Peptide Sequence Research Articles

Redesign of Translocon EXP2 Nanopore for Detecting Peptide Fragments.

Nanopore sensing is a rapid, label-free technique that enables single-molecule detection and is successfully applied to nucleic acid sequencing. Extending this technology to the detection and sequencing of peptides and proteins is a key area of interest. However, the complex structures and diverse charge distributions of peptides and proteins present challenges for extensive detection using existing nanopores. In this study, the focus is on the EXP2 nanopore derived from the malaria parasite Plasmodium falciparum to address these challenges. Previously, it is characterized wild-type EXP2 (WT-EXP2) nanopores and demonstrated their ability to detect polypeptides, although intrinsic electrical noise from the pore posed difficulties for accurate detection. To overcome these limitations, several EXP2 nanopore mutants are designed, including EXP2ΔD231, EXP2NC, and EXP2NC K42D/S46F, to reduce electrical noise and improve peptide detection accuracy. The EXP2ΔD231 mutant reduced electrical noise by more than 50% compared to WT-EXP2 and improved the discrimination accuracy of oligoarginine peptides. In addition, the EXP2ΔD231 detected and discriminated eight different peptides, ranging in molecular weight from small to large, that are previously challenging to detect using a single nanopore type. These results suggest that engineered EXP2 nanopores could serve as effective tools for peptide and protein detection and sequencing, contributing to the broader application of nanopore technology in biochemical and clinical research.

Pilot-Scale Production of Sericin-Derived Oligopeptides (SDOs) from Yellow Silk Cocoons: Peptide Characterization and Specifications

Our previous research demonstrated the health benefits of sericin-derived oligopeptides (SDOs) from yellow silk cocoons, particularly their hypoglycemic and antihypertensive properties. This study aims to produce SDOs at a pilot scale, preparing them for large commercial production as a novel food ingredient, and investigates the impact of scale-up on their characteristics and specifications. We compared the productivity of SDOs generated from 25 L and 300 L batches via the hydrolysis of sericin using 5% Neutrase (E/S) at 50 °C for 4 h. The 300 L production scale outperformed the 25 L scale, achieving a hydrolysis degree (DH) of 8.63%, a solid recovery rate of 94.35%, and enhanced inhibitory actions for dipeptidyl peptidase IV (DPP-IV) and angiotensin-converting enzyme (ACE). The characterization of peptides was carried out in ultrafiltered SDOs. Peptides < 3 kDa demonstrated optimal enzyme inhibition and were then fractionated by size exclusion chromatography into nine distinct fractions. Of the nine fractions, F1, F8, and F9 had significant enzyme inhibitory activity. LC-MS/MS analysis revealed 32 unique peptide sequences, with YPDLPYH exhibiting significant dual inhibitory effects on both DPP-IV (IC50 1.35 mM) and ACE (IC50 18.10 μM). The maximum residue limit (MRL) for trace metals, pesticide residues, and microbiological contamination in SDOs complies with food regulations. SDOs exhibited stability at 4, 25, and 45 °C for six months, based on their physical characteristics and biological activity. Considering their investigated characteristics, SDOs could be manufactured at a pilot capacity and used as a functional food component in commercial applications designed to improve metabolic health.

Twin-Tail Tadpole-Shaped Ce6-Peptide Conjugate for Enhanced Photodynamic Cancer Therapy.

Despite its therapeutic potential, photodynamic therapy faces several key limitations in clinical applications, including poor drug delivery and insufficient tumor selectivity. We engineered RFYFYR-Ce6-RFYFYR (R-Ce6-R), a twin-tail peptide-photosensitizer conjugate that self-assembles into nanostructures for improved cancer treatment. By incorporating arginine-rich peptide sequences, this design not only enhances cellular internalization but also promotes peroxynitrite (ONOO-) formation, amplifying the therapeutic effect. Our studies revealed that R-Ce6-R achieves 33-fold higher potency than unmodified Ce6, with an IC50 of 0.18 μM. The conjugate demonstrated selective accumulation in tumor tissue, robust ROS generation, and complete tumor regression in animal models while maintaining a favorable safety profile. These results establish R-Ce6-R as an innovative approach for advancing photodynamic therapy in cancer treatment.

Molecular Dynamics (MD)-Derived Features for Canonical and Noncanonical Amino Acids.

Machine learning (ML) models have become increasingly popular for predicting and designing structures and properties of peptides and proteins. These ML models typically use peptides and proteins containing only canonical amino acids as the training data. Consequently, these models struggle to make accurate predictions for peptides and proteins containing new amino acids that are absent in the training data set (e.g., noncanonical amino acids). One approach to improve the accuracy of the models is to collect more training data with the desired amino acids. However, this strategy is suboptimal as new data may not be easily attainable, and additional time is required to retrain the ML models. Alternatively, the extendibility of the ML models can be improved if the amino acid features used are representative and generalizable to the unseen amino acids. Herein, we develop amino acid features using molecular dynamics (MD) simulation results. Specifically, for a given amino acid, we perform MD simulation of its dipeptide to create features based on its backbone (ϕ, ψ) distributions and its electrostatic potentials. We demonstrate that these new features enable our ML models to more accurately predict the structural ensembles of cyclic peptides containing amino acids not present in the original training data set. For example, we build ML models to predict cyclic pentapeptide structures, with the training data set containing a library of 15 amino acids and the test data set containing the same 15-amino-acid library or an extended 50-amino-acid library. When using popular features such as Morgan fingerprints and MACCS keys to represent amino acids, the ML models achieve R2 = 0.963 for structural predictions of test cyclic pentapeptides containing the same 15-amino-acid library. However, these models' performances decrease significantly to R2 = 0.430 and R2 = 0.508, respectively, when tasked to predict the structures of cyclic pentapeptides containing a library of 50 amino acids. On the other hand, the model using our backbone (ϕ, ψ) features outperforms those using Morgan fingerprints and MACCS keys, with R2 = 0.700. Overall, instead of having to collect more training data, our new features enable predictions of peptide sequences containing amino acids not originally present in the training data set at the mere cost of performing new dipeptide simulations for the new amino acids.

StackAHTPs: An explainable antihypertensive peptides identifier based on heterogeneous features and stacked learning approach.

Hypertension, often known as high blood pressure, is a major concern to millions of individuals globally. Recent studies have demonstrated the significant efficacy of naturally derived peptides in reducing blood pressure. Hypertension is one of the risks associated with cardiovascular disorders and other health problems. Naturally sourced bioactive peptides possessing antihypertensive properties provide considerable potential as viable substitutes for conventional pharmaceutical medications. Currently, thorough examination of antihypertensive peptide (AHTPs), by using traditional wet-lab methods is highly expensive and labours. Therefore, in-silico approaches especially machine-learning (ML) algorithms are favourable due to saving time and cost in the discovery of AHTPs. In this study, a novel ML-based predictor, called StackAHTP was developed for predicting accurate AHTPs from sequence only. The proposed method, utilise two types of feature descriptors Pseudo-Amino Acid Composition and Dipeptide Composition to encode the local and global hidden information from peptide sequences. Furthermore, the encoded features are serially merged and ranked through SHapley Additive explanations (SHAP) algorithm. Then, the top ranked are fed into three different ensemble classifiers (Bagging, Boosting, and Stacking) for enhancing the prediction performance of the model. The StackAHTPs method achieved superior performance compare to other ML classifiers (AdaBoost, XGBoost and Light Gradient Boosting (LightGBM), Bagging and Boosting) on 10-fold cross validation and independent test. The experimental outcomes demonstrate that our proposed method outperformed the existing methods and achieved an accuracy of 92.25% and F1-score of 89.67% on independent test for predicting AHTPs and non-AHTPs. The authors believe this research will remarkably contribute in predicting large-scale characterisation of AHTPs and accelerate the drug discovery process. At https://github.com/ali-ghulam/StackAHTPs you may find datasets features used.

Methods of extraction, separation and identification of cyclic peptides from flaxseed (Linum usitatissimum L.): A review

Oilseed flax (Linum usitatissimum L.) is a valuable crop characterized by a high content of fats, dietary fiber, protein and various biologically active substances, in particular cyclopeptides. Cyclic peptides are a group of cyclic hydrophobic peptides consisting of eight to ten amino acids with a molecular weight in the range of 950–2300 Da. Flax oil and seeds contain from 0.1 to 0.3% cyclopeptides, which can exhibit antioxidant, anti-inflammatory, immunosuppressive, antihypertensive and antitumor activity. The aim of this review was to systematize and summarize the available literature data on methods of extraction, separation and identification of cyclopeptides from flaxseed oil. It was found that the main methods for obtaining cyclopeptides are solid-liquid, liquid-liquid or solid-phase extraction. Commonly used solvents include methanol, hexane, ethyl acetate, dichloromethane, acetonitrile and deionized water. Preparative flash chromatography on silica gel or polymer adsorbents is used to purify and concentrate cyclopeptides, and high-performance liquid chromatography (HPLC) is used to obtain individual standards. The most commonly used stationary phases are non-polar modified sorbents — octadecyl (C18) and phenylhexyl functional groups. Identification is carried out using instrumental methods of analysis: IR spectroscopy, NMR, HPLC with a diode array detector (HPLC-PDA/DAD), high-resolution tandem mass spectrometry with electrospray ionization (ESI-HR-MS/MS). For the qualitative and quantitative determination of cyclopeptides, the HPLC with a diode array detector at a wavelength of 214 nm is sufficient. In turn, mass spectral methods, including tandem mass spectrometry, make it possible to confirm the qualitative composition and establish the amino acid sequence of cyclic peptides.

Overcoming cross-reactivity of antibodies against human lactate dehydrogenase.

Lactate dehydrogenase subunit A (LD-A) plays an important role in cancer regulation and therapy. We attempted to develop an enzyme-linked immune-solvent assay (ELISA) for LD-A in human serum. However, commercial antibodies against LD-A exhibited cross-reactivity with an unknown protein. The unknown protein was purified and characterized by protein sequencing and Western blotting. In addition, we attempted to prepare a specific antibody for the ELISA using partially synthesized peptides of LD-A as immunogens. The epitope position in LD-A was carefully selected based on bioinformatics analysis. Peptide sequencer elucidated a ten amino acid sequence of the purified protein at the N-terminal. A BLAST search revealed that this sequence perfectly matched that at the N-terminus of the IgG heavy chain (H-chain). Furthermore, we demonstrated that twelve commercially available antibodies targeting LD-A or LD-B (subunit B) primarily cross-reacted with IgG or its H-chain, with only one specific antibody for each subunit. As the specific antibody against LD-A is no longer commercially accessible, we successfully produced two kinds of specific antibodies using partially synthesized LD-A peptides as immunogens. In conclusion, we have successfully produced specific antibodies against LD-A. Moreover, our findings underscore the utility of bioinformatics tools for determining the optimal positions of immunizing peptides.

Modular functionalization of cellular nanodiscs enables targeted delivery of chemotherapeutics into tumors.

The effective delivery of chemotherapeutic drugs to tumor sites is critical for cancer treatment and remains a significant challenge. The advent of nanomedicine has provided additional avenues for altering the in vivo distribution of drug payloads and increasing tumor localization. More recently, cell-derived nanoparticles, with their biocompatibility and unique biointerfacing properties, have demonstrated considerable utility for drug delivery applications. Here, we demonstrate that cell membrane-derived nanodiscs can be employed for tumor-targeted delivery. To bestow active targeting capabilities to the cellular nanodiscs, we utilize a modular functionalization strategy based on the SpyCatcher system. This enables the nanodiscs to be covalently modified with any targeting ligand labeled with a short SpyTag peptide sequence. As a proof-of-concept, a model chemotherapeutic doxorubicin is loaded into nanodiscs functionalized with an affibody targeting epidermal growth factor receptor. The resulting nanoformulation demonstrates strong tumor targeting both in vitro and in vivo, and it is able to significantly inhibit tumor growth in a murine breast cancer model.

α-N-Methyltransferase regiospecificity is mediated by proximal, redundant enzyme-substrate interactions.

N-Methylation of the peptide backbone confers pharmacologically beneficial characteristics to peptides that include greater membrane permeability and resistance to proteolytic degradation. The borosin family of ribosomally synthesized and post-translationally modified peptides offer a post-translational route to install amide backbone α-N-methylations. Previous work has elucidated the substrate scope and engineering potential of two examples of type I borosins, which feature autocatalytic precursors that encode N-methyltransferases that methylate their own C-termini in trans. We recently reported the first discrete N-methyltransferase and precursor peptide from Shewanella oneidensis MR-1, a minimally iterative, type IV borosin that allowed the first detailed kinetic analyses of borosin N-methyltransferases. Herein, we characterize the substrate scope and resilient regiospecificity of this discrete N-methyltransferase by comparison of relative rates and methylation patterns of over 40 precursor peptide variants along with structure analyses of nine enzyme-substrate complexes. Sequences critical to methylation are identified and demonstrated in assaying minimal peptide substrates and non-native peptide sequences for assessment of secondary structure requirements and engineering potential. This work grants understanding towards the mechanism of substrate recognition and iterative activity by discrete borosin N-methyltransferases.

Ion Mobility-Assisted Free Radical-Initiated Peptide Sequencing.

Free radical-initiated peptide sequencing (FRIPS) is a tandem mass spectrometry technique (MS/MS) that enables radical-based dissociation on instruments only capable of collisional activation. In FRIPS, peptides are chemically-derivatized with a compound that undergoes homolytic cleavage and generates radicals upon collisional activation. These radicals then propagate through the peptide backbone enabling the sequencing of peptide ions. This MS/MS technique has shown promise in sequencing post-translationally modified peptides, but it is typically performed in an MS3 workflow and single-step MS/MS approaches result in the generation of both collisional- and radical-driven dissociation products and highly complex spectra. Recently, our group developed a method to dissociate peptide ions prior to ion mobility analysis within a trapped-ion mobility spectrometry (TIMS) device. In this work, we examine if this "CIDtims" technique can initiate the homolytic cleavage of the FRIPS precursor. We then examine if the resultant ion mobility separation results in additional assignments of product ions and improved sequence coverage. We demonstrate that activation within the TIMS device does indeed promote robust radical initiation and fragmentation of peptide cations and that the generated product ions are mobility separated enabling facile assignment and increased sequence coverage.

Engineered PepFect14 analog for efficient cellular delivery of oligonucleotides.

The broad use of oligonucleotides (ON) in therapeutic and biotechnological applications is limited due to inefficient delivery methods. In parallel with lipids and polymeric carriers, cell-penetrating peptides (CPPs) are efficient vehicles for delivering nucleic acids of various types and activity into cells. In the current work, we examined the structural motifs required for the high efficacy of PepFect14, an often-used CPP for ON delivery, by introducing point mutations into the peptide sequence. We predicted the characteristics of modified CPPs, and analyzed their structure and ability to condense ONs into nanoparticles (NPs) using biophysical methods. We evaluated the ability of new PF14 analogs to deliver splicing switching oligonucleotides (SCO) and small interfering RNA (siRNA) in reporter cell lines, as well as microRNA miR-146a in human primary keratinocytes and in a mouse skin inflammation in vivo. Our findings indicate that the α-helical structure of PF14 is essential for efficient ON delivery, and mutations that disrupt the hydrophobic or cationic face in the peptide abolish NP formation and cellular internalization. PF14-Lys, an analog containing lysine residues instead of original ornithines, yielded a higher biological response to SCO and siRNA in the respective reporter cells than PF14. Furthermore, PF14-Lys efficiently delivered miRNA into keratinocytes and led to the subsequent downregulation of the target genes. Importantly, subcutaneously administered PF14-Lys-miR-146a NPs suppressed the inflammatory responses in mouse model of irritant contact dermatitis. In conclusion, our results suggest that PF14-Lys is a highly promising delivery vector for various oligonucleotides, applicable both in vitro and in vivo.

Formulation of new drug delivery systems for insulin from natural bioactive biocompatible polymers

New insulin drug delivery systems (IDDs): insulin@chitin; insulin@chitin-grafted (g)-guar gum were prepared by using a modified sol–gel method. Insulin vials were loaded on the safe natural inert bioactive polymers (chitin and chitin-g-GG copolymer) carriers using water green solvent. Traces amount additives were below toxicity limits. Guar gum increased the numbers of the functional groups of the polymer carrier. Insulin release monitored at 37 ± 0.5 °C and buffer solutions of pH (1.2, 6.8 and 7.4) simulating physiological body fluids: stomach, intestine colon and blood stream. Insulin released from insulin@chitin only at pH 7.4. No release observed at pH 1.2, 6.8 due strong bonding to acetyl group of chitin. Insulin@chitin-GG system showed sustained targeting insulin-release at pH: 6.8 > 7.4 > 1.2. Release data obeyed pseudo second order kinetic model indicating that IDDs is heterogeneous solid surface of energetically different active sites. Each insulin molecule occupied two active sites. The slow release at pH 1.2 indicated protection against stomach juice. Release kinetic depend on physicochemical characteristics (porosity, swelling ratio as well as peptide and amino acid sequence). Both IDDs showed negative zeta potential indicating stability against aggregation. Gaur gum improved particle size distribution and insulin release.

Metaproteomics Beyond Databases: Addressing the Challenges and Potentials of De Novo Sequencing.

Metaproteomics enables the large-scale characterization of microbial community proteins, offering crucial insights into their taxonomic composition, functional activities, and interactions within their environments. By directly analyzing proteins, metaproteomics offers insights into community phenotypes and the roles individual members play in diverse ecosystems. Although database-dependent search engines are commonly used for peptide identification, they rely on pre-existing protein databases, which can be limiting for complex, poorly characterized microbiomes. De novo sequencing presents a promising alternative, which derives peptide sequences directly from mass spectra without requiring a database. Over time, this approach has evolved from manual annotation to advanced graph-based, tag-based, and deep learning-based methods, significantly improving the accuracy of peptide identification. This Viewpoint explores the evolution, advantages, limitations, and future opportunities of de novo sequencing in metaproteomics. We highlight recent technological advancements that have improved its potential for detecting unsequenced species and for providing deeper functional insights into microbial communities.

PLM4CPPs: Protein Language Model-Based Predictor for Cell Penetrating Peptides.

Cell-penetrating peptides (CPPs) are short peptides capable of penetrating cell membranes, making them valuable for drug delivery and intracellular targeting. Accurate prediction of CPPs can streamline experimental validation in the lab. This study aims to assess pretrained protein language models (pLMs) for their effectiveness in representing CPPs and develop a reliable model for CPP classification. We evaluated peptide embeddings generated from BEPLER, CPCProt, SeqVec, various ESM variants (ESM, ESM-2 with expanded feature set, ESM-1b, and ESM-1v), ProtT5-XL UniRef50, ProtT5-XL BFD, and ProtBERT. We developed pLM4CCPs, a novel deep learning architecture using convolutional neural networks (CNNs) as the classifier for binary classification of CPPs. pLM4CCPs demonstrated superior performance over existing state-of-the-art CPP prediction models, achieving improvements in accuracy (ACC) by 4.9-5.5%, Matthews correlation coefficient (MCC) by 9.3-10.2%, and sensitivity (Sn) by 14.1-19.6%. Among all the tested models, ESM-1280 and ProtT5-XL BFD demonstrated the highest overall performance on the kelm data set. ESM-1280 achieved an ACC of 0.896, an MCC of 0.796, a Sn of 0.844, and a specificity (Sp) of 0.978. ProtT5-XL BFD exhibited superior performance with an ACC of 0.901, an MCC of 0.802, an Sn of 0.885, and an Sp of 0.917. pLM4CCPs combine predictions from multiple models to provide a consensus on whether a given peptide sequence is classified as a CPP or non-CPP. This approach will enhance prediction reliability by leveraging the strengths of each individual model. A user-friendly web server for bioactivity predictions, along with data sets, is available at https://ry2acnp6ep.us-east-1.awsapprunner.com. The source code and protocol for adapting pLM4CPPs can be accessed on GitHub at https://github.com/drkumarnandan/pLM4CPPs. This platform aims to advance CPP prediction and peptide functionality modeling, aiding researchers in exploring peptide functionality effectively.

Programmable Stapling Peptide Based on Sulfonium as Universal Vaccine Adjuvants for Multiple Types of Vaccines.

Adjuvants are non-specific immune enhancers commonly used to improve the responsiveness and persistence of the immune system toward antigens. However, due to the undefined chemical structure, toxicity, non-biodegradability, and lack of design technology in many existing adjuvants, it remains difficult to achieve substantive breakthroughs in the adjuvant research field. Here, a novel adjuvant development strategy based on stapling peptides is reported to overcome this challenge. The nano-vaccine incorporating peptide adjuvant and recombinant HBsAg protein not only induced strong antibody titers that are equivalent to aluminum adjuvanted vaccines but also simultaneously activated T-cell immune response. Similar results are also observed in herpes zoster vaccine and more complex influenza vaccine. The mechanism analysis demonstrates that antigen is efficiently carried into antigen-presenting cells (APCs) by peptide, further promoting the secretion of cytokines and activation of APCs. In addition, by redesigning the adjuvant, it is found that the sulfonium centers, rather than the sequence of peptide played an important role in immune activation. This discovery may provide a new paradigm for the rational design of peptide-based adjuvants. In brief, this study demonstrates that stapling peptides with sulfonium centers can provide a well-defined, programmable, biocompatible, and effective adjuvant for multiple types of vaccines.

Individualized Neoantigen-Directed Melanoma Therapy.

Individualized neoantigen-directed therapy represents a groundbreaking approach in melanoma treatment that leverages the patient's own immune system to target cancer cells. Thisinnovative strategyinvolvesthe identification of unique immunogenic neoantigens (mutated proteins specific to an individual's tumor) and the development of therapeutic vaccines that either consist of peptide sequences or RNA encoding these neoantigens. The goal of these therapies is to induce neoantigen-specific immune responses, enabling the immune system to recognize and destroy cancer cells presenting the targeted neoantigens. This individualized approach is particularly advantageous given the genetic heterogeneity of melanoma, which exhibits distinct mutations among different patients. In contrast to traditional therapies, neoantigen-directed therapy offers a tailored treatment that potentially reduces off-target side effects and enhances therapeutic efficacy. Recent advances in neoantigen prediction and vaccine development have facilitated clinical trials exploring the combination of neoantigen vaccines with immune checkpoint inhibitors. These trials have shown promising clinical outcomes, underscoring the potential of this personalized approach. This review provides an overview of the rationale behind neoantigen-directed therapies and summarizes the current state of knowledge regarding personalized neoantigen vaccines in melanoma treatment.

P-1971. Patterns of T cell receptor usage reveal associations with COVID-19 severity

Abstract Background T cells are associated with viral clearance and improved outcomes in COVID-19, but also with immunopathology. Defining T cell diversity in response to SARS-CoV-2 infection enables implementation of T cells in prevention and treatment strategies. T cells recognize peptide sequences from pathogens upon presentation in major histocompatibility complexes (MHC). Human MHC molecule diversity results in a wide array of peptide recognition by T cells making viral escape difficult, but also challenging to measure. Methods Using the ImmunoSEQ Assay to sequence complementarity-determining regions in the β chains (CDR3β) of the T cell receptor (TCR) from peripheral blood collected from participants in the EPICC COVID-19 cohort study, we quantified and characterized patterns of TCR usage. Multiple bioinformatic pipelines were compared for analysis. Results Our study evaluated TCR repertoires from 39 participants who had mild (20 outpatient) versus severe (19 hospitalized) acute COVID-19, and either experienced unvaccinated primary infection (n = 15) or vaccine breakthrough infection (n = 24) (Table 1). From these four clinical groups, we used the ImmuneCODE database, VDJdatabase and healthy controls to identify CD8 TCR sequences and amino acid motifs in the CDR3β region specific for the SARS-CoV-2 proteome. We were surprised to find that vaccine breakthrough and primary infection participants exhibited similar frequencies of SARS-CoV-2-specific TCR sequences over 14-21 days post-symptom onset. Increased use of common TCR clones occurred in participants with older age and comorbidities, which correlated with more severe disease (Figure 1). Strikingly, an increased frequency of non-SARS-CoV-2 TCRs was significantly associated with severe disease in unvaccinated (p = 0.006) and vaccinated participants (p = 0.045). Conclusion In conclusion, our study highlights the opportunities and challenges of studying the complexity of T cell diversity and the utility of combining databases and bioinformatics for identification of patterns of TCR recognition. Interestingly, participants who were hospitalized were more likely to have circulating T cells non-specific for SARS-CoV-2 suggesting that vaccines that increase T cell specificity may reduce disease severity. Disclosures Simon Pollett, MBBS, AstraZeneca: The IDCRP and HJF were funded to conduct an unrelated phase III COVID-19 monoclonal antibody immunoprophylaxis trial as part of US Govt COVID Response

Photoredox-Catalyzed Glycosylation at Dha in Peptide Sequences

Photoredox-Catalyzed Glycosylation at Dha in Peptide Sequences

Deep Learning Predicts Non-Normal Transmission Distributions in High-Field Asymmetric Waveform Ion Mobility (FAIMS) Directly from Peptide Sequence.

Peptide ion mobility adds an extra dimension of separation to mass spectrometry-based proteomics. The ability to accurately predict peptide ion mobility would be useful to expedite assay development and to discriminate true answers in a database search. There are methods to accurately predict peptide ion mobility through drift tube devices, but methods to predict mobility through high-field asymmetric waveform ion mobility (FAIMS) are underexplored. Here, we successfully model peptide ions' FAIMS mobility using a multi-label classification scheme to account for non-normal transmission distributions. We trained two models from over 100,000 human peptide precursors: a random forest and a long-term short-term memory (LSTM) neural network. Both models had different strengths, and the ensemble average of model predictions produced a higher F2 score than either model alone. Finally, we explored cases where the models make mistakes and demonstrate the predictive performance of F2 = 0.66 (AUROC = 0.928) on a new test data set of nearly 40,000 E. coli peptide ions. The deep learning model is easily accessible via https://faims.xods.org.

Gold Nanoparticles as a Platform for Delivery of Immunogenic Peptides to THP-1 Derived Macrophages: Insights into Nanotoxicity

Background: Peptide-based nanovaccines have emerged as a promising strategy for combating infectious diseases, as they overcome the low immunogenicity that is inherent to short epitope-containing synthetic peptides. Gold nanoparticles (AuNPs) present several advantages as peptide nanocarriers, but a deeper understanding of the design criteria is paramount to accelerate the development of peptide-AuNPs nanoconjugates (p-AuNPs). Methods: Herein, we synthesized and characterized p-AuNPs of 23 nm (p-Au23) and 68 nm (p-Au68) with varying levels of peptide surface coverage and different peptide designs, investigating their effect on the cell viability (cell death and mitochondrial activity), cellular uptake, and cathepsin B activity in THP-1 macrophages. Results: p-Au23 proved no negative effect in the cell viability and high levels of nanoconjugate uptake, but p-Au68 induced strong toxicity to the cell line. The peptide sequences were successfully designed with spacer regions and a cell-penetrating peptide (pTAT) that enhanced cellular uptake and cathepsin B activity for p-Au23, while pTAT induced severe effects in the THP-1 viability (~40–60% cell death). Conclusions: These findings provide valuable insight into the design criteria of AuNPs and immunogenic peptides, along with nanotoxicity effects associated with AuNP size and surface charge in human monocyte-derived macrophages.