Properties of MHC Class I Presented Peptides That Enhance Immunogenicity

T-cells have to recognize peptides presented on MHC molecules to be activated and elicit their effector functions. Several studies demonstrate that some peptides are more immunogenic than others and therefore more likely to be T-cell epitopes. We set out to determine which properties cause such differences in immunogenicity. To this end, we collected and analyzed a large set of data describing the immunogenicity of peptides presented on various MHC-I molecules. Two main conclusions could be drawn from this analysis: First, in line with previous observations, we showed that positions P4–6 of a presented peptide are more important for immunogenicity. Second, some amino acids, especially those with large and aromatic side chains, are associated with immunogenicity. This information was combined into a simple model that was used to demonstrate that immunogenicity is, to a certain extent, predictable. This model (made available at http://tools.iedb.org/immunogenicity/) was validated with data from two independent epitope discovery studies. Interestingly, with this model we could show that T-cells are equipped to better recognize viral than human (self) peptides. After the past successful elucidation of different steps in the MHC-I presentation pathway, the identification of variables that influence immunogenicity will be an important next step in the investigation of T-cell epitopes and our understanding of cellular immune responses.     

ISOTOPE: ISOform-guided prediction of epiTOPEs in cancer

Immunotherapies provide effective treatments for previously untreatable tumors and identifying tumor-specific epitopes can help elucidate the molecular determinants of therapy response. Here, we describe a pipeline, ISOTOPE (ISOform-guided prediction of epiTOPEs In Cancer), for the comprehensive identification of tumor-specific splicing-derived epitopes. Using RNA sequencing and mass spectrometry for MHC-I associated proteins, ISOTOPE identified neoepitopes from tumor-specific splicing events that are potentially presented by MHC-I complexes. Analysis of multiple samples indicates that splicing alterations may affect the production of self-epitopes and generate more candidate neoepitopes than somatic mutations. Although there was no difference in the number of splicing-derived neoepitopes between responders and non-responders to immune therapy, higher MHC-I binding affinity was associated with a positive response. Our analyses highlight the diversity of the immunogenic impacts of tumor-specific splicing alterations and the importance of studying splicing alterations to fully characterize tumors in the context of immunotherapies. ISOTOPE is available at https://github.com/comprna/ISOTOPE.     

AntiAngioPred: A Server for Prediction of Anti-Angiogenic Peptides

The process of angiogenesis is a vital step towards the formation of malignant tumors. Anti-angiogenic peptides are therefore promising candidates in the treatment of cancer. In this study, we have collected anti-angiogenic peptides from the literature and analyzed the residue preference in these peptides. Residues like Cys, Pro, Ser, Arg, Trp, Thr and Gly are preferred while Ala, Asp, Ile, Leu, Val and Phe are not preferred in these peptides. There is a positional preference of Ser, Pro, Trp and Cys in the N terminal region and Cys, Gly and Arg in the C terminal region of anti-angiogenic peptides. Motif analysis suggests the motifs “CG-G”, “TC”, “SC”, “SP-S”, etc., which are highly prominent in anti-angiogenic peptides. Based on the primary analysis, we developed prediction models using different machine learning based methods. The maximum accuracy and MCC for amino acid composition based model is 80.9% and 0.62 respectively. The performance of the models on independent dataset is also reasonable. Based on the above study, we have developed a user-friendly web server named “AntiAngioPred” for the prediction of anti-angiogenic peptides. AntiAngioPred web server is freely accessible at http://clri.res.in/subramanian/tools/antiangiopred/index.html (mirror site: http://crdd.osdd.net/raghava/antiangiopred/).     

Mg2+, K+, and the Ribosome

Mg²⁺ and K⁺ are the prevalent di- and monovalent cations inside the cells in all three domains, playing a dominant role in structure and function of biological macromolecules. Ribosomes bind a substantial fraction of total Mg²⁺ and K⁺ cations. In this issue of the Journal of Bacteriology, Akanuma and coworkers (G. Akanuma et al., J. Bacteriol. 196:3820–3830, 2014, doi:10.1128/JB.01896-14) report a surprising genetic link between ribosome amounts per cell and the intracellular Mg²⁺ concentrations.     

Epitope Predictions Indicate the Presence of Two Distinct Types of Epitope-Antibody-Reactivities Determined by Epitope Profiling of Intravenous Immunoglobulins

Epitope-antibody-reactivities (EAR) of intravenous immunoglobulins (IVIGs) determined for 75,534 peptides by microarray analysis demonstrate that roughly 9% of peptides derived from 870 different human protein sequences react with antibodies present in IVIG. Computational prediction of linear B cell epitopes was conducted using machine learning with an ensemble of classifiers in combination with position weight matrix (PWM) analysis. Machine learning slightly outperformed PWM with area under the curve (AUC) of 0.884 vs. 0.849. Two different types of epitope-antibody recognition-modes (Type I EAR and Type II EAR) were found. Peptides of Type I EAR are high in tyrosine, tryptophan and phenylalanine, and low in asparagine, glutamine and glutamic acid residues, whereas for peptides of Type II EAR it is the other way around. Representative crystal structures present in the Protein Data Bank (PDB) of Type I EAR are PDB 1TZI and PDB 2DD8, while PDB 2FD6 and 2J4W are typical for Type II EAR. Type I EAR peptides share predicted propensities for being presented by MHC class I and class II complexes. The latter interaction possibly favors T cell-dependent antibody responses including IgG class switching. Peptides of Type II EAR are predicted not to be preferentially presented by MHC complexes, thus implying the involvement of T cell-independent IgG class switch mechanisms. The high extent of IgG immunoglobulin reactivity with human peptides implies that circulating IgG molecules are prone to bind to human protein/peptide structures under non-pathological, non-inflammatory conditions. A webserver for predicting EAR of peptide sequences is available at www.sysmed-immun.eu/EAR.     

Seed Pro-Nutra Care: A tool for characterization of seed storage proteins and database of bioactive peptides having potential health benefits

Seed storage proteins, the major food proteins, possess unique physicochemical characteristics which determine their nutritional importance and influence their utilization by humans. Here, we describe a database driven tool named Seed Pro-Nutra Care which comprises a systematic compendium of seed storage proteins and their bioactive peptides influencing several vital organ systems for maintenance of health. Seed Pro-Nutra Careis an integrated resource on seed storage protein. This resource help in the (I) Characterization of proteins whether they belong to seed storage protein group or not. (II) Identification the bioactive peptides with their sequences using peptide name (III) Determination of physico chemical properties of seed storage proteins. (IV) Epitope identification and mapping (V) Allergenicity prediction and characterization. Seed Pro-Nutra Care is a compilation of data on bioactive peptides present in seed storage proteins from our own collections and other published and unpublished sources. The database provides an information resource of a variety of seed related biological information and its use for nutritional and biomedical application.

Availability

http://www.gbpuat-cbsh.ac.in/departments/bi/database/seed_pro_nutra_care/     

The Mps1 Kinase Modulates the Recruitment and Activity of Cnn1CENP-T at Saccharomyces cerevisiae Kinetochores

Kinetochores are conserved protein complexes that bind the replicated chromosomes to the mitotic spindle and then direct their segregation. To better comprehend Saccharomyces cerevisiae kinetochore function, we dissected the phospho-regulated dynamic interaction between conserved kinetochore protein Cnn1^CENP-T, the centromere region, and the Ndc80 complex through the cell cycle. Cnn1 localizes to kinetochores at basal levels from G1 through metaphase but accumulates abruptly at anaphase onset. How Cnn1 is recruited and which activities regulate its dynamic localization are unclear. We show that Cnn1 harbors two kinetochore-localization activities: a C-terminal histone-fold domain (HFD) that associates with the centromere region and a N-terminal Spc24/Spc25 interaction sequence that mediates linkage to the microtubule-binding Ndc80 complex. We demonstrate that the established Ndc80 binding site in the N terminus of Cnn1, Cnn1^60–84, should be extended with flanking residues, Cnn1^25–91, to allow near maximal binding affinity to Ndc80. Cnn1 localization was proposed to depend on Mps1 kinase activity at Cnn1–S74, based on in vitro experiments demonstrating the Cnn1–Ndc80 complex interaction. We demonstrate that from G1 through metaphase, Cnn1 localizes via both its HFD and N-terminal Spc24/Spc25 interaction sequence, and deletion or mutation of either region results in anomalous Cnn1 kinetochore levels. At anaphase onset (when Mps1 activity decreases) Cnn1 becomes enriched mainly via the N-terminal Spc24/Spc25 interaction sequence. In sum, we provide the first in vivo evidence of Cnn1 preanaphase linkages with the kinetochore and enrichment of the linkages during anaphase.     

mMass as a Software Tool for the Annotation of Cyclic Peptide Tandem Mass Spectra

Natural or synthetic cyclic peptides often possess pronounced bioactivity. Their mass spectrometric characterization is difficult due to the predominant occurrence of non-proteinogenic monomers and the complex fragmentation patterns observed. Even though several software tools for cyclic peptide tandem mass spectra annotation have been published, these tools are still unable to annotate a majority of the signals observed in experimentally obtained mass spectra. They are thus not suitable for extensive mass spectrometric characterization of these compounds. This lack of advanced and user-friendly software tools has motivated us to extend the fragmentation module of a freely available open-source software, mMass (http://www.mmass.org), to allow for cyclic peptide tandem mass spectra annotation and interpretation. The resulting software has been tested on several cyanobacterial and other naturally occurring peptides. It has been found to be superior to other currently available tools concerning both usability and annotation extensiveness. Thus it is highly useful for accelerating the structure confirmation and elucidation of cyclic as well as linear peptides and depsipeptides.     

The Overlap of Small Molecule and Protein Binding Sites within Families of Protein Structures

Protein–protein interactions are challenging targets for modulation by small molecules. Here, we propose an approach that harnesses the increasing structural coverage of protein complexes to identify small molecules that may target protein interactions. Specifically, we identify ligand and protein binding sites that overlap upon alignment of homologous proteins. Of the 2,619 protein structure families observed to bind proteins, 1,028 also bind small molecules (250–1000 Da), and 197 exhibit a statistically significant (p<0.01) overlap between ligand and protein binding positions. These “bi-functional positions”, which bind both ligands and proteins, are particularly enriched in tyrosine and tryptophan residues, similar to “energetic hotspots” described previously, and are significantly less conserved than mono-functional and solvent exposed positions. Homology transfer identifies ligands whose binding sites overlap at least 20% of the protein interface for 35% of domain–domain and 45% of domain–peptide mediated interactions. The analysis recovered known small-molecule modulators of protein interactions as well as predicted new interaction targets based on the sequence similarity of ligand binding sites. We illustrate the predictive utility of the method by suggesting structural mechanisms for the effects of sanglifehrin A on HIV virion production, bepridil on the cellular entry of anthrax edema factor, and fusicoccin on vertebrate developmental pathways. The results, available at http://pibase.janelia.org, represent a comprehensive collection of structurally characterized modulators of protein interactions, and suggest that homologous structures are a useful resource for the rational design of interaction modulators.     

PROCAIN: protein profile comparison with assisting information

Detection of remote sequence homology is essential for the accurate inference of protein structure, function and evolution. The most sensitive detection methods involve the comparison of evolutionary patterns reflected in multiple sequence alignments (MSAs) of protein families. We present PROCAIN, a new method for MSA comparison based on the combination of ‘vertical’ MSA context (substitution constraints at individual sequence positions) and ‘horizontal’ context (patterns of residue content at multiple positions). Based on a simple and tractable profile methodology and primitive measures for the similarity of horizontal MSA patterns, the method achieves the quality of homology detection comparable to a more complex advanced method employing hidden Markov models (HMMs) and secondary structure (SS) prediction. Adding SS information further improves PROCAIN performance beyond the capabilities of current state-of-the-art tools. The potential value of the method for structure/function predictions is illustrated by the detection of subtle homology between evolutionary distant yet structurally similar protein domains. ProCAIn, relevant databases and tools can be downloaded from: http://prodata.swmed.edu/procain/download. The web server can be accessed at http://prodata.swmed.edu/procain/procain.php.     

The Structure of Misfolded Amyloidogenic Dimers: Computational Analysis of Force Spectroscopy Data

Progress in understanding the molecular mechanism of self-assembly of amyloidogenic proteins and peptides requires knowledge about their structure in misfolded states. Structural studies of amyloid aggregates formed during the early aggregation stage are very limited. Atomic force microscopy (AFM) spectroscopy is widely used to analyze misfolded proteins and peptides, but the structural characterization of transiently formed misfolded dimers is limited by the lack of computational approaches that allow direct comparison with AFM experiments. Steered molecular dynamics (SMD) simulation is capable of modeling force spectroscopy experiments, but the modeling requires pulling rates 10⁷ times higher than those used in AFM experiments. In this study, we describe a computational all-atom Monte Carlo pulling (MCP) approach that enables us to model results at pulling rates comparable to those used in AFM pulling experiments. We tested the approach by modeling pulling experimental data for I91 from titin I-band (PDB ID: 1TIT) and ubiquitin (PDB ID: 1UBQ). We then used MCP to analyze AFM spectroscopy experiments that probed the interaction of the peptides [Q6C] Sup35 (6–13) and [H13C] Aβ (13–23). A comparison of experimental results with the computational data for the Sup35 dimer with out-of-register and in-register arrangements of β-sheets suggests that Sup35 monomers adopt an out-of-register arrangement in the dimer. A similar analysis performed for Aβ peptide demonstrates that the out-of-register antiparallel β-sheet arrangement of monomers also occurs in this peptide. Although the rupture of hydrogen bonds is the major contributor to dimer dissociation, the aromatic-aromatic interaction also contributes to the dimer rupture process.     

Interaction-based discovery of functionally important genes in cancers

A major challenge in cancer genomics is uncovering genes with an active role in tumorigenesis from a potentially large pool of mutated genes across patient samples. Here we focus on the interactions that proteins make with nucleic acids, small molecules, ions and peptides, and show that residues within proteins that are involved in these interactions are more frequently affected by mutations observed in large-scale cancer genomic data than are other residues. We leverage this observation to predict genes that play a functionally important role in cancers by introducing a computational pipeline (http://canbind.princeton.edu) for mapping large-scale cancer exome data across patients onto protein structures, and automatically extracting proteins with an enriched number of mutations affecting their nucleic acid, small molecule, ion or peptide binding sites. Using this computational approach, we show that many previously known genes implicated in cancers are enriched in mutations within the binding sites of their encoded proteins. By focusing on functionally relevant portions of proteins—specifically those known to be involved in molecular interactions—our approach is particularly well suited to detect infrequent mutations that may nonetheless be important in cancer, and should aid in expanding our functional understanding of the genomic landscape of cancer.     

Complete genome sequence of the Phaeobacter gallaeciensis type strain CIP 105210T (= DSM 26640T = BS107T)

Phaeobacter gallaeciensis CIP 105210^T (= DSM 26640^T = BS107^T) is the type strain of the species Phaeobacter gallaeciensis. The genus Phaeobacter belongs to the marine Roseobacter group (Rhodobacteraceae, Alphaproteobacteria). Phaeobacter species are effective colonizers of marine surfaces, including frequent associations with eukaryotes. Strain BS107^T was isolated from a rearing of the scallop Pecten maximus. Here we describe the features of this organism, together with the complete genome sequence, comprising eight circular replicons with a total of 4,448 genes. In addition to a high number of extrachromosomal replicons, the genome contains six genomic island and three putative prophage regions, as well as a hybrid between a plasmid and a circular phage. Phylogenomic analyses confirm previous results, which indicated that the originally reported P. gallaeciensis type-strain deposit DSM 17395 belongs to P. inhibens and that CIP 105210^T (= DSM 26640^T) is the sole genome-sequenced representative of P. gallaeciensis.     

Identification of a Cholesterol-Binding Pocket in Inward Rectifier K+ (Kir) Channels

Cholesterol is the major sterol component of all mammalian plasma membranes. Recent studies have shown that cholesterol inhibits both bacterial (KirBac1.1 and KirBac3.1) and eukaryotic (Kir2.1) inward rectifier K⁺ (Kir) channels. Lipid-sterol interactions are not enantioselective, and the enantiomer of cholesterol (ent-cholesterol) does not inhibit Kir channel activity, suggesting that inhibition results from direct enantiospecific binding to the channel, and not indirect effects of changes to the bilayer. Furthermore, conservation of the effect of cholesterol among prokaryotic and eukaryotic Kir channels suggests an evolutionary conserved cholesterol-binding pocket, which we aimed to identify. Computational experiments were performed by docking cholesterol to the atomic structures of Kir2.2 (PDB: 3SPI) and KirBac1.1 (PDB: 2WLL) using Autodock 4.2. Poses were assessed to ensure biologically relevant orientation and then clustered according to location and orientation. The stability of cholesterol in each of these poses was then confirmed by molecular dynamics simulations. Finally, mutation of key residues (S95H and I171L) in this putative binding pocket found within the transmembrane domain of Kir2.1 channels were shown to lead to a loss of inhibition by cholesterol. Together, these data provide support for this location as a biologically relevant pocket.     

Designing of peptides with desired half-life in intestine-like environment

Background

In past, a number of peptides have been reported to possess highly diverse properties ranging from cell penetrating, tumor homing, anticancer, anti-hypertensive, antiviral to antimicrobials. Owing to their excellent specificity, low-toxicity, rich chemical diversity and availability from natural sources, FDA has successfully approved a number of peptide-based drugs and several are in various stages of drug development. Though peptides are proven good drug candidates, their usage is still hindered mainly because of their high susceptibility towards proteases degradation. We have developed an in silico method to predict the half-life of peptides in intestine-like environment and to design better peptides having optimized physicochemical properties and half-life.

Results

In this study, we have used 10mer (HL10) and 16mer (HL16) peptides dataset to develop prediction models for peptide half-life in intestine-like environment. First, SVM based models were developed on HL10 dataset which achieved maximum correlation R/R² of 0.57/0.32, 0.68/0.46, and 0.69/0.47 using amino acid, dipeptide and tripeptide composition, respectively. Secondly, models developed on HL16 dataset showed maximum R/R² of 0.91/0.82, 0.90/0.39, and 0.90/0.31 using amino acid, dipeptide and tripeptide composition, respectively. Furthermore, models that were developed on selected features, achieved a correlation (R) of 0.70 and 0.98 on HL10 and HL16 dataset, respectively. Preliminary analysis suggests the role of charged residue and amino acid size in peptide half-life/stability. Based on above models, we have developed a web server named HLP (Half Life Prediction), for predicting and designing peptides with desired half-life. The web server provides three facilities; i) half-life prediction, ii) physicochemical properties calculation and iii) designing mutant peptides.

Conclusion

In summary, this study describes a web server ‘HLP’ that has been developed for assisting scientific community for predicting intestinal half-life of peptides and to design mutant peptides with better half-life and physicochemical properties. HLP models were trained using a dataset of peptides whose half-lives have been determined experimentally in crude intestinal proteases preparation. Thus, HLP server will help in designing peptides possessing the potential to be administered via oral route (http://www.imtech.res.in/raghava/hlp/).

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-282) contains supplementary material, which is available to authorized users.     

A Mass Spectrometric-Derived Cell Surface Protein Atlas

Cell surface proteins are major targets of biomedical research due to their utility as cellular markers and their extracellular accessibility for pharmacological intervention. However, information about the cell surface protein repertoire (the surfaceome) of individual cells is only sparsely available. Here, we applied the Cell Surface Capture (CSC) technology to 41 human and 31 mouse cell types to generate a mass-spectrometry derived Cell Surface Protein Atlas (CSPA) providing cellular surfaceome snapshots at high resolution. The CSPA is presented in form of an easy-to-navigate interactive database, a downloadable data matrix and with tools for targeted surfaceome rediscovery (http://wlab.ethz.ch/cspa). The cellular surfaceome snapshots of different cell types, including cancer cells, resulted in a combined dataset of 1492 human and 1296 mouse cell surface glycoproteins, providing experimental evidence for their cell surface expression on different cell types, including 136 G-protein coupled receptors and 75 membrane receptor tyrosine-protein kinases. Integrated analysis of the CSPA reveals that the concerted biological function of individual cell types is mainly guided by quantitative rather than qualitative surfaceome differences. The CSPA will be useful for the evaluation of drug targets, for the improved classification of cell types and for a better understanding of the surfaceome and its concerted biological functions in complex signaling microenvironments.