Synthetic Peptide Libraries Research Articles

Autoantibodies to ADAMTS13 in human immunodeficiency virus-associated thrombotic thrombocytopenic purpura.

Thrombotic thrombocytopenic purpura (TTP) is a potentially fatal thrombotic microangiopathic disorder that can result from human immunodeficiency virus (HIV) infection. The pathogenesis involves a deficiency of the von Willebrand factor (vWF) cleaving protease ADAMTS13 (a disintegrin and metalloprotease with thrombospondin motifs member 13) and the presence of anti-ADAMTS13 autoantibodies. However, there is insufficient information regarding the epitope specificity and reactivity of these autoantibodies. This study aimed to perform epitope-mapping analysis to provide novel insights into the specific epitopes on ADAMTS13 domains affected by autoantibodies. The study analysed 59 frozen citrate plasma samples from HIV-associated TTP patients in South Africa, measuring ADAMTS13 activity using Technozyme® ADAMTS13 activity test, total immunoglobulin (Ig) M and IgA antibodies levels using ELISA kit and purifying IgG antibodies using NAb™ Protein G spin columns. A synthetic ADAMTS13 peptide library was used for epitope mapping. Overall, 90% of samples showed anti-ADAMTS13 IgG autoantibodies, with 64% of these antibodies being inhibitory, as revealed by mixing studies. Samples with ADAMTS13 antigen levels below 5% showed high anti-ADAMTS13 IgG autoantibody titres (≥50 IU/mL), whereas those with 5%-10% levels had low autoantibody titres (<50 IU/mL).The metalloprotease, cysteine-rich and spacer domains were 100% involved in binding anti-ADAMTS13 IgG antibodies, with 58% of samples containing antibodies binding to the C-terminal part of the ADAMTS13 disintegrin-like domain, indicating different pathogenic mechanisms. The metalloprotease, cysteine-rich and spacer domains are the primary targets for anti-ADAMTS13 IgG autoantibodies in patients with HIV-associated TTP. These findings suggest potential effects on the proteolytic activity of ADAMTS13, highlighting the complex nature of the pathogenic mechanisms involved.

Decoding Arabidopsis thaliana CPK/SnRK Superfamily Kinase Client Signaling Networks Using Peptide Library and Mass Spectrometry.

Members of the calcium-dependent protein kinase (CDPK/CPK) and SNF-related protein kinase (SnRK) superfamilies are commonly found in plants and some protists. Our knowledge of client specificity of the members of this superfamily is fragmentary. As this family is represented by over 30 members in Arabidopsis thaliana, the identification of kinase-specific and overlapping client relationships is crucial to our understanding the nuances of this large family of kinases as directed towards signal transduction pathways. Herein, we used the kinase client (KiC) assay-a relative, quantitative, high-throughput mass spectrometry-based in vitro phosphorylation assay-to identify and characterize potential CPK/SnRK targets of Arabidopsis. Eight CPKs (1, 3, 6, 8, 17, 24, 28, and 32), four SnRKs (subclass 1 and 2), and PPCK1 and PPCK2 were screened against a synthetic peptide library that contains 2095 peptides and 2661 known phosphorylation sites. A total of 625 in vitro phosphorylation sites corresponding to 203 non-redundant proteins were identified. The most promiscuous kinase, CPK17, had 105 candidate target proteins, many of which had already been discovered. Sequence analysis of the identified phosphopeptides revealed four motifs: LxRxxS, RxxSxxR, RxxS, and LxxxxS, that were significantly enriched among CPK/SnRK clients. The results provide insight into both CPK- and SnRK-specific and overlapping signaling network architectures and recapitulate many known in vivo relationships validating this large-scale approach towards discovering kinase targets.

Non-prime- and prime-side profiling of Pro-Pro endopeptidase specificity using synthetic combinatorial peptide libraries and mass spectrometry.

A group of bacterial proteases, the Pro-Pro endopeptidases (PPEPs), possess the unique ability to hydrolyze proline-proline bonds in proteins. Since a protease's function is largely determined by its substrate specificity, methods that can extensively characterize substrate specificity are valuable tools for protease research. Previously, we achieved an in-depth characterization of PPEP prime-side specificity. However, PPEP specificity is also determined by the non-prime-side residues in the substrate. To gain a more complete insight into the determinants of PPEP specificity, we characterized the non-prime- and prime-side specificity of various PPEPs using a combination of synthetic combinatorial peptide libraries and mass spectrometry. With this approach, we deepened our understanding of the P3-P3' specificities of PPEP-1 and PPEP-2, while identifying the endogenous substrate of PPEP-2 as the most optimal substrate in our library data. Furthermore, by employing the library approach, we investigated the altered specificity of mutants of PPEP-1 and PPEP-2. Additionally, we characterized a novel PPEP from Anoxybacillus tepidamans, which we termed PPEP-4. Based on structural comparisons, we hypothesized that PPEP-4 displays a PPEP-1-like prime-side specificity, which was substantiated by the experimental data. Intriguingly, another putative PPEP from Clostridioides difficile, CD1597, did not display Pro-Pro endoproteolytic activity. Collectively, we characterized PPEP specificity in detail using our robust peptide library method and, together with additional structural information, provide more insight into the intricate mechanisms that govern protease specificity.

SAMP-VGG16: Force-field assisted image-based deep neural network prediction model for short antimicrobial peptides.

During the last three decades, antimicrobial peptides (AMPs) have emerged as a promising therapeutic alternative to antibiotics. The approaches for designing AMPs span from experimental trial-and-error methods to synthetic hybrid peptide libraries. To overcome the exceedingly expensive and time-consuming process of designing effective AMPs, many computational and machine-learning tools for AMP prediction have been recently developed. In general, to encode the peptide sequences, featurization relies on approaches based on (a) amino acid (AA) composition, (b) physicochemical properties, (c) sequence similarity, and (d) structural properties. In this work, we present an image-based deep neural network model to predict AMPs, where we are using feature encoding based on Drude polarizable force-field atom types, which can capture the peptide properties more efficiently compared to conventional feature vectors. The proposed prediction model identifies short AMPs (≤30 AA) with promising accuracy and efficiency and can be used as a next-generation screening method for predicting new AMPs. The source code is publicly available at the Figshare server sAMP-VGG16.

Identification of mitogen-activated protein kinases substrates in Arabidopsis using kinase client assay

ABSTRACT Mitogen-activated protein kinase (MPK) cascades are essential signal transduction components that control a variety of cellular responses in all eukaryotes. MPKs convert extracellular stimuli into cellular responses by the phosphorylation of downstream substrates. Although MPK cascades are predicted to be very complex, only limited numbers of MPK substrates have been identified in plants. Here, we used the kinase client (KiC) assay to identify novel substrates of MPK3 and MPK6. Recombinant MPK3 or MPK6 were tested against a large synthetic peptide library representing in vivo phosphorylation sites, and phosphorylated peptides were identified by high-resolution tandem mass spectrometry. From this screen, we identified 23 and 21 putative client peptides of MPK3 and MPK6, respectively. To verify the phosphorylation of putative client peptides, we performed in vitro kinase assay with recombinant fusion proteins of isolated client peptides. We found that 13 and 9 recombinant proteins were phosphorylated by MPK3 and MPK6. Among them, 11 proteins were proven to be the novel substrates of two MPKs. This study suggests that the KiC assay is a useful method to identify new substrates of MPKs.

MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer

Understanding the nature and extent of non-canonical human leukocyte antigen (HLA) presentation in tumour cells is a priority for target antigen discovery for the development of next generation immunotherapies in cancer. We here employ a de novo mass spectrometric sequencing approach with a refined, MHC-centric analysis strategy to detect non-canonical MHC-associated peptides specific to cancer without any prior knowledge of the target sequence from genomic or RNA sequencing data. Our strategy integrates MHC binding rank, Average local confidence scores, and peptide Retention time prediction for improved de novo candidate Selection; culminating in the machine learning model MARS. We benchmark our model on a large synthetic peptide library dataset and reanalysis of a published dataset of high-quality non-canonical MHC-associated peptide identifications in human cancer. We achieve almost 2-fold improvement for high quality spectral assignments in comparison to de novo sequencing alone with an estimated accuracy of above 85.7% when integrated with a stepwise peptide sequence mapping strategy. Finally, we utilize MARS to detect and validate lncRNA-derived peptides in human cervical tumour resections, demonstrating its suitability to discover novel, immunogenic, non-canonical peptide sequences in primary tumour tissue.

Investigation of the Catalytic Mechanism of a Soluble N-glycosyltransferase Allows Synthesis of N-glycans at Noncanonical Sequons.

The soluble N-glycosyltransferase from Actinobacillus pleuropneumoniae (ApNGT) can establish an N-glycosidic bond at the asparagine residue in the Asn-Xaa-Ser/Thr consensus sequon and is one of the most promising tools for N-glycoprotein production. Here, by integrating computational and experimental strategies, we revealed the molecular mechanism of the substrate recognition and following catalysis of ApNGT. These findings allowed us to pinpoint a key structural motif (215DVYM218) in ApNGT responsible for the peptide substrate recognition. Moreover, Y222 and H371 of ApNGT were found to participate in activating the acceptor Asn. The constructed models were supported by further crystallographic studies and the functional roles of the identified residues were validated by measuring the glycosylation activity of various mutants against a library of synthetic peptides. Intriguingly, with particular mutants, site-selective N-glycosylation of canonical or noncanonical sequons within natural polypeptides from the SARS-CoV-2 spike protein could be achieved, which were used to investigate the biological roles of the N-glycosylation in membrane fusion during virus entry. Our study thus provides in-depth molecular mechanisms underlying the substrate recognition and catalysis for ApNGT, leading to the synthesis of previously unknown chemically defined N-glycoproteins for exploring the biological importance of the N-glycosylation at a specific site.

In-Depth Specificity Profiling of Endopeptidases Using Dedicated Mix-and-Split Synthetic Peptide Libraries and Mass Spectrometry.

Proteases comprise the class of enzymes that catalyzes the hydrolysis of peptide bonds, thereby playing a pivotal role in many aspects of life. The amino acids surrounding the scissile bond determine the susceptibility toward protease-mediated hydrolysis. A detailed understanding of the cleavage specificity of a protease can lead to the identification of its endogenous substrates, while it is also essential for the design of inhibitors. Although many methods for protease activity and specificity profiling exist, none of these combine the advantages of combinatorial synthetic libraries, i.e., high diversity, equimolar concentration, custom design regarding peptide length, and randomization, with the sensitivity and detection power of mass spectrometry. Here, we developed such a method and applied it to study a group of bacterial metalloproteases that have the unique specificity to cleave between two prolines, i.e., Pro-Pro endopeptidases (PPEPs). We not only confirmed the prime-side specificity of PPEP-1 and PPEP-2, but also revealed some new unexpected peptide substrates. Moreover, we have characterized a new PPEP (PPEP-3) that has a prime-side specificity that is very different from that of the other two PPEPs. Importantly, the approach that we present in this study is generic and can be extended to investigate the specificity of other proteases.

289-OR: Defining Pathway-Selective Insulin Resistance in Type 2 Diabetes Using iPS Cell-Derived Hepatocytes

Hepatic insulin resistance is central to type 2 diabetes (T2D), fatty liver disease, and metabolic syndrome. To identify the fundamental, cell-autonomous defects underlying hepatic insulin resistance in T2D, we have used inducible human pluripotent stem (iPS) cells derived from 16 individuals with and without T2D differentiated to hepatocytes (iHeps) and studied in vitro. Consistent with pathway selective insulin resistance, we find that insulin failed to suppress gene expression of critical gluconeogenic enzymes (PCK1, G6PC) but continued to stimulate increased expression of the lipogenic enzymes, fatty acid synthase and acetyl Co-A carboxylase 1 in T2D iHeps. iHeps from T2D also displayed reduced insulin receptor tyrosine phosphorylation and reduced phosphorylation through the IRS/Akt pathway. Global phosphoproteomics in control and T2D iHeps identified 378 unique phosphosites regulated by insulin, and many dysregulated phosphosites within the classical insulin signaling pathway (IRS, AKT1/2, GSK3, FOXO1, RPS6KB1, TSC2) and components of gene transcription regulation, membrane trafficking, Rho-GTPases, vesicle transport. Using a kinome-wide data set created with synthetic peptide libraries to profile almost all functional known human serine/threonine kinases, we found the alterations in insulin signaling mapped to AKT, P70S6K, P90S6K, RSK, PKCτ, and SGK kinases. Thus, iHeps from T2D patients show pathway-selective insulin resistance with a loss of suppression of gluconeogenesis by insulin yet persistent activation of lipogenesis, even in vitro, in the absence of any circulating factor exposure. T2D iHeps also show a dysregulated phosphorylation network that provides new clues to the kinases whose action is altered in T2D and hepatic insulin resistance. Disclosure A.Gattu: None. A.Krook: None. J.R.Zierath: None. M.Mann: None. C.Kahn: None. Funding National Institutes of Health (T32DK007260-46)

Early Selection of the Amino Acid Alphabet Was Adaptively Shaped by Biophysical Constraints of Foldability.

Whereas modern proteins rely on a quasi-universal repertoire of 20 canonical amino acids (AAs), numerous lines of evidence suggest that ancient proteins relied on a limited alphabet of 10 "early" AAs and that the 10 "late" AAs were products of biosynthetic pathways. However, many nonproteinogenic AAs were also prebiotically available, which begs two fundamental questions: Why do we have the current modern amino acid alphabet and would proteins be able to fold into globular structures as well if different amino acids comprised the genetic code? Here, we experimentally evaluate the solubility and secondary structure propensities of several prebiotically relevant amino acids in the context of synthetic combinatorial 25-mer peptide libraries. The most prebiotically abundant linear aliphatic and basic residues were incorporated along with or in place of other early amino acids to explore these alternative sequence spaces. The results show that foldability was likely a critical factor in the selection of the canonical alphabet. Unbranched aliphatic amino acids were purged from the proteinogenic alphabet despite their high prebiotic abundance because they generate polypeptides that are oversolubilized and have low packing efficiency. Surprisingly, we find that the inclusion of a short-chain basic amino acid also decreases polypeptides' secondary structure potential, for which we suggest a biophysical model. Our results support the view that, despite lacking basic residues, the early canonical alphabet was remarkably adaptive at supporting protein folding and explain why basic residues were only incorporated at a later stage of protein evolution.

An atlas of substrate specificities for the human serine/threonine kinome

Protein phosphorylation is one of the most widespread post-translational modifications in biology1,2. With advances in mass-spectrometry-based phosphoproteomics, 90,000 sites of serine and threonine phosphorylation have so far been identified, and several thousand have been associated with human diseases and biological processes3,4. For the vast majority of phosphorylation events, it is not yet known which of the more than 300 protein serine/threonine (Ser/Thr) kinases encoded in the human genome are responsible3. Here we used synthetic peptide libraries to profile the substrate sequence specificity of 303 Ser/Thr kinases, comprising more than 84% of those predicted to be active in humans. Viewed in its entirety, the substrate specificity of the kinome was substantially more diverse than expected and was driven extensively by negative selectivity. We used our kinome-wide dataset to computationally annotate and identify the kinases capable of phosphorylating every reported phosphorylation site in the human Ser/Thr phosphoproteome. For the small minority of phosphosites for which the putative protein kinases involved have been previously reported, our predictions were in excellent agreement. When this approach was applied to examine the signalling response of tissues and cell lines to hormones, growth factors, targeted inhibitors and environmental or genetic perturbations, it revealed unexpected insights into pathway complexity and compensation. Overall, these studies reveal the intrinsic substrate specificity of the human Ser/Thr kinome, illuminate cellular signalling responses and provide a resource to link phosphorylation events to biological pathways.

Identification of specific carbonic anhydrase inhibitors via in situ click chemistry, phage-display and synthetic peptide libraries: comparison of the methods and structural study.

The development of highly active and selective enzyme inhibitors is one of the priorities of medicinal chemistry. Typically, various high-throughput screening methods are used to find lead compounds from a large pool of synthetic compounds, and these are further elaborated and structurally refined to achieve the desired properties. In an effort to streamline this complex and laborious process, new selection strategies based on different principles have recently emerged as an alternative. Herein, we compare three such selection strategies with the aim of identifying potent and selective inhibitors of human carbonic anhydrase II. All three approaches, in situ click chemistry, phage-display libraries and synthetic peptide libraries, led to the identification of more potent inhibitors when compared to the parent compounds. In addition, one of the inhibitor-peptide conjugates identified from the phage libraries showed greater than 100-fold selectivity for the enzyme isoform used for the compound selection. In an effort to rationalize the binding properties of the conjugates, we performed detailed crystallographic and NMR structural analysis, which revealed the structural basis of the compound affinity towards the enzyme and led to the identification of a novel exosite that could be utilized in the development of isoform specific inhibitors.

Identification of Specific Carbonic Anhydrase Inhibitors via In Situ Click Chemistry, Phage-Displayed and Synthetic Peptide Libraries: Comparison of the Methods and Structural Study

Identification of Specific Carbonic Anhydrase Inhibitors via In Situ Click Chemistry, Phage-Displayed and Synthetic Peptide Libraries: Comparison of the Methods and Structural Study

IBench: A ground truth approach for advanced validation of mass spectrometry identification method.

The discovery of many noncanonical peptides detectable with sensitive mass spectrometry inside, outside, and on cells shepherded the development of novel methods for their identification, often not supported by a systematic benchmarking with other methods. We here propose iBench, a bioinformatic tool that can construct ground truth proteomics datasets and cognate databases, thereby generating a training court wherein methods, search engines, and proteomics strategies can be tested, and their performances estimated by the same tool. iBench can be coupled to the main database search engines, allows the selection of customized features of mass spectrometry spectra and peptides, provides standard benchmarking outputs, and is open source. The proof-of-concept application to tryptic proteome digestions, immunopeptidomes, and synthetic peptide libraries dissected the impact that noncanonical peptides could have on the identification of canonical peptides by Mascot search with rescoring via Percolator (Mascot+Percolator).

Therapeutic potential of WKYMVm in diseases

The synthetic hexapeptide WKYMVm, screened from a synthetic peptide library, has been identified as an agonist of FPRs with the strongest activating effect on FPR2. WKYMVm plays an anti-inflammatory role in most inflammatory diseases by increasing the chemotaxis of phagocytes and regulating the secretion of inflammatory factors. WKYMVm can inhibit or promote the progression of different types of tumors, which depends on the regulation of WKYMVm on various components such as immune cells, inflammatory factors, chemokines, and tumor epithelial cells. Another major function of WKYMVm is to promote angiogenesis, which is reflected in its therapeutic value in ischemic diseases, wound healing and bone repair. In addition to the above functions, this paper also reviews the effects of WKYMVm on fibrosis, insulin resistance, osteolytic diseases and neurodegenerative diseases. By summarizing related studies, this review can increase people’s comprehensive understanding of WKYMVm, promote its broad and in-depth research, and help to exert its therapeutic value as soon as possible.

Mimicked synthetic ribosomal protein complex for benchmarking crosslinking mass spectrometry workflows

Cross-linking mass spectrometry has matured to a frequently used tool for the investigation of protein structures as well as interactome studies up to a system-wide level. The growing community generated a broad spectrum of applications, linker types, acquisition strategies and specialized data analysis tools, which makes it challenging to decide for an appropriate analysis workflow. Here, we report a large and flexible synthetic peptide library as reliable instrument to benchmark crosslink workflows. Additionally, we provide a tool, IMP-X-FDR, that calculates the real, experimentally validated, FDR, compares results across search engine platforms and analyses crosslink properties in an automated manner. We apply the library with 6 commonly used linker reagents and analyse the data with 6 established search engines. We thereby show that the correct algorithm and search setting choice is highly important to improve identification rate and reliability. We reach identification rates of up to ~70 % of the theoretical maximum (i.e. 700 unique lysine-lysine cross-links) while maintaining a real false-discovery-rate of <3 % at cross-link level with high reproducibility, representatively showing that our test system delivers valuable and statistically solid results.

Artificial intelligence channelizing protein-peptide interactions pipeline for host-parasite paradigm in IL-10 and IL-12 reciprocity by SHP-1

Identification of molecular targets in any cellular phenomena is a challenge and a path that one endeavors upon independently. We have identified a phosphatase SHP-1 as a point of intervention of IL-10 and IL-12 reciprocity in leishmaniasis. The therapeutic model that we have developed uniquely targets this protein but the pipeline in general can be used by the researchers for their unique targets. Naturally occurring peptides are well known for their biochemical participation in cellular functions hence we were motivated to use this uniqueness of physico-chemical properties of peptides conferred by amino acids through machine learning to channelize a mode of therapeutic exploration in infectious disease. Using computational approaches, we identified high order sequence conservation and similarity in SHP-1 sequence which was also evolutionarily conserved, complete structure of Mouse SHP-1 was predicted and validated, a unique motif of the same was identified against which library of synthetic peptides were designed and validated followed by screening the library by docking them with MuSHP-1 protein structure. Our findings showed 3 peptides had high binding affinity and in future can be validated using cell based and in vivo assays.

ADAMTS4-specific MR probe to assess aortic aneurysms in vivo using synthetic peptide libraries

The incidence of abdominal aortic aneurysms (AAAs) has substantially increased during the last 20 years and their rupture remains the third most common cause of sudden death in the cardiovascular field after myocardial infarction and stroke. The only established clinical parameter to assess AAAs is based on the aneurysm size. Novel biomarkers are needed to improve the assessment of the risk of rupture. ADAMTS4 (A Disintegrin And Metalloproteinase with ThromboSpondin motifs 4) is a strongly upregulated proteoglycan cleaving enzyme in the unstable course of AAAs. In the screening of a one-bead-one-compound library against ADAMTS4, a low-molecular-weight cyclic peptide is discovered with favorable properties for in vivo molecular magnetic resonance imaging applications. After identification and characterization, it’s potential is evaluated in an AAA mouse model. The ADAMTS4-specific probe enables the in vivo imaging-based prediction of aneurysm expansion and rupture.

A peptide from the staphylococcal protein Efb binds P‐selectin and inhibits the interaction of platelets with leukocytes

AimsP‐selectin is a key surface adhesion molecule for the interaction of platelets with leukocytes. We have shown previously that the N‐terminal domain of Staphylococcus aureus extracellular fibrinogen‐binding protein (Efb) binds to P‐selectin and interferes with platelet‐leukocyte aggregate formation. Here, we aimed to identify the minimal Efb motif required for binding platelets and to characterize its ability to interfering with the formation of platelet‐leukocyte aggregates. Methods and ResultsUsing a library of synthetic peptides, we mapped the platelet‐binding site to a continuous 20 amino acid stretch. The peptide Efb68‐87 was able to bind to resting and, to a greater extent, thrombin‐stimulated platelets in the absence of fibrinogen. Dot blots, pull‐down assays and P‐selectin glycoprotein ligand‐1 (PSGL‐1) competitive binding experiments identified P‐selectin as the cellular docking site mediating Efb68‐87 platelet binding. Accordingly, Efb68‐87 did not bind to other blood cells and captured platelets from human whole blood under low shear stress conditions. Efb68‐87 did not affect platelet activation as tested by aggregometry, flow cytometry and immunoblotting, but inhibited the formation of platelet‐leukocyte aggregates (PLAs). Efb68‐87 also interfered with the platelet‐dependent stimulation of neutrophil extracellular traps (NETs) formation in vitro. ConclusionsWe have identified Efb68‐87 as a novel selective platelet‐binding peptide. Efb68‐87 binds directly to P‐selectin and inhibits interactions of platelets with leukocytes that lead to PLA and NET formation. As PLAs and NETs play a key role in thromboinflammation, Efb68‐87 is an exciting candidate for the development of novel selective inhibitors of the proinflammatory activity of platelets.

Affinity Selection from Synthetic Peptide Libraries Enabled by De Novo MS/MS Sequencing

Recently, de novo MS/MS peptide sequencing has enabled the application of affinity selections to synthetic peptide mixtures that approach the diversity of phage libraries (> 108 random peptides). In conjunction with ‘split-mix’ solid phase synthesis to access equimolar peptide mixtures, this approach provides a straightforward means to examine synthetic peptide libraries of considerably higher diversity than has been feasible historically. Here, we offer a critical perspective on this work, report emerging data, and highlight opportunities for further methods refinement. With continued development, ‘affinity selection–mass spectrometry’ may become a complimentary approach to phage display, in vitro selection, and DNA-encoded libraries for the discovery of synthetic ligands that modulate protein function.