Not all transmembrane helices are born equal: Towards the extension of the sequence homology concept to membrane proteins

<p/> <p>Background</p> <p>Sequence homology considerations widely used to transfer functional annotation to uncharacterized protein sequences require special precautions in the case of non-globular sequence segments including membrane-spanning stretches composed of non-polar residues. Simple, quantitative criteria are desirable for identifying transmembrane helices (TMs) that must be included into or should be excluded from start sequence segments in similarity searches aimed at finding distant homologues.</p> <p>Results</p> <p>We found that there are two types of TMs in membrane-associated proteins. On the one hand, there are so-called simple TMs with elevated hydrophobicity, low sequence complexity and extraordinary enrichment in long aliphatic residues. They merely serve as membrane-anchoring device. In contrast, so-called complex TMs have lower hydrophobicity, higher sequence complexity and some functional residues. These TMs have additional roles besides membrane anchoring such as intra-membrane complex formation, ligand binding or a catalytic role. Simple and complex TMs can occur both in single- and multi-membrane-spanning proteins essentially in any type of topology. Whereas simple TMs have the potential to confuse searches for sequence homologues and to generate unrelated hits with seemingly convincing statistical significance, complex TMs contain essential evolutionary information.</p> <p>Conclusion</p> <p>For extending the homology concept onto membrane proteins, we provide a necessary quantitative criterion to distinguish simple TMs (and a sufficient criterion for complex TMs) in query sequences prior to their usage in homology searches based on assessment of hydrophobicity and sequence complexity of the TM sequence segments.</p> <p>Reviewers</p> <p>This article was reviewed by Shamil Sunyaev, L. Aravind and Arcady Mushegian.</p

SNPeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants

Single nucleotide variants (SNVs) are, together with copy number variation, the primary source of variation in the human genome and are associated with phenotypic variation such as altered response to drug treatment and susceptibility to disease. Linking structural effects of non-synonymous SNVs to functional outcomes is a major issue in structural bioinformatics. The SNPeffect database (http://snpeffect.switchlab.org) uses sequence- and structure-based bioinformatics tools to predict the effect of protein-coding SNVs on the structural phenotype of proteins. It integrates aggregation prediction (TANGO), amyloid prediction (WALTZ), chaperone-binding prediction (LIMBO) and protein stability analysis (FoldX) for structural phenotyping. Additionally, SNPeffect holds information on affected catalytic sites and a number of post-translational modifications. The database contains all known human protein variants from UniProt, but users can now also submit custom protein variants for a SNPeffect analysis, including automated structure modeling. The new meta-analysis application allows plotting correlations between phenotypic features for a user-selected set of variants

Amyloid-based nanosensors and nanodevices

This journal is © The Royal Society of Chemistry 2014Self-assembling amyloid-like peptides and proteins give rise to promising biomaterials with potential applications in many fields. Amyloid structures are formed by the process of molecular recognition and self-assembly, wherein a peptide or protein monomer spontaneously self-associates into dimers and oligomers and subsequently into supramolecular aggregates, finally resulting in condensed fibrils. Mature amyloid fibrils possess a quasi-crystalline structure featuring a characteristic fiber diffraction pattern and have well-defined properties, in contrast to many amorphous protein aggregates that arise when proteins misfold. Core sequences of four to seven amino acids have been identified within natural amyloid proteins. They are capable to form amyloid fibers and fibrils and have been used as amyloid model structures, simplifying the investigations on amyloid structures due to their small size. Recent studies have highlighted the use of self-assembled amyloid-based fibers as nanomaterials. Here, we discuss the latest advances and the major challenges in developing amyloids for future applications in nanotechnology and nanomedicine, with the focus on development of sensors to study protein–ligand interactions.This work is supported by the Institute of Bioengineering and Nanotechnology (Biomedical Research Council, Agency for Science, Technology and Research, Singapore). Dr Ivo C. Martins acknowledges the funding from the Portuguese Foundation for Science and Technology (project PTDC/SAU-ENB/117013/2010 and program Investigador FCT, Research Contract IF/00772/2013)

Recognizing flu-like symptoms from videos

© 2014 Hue Thi et al.; licensee BioMed Central Ltd. Background: Vision-based surveillance and monitoring is a potential alternative for early detection of respiratory disease outbreaks in urban areas complementing molecular diagnostics and hospital and doctor visit-based alert systems. Visible actions representing typical flu-like symptoms include sneeze and cough that are associated with changing patterns of hand to head distances, among others. The technical difficulties lie in the high complexity and large variation of those actions as well as numerous similar background actions such as scratching head, cell phone use, eating, drinking and so on. Results: In this paper, we make a first attempt at the challenging problem of recognizing flu-like symptoms from videos. Since there was no related dataset available, we created a new public health dataset for action recognition that includes two major flu-like symptom related actions (sneeze and cough) and a number of background actions. We also developed a suitable novel algorithm by introducing two types of Action Matching Kernels, where both types aim to integrate two aspects of local features, namely the space-time layout and the Bag-of-Words representations. In particular, we show that the Pyramid Match Kernel and Spatial Pyramid Matching are both special cases of our proposed kernels. Besides experimenting on standard testbed, the proposed algorithm is evaluated also on the new sneeze and cough set. Empirically, we observe that our approach achieves competitive performance compared to the state-of-the-arts, while recognition on the new public health dataset is shown to be a non-trivial task even with simple single person unobstructed view. Conclusions: Our sneeze and cough video dataset and newly developed action recognition algorithm is the first of its kind and aims to kick-start the field of action recognition of flu-like symptoms from videos. It will be challenging but necessary in future developments to consider more complex real-life scenario of detecting these actions simultaneously from multiple persons in possibly crowded environments

Importance of extended protease substrate recognition motifs in steering BNIP-2 cleavage by human and mouse granzymes B

Background: Previous screening of the substrate repertoires and substrate specificity profiles of granzymes resulted in long substrate lists highly likely containing bystander substrates. Here, a recently developed degradomics technology that allows distinguishing efficiently from less efficiently cleaved substrates was applied to study the degradome of mouse granzyme B (mGrB). Results: In vitro kinetic degradome analysis resulted in the identification of 37 mGrB cleavage events, 9 of which could be assigned as efficiently targeted ones. Previously, cleavage at the IEAD(75) tetrapeptide motif of Bid was shown to be efficiently and exclusively targeted by human granzyme B (hGrB) and thus not by mGrB. Strikingly, and despite holding an identical P4-P1 human Bid (hBid) cleavage motif, mGrB was shown to efficiently cleave the BCL2/adenovirus E1B 19 kDa protein-interacting protein 2 or BNIP-2 at IEAD(28). Like Bid, BNIP-2 represents a pro-apoptotic Bcl-2 protein family member and a potential regulator of GrB induced cell death. Next, in vitro analyses demonstrated the increased efficiency of human and mouse BNIP-2 cleavage by mGrB as compared to hGrB indicative for differing Bid/BNIP-2 substrate traits beyond the P4-P1 IEAD cleavage motif influencing cleavage efficiency. Murinisation of differential primed site residues in hBNIP-2 revealed that, although all contributing, a single mutation at the P3' position was found to significantly increase the mGrB/hGrB cleavage ratio, whereas mutating the P1' position from I-29 > T yielded a 4-fold increase in mGrB cleavage efficiency. Finally, mutagenesis analyses revealed the composite BNIP 2 precursor patterns to be the result of alternative translation initiation at near-cognate start sites within the 5' leader sequence (5'UTR) of BNIP-2. Conclusions: Despite their high sequence similarity, and previously explained by their distinct tetrapeptide specificities observed, the substrate repertoires of mouse and human granzymes B only partially overlap. Here, we show that the substrate sequence context beyond the P4-P1 positions can influence orthologous granzyme B cleavage efficiencies to an unmatched extent. More specifically, in BNIP-2, the identical and hGrB optimal IEAD tetrapeptide substrate motif is targeted highly efficiently by mGrB, while this tetrapeptide motif is refractory towards mGrB cleavage in Bid

Application of AllerCatPro 2.0 for protein safety assessments of consumer products

Foreign proteins are potentially immunogenic, and a proportion of these are able to induce immune responses that result in allergic sensitization. Subsequent exposure of sensitized subjects to the inducing protein can provoke a variety of allergic reactions that may be severe, or even fatal. It has therefore been recognized for some time that it is important to determine a priori whether a given protein has the potential to induce allergic responses in exposed subjects. For example, the need to assess whether transgene products expressed in genetically engineered crop plants have allergenic properties. This is not necessarily a straightforward exercise (as discussed elsewhere in this edition), but the task becomes even more challenging when there is a need to conduct an overall allergenicity safety assessment of complex mixtures of proteins in botanicals or other natural sources that are to be used in consumer products. This paper describes a new paradigm for the allergenicity safety assessment of proteins that is based on the use of AllerCatPro 2.0, a new version of a previously described web application model developed for the characterization of the allergenic potential of proteins. Operational aspects of AllerCatPro 2.0 are described with emphasis on the application of new features that provide improvements in the predictions of allergenic properties such as the identification of proteins with high allergenic concern. Furthermore, the paper provides a description of strategies of how AllerCatPro 2.0 can best be deployed as a screening tool for identifying suitable proteins as ingredients in consumer products as well as a tool, in conjunction with label-free proteomic analysis, for identifying and semiquantifying protein allergens in complex materials. Lastly, the paper discusses the steps that are recommended for formal allergenicity safety assessment of novel consumer products which contain proteins, including consideration and integration of predicted consumer exposure metrics. The article therefore provides a holistic perspective of the processes through which effective protein safety assessments can be made of potential allergenic hazards and risks associated with exposure to proteins in consumer products, with a particular focus on the use of AllerCatPro 2.0 for this purpose

Mapping the sequence mutations of the 2009 H1N1 influenza A virus neuraminidase relative to drug and antibody binding sites

In this work, we study the consequences of sequence variations of the "2009 H1N1" (swine or Mexican flu) influenza A virus strain neuraminidase for drug treatment and vaccination. We find that it is phylogenetically more closely related to European H1N1 swine flu and H5N1 avian flu rather than to the H1N1 counterparts in the Americas. Homology-based 3D structure modeling reveals that the novel mutations are preferentially located at the protein surface and do not interfere with the active site. The latter is the binding cavity for 3 currently used neuraminidase inhibitors: oseltamivir (Tamiflu®), zanamivir (Relenza®) and peramivir; thus, the drugs should remain effective for treatment. However, the antigenic regions of the neuraminidase relevant for vaccine development, serological typing and passive antibody treatment can differ from those of previous strains and already vary among patients

Towards Complete Sets of Farnesylated and Geranylgeranylated Proteins

Three different prenyltransferases attach isoprenyl anchors to C-terminal motifs in substrate proteins. These lipid anchors serve for membrane attachment or protein–protein interactions in many pathways. Although well-tolerated selective prenyltransferase inhibitors are clinically available, their mode of action remains unclear since the known substrate sets of the various prenyltransferases are incomplete. The Prenylation Prediction Suite (PrePS) has been applied for large-scale predictions of prenylated proteins. To prioritize targets for experimental verification, we rank the predictions by their functional importance estimated by evolutionary conservation of the prenylation motifs within protein families. The ranked lists of predictions are accessible as PRENbase (http://mendel.imp.univie.ac.at/sat/PrePS/PRENbase) and can be queried for verification status, type of modifying enzymes (anchor type), and taxonomic distribution. Our results highlight a large group of plant metal-binding chaperones as well as several newly predicted proteins involved in ubiquitin-mediated protein degradation, enriching the known functional repertoire of prenylated proteins. Furthermore, we identify two possibly prenylated proteins in Mimivirus. The section HumanPRENbase provides complete lists of predicted prenylated human proteins—for example, the list of farnesyltransferase targets that cannot become substrates of geranylgeranyltransferase 1 and, therefore, are especially affected by farnesyltransferase inhibitors (FTIs) used in cancer and anti-parasite therapy. We report direct experimental evidence verifying the prediction of the human proteins Prickle1, Prickle2, the BRO1 domain–containing FLJ32421 (termed BROFTI), and Rab28 (short isoform) as exclusive farnesyltransferase targets. We introduce PRENbase, a database of large-scale predictions of protein prenylation substrates ranked by evolutionary conservation of the motif. Experimental evidence is presented for the selective farnesylation of targets with an evolutionary conserved modification site

AllerCatPro 2.0: a web server for predicting protein allergenicity potential

Proteins in food and personal care products can pose a risk for an immediate immunoglobulin E (IgE)-mediated allergic response. Bioinformatic tools can assist to predict and investigate the allergenic potential of proteins. Here we present AllerCatPro 2.0, a web server that can be used to predict protein allergenicity potential with better accuracy than other computational methods and new features that help assessors making informed decisions. AllerCatPro 2.0 predicts the similarity between input proteins using both their amino acid sequences and predicted 3D structures towards the most comprehensive datasets of reliable proteins associated with allergenicity. These datasets currently include 4979 protein allergens, 162 low allergenic proteins, and 165 autoimmune allergens with manual expert curation from the databases of WHO/International Union of Immunological Societies (IUIS), Comprehensive Protein Allergen Resource (COMPARE), Food Allergy Research and Resource Program (FARRP), UniProtKB and Allergome. Various examples of profilins, autoimmune allergens, low allergenic proteins, very large proteins, and nucleotide input sequences showcase the utility of AllerCatPro 2.0 for predicting protein allergenicity potential. The AllerCatPro 2.0 web server is freely accessible at https://allercatpro.bii.a-star.edu.sg