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The coating of surfaces with bio-functional proteins is a promising strategy for the creation of highly biocompatible medical implants. Bio-functional proteins from the extracellular matrix (ECM) provide effective surface functions for controlling cellular behavior. We have previously screened bio-functional tripeptides for feasibility of mass production with the aim of identifying those that are medically useful, such as cell-selective peptides. In this work, we focused on the screening of tripeptides that selectively accumulate collagen type IV (Col IV), an ECM protein that accelerates the re-endothelialization of medical implants. A SPOT peptide microarray was selected for screening owing to its unique cellulose membrane platform, which can mimic fibrous scaffolds used in regenerative medicine. However, since the library size on the SPOT microarray was limited, physicochemical clustering was used to provide broader variation than that of random peptide selection. Using the custom focused microarray of 500 selected peptides, we assayed the relative binding rates of tripeptides to Col IV, collagen type I (Col I), and albumin. We discovered a cluster of Col IV-selective adhesion peptides that exhibit bio-safety with endothelial cells. The results from this study can be used to improve the screening of regeneration-enhancing peptides.

and are rickettsial pathogens infecting dogs, with a worldwide distribution. Both species are obligate intracellular pathogens and colonize bone marrow-derived cells, with coinfections frequently reported in dogs. Although immunodominant proteins have been thoroughly characterized, very few high-throughput studies have been conducted to identify immunogenic proteins from spp. In this study, we used a methodology based on peptide microarray chips to identify immunoreactive peptides, either shared or species-specific, in the complete theoretical proteomes of both pathogens. B-cell epitopes were predicted in the corresponding proteins from both species and ranked for synthesis on the peptide microarrays. These microarrays were screened with serum samples from antibody-positive dogs, as well as negative control sera from unexposed dogs. Additionally, we assessed the feasibility of integrating evidence gathered at the level of individual peptides to identify potentially immunogenic proteins contributing to the patterns of immunoreactivity observed on microarrays. Screening of peptide microarrays resulted in complex antibody reactivity patterns against thousands of peptides. After discarding peptides with cross-reactivity to negative control sera, we identified over 1,200 immunoreactive peptides, including ~80 peptides shared between the two species with almost identical sequences. Despite screening linear peptides, we were able to identify proteins previously reported as immunodominant in , some of which contain predominantly conformational epitopes. Our results suggest that a high-throughput strategy based on peptide microarrays is an effective approach for the rapid identification of immunoreactive peptides and the underlying immunogenic proteins. This study provides a foundation for developing novel diagnostic tools and vaccine candidates against and , including potential combined or multivalent formulations targeting both pathogens.

A novel resemblance-ranking peptide library with 160,000 10-meric peptides was designed to search for selective binders to antibodies. The resemblance-ranking principle enabled the selection of sequences that are most similar to the human peptidome. The library was synthesized with ultra-high-density peptide arrays. As proof of principle, screens for selective binders were performed for the therapeutic anti-CD20 antibody rituximab. Several features in the amino acid composition of antibody-binding peptides were identified. The selective affinity of rituximab increased with an increase in the number of hydrophobic amino acids in a peptide, mainly tryptophan and phenylalanine, while a total charge of the peptide remained relatively small. Peptides with a higher affinity exhibited a lower sum helix propensity. For the 30 strongest peptide binders, a substitutional analysis was performed to determine dissociation constants and the invariant amino acids for binding to rituximab. The strongest selective peptides had a dissociation constant in the hundreds of the nano-molar range. The substitutional analysis revealed a specific hydrophobic epitope for rituximab. To show that conformational binders can, in principle, be detected in array format, cyclic peptide substitutions that are similar to the target of rituximab were investigated. Since the specific binders selected via the resemblance-ranking peptide library were based on the hydrophobic interactions that are widespread in the world of biomolecules, the library can be used to screen for potential linear epitopes that may provide information about the cross-reactivity of antibodies.

Using the commercially available PEPperCHIP® microarray platform, a peptide microarray was developed to identify immunodominant epitopes for the detection of antibodies against Equine arteritis virus (EAV). For this purpose, the whole EAV Bucyrus sequence was used to design a total of 1250 peptides that were synthesized and spotted onto a microarray slide. A panel of 28 serum samples representing a selection of EAV strains was tested using the microarray. Of the 1250 peptides, 97 peptides (7.76%) showed reactivity with the EAV-positive samples. No single peptide was detected by all the positive serum samples. Seven peptides repeatedly showed reactivity above the cut-off and were considered to have diagnostic potential. Five of these peptides were within the immunodominant GP5 protein and two were within the replicase polyprotein regions NSP2 and NSP10, located in ORF1. The diagnostic sensitivity of the seven peptides selected was low, ranging from 5% to 55%; however, the combined diagnostic sensitivity and specificity of the seven peptides was 90% and 100%, respectively. This data demonstrate that multiple peptide sequences would be required to design a comprehensive serological test to cover the diversity of the EAV strains and the individual immune responses of horses.

Extensive genetic diversity plays a role in immune evasion, and antibody responses can be strain-specific or broadly reactive depending on the epitope. Controlled human malaria infection (CHMI) allows investigation of immune responses to variant parasite proteins after a single infection with a known strain. We designed a novel diversity-reflecting peptide microarray containing 638,817 unique peptides representing 22,655 variants of 227 proteins from 23 P genome assemblies and 379 field isolates. Using this array, we probed sera from 38 malaria naïve adults before and 28 days after CHMI with one of two genetically distinct strains, NF54 (n = 21) or 7G8 (n = 17). We examined fold-increase in antibody response (intensity) and cross-reactivity to protein variants (breadth). ABCPred was used to predict linear epitopes for all 227 proteins. We used MEME to identify enriched motifs in regions of high intensity or breadth, which were presumed to be potential epitopes. While the two CHMI groups had similar intensity of responses to all proteins on the array, 20 proteins on the array had differential breadth of responses and participants infected with 7G8 strain had a higher breadth of responses to 17 of them. Of 543 ABCPred-predicted epitopes, 66 overlapped with MEME-identified epitopes, six of which were highly cross-reactive with >95% of peptide variants serorecognized by at least one CHMI group. Overall, we found most antibody responses to be comparable after infection with the NF54 strain or 7G8 strain, but we saw notable differences for ~10% of proteins on the array. While many MEME-identified epitopes from highly cross-reactive proteins were asparagine rich, an epitope from PF3D7_1033200 (ETRAMP10.2) was not. Highly cross-reactive responses to ETRAMP10.2 could be further characterized and ETRAMP10.2 could be considered for inclusion in a next generation vaccine.

Purpose To evaluate a new class of blood-based biomarkers, anti-frameshift peptide antibodies, for predicting both tumor responses and adverse immune events to immune checkpoint inhibitor (ICI) therapies in advanced lung cancer patients. Experimental design Serum samples were obtained from 74 lung cancer patients prior to palliative PD-(L)1 therapies with subsequently recorded tumor responses and immune adverse events (irAEs). Pretreatment samples were assayed on microarrays of frameshift peptides (FSPs), representing ~ 375,000 variant peptides that tumor cells can be informatically predicted to produce from translated mRNA processing errors. Serum-antibodies specifically recognizing these ligands were measured. Binding activities preferentially associated with best-response and adverse-event outcomes were determined. These antibody bound FSPs were used in iterative resampling analyses to develop predictive models of tumor response and immune toxicity. Results Lung cancer serum samples were classified based on predictive models of ICI treatment outcomes. Disease progression was predicted pretreatment with ~ 98% accuracy in the full cohort of all response categories, though ~ 30% of the samples were indeterminate. This model was built with a heterogeneous sample cohort from patients that (i) would show either clear response or stable outcomes, (ii) would be administered either single or combination therapies and (iii) were diagnosed with different lung cancer subtypes. Removing the stable disease, combination therapy or SCLC groups from model building increased the proportion of samples classified while performance remained high. Informatic analyses showed that several of the FSPs in the all-response model mapped to translations of variant mRNAs from the same genes. In the predictive model for treatment toxicities, binding to irAE-associated FSPs provided 90% accuracy pretreatment, with no indeterminates. Several of the classifying FSPs displayed sequence similarity to self-proteins. Conclusions Anti-FSP antibodies may serve as biomarkers for predicting ICI outcomes when tested against ligands corresponding to mRNA-error derived FSPs. Model performances suggest this approach might provide a single test to predict treatment response to ICI and identify patients at high risk for immunotherapy toxicities.

A workflow for rapid SARS-CoV-2 epitope discovery on peptide microarrays is herein reported. The process started with a proteome-wide screening of immunoreactivity based on the use of a high-density microarray followed by a refinement and validation phase on a restricted panel of probes using microarrays with tailored peptide immobilization through a click-based strategy. Progressively larger, independent cohorts of Covid-19 positive sera were tested in the refinement processes, leading to the identification of immunodominant regions on SARS-CoV-2 spike (S), nucleocapsid (N) protein and Orf1ab polyprotein. A summary study testing 50 serum samples highlighted an epitope of the N protein (region 155–71) providing good diagnostic performance in discriminating Covid-19 positive vs. healthy individuals. Using this epitope, 92% sensitivity and 100% specificity were reached for IgG detection in Covid-19 samples, and no cross-reactivity with common cold coronaviruses was detected. Likewise, IgM immunoreactivity in samples collected within the first month after symptoms onset showed discrimination ability. Overall, epitope 155–171 from N protein represents a promising candidate for further development and rapid implementation in serological tests.

Vaccination is effective tool for preventing and controlling SARS-CoV-2 infections, and inactivated vaccines are the most widely used type of vaccine. In order to identify antibody-binding peptide epitopes that can distinguish between individuals who have been vaccinated and those who have been infected, this study aimed to compare the immune responses of vaccinated and infected individuals. SARS-CoV-2 peptide microarrays were used to assess the differences between 44 volunteers inoculated with the inactivated virus vaccine BBIBP-CorV and 61 patients who were infected with SARS-CoV-2. Clustered heatmaps were used to identify differences between the two groups in antibody responses to peptides such as M1, N24, S15, S64, S82, S104, and S115. Receiver operating characteristic curve analysis was used to determine whether a combined diagnosis with S15, S64, and S104 could effectively distinguish infected patients from vaccinated individuals. Our findings showed that the specific antibody responses against S15, S64, and S104 peptides were stronger in vaccinators than in infected persons, while responses to M1, N24, S82, and S115 were weaker in asymptomatic patients than in symptomatic patients. Additionally, two peptides (N24 and S115) were found to correlate with the levels of neutralizing antibodies. Our results suggest that antibody profiles specific to SARS-CoV-2 can be used to distinguish between vaccinated individuals and those who are infected. The combined diagnosis with S15, S64, and S104 was found to be more effective in distinguishing infected patients from those who have been vaccinated than the diagnosis using individual peptides. Moreover, the specific antibody responses against the N24 and S115 peptides were found to be consistent with the changing trend of neutralizing antibodies.

The current COVID-19 pandemic is caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). A better understanding of its immunogenicity can be important for the development of improved diagnostics, therapeutics, and vaccines. Here, we report the longitudinal analysis of three COVID-19 patients with moderate (#1) and mild disease (#2 and #3). Antibody serum responses were analyzed using spike glycoprotein enzyme linked immunosorbent assay (ELISA), full-proteome peptide, and glycan microarrays. ELISA immunoglobulin A, G, and M (IgA, IgG, and IgM) signals increased over time for individuals #1 and #2, whereas #3 only showed no clear positive IgG and IgM result. In contrast, peptide microarrays showed increasing IgA/G signal intensity and epitope spread only in the moderate patient #1 over time, whereas early but transient IgA and stable IgG responses were observed in the two mild cases #2 and #3. Glycan arrays showed an interaction of antibodies to fragments of high-mannose and core N-glycans, present on the viral shield. In contrast to protein ELISA, microarrays allow for a deeper understanding of IgA, IgG, and IgM antibody responses to specific epitopes of the whole proteome and glycans of SARS-CoV-2 in parallel. In the future, this may help to better understand and to monitor vaccination programs and monoclonal antibodies as therapeutics.

Many patients with chronic myeloid leukemia in deep remission experience return of clinical disease after withdrawal of tyrosine kinase inhibitors (TKIs). This suggests signaling of inactive BCR–ABL, which allows the survival of cancer cells, and relapse. We show that TKI treatment inhibits catalytic activity of BCR–ABL, but does not dissolve BCR–ABL core signaling complex, consisting of CRKL, SHC1, GRB2, SOS1, cCBL, p85a-PI3K, STS1 and SHIP2. Peptide microarray and co-immunoprecipitation results demonstrate that CRKL binds to proline-rich regions located in C-terminal, intrinsically disordered region of BCR–ABL, that SHC1 requires pleckstrin homology, src homology and tyrosine kinase domains of BCR–ABL for binding, and that BCR–ABL sequence motif located in disordered region around phosphorylated tyrosine 177 mediates binding of three core complex members, i.e., GRB2, SOS1, and cCBL. Further, SHIP2 binds to the src homology and tyrosine kinase domains of BCR–ABL and its inositol phosphatase activity contributes to BCR–ABL-mediated phosphorylation of SHC1. Together, this study characterizes protein–protein interactions within the BCR–ABL core complex and determines the contribution of particular BCR–ABL domains to downstream signaling. Understanding the structure and dynamics of BCR–ABL interactome is critical for the development of drugs targeting integrity of the BCR–ABL core complex.