Peptide Binding Affinity Research Articles

The interaction of half-sandwich (η5-Cp*)Rh(III) cation with histidine containing peptides and their ternary species with (N,N) bidentate ligands

Our goal was to explore the possible interactions of the potential metallodrug (η5-Cp*)Rh(III) complexes with histidine containing biomolecules (peptides/proteins) in order to understand the most important thermodynamic factors influencing the biospeciation and biotransformation of (η5-Cp*)Rh(III) complexes. To this end, here we report systematic solution thermodynamic and solution structural study on the interaction of (η5-Cp*)Rh(III) cation with histidine containing peptides and their constituents ((N-methyl)imidazole, GGA-OH, GGH-OH, histidine-amide, HGG-OH, GHG-NH2), based on extensive 1H NMR, ESI-MS and potentiometric investigations. The comparative evaluation of our data indicated that (η5-Cp*)Rh(III) cation is able to induce the deprotonation of amide nitrogen well below pH 7. Consequently, at physiological pH the peptides are coordinated to Rh(III) by tridentate manner, with the participation of amide nitrogen. At pH 7.4 the (η5-Cp*)Rh(III) binding affinity of peptides follow the order GGA-OH < < GGH-OH < < histidine-amide < HGG-OH < GHG-NH2, i.e. the observed binding strength essentially depends on the presence and position of histidine within the peptide sequence. We also performed computational study on the possible solution structures of complexes present at near physiological pH. At pH 7.4 all histidine containing peptides form ternary complexes with strongly coordinating (N,N) bidentate ligands (ethylenediamine or bipyridyl), in which the peptides are monodentately coordinated to Rh(III) through their imidazole N1‑nitrogens. In addition, the strongest chelators histidine-amide, HGG-OH and GHG-NH2 are also able to displace these powerful bidentate ligands from the coordination sphere of Rh(III).

Asn-linked N-acetylglucosamine of the amylin receptor 2 extracellular domain enhances peptide ligand affinity.

The calcitonin receptor (CTR) has a large extracellular domain (ECD) with multiple N-glycosylation sites. An asparagine (Asn)-linked N-acetylglucosamine (GlcNAc) of CTR ECD N130 was previously reported to enhance peptide hormone binding affinity for CTR ECD. CTR forms a complex with an accessory protein RAMP, and the RAMP:CTR complex gains affinity for peptide hormone amylin as the amylin receptor (AMY). Although N-glycosylation of AMY ECD was reported to enhance peptide hormone affinity, it remains underexplored which N-glycosites of AMY ECD are responsible for peptide affinity enhancement and it is unclear whether an Asn-linked GlcNAc of the N-glycosites plays a critical role. Here, I investigated the role of the Asn-linked GlcNAc of CTR N130 in the affinity of an antagonistic amylin analog (AC413) for AMY2 ECD (the RAMP2 ECD:CTR ECD complex). I used Endo H-treated CTR ECD in which N-glycans were trimmed to an Asn-linked GlcNAc on each of the N-glycosites. I incubated Endo H-treated CTR ECD with excess of glycan-free RAMP2 ECD to produce the RAMP2 ECD:CTR ECD complex. Using this coincubation system, I found that the RAMP2 ECD complex with Endo H-treated CTR ECD with N130D mutation showed a fourfold decrease in AC413 affinity compared with the RAMP2 ECD complex with Endo H-treated CTR ECD WT. In contrast, RAMP2 ECD N-glycosylation did not affect peptide binding affinity. These results indicate that the Asn-linked GlcNAc of CTR N130 is an important peptide affinity enhancer for AMY2 ECD and reveals a significant role of the Asn-linked GlcNAc in AMY2 function.

Peptide Binding to HLA-E Molecules in Humans, Nonhuman Primates, and Mice Reveals Unique Binding Peptides but Remarkably Conserved Anchor Residues.

Ag presentation via the nonclassical MHC class Ib molecule HLA-E, with nearly complete identity between the two alleles expressed in humans, HLA-E*01:01 and HLA-E*01:03, can lead to the activation of unconventional T cells in humans. Despite this virtual genetic monomorphism, differences in peptide repertoires binding to the two allelic variants have been reported. To further dissect and compare peptide binding to HLA-E*01:01 and HLA-E*01:03, we used an UV-mediated peptide exchange binding assay and an HPLC-based competition binding assay. In addition, we investigated binding of these same peptides to Mamu-E, the nonhuman primate homologue of human HLA-E, and to the HLA-E-like molecule Qa-1b in mice. We next exploited the differences and homologies in the peptide binding pockets of these four molecules to identify allele specific as well as common features of peptide binding motifs across species. Our results reveal differences in peptide binding preferences and intensities for each human HLA-E variant compared with Mamu-E and Qa-1b Using extended peptide libraries, we identified and refined the peptide binding motifs for each of the four molecules and found that they share main anchor positions, evidenced by conserved amino acid preferences across the four HLA-E molecules studied. In addition, we also identified differences in peptide binding motifs, which could explain the observed variations in peptide binding preferences and affinities for each of the four HLA-E-like molecules. Our results could help with guiding the selection of candidate pathogen-derived peptides with the capacity to target HLA-E-restricted T cells that could be mobilized in vaccination and immunotherapeutic strategies.

Structure-Based Optimization of Conformationally Constrained Peptides to Target Esophageal Cancer TEAD Transcription Factor

The transcriptional enhanced associate domain (TEAD) transcription factor is a core regulator of Hippo signaling pathway and has been recognized as a promising therapeutic target against gastrointestinal tumors. The intermolecular interaction of TEAD with its transcription coactivator vestigial-like protein (VGLL) is involved in the tumorigenesis of esophageal cancer, which possesses an additional double-stranded β-sheet (DSβS) motif in its TEAD-recognition domain as compared to the classic coactivators YAP and TAZ. Here, the DSβS motif is examined systematically based on the complex crystal structure of TEAD with VGL. It is found that the DSβS is one of the most important recognition sites at TEAD–VGLL complex interface, which can confer stability and specificity to the complex recognition and elicit the transcription activation event. Protein segments with different lengths are derived from the DSβS motif of VGLL to define a number of linear peptides; their binding modes and affinities to TEAD are computationally analyzed to reveal that these linear peptides cannot maintain in native double-strand conformation in free solution state, thus unfavorable for their rebinding to TEAD–VGLL interface. A head-to-tail cyclization strategy is described to constrain the conformation of linear peptides, which can considerably reduce the peptide flexibility and help the peptide folding to an approximately native conformation in solution. We also demonstrate that the binding affinity and biological activity of cyclic peptides are improved effectively relative to their linear counterparts at molecular and cellular levels.

Computational evaluation of modified peptides from human neutrophil peptide 1 (HNP-1)

The development of bacterial resistance toward antibiotics has been led to pay attention to the antimicrobial peptides (AMPs). The common mechanism of AMPs is disrupting the integrity of the bacterial membrane. One of the most accessible targets for α-defensins human neutrophil peptide-1 (HNP-1) is lipid II. In the present study, we performed homology modeling and geometrical validation of human neutrophil defensin 1. Then, the conformational and physicochemical properties of HNP-1 derived peptides 2Abz14S29, 2Abz23S29, and HNP1ΔC18A, as well as their interaction with lipid II were studied computationally. The overall quality of the predicted model of full protein was −5.14, where over 90% of residues were in the most favored and allowed regions in the Ramachandran plot. Although HNP-1 and HNP1ΔC18A were classified as unstable peptides, 2Abz14S29 and 2Abz23S29 were stable, based on the instability index values. Molecular docking showed similar interaction pattern of peptides and HNP-1 to lipid II. Molecular dynamic simulations revealed the overall stability of conformations, though the fluctuations of amino acids in the modified peptides were relatively higher than HNP-1. Further, the binding affinity constant (Kd) of HNP-1 and 2Abz23S29 in complex with lipid II was 10 times stronger than 2Abz14S29 and HNP1ΔC18A. Overall, computational studies of conformational and interaction patterns have signified how derived peptides could have displayed relatively similar antimicrobial results compared to HNP-1 in the reported experimental studies. Chemical modifications not only have improved the physicochemical properties of derived peptides compared to HNP-1, but also they have retained the similar pattern and binding affinity of peptides. Communicated by Ramaswamy H. Sarma

Identification of Putative Cell-entry-inhibitory Peptides against SARS-CoV-2 from Edible Insects: An in silico Study

Coronavirus Disease 2019 (COVID-19), an infectious disease caused by SARS-CoV-2, has become the most contagious pandemic in the world. The binding of SARS-CoV-2 spike glycoprotein Receptor-binding Domain (RBD) to human angiotensin-converting enzyme 2 receptor precedes cell entry. Thus, RBD of SARS-CoV-2 spike glycoprotein is a key target for the development of drugs and vaccines to curb COVID-19. The aim of this in silico study was to identify potential SARS-CoV-2 cell entry inhibitors from peptides derived from edible insects. Twenty-four major proteins from mealworms, silkworm cocoons and housefly larvae were subjected to in silico gastrointestinal (GI) digestion, yielding 3560 fragments. Further screening led to 82 high-GI-absorption peptides with unique sequences. Molecular docking revealed 10 promising peptides (VPW, PPY, PIF, VW, PSF, PGF, PAY, VGF, PF and TW), predicted to interact with at least one key binding residue on RBD. Notably, VPW had the lowest docking energy score (−144.359) and binding affinity (−7.0 kcal/mol), highlighting its potency among the ten peptides. Binding affinities of the insect peptides were superior to some reported natural products. Our study suggests that when consumed, edible insects may be a source of putative SARS-CoV-2 cell entry inhibitors in the form of RBD-binding peptides.

Repertoire-scale determination of class II MHC peptide binding via yeast display improves antigen prediction

CD4+ helper T cells contribute important functions to the immune response during pathogen infection and tumor formation by recognizing antigenic peptides presented by class II major histocompatibility complexes (MHC-II). While many computational algorithms for predicting peptide binding to MHC-II proteins have been reported, their performance varies greatly. Here we present a yeast-display-based platform that allows the identification of over an order of magnitude more unique MHC-II binders than comparable approaches. These peptides contain previously identified motifs, but also reveal new motifs that are validated by in vitro binding assays. Training of prediction algorithms with yeast-display library data improves the prediction of peptide-binding affinity and the identification of pathogen-associated and tumor-associated peptides. In summary, our yeast-display-based platform yields high-quality MHC-II-binding peptide datasets that can be used to improve the accuracy of MHC-II binding prediction algorithms, and potentially enhance our understanding of CD4+ T cell recognition.

OnionMHC: A deep learning model for peptide — HLA-A*02:01 binding predictions using both structure and sequence feature sets

The peptide binding to Major Histocompatibility Complex (MHC) proteins is an important step in the antigen-presentation pathway. Thus, predicting the binding potential of peptides with MHC is essential for the design of peptide-based therapeutics. Most of the available machine learning-based models predict the peptide-MHC binding based on the sequence of amino acids alone. Given the importance of structural information in determining the stability of the complex, here we have utilized both the complex structure and the peptide sequence features to predict the binding affinity of peptides to human receptor HLA-A*02:01. To our knowledge, no such model has been developed for the human HLA receptor before that incorporates both structure and sequence-based features. Results: We have applied machine learning techniques through the natural language processing (NLP) and convolutional neural network to design a model that performs comparably with the existing state-of-the-art models. Our model shows that the information from both sequence and structure domains results in enhanced performance in the binding prediction compared to the information from one domain alone. The testing results in 18 weekly benchmark datasets provided by the Immune Epitope Database (IEDB) as well as experimentally validated peptides from the whole-exome sequencing analysis of the breast cancer patients indicate that our model has achieved state-of-the-art performance. Conclusion: We have developed a deep-learning model (OnionMHC) that incorporates both structure as well as sequence-based features to predict the binding affinity of peptides with human receptor HLA-A*02:01. The model demonstrates state-of-the-art performance on the IEDB benchmark dataset as well as the experimentally validated peptides. The model can be used in the screening of potential neo-epitopes for the development of cancer vaccines or designing peptides for peptide-based therapeutics. OnionMHC is freely available at https://github.com/shikhar249/OnionMHC .

Possible role of HLA class-I genotype in SARS-CoV-2 infection and progression: A pilot study in a cohort of Covid-19 Spanish patients

Human Leukocyte Antigen (HLA) includes a large set of genes with important actions in immune response against viral infection. Numerous studies have revealed the existence of significant associations between certain HLA alleles and the susceptibility and prognosis of different infectious diseases. In this pilot study we analyse the binding affinity between 66 class I HLA alleles and SARS-CoV-2 viral peptides, and its association with the severity of the disease.A total of 45 Spanish patients with mild, moderate and severe SARS-CoV-2 infection were typed for HLA class I; after that, we analysed if an in silico model of HLA I-viral peptide binding affinity and classical HLA supertypes could be correlated to the severity of the disease. Our results suggest that patients with mild disease present Class I HLA molecules with a higher theoretical capacity for binding SARS-Cov-2 peptides and showed greater heterozygosity when comparing them with moderate and severe groups. In this regard, identifying HLA-SARS-CoV-2 peptides binding differences between individuals would help to clarify the heterogeneity of clinical responses to the disease and will also be useful to guide a personalized treatment according to its particular risk.

Abstract 6610: To identify neoantigens relevant to anti-tumor activity is challenging even with NGS-based prediction combined with MHC ligandome profiling

Abstract Background T cells that target neoantigens, which are mutant peptides encoded by tumor-specific mutations in cancer cells, have a pivotal role in response to immunotherapy. Current affordable NGS and bioinformatics have facilitated neoantigen research. However, the prediction of neoantigens in silico analysis lacks actual proof that the predicted neoantigens are indeed relevant to anti-tumor immune response. In this study, immunogenicity and anti-tumor activity of in silico predicted neoepitopes of gastric cancer cell lines, YTN2 and YTN16, were evaluated in vitro and in vivo. Besides, MHC ligandome profiling was performed on whether predicted neoantigens were presented by cancer cells. Methods YTN2 and YTN16 are subclones established from a chemically induced murine gastric cancer (Cancer Sci. 2018;109:1480-1492). Whole-exome sequencing and microarray analysis were performed. Binding affinities of peptides with MHC-I were predicted using NetMHCpan. Peptides with IC50≤250 nM were considered as candidate neoantigen. Results Exome sequencing identified 3329 and 3347 missense mutations in YTN2 and YTN16 tumors, respectively. Most of the mutations (3007) were common in YTN2 and YTN16. Among them, 2356 and 2166 mutations were expressed in YTN2 and YTN16, respectively. Candidate neoantigen peptides were 263 and 250 in YTN2 and YTN16, respectively. Although we detected 75 neoantigen-peptide-specific T cell responses, only six neoantigens (Cdt1, Cers4, Nlrp1a, Scarb2, Vmn2r121, and Zfp106)-specific T cells could recognize YTN2 or YTN16 cell line and produced IFNγ. To evaluate in vivo anti-tumor effects of neoantigen-specific T cells, we expanded four neoantigens (Cdt1, Nlrp1a, Vmn2r121, and Zfp106)-specific CD8+ T cell lines and injected them into YTN16 tumor-bearing mice. Only Cdt1-specific T cells completely eradicated the tumor, and Nlrp1a-specific T cells partially inhibited tumor growth. However, Vmn2r121- or Zfp106-specific CD8+ T cell lines could not inhibit the tumor growth. MHC ligandome profiling identified 424 Db-binding peptides and 228 Kb-binding peptides in YTN2, while 317 Db-binding peptides and 228 Kb-binding peptides were detected in YTN16. Almost all MHC-binding peptides had wild type sequences. Only one mutated peptide, Cdt1, was detected in YTN2 but not YTN16. Although Cdt1-specific CD8+ T cells recognized both YTN2 and YTN16. Other neoantigens, Cers4, Nlrp1a, Scarb2, Vmn2r121, and Zfp106, were not detected by MHC ligandome profiling, though T cells specific to these neoantigens responded to cancer cells. Conclusion Currently available technologies anyhow identified neoantigens that are relevant to anti-tumor immune response. However, any single methodology is not sufficient to identify real neoantigens. Detection of neoantigen-specific T cells is still important. Citation Format: Koji Nagaoka, Takayuki Kanaseki, Changbo Sun, Akihiro Hosoi, Serina Tokita, Kazuhiro Maejima, Masashi Fujita, Hidewaki Nakagawa, Toshihiko Torigoe, Kazuhiro Kakimi. To identify neoantigens relevant to anti-tumor activity is challenging even with NGS-based prediction combined with MHC ligandome profiling [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6610.

FVIII Immunogenicity-Bioinformatic Approaches to Evaluate Inhibitor Risk in Non-severe Hemophilia A.

The life-long inhibitor risk in non-severe hemophilia A has been an important clinical and research focus in recent years. Non-severe hemophilia A is most commonly caused by point mutation, missense F8 genotypes, of which over 500 variants are described. The immunogenic potential of just a single amino acid change within a complex 2,332 amino acid protein is an important reminder of the challenges of protein replacement therapies in diverse, global populations. Although some F8 genotypes have been identified as “high risk” mutations in non-severe hemophilia A (e.g., R593C), this is likely, in part at least, a reporting bias and oversimplification of the underlying immunological mechanism. Bioinformatic approaches offer a strategy to dissect the contribution of F8 genotype in the context of the wider HLA diversity through which antigenic peptides will necessarily be presented. Extensive modeling of all permutations of FVIII-derived fifteen-mer peptides straddling all reported F8 genotype positions demonstrate the likely heterogeneity of peptide binding affinity to different HLA II grooves. For the majority of F8 genotypes it is evident that inhibitor risk prediction is dependent on the combination of F8 genotype and available HLA II. Only a minority of FVIII-derived peptides are predicted to bind to all candidate HLA molecules. In silico predictions still over call the risk of inhibitor occurrence, suggestive of mechanisms of “protection” against clinically meaningful inhibitor events. The structural homology between FVIII and FV provides an attractive mechanism by which some F8 genotypes may be afforded co-incidental tolerance through homology of FV and FVIII primary amino sequence. In silico strategies enable the extension of this hypothesis to analyse the extent to which co-incidental cross-matching exists between FVIII-derived primary peptide sequences and any other protein in the entire human proteome and thus potential central tolerance. This review of complimentary in vitro, in silico, and clinical epidemiology data documents incremental insights into immunological mechanism of inhibitor occurrence in non-severe hemophilia A over the last decade. However, complex questions remain about antigenic processing and presentation to truly understand and predict an individual person with hemophilia risk of inhibitor occurrence.

Polyvalent therapeutic vaccine for type 2 diabetes mellitus: Immunoinformatics approach to study co-stimulation of cytokines and GLUT1 receptors

BackgroundType 2 diabetes mellitus (T2DM) is a worldwide disease that have an impact on individuals of all ages causing micro and macro vascular impairments due to hyperglycemic internal environment. For ultimate treatment to cure T2DM, association of diabetes with immune components provides a strong basis for immunotherapies and vaccines developments that could stimulate the immune cells to minimize the insulin resistance and initiate gluconeogenesis through an insulin independent route.MethodologyImmunoinformatics based approach was used to design a polyvalent vaccine for T2DM that involved data accession, antigenicity analysis, T-cell epitopes prediction, conservation and proteasomal evaluation, functional annotation, interactomic and in silico binding affinity analysis.ResultsWe found the binding affinity of antigenic peptides with major histocompatibility complex (MHC) Class-I molecules for immune activation to control T2DM. We found 13-epitopes of 9 amino acid residues for multiple alleles of MHC class-I bears significant binding affinity. The downstream signaling resulted by T-cell activation is directly regulated by the molecular weight, amino acid properties and affinity of these epitopes. Each epitope has important percentile rank with significant ANN IC50 values. These high score potential epitopes were linked using AAY, EAAAK linkers and HBHA adjuvant to generate T-cell polyvalent vaccine with a molecular weight of 35.6 kDa containing 322 amino acids residues. In silico analysis of polyvalent construct showed the significant binding affinity (− 15.34 Kcal/mol) with MHC Class-I. This interaction would help to understand our hypothesis, potential activation of T-cells and stimulatory factor of cytokines and GLUT1 receptors.ConclusionOur system-level immunoinformatics approach is suitable for designing potential polyvalent therapeutic vaccine candidates for T2DM by reducing hyperglycemia and enhancing metabolic activities through the immune system.

IConMHC: a deep learning convolutional neural network model to predict peptide and MHC-I binding affinity.

Tumor-specific neoantigens are mutated self-peptides presented by tumor cell major histocompatibility complex (MHC) molecules and are necessary to elicit host's anti-cancer cytotoxic T cell responses. It could be specifically recognized by neoantigen-specific T cell receptors (TCRs). However, current wet-lab assays for identifying peptide MHC binding are too expensive and time-consuming to meet the clinical needs. In this study, we developed an in silico method with a deep convolutional neural network (CNN) model, iConMHC, to predict peptide MHC binding affinity. Unlike other in silico methods that only learn from properties of amino acid in neoantigen peptides alone and/or MHCs alone, iConMHC learns from physical and chemical interaction properties between pairwise amino acids from the two molecules. These properties, such as contact potentials and distances in folded proteins, directly affect neoantigen-MHC binding affinity. In addition, IConMHC is a pan-allele model that is capable of making predictions for all the MHC alleles. Even for those rare MHC alleles without training data, iConMHC can make predictions with reasonable accuracy. We benchmarked iConMHC with other commonly used MHC-I binding predictors and found our model performs better than most of the pan-allele models.

Mapping of CaM, S100A1 and PIP2-Binding Epitopes in the Intracellular N- and C-Termini of TRPM4.

Molecular determinants of the binding of various endogenous modulators to transient receptor potential (TRP) channels are crucial for the understanding of necessary cellular pathways, as well as new paths for rational drug designs. The aim of this study was to characterise interactions between the TRP cation channel subfamily melastatin member 4 (TRPM4) and endogenous intracellular modulators—calcium-binding proteins (calmodulin (CaM) and S100A1) and phosphatidylinositol 4, 5-bisphosphate (PIP2). We have found binding epitopes at the N- and C-termini of TRPM4 shared by CaM, S100A1 and PIP2. The binding affinities of short peptides representing the binding epitopes of N- and C-termini were measured by means of fluorescence anisotropy (FA). The importance of representative basic amino acids and their combinations from both peptides for the binding of endogenous TRPM4 modulators was proved using point alanine-scanning mutagenesis. In silico protein–protein docking of both peptides to CaM and S100A1 and extensive molecular dynamics (MD) simulations enabled the description of key stabilising interactions at the atomic level. Recently solved cryo-Electron Microscopy (EM) structures made it possible to put our findings into the context of the entire TRPM4 channel and to deduce how the binding of these endogenous modulators could allosterically affect the gating of TRPM4. Moreover, both identified binding epitopes seem to be ideally positioned to mediate the involvement of TRPM4 in higher-order hetero-multimeric complexes with important physiological functions.

Truncation of Huia versabilis Bowman-Birk inhibitor increases its selectivity, matriptase-1 inhibitory activity and proteolytic stability

A gradual truncation of the primary structure of frog skin-derived Huia versabilis Bowman-Birk peptidic inhibitor (HV-BBI) resulted in 18-times stronger inhibitor of matriptase-1 (peptide 6, Ki = 8 nm) in comparison to the full-length HV-BBI (Ki = 155 nm). Analogous increase in the inhibitory activity in correlation with the peptide length reduction was not observed in case of other serine proteases, bovine trypsin (Ki = 151 nm for peptide 6 and Ki = 120 nm for HV-BBI) and plasmin (Ki = 120 nm for peptide 6 and 82 nm for HV-BBI). Weaker binding affinity to these enzymes emphasized an inhibitory specificity of peptide 6. Molecular dynamic analysis revealed that the observed variations in the binding affinity of peptide 6 and HV-BBI with matriptase-1 are associated with the entropic differences of the unbound peptides. Moreover, several aspects explaining differences in the inhibition of matriptase-1 by peptide 6 (bearing the C-terminal amide group) and its two analogues, peptide 6∗ (having the C-terminal carboxyl group, Ki = 473 nm) and cyclic peptide 6∗∗ (Ki = 533 nm), both exhibiting more than 50-fold reduced inhibitory potency, were discovered. It was also shown that peptide 6 presented significantly higher resistance to proteolytic degradation in human serum than HV-BBI. Additional investigations revealed that, in contrast to some amphibian-derived inhibitors, HV-BBI and its truncated analogues do not possess bactericidal activity, thus they cannot be considered as bifunctional agents.

Interaction of an IκBα Peptide with 14-3-3.

Inflammatory responses mediated by the transcription factor nuclear factor kappa-light-chain enhancer of activated B cells (NF-κB) play key roles in immunity, autoimmune diseases, and cancer. NF-κB is directly regulated through protein–protein interactions, including those with IκB and 14-3-3 proteins. These two important regulatory proteins have been reported to interact with each other, although little is known about this interaction. We analyzed the inhibitor of nuclear factor kappa B α (IκBα)/14-3-3σ interaction via a peptide/protein-based approach. Structural data were acquired via X-ray crystallography, while binding affinities were measured with fluorescence polarization assays and time-resolved tryptophan fluorescence. A high-resolution crystal structure (1.13 Å) of the uncommon 14-3-3 interaction motif of IκBα (IκBαpS63) in a complex with 14-3-3σ was evaluated. This motif harbors a tryptophan that makes this crystal structure the first one with such a residue visible in the electron density at that position. We used this tryptophan to determine the binding affinity of the unlabeled IκBα peptide to 14-3-3 via tryptophan fluorescence decay measurements.

Structural basis of ligand binding modes at the human formyl peptide receptor 2

The human formyl peptide receptor 2 (FPR2) plays a crucial role in host defense and inflammation, and has been considered as a drug target for chronic inflammatory diseases. A variety of peptides with different structures and origins have been characterized as FPR2 ligands. However, the ligand-binding modes of FPR2 remain elusive, thereby limiting the development of potential drugs. Here we report the crystal structure of FPR2 bound to the potent peptide agonist WKYMVm at 2.8 Å resolution. The structure adopts an active conformation and exhibits a deep ligand-binding pocket. Combined with mutagenesis, ligand binding and signaling studies, key interactions between the agonist and FPR2 that govern ligand recognition and receptor activation are identified. Furthermore, molecular docking and functional assays reveal key factors that may define binding affinity and agonist potency of formyl peptides. These findings deepen our understanding about ligand recognition and selectivity mechanisms of the formyl peptide receptor family.

PVACtools: A Computational Toolkit to Identify and Visualize Cancer Neoantigens.

Identification of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. This is a cross-disciplinary challenge, involving genomics, proteomics, immunology, and computational approaches. We have built a computational framework called pVACtools that, when paired with a well-established genomics pipeline, produces an end-to-end solution for neoantigen characterization. pVACtools supports identification of altered peptides from different mechanisms, including point mutations, in-frame and frameshift insertions and deletions, and gene fusions. Prediction of peptide:MHC binding is accomplished by supporting an ensemble of MHC Class I and II binding algorithms within a framework designed to facilitate the incorporation of additional algorithms. Prioritization of predicted peptides occurs by integrating diverse data, including mutant allele expression, peptide binding affinities, and determination whether a mutation is clonal or subclonal. Interactive visualization via a Web interface allows clinical users to efficiently generate, review, and interpret results, selecting candidate peptides for individual patient vaccine designs. Additional modules support design choices needed for competing vaccine delivery approaches. One such module optimizes peptide ordering to minimize junctional epitopes in DNA vector vaccines. Downstream analysis commands for synthetic long peptide vaccines are available to assess candidates for factors that influence peptide synthesis. All of the aforementioned steps are executed via a modular workflow consisting of tools for neoantigen prediction from somatic alterations (pVACseq and pVACfuse), prioritization, and selection using a graphical Web-based interface (pVACviz), and design of DNA vector-based vaccines (pVACvector) and synthetic long peptide vaccines. pVACtools is available at http://www.pvactools.org.

MAPDP: A Cloud-Based Computational Platform for Immunopeptidomics Analyses.

The immunopeptidome corresponds to the repertoire of peptides presented at the cell surface by the major histocompatibility complex (MHC) molecules. Cytotoxic T cells scan this repertoire to identify nonself antigens that can arise from tumors or infected cells. The identification of actionable antigenic targets is key to the development of therapeutic cancer vaccines, T-cell therapy, and other T-cell receptor-based biologics. The growing clinical interest for immunopeptidomics has accelerated the development of high throughput proteogenomic platforms that provide a system-level analysis of MHC-associated peptides. Improvement in sensitivity and throughput of mass spectrometers now allows the detection of a few thousands of peptides from less than 100 million cells. To manage the amount of data generated by these instruments, we have developed the MHC-associated peptide discovery platform (MAPDP), a novel open-source cloud-based computational platform for immunopeptidomic analyses. It provides convenient access from a web portal to immunopeptidomes stored in the database, filtering tools, various visualizations, annotations (e.g., IEDB, dbSNP, gnomAD), peptide-binding affinity prediction (mhcflurry, NetMHC), HLA genotyping, and the generation of personalized proteome databases. MAPDP functionalities are demonstrated here by the discovery of MHC peptides featuring new genetic variants identified in two previously published ovarian carcinoma data sets.

α-Actinin-1 promotes activity of the L-type Ca2+ channel Cav 1.2.

The L-type Ca2+ channel CaV 1.2 governs gene expression, cardiac contraction, and neuronal activity. Binding of α-actinin to the IQ motif of CaV 1.2 supports its surface localization and postsynaptic targeting in neurons. We report a bi-functional mechanism that restricts CaV 1.2 activity to its target sites. We solved separate NMR structures of the IQ motif (residues 1,646-1,664) bound to α-actinin-1 and to apo-calmodulin (apoCaM). The CaV 1.2 K1647A and Y1649A mutations, which impair α-actinin-1 but not apoCaM binding, but not the F1658A and K1662E mutations, which impair apoCaM but not α-actinin-1 binding, decreased single-channel open probability, gating charge movement, and its coupling to channel opening. Thus, α-actinin recruits CaV 1.2 to defined surface regions and simultaneously boosts its open probability so that CaV 1.2 is mostly active when appropriately localized.