functional importance of disordered regions. Nevertheless, they have not yet been extensively characterised. Shifting the attention from globular domains to disordered regions of the proteome might bring us closer to elucidating the dense and complex connectivity of the interactome. An important class of disordered interfaces are the compact mono-partite, short linear motifs (SLiMs, or eukaryotic linear motifs (ELMs)). They are evolutionarily plastic and interact with relatively low affinity due to the limited number of residues that make direct contact with the binding partner. These features confer to SLiMs the ability to evolve convergently and mediate transient interactions, which is imperative to network evolution and to maintain robust cell signalling, respectively. The ability to discriminate biologically relevant SLiMs by means of different attributes will improve our understanding of the complexity of the interactome and aid development of bioinformatics tools for motif discovery. In this paper, the curated instances currently available in the Eukaryotic Linear Motif (ELM) database are analysed to provide a clear overview of the defining attributes of SLiMs. These analyses suggest that functional SLiMs have higher levels of conservation than their surrounding residues, frequently evolve convergently, preferentially occur in disordered regions and often form a secondary structure when bound to their interaction partner. These results advocate searching for small groupings of residues in disordered regions with higher relative conservation and a propensity to form the secondary structure. Finally, the most interesting conclusions are examined in regard to their functional consequences.

KvSNP: accurately predicting the effect of genetic variants in voltage-gated potassium channels

MOTIVATION

Non-synonymous single nucleotide polymorphisms (nsSNPs) in voltage-gated potassium (Kv) channels cause diseases with potentially fatal consequences in seemingly healthy individuals. Identifying disease-causing genetic variation will aid presymptomatic diagnosis and treatment of such disorders. NsSNP-effect predictors are hypothesized to perform best when developed for specific gene families. We, thus, created KvSNP: a method that assigns a disease-causing probability to Kv-channel nsSNPs.

RESULTS

KvSNP outperforms popular non gene-family-specific methods (SNPs&GO, SIFT and Polyphen) in predicting the disease potential of Kv-channel variants, according to all tested metrics (accuracy, Matthews correlation coefficient and area under receiver operator characteristic curve). Most significantly, it increases the separation of the median predicted disease probabilities between benign and disease-causing SNPs by 26% on the next-best competitor. KvSNP has ranked 172 uncharacterized Kv-channel nsSNPs by disease-causing probability.

AVAILABILITY AND IMPLEMENTATION

KvSNP, a WEKA implementation is available at www.bioinformatics.leeds.ac.uk/KvDB/KvSNP.html.

CONTACT

d.r.westhead@leeds.ac.uk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A mutation in the mitochondrial fission gene Dnm1l leads to cardiomyopathy

Mutations in a number of genes have been linked to inherited dilated cardiomyopathy (DCM). However, such mutations account for only a small proportion of the clinical cases emphasising the need for alternative discovery approaches to uncovering novel pathogenic mutations in hitherto unidentified pathways. Accordingly, as part of a large-scale N-ethyl-N-nitrosourea mutagenesis screen, we identified a mouse mutant, Python, which develops DCM. We demonstrate that the Python phenotype is attributable to a dominant fully penetrant mutation in the dynamin-1-like (Dnm1l) gene, which has been shown to be critical for mitochondrial fission. The C452F mutation is in a highly conserved region of the M domain of Dnm1l that alters protein interactions in a yeast two-hybrid system, suggesting that the mutation might alter intramolecular interactions within the Dnm1l monomer. Heterozygous Python fibroblasts exhibit abnormal mitochondria and peroxisomes. Homozygosity for the mutation results in the death of embryos midway though gestation. Heterozygous Python hearts show reduced levels of mitochondria enzyme complexes and suffer from cardiac ATP depletion. The resulting energy deficiency may contribute to cardiomyopathy. This is the first demonstration that a defect in a gene involved in mitochondrial remodelling can result in cardiomyopathy, showing that the function of this gene is needed for the maintenance of normal cellular function in a relatively tissue-specific manner. This disease model attests to the importance of mitochondrial remodelling in the heart; similar defects might underlie human heart muscle disease.

Flanking signal and mature peptide residues influence signal peptide cleavage

Background

Signal peptides (SPs) mediate the targeting of secretory precursor proteins to the correct subcellular compartments in prokaryotes and eukaryotes. Identifying these transient peptides is crucial to the medical, food and beverage and biotechnology industries yet our understanding of these peptides remains limited. This paper examines the most common type of signal peptides cleavable by the endoprotease signal peptidase I (SPase I), and the residues flanking the cleavage sites of three groups of signal peptide sequences, namely (i) eukaryotes (Euk) (ii) Gram-positive (Gram+) bacteria, and (iii) Gram-negative (Gram-) bacteria.

Results

In this study, 2352 secretory peptide sequences from a variety of organisms with amino-terminal SPs are extracted from the manually curated SPdb database for analysis based on physicochemical properties such as pI, aliphatic index, GRAVY score, hydrophobicity, net charge and position-specific residue preferences. Our findings show that the three groups share several similarities in general, but they display distinctive features upon examination in terms of their amino acid compositions and frequencies, and various physico-chemical properties. Thus, analysis or prediction of their sequences should be separated and treated as distinct groups.

Conclusion

We conclude that the peptide segment recognized by SPase I extends to the start of the mature protein to a limited extent, upon our survey of the amino acid residues surrounding the cleavage processing site. These flanking residues possibly influence the cleavage processing and contribute to non-canonical cleavage sites. Our findings are applicable in defining more accurate prediction tools for recognition and identification of cleavage site of SPs.