Peptide Binding Affinity Research Articles

Significance of D- tryptophan in Contryphan-Ar1131 Conus peptide: Oxidative folding, trypsin binding, and photostabilization activity

Distinct differences have been observed between L-tryptophan and D-tryptophan containing contryphan-Ar1131 in oxidative folding, trypsin binding, and photostabilization activity on avobenzone. [W5] contryphan-Ar1131 and [w5] contryphan-Ar1131 were chemically synthesized and characterized using RP-HPLC and mass spectrometry. Structural differences due to the change of configuration of tryptophan were evident from the optimized structures of contryphan-Ar1131 using density functional theory (DFT). The comparison of early events of oxidative folding has revealed the role of D-tryptophan in accelerating the formation of a disulfide bond. The optimized structures of the reduced form of peptides revealed the occurrence of aromatic-aromatic and aromatic-proline interactions in [w5] contryphan-Ar1131 which may be critical in aiding the oxidative folding reaction. The presence of the Lys6-Pro7 peptide bond indicates that contryphan-Ar1131 is resistant but may bind to trypsin allowing to assign the binding affinity of peptides to the protein surface. Competitive binding studies and molecular docking along with molecular dynamic (MD) simulations have revealed that [w5] contryphan-Ar1131 has more affinity for the active site of trypsin. Given tryptophan is a photostabilizer of FDA-approved chemical UV-A filter avobenzone, the report has compared the photostabilization activity of [W5]/ [w5] contryphan-Ar1131 on avobenzone under natural sunlight. [w5] contryphan-Ar1131 has better photostabilization activity than that of [W5] contryphan-Ar1131 and also individual D-tryptophan and L-tryptophan amino acids. These biochemical studies have highlighted the significance of D-tryptophan in contryphan-Ar1131 and its photostabilization activity on avobenzone may find applications in cosmetics.

Characterization of the Immunological Status of Hypermutated Solid Tumors in the Cancer Genome Analysis Project HOPE.

Many reports demonstrate that a high tumor mutation burden (TMB-H) is closely associated with good prognosis of cancer. However, specific studies investigating the association of various TMB statuses with overall survival in patients with solid tumors are scarce. In the present study, we investigated the association of TMB status with overall survival in 5,072 patients with cancer from the HOPE project and clarified the specific mechanism responsible for the good prognosis of the TMB-H group. All tumors were classified into one of four groups based on TMB: ultralow (UL), low (L), intermediate (I) and high (H). The TMB-H group had a better prognosis than the TMB-I and TMB-L groups, but not than the TMB-UL group. Analyzing the expression of 293 immune response-associated genes, 17 genes were up-regulated in the TMB-H group compared to the TMB-I and TNB-L groups, and two genes [CD274 and interferon-γ (IFNG)] were identified as good prognostic factors. Analysis of immune cell populations inside tumors demonstrated that the frequencies of exhausted CD8+ T-cells, activated effector CD8+ T-cells and natural killer cells were significantly higher in the TMB-H group. The T-cell receptor repertoire numbers and the diversity evenness score (DE50) were lower in the TMB-H group than in TMB-UL group; however, no association of the DE50 value with the binding or elution affinity of epitope peptides from neoantigens was found. One possible mechanism for the good prognosis of the TMB-UL group compared to the TMB-H group might be that the TMB-UL group features a balance between immunosuppression and immunostimulation.

Molecular insight into the systematic affinity and selectivity of partner recognition sites between the WW1 and WW2 domains of human KIBRA neuroprotein

Human KIBRA, a member of the WWC family proteins, is an upstream regulator of the Salvador/Warts/Hippo (SWH) signaling pathway and predominately expressed in nervous system. The protein has two functionally regulatory domains WW1 and WW2 at N-terminal region, which recognize and bind to the PY-motif segments of their partner proteins to serve as a signaling scaffold role in the SWH pathway. The two domains are highly conserved, but their downstream ligands and biological functions may not be fully consistent. In this study, we attempted to systematically profile the PY-motif affinity to and selectivity between KIBRA WW1 and WW2 domains involved in partner recognition sites. Ontology mining was used to enrich the KIBRA-interacting proteins in literature libraries, from which a variety of PY-motif peptide segments were identified, and their binding behavior to each domain was then analyzed by integrating computational modeling and experimental assay. Most PY-motif peptides were found to interact potently with WW1 and WW2, but they generally only exhibit a moderate or modest selectivity between the two domains. Subsequently, several representative peptides were further examined in detail to elucidate the molecular mechanism underlying their affinity and selectivity. It is revealed that the middle motif region of PY-motif peptides is primarily responsible for the affinity and stability of peptide binding, but only contributes marginally to peptide selectivity. Instead, the N-terminal region and, particularly, C-terminal region of PY-motif peptides play a crucial role in the selectivity. Hydrophobic contacts and hydrogen bonds confer stability and specificity to the domain–peptide interaction, respectively.

DeepMHCII: a novel binding core-aware deep interaction model for accurate MHC-II peptide binding affinity prediction.

MotivationComputationally predicting major histocompatibility complex (MHC)-peptide binding affinity is an important problem in immunological bioinformatics. Recent cutting-edge deep learning-based methods for this problem are unable to achieve satisfactory performance for MHC class II molecules. This is because such methods generate the input by simply concatenating the two given sequences: (the estimated binding core of) a peptide and (the pseudo sequence of) an MHC class II molecule, ignoring biological knowledge behind the interactions of the two molecules. We thus propose a binding core-aware deep learning-based model, DeepMHCII, with a binding interaction convolution layer, which allows to integrate all potential binding cores (in a given peptide) with the MHC pseudo (binding) sequence, through modeling the interaction with multiple convolutional kernels.ResultsExtensive empirical experiments with four large-scale datasets demonstrate that DeepMHCII significantly outperformed four state-of-the-art methods under numerous settings, such as 5-fold cross-validation, leave one molecule out, validation with independent testing sets and binding core prediction. All these results and visualization of the predicted binding cores indicate the effectiveness of our model, DeepMHCII, and the importance of properly modeling biological facts in deep learning for high predictive performance and efficient knowledge discovery.Availability and implementationDeepMHCII is publicly available at https://github.com/yourh/DeepMHCII.Supplementary information Supplementary data are available at Bioinformatics online.

3pHLA-score improves structure-based peptide-HLA binding affinity prediction

Binding of peptides to Human Leukocyte Antigen (HLA) receptors is a prerequisite for triggering immune response. Estimating peptide-HLA (pHLA) binding is crucial for peptide vaccine target identification and epitope discovery pipelines. Computational methods for binding affinity prediction can accelerate these pipelines. Currently, most of those computational methods rely exclusively on sequence-based data, which leads to inherent limitations. Recent studies have shown that structure-based data can address some of these limitations. In this work we propose a novel machine learning (ML) structure-based protocol to predict binding affinity of peptides to HLA receptors. For that, we engineer the input features for ML models by decoupling energy contributions at different residue positions in peptides, which leads to our novel per-peptide-position protocol. Using Rosetta’s ref2015 scoring function as a baseline we use this protocol to develop 3pHLA-score. Our per-peptide-position protocol outperforms the standard training protocol and leads to an increase from 0.82 to 0.99 of the area under the precision-recall curve. 3pHLA-score outperforms widely used scoring functions (AutoDock4, Vina, Dope, Vinardo, FoldX, GradDock) in a structural virtual screening task. Overall, this work brings structure-based methods one step closer to epitope discovery pipelines and could help advance the development of cancer and viral vaccines.

Abstract 5036: pVACview: An integrative visualization tool for efficient neoantigen prioritization and selection

Abstract Neoantigen vaccines have demonstrated strong efficacy in the treatment of cancers in the realm of personalized medicine when used in combination with checkpoint blockade therapy. Numerous clinical trials involving these therapies are underway across the world. Accurate identification and prioritization of neoantigens is highly relevant to the design of these trials, and for predicting treatment response and understanding mechanisms of resistance. With the advent of whole exome and RNA sequencing technologies, researchers and clinicians are now able to computationally predict neoantigens and design personalized vaccines based on patient-specific mutation information. However, numerous factors must be considered when prioritizing neoantigens for use in personalized medicine, including somatic variant identification and expression, peptide processing and transportation, peptide-MHC binding and stability, recognition by cytotoxic T cells, etc. There has been a rapid development of computational tools that attempt to account for these complexities. Pipelines have been developed to allow researchers to run an ensemble of many potentially informative tools for individual patients. However, output results from these complex pipelines are often unorganized, difficult to navigate and require extensive knowledge of the underlying tools for accurate interpretation. Additionally, while current pipelines analyze neoantigens from the perspective of variants, transcripts and peptides, they fail to present the underlying details for efficient prioritization of the candidates. In addition to variant detection, gene expression and predicted peptide binding affinities, recent studies have also shown the importance of mutation location, allele-specific anchor locations, and variation of T-cell response to long versus short peptides. Due to the intricate nature of neoantigen features, presenting all relevant information to experimentalists and immunogenomics tumor boards for candidate selection is a difficult challenge. In response, we have created pVACview, an interactive tool designed to aid in the prioritization and selections of neoantigen candidates for personalized cancer vaccines. pVACview has a user-friendly and intuitive interface where users can upload, explore, select and export their neoantigen candidates. The tool allows users to visualize neoantigen candidates across three different levels, including variant, transcript and peptide level information. We hope pVACview will allow researchers and clinicians to analyze and prioritize neoantigen candidates with greater efficiency and accuracy. The application is available as part of the pVACtools pipeline at https://github.com/griffithlab/pVACtools and the online server can be accessed at pvacview.org. Citation Format: Huiming Xia, Susanna Kiwala, Zachary L. Skidmore, Bryan Fisk, S. Peter Goedegebuure, Christopher A. Miller, William E. Gillanders, Obi L. Griffith, Malachi Griffith. pVACview: An integrative visualization tool for efficient neoantigen prioritization and selection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5036.

PPI-Affinity: A Web Tool for the Prediction and Optimization of Protein-Peptide and Protein-Protein Binding Affinity.

Virtual screening of protein–protein and protein–peptide interactions is a challenging task that directly impacts the processes of hit identification and hit-to-lead optimization in drug design projects involving peptide-based pharmaceuticals. Although several screening tools designed to predict the binding affinity of protein–protein complexes have been proposed, methods specifically developed to predict protein–peptide binding affinity are comparatively scarce. Frequently, predictors trained to score the affinity of small molecules are used for peptides indistinctively, despite the larger complexity and heterogeneity of interactions rendered by peptide binders. To address this issue, we introduce PPI-Affinity, a tool that leverages support vector machine (SVM) predictors of binding affinity to screen datasets of protein–protein and protein–peptide complexes, as well as to generate and rank mutants of a given structure. The performance of the SVM models was assessed on four benchmark datasets, which include protein–protein and protein–peptide binding affinity data. In addition, we evaluated our model on a set of mutants of EPI-X4, an endogenous peptide inhibitor of the chemokine receptor CXCR4, and on complexes of the serine proteases HTRA1 and HTRA3 with peptides. PPI-Affinity is freely accessible at https://protdcal.zmb.uni-due.de/PPIAffinity.

Cancer immunotherapy: Identifying cancer testis antigen peptides to enhance antitumor response.

e20022 Background: Cancer testis antigens (CTAs) are highly immunogenic, tissue-restricted proteins that may be over expressed in hematological malignancies. In this study, an in silico analysis of CTA-derived peptide presentation on HLA class I molecules is reported with an aim to identify putative CTA peptides with a high estimated binding affinity to known HLA class I molecules. Methods: Eighty unique HLA Class I alleles (HLA-A, B, and C), across 78 hematopoietic cell transplant recipients were computationally studied for their ability to bind peptides derived from three CTAs. The amino acid sequences of all three CTA proteins were obtained from UniProtKB. NetMHCPan-4.1 was utilized for in silico prediction of HLA class I binding affinities of the 9-mer peptides generated from these CTAs. Results: The CTA MAGE-A1 had 1202 unique peptides predicted to bind at least one HLA class I allele and 176 of these peptides were classified as strong binders (Rank <0.5 and IC50<500nM). Conversely, 71 unique HLA class 1 alleles were predicted to bind strongly to at least one MAGE-A1 derived peptide. The ability of different MAGE-A1 peptides to bind HLA varied considerably, e.g., peptide TSYVKVLEY, was predicted to bind strongly to 22 different HLA class I alleles. Similarly, 41 CTAG-1B-derived and 164 MAGE-A2-derived peptides were predicted to generate strong binders with at least one HLA class 1 allele. Conversely, 37 and 71 unique HLA class I alleles were predicted to bind strongly to at least one CTAG1B peptide and one MAGE-A1 peptide, respectively. CTAG1-B derived peptide FATPMEAEL and MAGE-A2-derived peptide FAHPRKLLM were predicted to bind strongly to 17 and 22 HLA class 1 alleles, respectively. In aggregate most patients presented multiple peptides across the 6 HLA class I molecules studied (Table). The number of strongly binding MAGE-A1 and CTAG-1B peptides from unique patients. Strong binding defined by Rank <0.5 and IC50<500nM. Conclusions: Most patients presented several CTA derived peptides, suggesting that the aggregate HLA presentation across the 6 classical HLA class I molecules will yield high odds of presentation of CTA expressed by target tissues. These HLA genotype-informed quantitative analyses raise the possibility that agents such as checkpoint inhibitors and immunomodulatory drugs may synergize with hypomethylating agents and augment CTA-directed T-cell responses.[Table: see text]

Solid-Phase Peptide Modification via Deaminative Photochemical Csp3 -Csp3 Bond Formation Using Katritzky Salts.

Introduction of unnatural amino acids can significantly improve the binding affinity and stability of peptides. Commercial availability of such amino acids is limited, and their synthesis is a long and tedious process. We here describe a method that allows the functionalization of peptides directly on solid‐support by converting lysine residues to Katritzky salts, and subjecting them to a photochemical Giese reaction under mild reaction conditions. The method avoids the need for amino acid synthesis and instead offers a late‐stage modification route for rapid peptide diversification. While numerous modification approaches at the lysine amine have been described, this work provides the first example of deaminative functionalization of peptides at lysine. The two‐step protocol is compatible with various substrates, lysine analogues, resins, and all proteinogenic amino acids. Finally, by leveraging solid‐phase modification, this protocol facilitates the functionalization of longer peptides as was demonstrated using biologically relevant peptides of up to 15 amino acids.

Characterization of a novel affinity binding ligand for tyrosine nitrated peptides from a phage-displayed peptide library.

As an important post-translational modification in response to oxidative and nitrosative stress, protein tyrosine nitration is deeply involved in many physiological and pathological processes. Identifying tyrosine nitration in proteins is challenging due to its low abundance.Consequently, pre-separation and enrichment of tyrosine-nitrated peptides (TNPs) are necessary before submitting them to mass spectrometry analysis. However, the most popularly used anti-nitrotyrosine antibody pull-down method showed limitations like sequence preference and unspecific binding. Therefore, developing novel affinity purification materials for TNPs is of significance. In the present study, we screened the phage-displayed 12-mer randomized peptide library for affinity binding peptide of the synthetic standard TNP (sTNP, sequence: H2N-GGGGY*GGG-COOH) and identified a peptide named NT-1 (H2N-TLWPFDLWLKTR-COOH) as a promising candidate. NT-1 at extremely low concentration (3nM) in solutions could be efficiently captured by immobilized sTNP as determined by pull-down and subsequent MALDI-TOF MS analysis. Surface plasmon resonance (SPR) measurement confirmed that NT-1 possesseed a good selectivity, showing more than 100-fold higher binding affinity with TNP than its non-nitrated counterpart. Moreover, NT-1 could efficiently capture various types of TNPs in solutions even in the presence of 1000-fold excessive amount of trypsinized BSA fragments. Most importantly, NT-1 showed superiority to commercially used nitrotyrosine antibody as the former captured more TNPs, with less sequence preference. In summary, our study provided NT-1 as a novel affinity binding ligand for TNPs and should be useful in developing an alternative enrichment strategy for TNPs.

QuickSwitch™: a validated platform for both screening MHC binding SARS-CoV-2 peptides and making SARS-CoV-2 specific MHC class I and II tetramers.

Abstract The success of SARS-CoV-2 vaccines is measured by their ability to mount long-lasting memory responses. To this end, it is important to identify viral protein fragments, or peptides, that generate the best immune response and lead to the development of memory T cells. For this purpose, researchers need a system that would allow them to rapidly test the ability of an antigenic peptide to be presented by the immune system and to identify T cells that can be stimulated by such a peptide in the context of the right MHC molecule. The QuickSwitch™ platform contains two modules for performing both functions. First a large number of SARS-CoV-2 peptides, including mutant peptides were incubated with QuickSwitch™ monomers or tetramers and exchanged against their exiting peptides in the first module. Peptide binding affinities to various human MHC Class I and Class II alleles, which are correlated with their exchange rates, were determined by the capture binding assay, the second QuickSwitch™ module. MHC tetramers built with SARS-CoV-2 high affinity MHC binding peptides were then used for T cell staining. Cell staining indicated that tetramers generated with the QuickSwitch™ platform were able to stain and identify T cells specific for the tested peptides. Further, peptides that bound to multiple MHC alleles, including potentially dominant peptides, were also detected. In conclusion, since QuickSwitch™ allows the screening of MHC binding SARS-CoV-2 peptides and, at the same time, the generation of corresponding tetramers, this platform is a useful tool for developing SARS-CoV-2 vaccines, monitoring T cell populations from vaccinated subjects and assessing the durability of mounted responses.

Structural Mapping of BMP Conformational Epitopes and Bioengineering Design of Osteogenic Peptides to Specifically Target the Epitope-Binding Sites.

Human bone morphogenetic proteins (BMPs) constitute a large family of cytokines related to members of the transforming growth factor-β superfamily, which fulfill biological functions by specificity binding to their cognate type I (BRI) and type II (BRII) receptors through conformational wrist and linear knuckle epitopes, respectively. We systematically examined the intermolecular recognition and interaction between the BMP proteins and BRI receptor at structural, energetic and dynamic levels. The BRI-binding site consists of three hotspot regions on BMP surface, which totally contribute ~70% potency to the BMP-BRI binding events and represent the core sections of BMP conformational wrist epitope; the contribution increases in the order: hotspot 2 (~8%) < hotspot 3 (~20%) < hotspot 1 (~40%). Multiple sequence alignment and structural superposition revealed a consensus sequence pattern and a similar binding mode of the three hotspots shared by most BMP members, indicating a high conservation of wrist epitope in BMP family. The three hotspots are natively folded into wellstructured U-shaped,, loop and double-stranded conformations in BMP proteins, which, however, would become largely disordered when splitting from the protein context to derive osteogenic peptides in free state, thus largely impairing their rebinding capability to BRI receptor. In this respect, cyclization strategy was employed to constrain hotspot 1/3-derived peptides into a native-like conformation, which was conducted by adding a disulfide bond across the ending arms of linear peptides based on their native conformations. Fluorescence-based assays substantiated that the cyclization can effectively improve the binding affinities of osteogenic peptides to BRI receptor by 3-6-fold. The cyclic peptides also exhibit a good selectivity for BRI over BRII (>5-fold), confirming that they can specifically target the wrist epitope-binding site of BRI receptor. The rationally designed cyclic peptides can be regarded as the promising lead entities that should be further chemically modified to enhance their in vivo biological stability for further bioengineering therapeutic osteogenic peptides againstchondrocyte senescence and bone disorder.

The Use of Molecular Dynamics Simulation Method to Quantitatively Evaluate the Affinity between HBV Antigen T Cell Epitope Peptides and HLA-A Molecules.

Chronic hepatitis B virus (HBV), a potentially life-threatening liver disease, makes people vulnerable to serious diseases such as cancer. T lymphocytes play a crucial role in clearing HBV virus, while the pathway depends on the strong binding of T cell epitope peptide and HLA. However, the experimental identification of HLA-restricted HBV antigenic peptides is extremely time-consuming. In this study, we provide a novel prediction strategy based on structure to assess the affinity between the HBV antigenic peptide and HLA molecule. We used residue scanning, peptide docking and molecular dynamics methods to obtain the molecular docking model of HBV peptide and HLA, and then adopted the MM-GBSA method to calculate the binding affinity of the HBV peptide–HLA complex. Overall, we collected 59 structures of HLA-A from Protein Data Bank, and finally obtained 352 numerical affinity results to figure out the optimal bind choice between the HLA-A molecules and 45 HBV T cell epitope peptides. The results were highly consistent with the qualitative affinity level determined by the competitive peptide binding assay, which confirmed that our affinity prediction process based on an HLA structure is accurate and also proved that the homologous modeling strategy for HLA-A molecules in this study was reliable. Hence, our work highlights an effective way by which to predict and screen for HLA-peptide binding that would improve the treatment of HBV infection.

Atomistic insight into the essential binding event of ACE2-derived peptides to the SARS-CoV-2 spike protein.

The pathogenic agent of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters into human cells through the interaction between the receptor binding domain (RBD) of its spike glycoprotein and the angiotensin-converting enzyme 2 (ACE2) receptor. Efforts have been made towards finding antivirals that block thisinteraction, therefore preventing infection. Here, we determined the binding affinity of ACE2-derived peptides to the RBD of SARS-CoV-2 experimentally and performed MD simulations in order to understand key characteristics of their interaction. One of the peptides, p6, binds to the RBD of SARS-CoV-2 with nM affinity. Although the ACE2-derived peptides retain conformational flexibility when bound to SARS-CoV-2 RBD, we identified residues T27 and K353 as critical anchors mediating the interaction. New ACE2-derived peptides were developed based on the p6-RBD interface analysis and expecting the native conformation of the ACE2 to be maintained. Furthermore, we found a correlation between the helicity in trifluoroethanol and the binding affinity to RBD of the new peptides. Under the hypothesis that the conservation of peptide secondary structure is decisive to the binding affinity, we developed a cyclized version of p6 which had more helicity than p6 and approximately half of its KD value.

Epitope-based peptide vaccine design and elucidation of novel compounds against 3C like protein of SARS-CoV-2.

Coronaviruses (CoVs) are positive-stranded RNA viruses with short clubs on their edges. CoVs are pathogenic viruses that infect several animals and plant organisms, as well as humans (lethal respiratory dysfunctions). A noval strain of CoV has been reported and named as SARS-CoV-2. Numerous COVID-19 cases were being reported all over the World. COVID-19 and has a high mortality rate. In the present study, immunoinformatics techniques were utilized to predict the antigenic epitopes against 3C like protein. B-cell epitopes and Cytotoxic T-lymphocyte (CTL) were designed computationally against SARS-CoV-2. Multiple Sequence Alignment (MSA) of seven complete strains (HCoV-229E, HCoV-NL63, HCoV-OC43, HCoV-HKU1, SARS-CoV, MERS-CoV, and SARS-CoV-2) was performed to elucidate the binding domain and interacting residues. MHC-I binding epitopes were evaluated by analyzing the binding affinity of the top-ranked peptides having HLA molecule. By utilizing the docked complexes of CTL epitopes with antigenic sites, the binding relationship and affinity of top-ranked predicted peptides with the MHC-I HLA protein were investigated. The molecular docking analyses were conducted on the ZINC database library and twelve compounds having least binding energy were scrutinized. In conclusion, twelve CTL epitopes (GTDLEGNFY, TVNVLAWLY, GSVGFNIDY, SEDMLNPNY, LSQTGIAV, VLDMCASLK, LTQDHVDIL, TTLNDFNLV, CTSEDMLNP, TTITVNVLA, YNGSPSGVY, and SMQNCVLKL) were identified against SARS-CoV-2.

Engineering Nanointerfaces of Au25 Clusters for Chaperone-Mediated Peptide Amyloidosis.

Synthetic nanomaterials possessing biomolecular-chaperone functions are good candidates for modulating physicochemical interactions in many bioapplications. Despite extensive research, no general principle to engineer nanomaterial surfaces is available to precisely manipulate biomolecular conformations and behaviors, greatly limiting attempts to develop high-performance nanochaperone materials. Here, we demonstrate that, by quantifying the length (-SCxR±, x = 3-11) and charges (R- = -COO-, R+ = -NH3+) of ligands on Au25 gold nanochaperones (AuNCs), simulating binding sites and affinities of amyloid-like peptides with AuNCs, and probing peptide folding and fibrillation in the presence of AuNCs, it is possible to precisely manipulate the peptides' conformations and, thus, their amyloidosis via customizing AuNCs nanointerfaces. We show that intermediate-length liganded AuNCs with a specific charge chaperone peptides' native conformations and thus inhibit their fibrillation, while other types of AuNCs destabilize peptides and promote their fibrillation. We offer a microscopic molecular insight into peptide identity on AuNCs and provide a guideline in customizing nanochaperones via manipulating their nanointerfaces.

Phosphosite-dependent presentation of dual phosphorylated peptides by MHC class I molecules

SummaryPhosphopeptides presented by major histocompatibility complex (MHC) class I have been regarded as a pivotal type of cancer neoantigens that are recognized by T cells. The structural basis of single-phosphorylated peptide presentation has been well studied. Diphosphorylation with one interval between two sites is one of the prevalent forms of multisite-phosphorylated peptides. Herein, we determined the molecular basis of presentation of two P4/P6 double pS-containing peptides by HLA-B27 and compared them with unmodified and single-phosphorylated peptide complexes. These data clarified not only the HLA allele-specific presentation of phosphopeptides by MHC class I molecules but also the cooperativity of peptide conformation within P4 and P6 phosphorylation sites. The phosphorylation of P6 site can influence the binding mode of P4 phosphorylated site to HLA-B27. And we found the diphospho-dependent attenuated effect of peptide binding affinity. This study provides insights into the MHC presentation features of diphosphopeptides, which is different from monophosphopeptides.

Efficacy of defensins as neutralizing agents against the deadly SARS-CoV-2

SARS-CoV-2 infection causes asymptomatic to severe human respiratory diseases. Vaccinations are effective only to a certain extent, and the disease recurs with milder symptoms even after booster doses. Hence, we hypothesize that antiviral therapy in conjunction with vaccination is the need of the hour for containing the disease. SARS-CoV-2 enters the host cell through interaction between viral spike (S) protein and human Angiotensin II converting enzyme2 (ACE2). So, any S-protein neutralizing molecule could be a potential antiviral moiety. The interaction-interface architecture indicates that cationic peptides effectively bind to anionic interface residues of S protein-receptor binding domain (S-RBD). Subsequently, we adopted molecular docking and simulation approaches to examine the binding affinity of cationic human α and β defensins, HNP1 and HBD2 with S-RBD. We observed strong hydrogen bonds, electrostatic, salt bridge, and hydrophobic interactions between these defensins and S-RBD with binding energy (BE) of −10.7 kcal/mol. Interestingly, defensins from Zea mays (ZmD32), Solanum lycopersicum (TPP3), and Sorghum bicolor (DEF1_SORBI) exhibited approximately similar BE of −11.1 kcal/mol, −11.9 kcal/mol, and −12.6 kcal/mol respectively, comparable to ACE2 (BE= −11.9 kcal/mol). Molecular dynamics simulation of S-RBD complexes formed with HBD2, ZmD32 and TPP3, showed stable associations for 100 ns. Results of in-silico studies demonstrated higher binding affinity of more positively-charged peptides with S-RBD, suggesting the potential of plant defensins to block ACE2 binding of S-RBD. These results warrant experimental validation. However these findings indicate the usefulness of plant defensin homologues as neutralizing antiviral agents for use as ideal prophylactic and therapeutic drugs for COVID-19. Communicated by Ramaswamy H. Sarma

Trans-ancestral fine-mapping of MHC reveals key amino acids associated with spontaneous clearance of hepatitis C in HLA-DQβ1

Spontaneous clearance of acute hepatitis C virus (HCV) infection is associated with single nucleotide polymorphisms (SNPs) on the MHC class II. We fine-mapped the MHC region in European (n = 1,600; 594 HCV clearance/1,006 HCV persistence) and African (n = 1,869; 340 HCV clearance/1,529 HCV persistence) ancestry individuals and evaluated HCV peptide binding affinity of classical alleles. In both populations, HLA-DQβ1Leu26 (p valueMeta = 1.24 × 10−14) located in pocket 4 was negatively associated with HCV spontaneous clearance and HLA-DQβ1Pro55 (p valueMeta = 8.23 × 10−11) located in the peptide binding region was positively associated, independently of HLA-DQβ1Leu26. These two amino acids are not in linkage disequilibrium (r2 < 0.1) and explain the SNPs and classical allele associations represented by rs2647011, rs9274711, HLA-DQB1∗03:01, and HLA-DRB1∗01:01. Additionally, HCV persistence classical alleles tagged by HLA-DQβ1Leu26 had fewer HCV binding epitopes and lower predicted binding affinities compared to clearance alleles (geometric mean of combined IC50 nM of persistence versus clearance; 2,321 nM versus 761.7 nM, p value = 1.35 × 10−38). In summary, MHC class II fine-mapping revealed key amino acids in HLA-DQβ1 explaining allelic and SNP associations with HCV outcomes. This mechanistic advance in understanding of natural recovery and immunogenetics of HCV might set the stage for much needed enhancement and design of vaccine to promote spontaneous clearance of HCV infection.

High cell surface expression and peptide binding affinity of HLA-DQA1*05:03, a susceptible allele of neuromyelitis optica spectrum disorders (NMOSD)

Neuromyelitis optica spectrum disorder (NMOSD) is a relapsing autoimmune disease characterized by the presence of pathogenic autoantibodies, anti-aquaporin 4 (AQP4) antibodies. Recently, HLA-DQA1*05:03 was shown to be significantly associated with NMOSD in a Japanese patient cohort. However, the specific mechanism by which HLA-DQA1*05:03 is associated with the development of NMOSD has yet to be elucidated. In the current study, we revealed that HLA-DQA1*05:03 exhibited significantly higher cell surface expression levels compared to other various DQA1 alleles, and that its expression strongly depended on the amino acid sequence of the α1 domain, with a preference for leucine at position 75. Moreover, in silico analysis indicated that the HLA-DQ encoded by HLA-DQA1*05:03 preferentially presents immunodominant AQP4 peptides, and that the peptide major histocompatibility complexes (pMHCs) are more energetically stable in the presence of HLA-DQA1*05:03 than other HLA-DQA1 alleles. In silico 3D structural models were also applied to investigate the validity of the energetic stability of pMHCs. Taken together, our findings indicate that HLA-DQA1*05:03 possesses a distinct property to play a pathogenic role in the development of NMOSD.