Veni, Vidi, Vici: Immobilized Peptide-Based Conjugates as Tools for Capture, Analysis, and Transformation

Analysis of peptide biomarkers of pathological states of the organism is often a serious challenge, due to a very complex composition of the cell and insufficient sensitivity of the current analytical methods (including mass spectrometry). One of the possible ways to overcome this problem is sample enrichment by capturing the selected components using a specific solid support. Another option is increasing the detectability of the desired compound by its selective tagging. Appropriately modified and immobilized peptides can be used for these purposes. In addition, they find application in studying the specificity and activity of proteolytic enzymes. Immobilized heterocyclic peptide conjugates may serve as metal ligands, to form complexes used as catalysts or analytical markers. In this review, we describe various applications of immobilized peptides, including selective capturing of cysteine-containing peptides, tagging of the carbonyl compounds to increase the sensitivity of their detection, enrichment of biological samples in deoxyfructosylated peptides, and fishing out of tyrosine–containing peptides by the formation of azo bond. Moreover, the use of the one-bead-one-compound peptide library for the analysis of substrate specificity and activity of caspases is described. Furthermore, the evolution of immobilization from the solid support used in peptide synthesis to nanocarriers is presented. Taken together, the examples presented here demonstrate immobilized peptides as a multifunctional tool, which can be successfully used to solve multiple analytical problems.

Evaluation of an integrative Bayesian peptide detection approach on a combinatorial peptide library

Detection of peptides lies at the core of bottom-up proteomics analyses. We examined a Bayesian approach to peptide detection, integrating match-based models (fragments, retention time, isotopic distribution, and precursor mass) and peptide prior probability models under a unified probabilistic framework. To assess the relevance of these models and their various combinations, we employed a complete- and a tail-complete search of a low-precursor-mass synthetic peptide library based on oncogenic KRAS peptides. The fragment match was by far the most informative match-based model, while the retention time match was the only remaining such model with an appreciable impact––increasing correct detections by around 8 %. A peptide prior probability model built from a reference proteome greatly improved the detection over a uniform prior, essentially transforming de novo sequencing into a reference-guided search. The knowledge of a correct sequence tag in advance to peptide-spectrum matching had only a moderate impact on peptide detection unless the tag was long and of high certainty. The approach also derived more precise error rates on the analyzed combinatorial peptide library than those estimated using PeptideProphet and Percolator, showing its potential applicability for the detection of homologous peptides. Although the approach requires further computational developments for routine data analysis, it illustrates the value of peptide prior probabilities and presents a Bayesian approach for their incorporation into peptide detection.

Amyloidogenesis of SARS-CoV-2 Spike Protein

SARS-CoV-2 infection is associated with a surprising number of morbidities. Uncanny similarities with amyloid-disease associated blood coagulation and fibrinolytic disturbances together with neurologic and cardiac problems led us to investigate the amyloidogenicity of the SARS-CoV-2 Spike protein (S-protein). Amyloid fibril assays of peptide library mixtures and theoretical predictions identified seven amyloidogenic sequences within the S-protein. All seven peptides in isolation formed aggregates during incubation at 37°C. Three 20-amino acid long synthetic Spike peptides (sequence 191-210, 599-618, 1165-1184) fulfilled three amyloid fibril criteria: nucleation dependent polymerization kinetics by ThT, Congo red positivity and ultrastructural fibrillar morphology. Full-length folded S-protein did not form amyloid fibrils, but amyloid-like fibrils with evident branching were formed during 24 hours of S-protein co-incubation with the protease neutrophil elastase (NE) in vitro. NE efficiently cleaved S-protein rendering exposure of amyloidogenic segments and accumulation of the peptide 193-202, part of the most amyloidogenic synthetic Spike peptide. NE is overexpressed at inflamed sites of viral infection and at vaccine injection sites. Our data propose a molecular mechanism for amyloidogenesis of SARS-CoV-2 S-protein in humans facilitated by endoproteolysis. The potential implications of S-protein amyloidogenesis in COVID-19 disease associated pathogenesis and consequences following S-protein based vaccines should be addressed in understanding the disease, long COVID-19, and vaccine side effects.

A high-throughput pipeline for design and selection of peptides targeting the SARS-Cov-2 Spike protein

Rapid design, screening, and characterization of biorecognition elements (BREs) is essential for the development of diagnostic tests and antiviral therapeutics needed to combat the spread of viruses such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To address this need, we developed a high-throughput pipeline combining in silico design of a peptide library specific for SARS-CoV-2 spike (S) protein and microarray screening to identify binding sequences. Our optimized microarray platform allowed the simultaneous screening of ~ 2.5 k peptides and rapid identification of binding sequences resulting in selection of four peptides with nanomolar affinity to the SARS-CoV-2 S protein. Finally, we demonstrated the successful integration of one of the top peptides into an electrochemical sensor with a clinically relevant limit of detection for S protein in spiked saliva. Our results demonstrate the utility of this novel pipeline for the selection of peptide BREs in response to the SARS-CoV-2 pandemic, and the broader application of such a platform in response to future viral threats.

Identification of a Novel Serological Marker in Seronegative Rheumatoid Arthritis Using the Peptide Library Approach

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by chronic inflammation mainly affecting the joints leading to cartilage and bone destruction. The definition of seropositive or seronegative RA is based on the presence or absence of rheumatoid factor (RF) and anti-citrullinated peptide antibodies (ACPAs). Other autoantibodies have been identified in the last decade such as antibodies directed against carbamylated antigens, peptidyl-arginine deiminase type 4 and v-Raf murine sarcoma viral oncogene homologue B. In order to identify relevant autoantigens, we screened a random peptide library (RPL) with pooled IgGs obtained from 50 patients with seronegative RA. Patients’ sera were then used in an ELISA test to identify the most frequently recognized peptide among those obtained by screening the RPL. Sera from age- and sex-matched healthy subjects were used as controls. We identified a specific peptide (RA-peptide) recognized by RA patients’ sera, but not by healthy subjects or by patients with other immune-mediated diseases. The majority of sera from seronegative and seropositive RA patients (73.8% and 63.6% respectively) contained IgG antibodies directed against the RA-peptide. Interestingly, this peptide shares homology with some self-antigens, such as Protein-tyrosine kinase 2 beta, B cell scaffold protein, Liprin-alfa1 and Cytotoxic T lymphocyte protein 4. Affinity purified anti-RA-peptide antibodies were able to cross react with these autoantigens. In conclusion, we identified a peptide that is recognized by seropositive and, most importantly, by seronegative RA patients’ sera, but not by healthy subjects, conferring to this epitope a high degree of specificity. This peptide shares also homology with other autoantigens which can be recognized by autoantibodies present in seronegative RA sera. These newly identified autoantibodies, although present also in a percentage of seropositive RA patients, may be considered as novel serum biomarkers for seronegative RA, which lacks the presence of RF and/or ACPAs.