Discovery Of Peptides Research Articles

Abstract 6024: A novel carbonic anhydrase IX targeting radiopeptide, 64Cu-PD-32766 and 177Lu-PD-32766, exhibit promising theranostic potential in ccRCC tumors

Abstract Introduction: Carbonic Anhydrase IX (CA9) is a zinc metalloenzyme that regulates the pH for cell growth. CA9 is considered an attractive target and is upregulated in a variety of cancers, especially, in 95% of clear cell renal cell carcinoma (ccRCC) where there remains a large clinical unmet need despite the availability of several newly approved medicines. PeptiDream has identified PD-32766, a novel macrocyclic peptide, targeting CA9 and can be labeled with radionuclides such as copper (64Cu) and lutetium (177Lu), which enables tumor-specific PET bioimaging and radiotherapy. Here we report the theranostic translational feasibility of PD-32766 for ccRCC. Materials and Methods: PD-32766 was discovered using Peptide Discovery Platform System (PDPS), a proprietary screening system of PeptiDream. Binding affinity of PD-32766, 63Cu-PD-32766 and 175Lu-PD-32766 to CA9 was measured by SPR. For in vivo evaluation, we used a xenograft model in which VMRC-RCW (ccRCC cell line with similar CA9 expression level to clinical samples) was subcutaneously transplanted into nude mice. To assess biodistribution, 64Cu-PD-32766 or 177Lu-PD-32766 was dosed to VMRC-RCW xenograft mice, and the percentage of injected dose in tumors and major organs was quantified by cut and count method. To evaluate whether PET imaging can detect tumors, 64Cu-PD-32766 was dosed to VMRC-RCW xenograft mice and after dosing, PET scanning was performed. For therapeutic experiments, 177Lu-PD-32766 was dosed to VMRC-RCW xenograft mice and tumor volume was measured for 45 days after dosing. Results: SPR analysis revealed that PD-32766, 63Cu-PD-32766 and 175Lu-PD-32766 exhibits an affinity of less than 0.2 nM for CA9. In in vivo biodistribution experiment, 64Cu-PD-32766 and 177Lu-PD-32766 showed specific and strong accumulation in tumors 4 hours after dosing (88 and 107% ID/g, for 64Cu-PD-32766 and 177Lu-PD-32766, respectively) and were highly retained in the tumors at 48 hours after dosing (37 and 51% ID/g, for 64Cu-PD-32766 and 177Lu-PD-32766, respectively). Maximal intensity in other normal tissues were 23 and 9 %ID/g (tumor/kidney=3.8 and 11.9 for 64Cu-PD-32766 and 177Lu-PD-32766, respectively at 4 hrs.). PET bioimaging of 64Cu-PD-32766 clearly detected only tumors, consistent with the biodistribution data. In therapeutic experiments, 177Lu-PD-32766 (30 MBq/mouse single or QW x3) was well tolerated and strikingly improved mouse survival compared with control animals for 45 days after transplantation, which suggests a robust tumor growth inhibition of 177Lu-PD-32766 dosing. Taken together, PD-32766 showed specific tumor accumulation and strong therapeutic effect in a clinically relevant xenograft model of ccRCC. Conclusion: PD-32766 has preferable properties for imaging and therapy with radionuclides and a great potential for theranostic use in ccRCC. Citation Format: Yoshihide Mizukoshi, Shota Tsuchida, Hiroko Inaba, Tatsuro Kotake, Takanori Aoki, Kai Orihara, Yuichi Funase, Naoki Kanazawa, Hikaru Shimizu, Kaita Sawano, Kentaro Suzuki, Hayato Yanagida, Takeru Ehara, Hidetomo Kitamura, Satoshi Matsushima, Masato Murakami. A novel carbonic anhydrase IX targeting radiopeptide, 64Cu-PD-32766 and 177Lu-PD-32766, exhibit promising theranostic potential in ccRCC tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6024.

Comprehensive Evaluation of 10 Docking Programs on a Diverse Set of Protein-Cyclic Peptide Complexes.

Cyclic peptides have emerged as a highly promising class of therapeutic molecules owing to their favorable pharmacokinetic properties, including stability and permeability. Currently, many clinically approved cyclic peptides are derived from natural products or their derivatives, and the development of molecular docking techniques for cyclic peptide discovery holds great promise for expanding the applications and potential of this class of molecules. Given the availability of numerous docking programs, there is a pressing need for a systematic evaluation of their performance, specifically on protein-cyclic peptide systems. In this study, we constructed an extensive benchmark data set called CPSet, consisting of 493 protein-cyclic peptide complexes. Based on this data set, we conducted a comprehensive evaluation of 10 docking programs, including Rosetta, AutoDock CrankPep, and eight protein-small molecule docking programs (i.e., AutoDock, AudoDock Vina, Glide, GOLD, LeDock, rDock, MOE, and Surflex). The evaluation encompassed the assessment of the sampling power, docking power, and scoring power of these programs. The results revealed that all of the tested protein-small molecule docking programs successfully sampled the binding conformations when using the crystal conformations as the initial structures. Among them, rDock exhibited outstanding performance, achieving a remarkable 94.3% top-100 sampling success rate. However, few programs achieved successful predictions of the binding conformations using tLEaP-generated conformations as the initial structures. Within this scheme, AutoDock CrankPep yielded the highest top-100 sampling success rate of 29.6%. Rosetta's scoring function outperformed the others in selecting optimal conformations, resulting in an impressive top-1 docking success rate of 87.6%. Nevertheless, all the tested scoring functions displayed limited performance in predicting binding affinity, with MOE@Affinity dG exhibiting the highest Pearson's correlation coefficient of 0.378. It is therefore suggested to use an appropriate combination of different docking programs for given tasks in real applications. We expect that this work will offer valuable insights into selecting the appropriate docking programs for protein-cyclic peptide complexes.

Discovery of a Novel Dual Targeting Peptide for Human Glioma: From In Silico Simulation to Acting as Targeting Ligand.

Receptor-mediated transcytosis (RMT) is a more specific, highly efficient, and reliable approach to crossing the blood-brain-barrier (BBB) and releasing the therapeutic cargos into the brain parenchyma. Here, we introduced and characterized a human/mouse-specific novel leptin-derived peptide using in silico, in vitro and in vivo experiments. Based on the bioinformatics analysis and molecular dynamics (MD) simulation, a 14 amino acid peptide sequence (LDP 14) was introduced and its interaction with leptin-receptor (ObR) was analyzed in comparison with an well known leptin-derived peptide, Lep 30. MD simulation data revealed a significant stable interaction between ligand binding domains (LBD) of ObR with LDP 14. Analyses demonstrated suitable cellular uptake of LDP 14 alone and its derivatives (LDP 14-modified G4 PAMAM dendrimer and LDP 14-modified G4 PAMAM/pEGFP-N1 plasmid complexes) via ObR, energy and species dependent manner (preferred uptake by human/mouse cell lines compared to rat cell line). Importantly, our findings illustrated that the entry of LDP 14-modified dendrimers in hBCEC-D3 cells not only is not affected by protein corona (PC) formation, as the main reason for diminishing the cellular uptake, but also PC per se can enhance uptake rate. Finally, fluorescein labeled LDP 14-modified G4 PAMAM dendrimers efficiently accumulated in the mice brain with lower biodistribution in other organs, in our in vivo study. LDP 14 introduced as a novel and highly efficient ligand, which can be used for drugs/genes delivery to brain tissue in different central nervous system (CNS) disorders.

Challenges in computational discovery of bioactive peptides in 'omics data.

Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available 'omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.

Discovery of novel peptide–dehydroepiandrosterone hybrids inducing endoplasmic reticulum stress with effective in vitro and in vivo anti-melanoma activities

Steroid hybrids have emerged as a type of advantageous compound as they could offer improved pharmacological and pharmaceutical properties. Here, we report a series of novel peptide–dehydroepiandrosterone hybrids, which would effectively induce endoplasmic reticulum stress (ERS) and lead to apoptosis with outstanding in vitro and in vivo anti-melanoma effects. The lead compound IId among various steroids conjugated with peptides and pyridines showed effective in vivo activity in B16 xenograft mice: in medium- and high-dose treatment groups (60 and 80 mg/kg), compound IId would significantly inhibit the growth of tumours by 98%–99% compared to the control group, with the highest survival rate as well. Further mechanism studies showed that compound IId would damage the endoplasmic reticulum and upregulate the ERS markers C/EBP homologous protein (CHOP) and glucose-regulated protein 78 (GRP78), which could further regulate caspase and Bcl-2 family proteins and lead to cell apoptosis. The compound IId was also proven to be effective in inhibiting B16 cell migration and invasion.

Advances in understanding migraine pathophysiology: a bench to bedside review of research insights and therapeutics.

The individual and global burden of migraine is of such significance that there are accelerated efforts to develop new therapies. New migraine therapeutics are needed to address the current deficiencies that exist in the efficacy and adherence rate of approved anti-migraine medications. The recent discovery of the calcitonin gene related peptide as an add-on to the role of serotonin has markedly increased the range of new treatment options for acute and chronic migraine. Despite this, tackling the complexity of migraine disorders requires a complete understanding of its pathophysiology. Preclinical animal models can shed light on disease-related pathophysiology, including migraine. Indeed, the use of animal models has been instrumental in developing many therapeutics. However, an animal model is limited by the predictive and face validity of that model, and this extends to preclinical migraine models. In this review, a summary of the current understanding of the pathophysiology of migraine is given from both a preclinical and clinical perspective, and an emphasis is placed on the animal models of migraine. We will discuss the strengths and pitfalls of common preclinical migraine models as well as experimental research areas to explore further.

A MassQL-Integrated Molecular Networking Approach for the Discovery and Substructure Annotation of Bioactive Cyclic Peptides.

The marine sponge-derived fungus Stachylidium bicolor 293 K04 is a prolific producer of specialized metabolites, including certain cyclic tetrapeptides called endolides, which are characterized by the presence of the unusual amino acid N-methyl-3-(3-furyl)-alanine. This rare feature can be used as bait to detect new endolide-like analogs through customized fragment pattern searches of tandem mass spectrometry data using the Mass Spec Query Language (MassQL). Here, we integrate endolide-specific MassQL queries with molecular networking to obtain substructural information guiding the targeted isolation and structure elucidation of the new proline-containing endolides E (1) and F (2). We showed that endolide F (but not E) is a moderate antagonist of the arginine vasopressin V1A receptor, a member of the G protein-coupled receptor superfamily.

Prediction, screening and characterization of novel bioactive tetrapeptide matrikines for skin rejuvenation.

Extracellular matrices play a critical role in tissue structure and function and aberrant remodelling of these matrices is a hallmark of many age-related diseases. In skin, loss of dermal collagens and disorganization of elastic fibre components are key features of photoageing. Although the application of some small matrix-derived peptides to aged skin has been shown to beneficially affect in vitro cell behaviour and, in vivo, molecular architecture and clinical appearance, the discovery of new peptides has lacked a guiding hypothesis. To identify, using protease cleavage site prediction, novel putative matrikines with beneficial activities for skin composition and structure. Here, we present an in silico (peptide cleavage prediction) to in vitro (proteomic and transcriptomic activity testing in cultured human dermal fibroblasts) to in vivo (short-term patch test and longer-term split-face clinical study) discovery pipeline, which enables the identification and characterization of peptides with differential activities. Using this pipeline we showed that cultured fibroblasts were responsive to all applied peptides, but their associated bioactivity was sequence-dependent. Based on bioactivity, toxicity and protein source, we further characterized a combination of two novel peptides, GPKG (glycine-proline-lysine-glycine) and LSVD (leucine-serine-valine-aspartate), that acted in vitro to enhance the transcription of matrix -organization and cell proliferation genes and in vivo (in a short-term patch test) to promote processes associated with epithelial and dermal maintenance and remodelling. Prolonged use of a formulation containing these peptides in a split-face clinical study led to significantly improved measures of crow's feet and firmness in a mixed population. This approach to peptide discovery and testing can identify new synthetic matrikines, providing insights into biological mechanisms of tissue homeostasis and repair and new pathways to clinical intervention.

Multi-omics resources for the Australian southern stuttering frog (Mixophyes australis) reveal assorted antimicrobial peptides

The number of genome-level resources for non-model species continues to rapidly expand. However, frog species remain underrepresented, with up to 90% of frog genera having no genomic or transcriptomic data. Here, we assemble the first genomic and transcriptomic resources for the recently described southern stuttering frog (Mixophyes australis). The southern stuttering frog is ground-dwelling, inhabiting naturally vegetated riverbanks in south-eastern Australia. Using PacBio HiFi long-read sequencing and Hi-C scaffolding, we generated a high-quality genome assembly, with a scaffold N50 of 369.3 Mb and 95.1% of the genome contained in twelve scaffolds. Using this assembly, we identified the mitochondrial genome, and assembled six tissue-specific transcriptomes. We also bioinformatically characterised novel sequences of two families of antimicrobial peptides (AMPs) in the southern stuttering frog, the cathelicidins and β-defensins. While traditional peptidomic approaches to peptide discovery have typically identified one or two AMPs in a frog species from skin secretions, our bioinformatic approach discovered 12 cathelicidins and two β-defensins that were expressed in a range of tissues. We investigated the novelty of the peptides and found diverse predicted activities. Our bioinformatic approach highlights the benefits of multi-omics resources in peptide discovery and contributes valuable genomic resources in an under-represented taxon.

Discovery of Species-Specific Proteotypic Peptides To Establish a Spectral Library Platform for Identification of Nontuberculosis Mycobacteria from Mass Spectrometry-Based Proteomics.

Nontuberculous mycobacteria are opportunistic bacteria pulmonary and extra-pulmonary infections in humans that closely resemble Mycobacterium tuberculosis. Although genome sequencing strategies helped determine NTMs, a common assay for the detection of coinfection by multiple NTMs with M. tuberculosis in the primary attempt of diagnosis is still elusive. Such a lack of efficiency leads to delayed therapy, an inappropriate choice of drugs, drug resistance, disease complications, morbidity, and mortality. Although a high-resolution LC-MS/MS-based multiprotein panel assay can be developed due to its specificity and sensitivity, it needs a library of species-specific peptides as a platform. Toward this, we performed an analysis of proteomes of 9 NTM species with more than 20 million peptide spectrum matches gathered from 26 proteome data sets. Our metaproteomic analyses determined 48,172 species-specific proteotypic peptides across 9 NTMs. Notably, M. smegmatis (26,008), M. abscessus (12,442), M. vaccae (6487), M. fortuitum (1623), M. avium subsp. paratuberculosis (844), M. avium subsp. hominissuis (580), and M. marinum (112) displayed >100 species-specific proteotypic peptides. Finally, these peptides and corresponding spectra have been compiled into a spectral library, FASTA, and JSON formats for future reference and validation in clinical cohorts by the biomedical community for further translation.

Discovery and characterization of anti-cancer peptides from a random peptide library.

We performed a forward genetic screen to discover peptides that specifically target breast cancer cells using a Penetratin tagged, random 15mer peptide library. We identified a group of novel peptides that specifically inhibited the proliferation and survival of breast cancer cells without affecting normal primary mammary epithelial cells or fibroblasts. The intrinsic apoptotic pathway is activated by these peptides in the face of abnormal expression of numerous cell cycle regulatory genes. Associated alterations in histone marks, nuclear structure, and levels of critical RNA binding proteins vary in a peptide specific manner. This study demonstrates a novel method for the discovery of new potential therapeutic peptides.

Accelerating Antimicrobial Peptide Discovery for WHO Priority Pathogens through Predictive and Interpretable Machine Learning Models.

The escalating menace of multidrug-resistant (MDR) pathogens necessitates a paradigm shift from conventional antibiotics to innovative alternatives. Antimicrobial peptides (AMPs) emerge as a compelling contender in this arena. Employing in silico methodologies, we can usher in a new era of AMP discovery, streamlining the identification process from vast candidate sequences, thereby optimizing laboratory screening expenditures. Here, we unveil cutting-edge machine learning (ML) models that are both predictive and interpretable, tailored for the identification of potent AMPs targeting World Health Organization's (WHO) high-priority pathogens. Furthermore, we have developed ML models that consider the hemolysis of human erythrocytes, emphasizing their therapeutic potential. Anchored in the nuanced physical-chemical attributes gleaned from the three-dimensional (3D) helical conformations of AMPs, our optimized models have demonstrated commendable performance-boasting an accuracy exceeding 75% when evaluated against both low-sequence-identified peptides and recently unveiled AMPs. As a testament to their efficacy, we deployed these models to prioritize peptide sequences stemming from PEM-2 and subsequently probed the bioactivity of our algorithm-predicted peptides vis-à-vis WHO's priority pathogens. Intriguingly, several of these new AMPs outperformed the native PEM-2 in their antimicrobial prowess, thereby underscoring the robustness of our modeling approach. To elucidate ML model outcomes, we probe via Shapley Additive exPlanations (SHAP) values, uncovering intricate mechanisms guiding diverse actions against bacteria. Our state-of-the-art predictive models expedite the design of new AMPs, offering a robust countermeasure to antibiotic resistance. Our prediction tool is available to the public at https://ai-meta.chem.ncu.edu.tw/amp-meta.

Immunomodulatory Peptides as Vaccine Adjuvants and Antimicrobial Agents.

The underdevelopment of adjuvant discovery and diversity, compared to core vaccine technology, is evident. On the other hand, antibiotic resistance is on the list of the top ten threats to global health. Immunomodulatory peptides that target a pathogen and modulate the immune system simultaneously are promising for the development of preventive and therapeutic molecules. Since investigating innate immunity in insects has led to prominent achievements in human immunology, such as toll-like receptor (TLR) discovery, we used the capacity of the immunomodulatory peptides of arthropods with concomitant antimicrobial or antitumor activity. An SVM-based machine learning classifier identified short immunomodulatory sequences encrypted in 643 antimicrobial peptides from 55 foe-to-friend arthropods. The critical features involved in efficacy and safety were calculated. Finally, 76 safe immunomodulators were identified. Then, molecular docking and simulation studies defined the target of the most optimal peptide ligands among all human cell-surface TLRs. SPalf2-453 from a crab is a cell-penetrating immunoadjuvant with antiviral properties. The peptide interacts with the TLR1/2 heterodimer. SBsib-711 from a blackfly is a TLR4/MD2 ligand used as a cancer vaccine immunoadjuvant. In addition, SBsib-711 binds CD47 and PD-L1 on tumor cells, which is applicable in cancer immunotherapy as a checkpoint inhibitor. MRh4-679 from a shrimp is a broad-spectrum or universal immunoadjuvant with a putative Th1/Th2-balanced response. We also implemented a pathway enrichment analysis to define fingerprints or immunological signatures for further in vitro and in vivo immunogenicity and reactogenicity measurements. Conclusively, combinatorial machine learning, molecular docking, and simulation studies, as well as systems biology, open a new opportunity for the discovery and development of multifunctional prophylactic and therapeutic lead peptides.

Multiplatform High-Definition Ion Mobility Separations of the Largest Epimeric Peptides.

Ion mobility spectrometry (IMS) coupled to mass spectrometry (MS) has become a versatile tool to fractionate complex mixtures, distinguish structural isomers, and elucidate molecular geometries. Along with the whole MS field, IMS/MS advances to ever larger species. A topical proteomic problem is the discovery and characterization of d-amino acid-containing peptides (DAACPs) that are critical to neurotransmission and toxicology. Both linear IMS and FAIMS previously disentangled d/l epimers with up to ∼30 residues. In the first study using all three most powerful IMS methodologies─trapped IMS, cyclic IMS, and FAIMS─we demonstrate baseline resolution of the largest known d/l peptides (CHH from Homarus americanus with 72 residues) with a dynamic range up to 100. This expands FAIMS analyses of isomeric modified peptides, especially using hydrogen-rich buffers, to the ∼50-100 residue range of small proteins. The spectra for d and l are unprecedentedly strikingly similar except for a uniform shift of the separation parameter, indicating the conserved epimer-specific structural elements across multiple charge states and conformers. As the interepimer resolution tracks the average for smaller DAACPs, the IMS approaches could help search for yet larger DAACPs. The a priori method to calibrate cyclic (including multipass) IMS developed here may be broadly useful.

Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools.

Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein-protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).

Enhancing antigenic peptide discovery: Improved MHC-I binding prediction and methodology

The Major Histocompatibility Complex (MHC) is a critical element of the vertebrate cellular immune system, responsible for presenting peptides derived from intracellular proteins. MHC-I presentation is pivotal in the immune response and holds considerable potential in the realms of vaccine development and cancer immunotherapy. This study delves into the limitations of current methods and benchmarks for MHC-I presentation. We introduce a novel benchmark designed to assess generalization properties and the reliability of models on unseen MHC molecules and peptides, with a focus on the Human Leukocyte Antigen (HLA)–a specific subset of MHC genes present in humans. Finally, we introduce HLABERT, a pretrained language model that outperforms previous methods significantly on our benchmark and establishes a new state-of-the-art on existing benchmarks.

Comprehensive workflow encompassing discovery, verification, and quantification of indicator peptide in snail mucin using LC-quadrupole Orbitrap high-resolution tandem mass spectrometry

Peptidomics analysis was conducted using high-resolution tandem mass spectrometry (MS2) to determine the peptide profile of snail-derived mucin extract (SM). The study was also aimed to identify an indicator peptide and validate a quantification method for this peptide. The peptide profiling and identification were conducted using discovery-based peptidomics analysis employing data-dependent acquisition, whereas the selected peptides were verified and quantified using parallel reaction monitoring acquisition. Among the 16 identified peptides, the selected octapeptide (TEAPLNPK) was quantified via precursor ion ionization (m/z 435.2400), followed by quantification of the corresponding quantifier ion fragment (m/z 639.3824) using MS2. The quantification method was optimized and validated in terms of specificity, linearity, accuracy, precision, and limit of detection/quantification. The validated method accurately quantified the TEAPLNPK content in the SM as 7.5 ± 0.2 μg/g. Our study not only identifies an indicator peptide from SM but also introduces a novel validation method, involving precursor ion ionization and quantification of specific fragments. Our findings may serve as a comprehensive workflow for the monitoring, selection, and quantification of indicator peptides from diverse food resources.

Real-world effectiveness of fremanezumab in patients with migraine switching from another mAb targeting the CGRP pathway.

This is a summary of the research article entitled "Real-world effectiveness of fremanezumabin patients with migraine switching from another mAb targeting the CGRP pathway:A subgroup analysis of the Finesse Study".The discovery of calcitonin gene-related peptide (CGRP) as a therapeutic target in migrainehas been one of the greatest achievements in neurology in recent years. Specific antibodiesagainst CGRP bind to it either via a receptor (erenumab) or ligand (fremanezumab,galcanezumab, eptinezumab). Monoclonal antibodies (mAbs) are effective, safe and welltolerateddrugs that have been approved for prophylactic treatment if there are at least4 days with migraine per month. However, in clinical practice, the failure of treatment withmAbs has been observed, and thus the question arises whether it is worthwhile to includetreatment using an antibody with a different mechanism of action.The Finesse Study was designed to evaluate the efficacy of fremanezumab in patients witha history of prior treatment failure with other mAbs against the CGRP pathway. Among the153 patients with priorly failed mAbs, switching to fremanezumab led to a ≥50% reductionin the number of days with migraine per month in 42.8% of patients. The conclusionemphasizes that switching to another antibody should be considered in patients with priortherapy failure.

Discovery, characterization, and application of a novel antimicrobial peptide produced by Lactiplantibacillus plantarum FB-2

Bacteriocins produced by lactic acid bacteria (LAB) are of interest due to their promising applications in the food industry. In this study, Plantaricin FB-2 was separated to two fractions (FB-2A, FB-2B) by gel filtration chromatography and FB-2A was chosen with higher antibacterial activity. FB-2A was further characterized by reversed-phase high performance liquid chromatography (RP-HPLC) coupled, purified AMP KMY15 was obtained and possessed antibacterial activity against Staphylococcus aureus ATCC6538 (MIC = 256 μg/mL), Escherichia coli DH5α (MIC = 128 μg/mL), Salmonella Typhimurium CICI10437 (MIC = 512 μg/mL), and Listeria monocytogenes ATCC19115 (MIC = 256 μg/mL). The amino acid sequence of KMY15 was identified as KMYKKGRLWLVAGLS. It hemolytic activities were lower than 5 %, and it co-cultures well with RAW 264.7 macrophage cells. Further Scanning Electron Microscope (SEM) also showed that KMY15 can distinctively act on the bacterial membrane leading to the leakage of cellular contents. The quantification of apoptosis using a flow cytometry (FCM) analysis further confirmed that the mortality rates of S. aureus ATCC6538 and E. coli DH5α were 63.0 % and 87.0 %, respectively, after 8 h of treatment with KMY15. It effectively mitigates the growth of S. aureus ATCC6538 in milk. Complete genome sequencing of Lactiplantibacillus plantarum FB-2 reveals the relationship between KMY15 and the genome. In conclusion, KMY15 is a potent preservative and antibacterial agent.

Introducing π-HelixNovo for practical large-scale de novo peptide sequencing.

De novo peptide sequencing is a promising approach for novel peptide discovery, highlighting the performance improvements for the state-of-the-art models. The quality of mass spectra often varies due to unexpected missing of certain ions, presenting a significant challenge in de novo peptide sequencing. Here, we use a novel concept of complementary spectra to enhance ion information of the experimental spectrum and demonstrate it through conceptual and practical analyses. Afterward, we design suitable encoders to encode the experimental spectrum and the corresponding complementary spectrum and propose a de novo sequencing model $\pi$-HelixNovo based on the Transformer architecture. We first demonstrated that $\pi$-HelixNovo outperforms other state-of-the-art models using a series of comparative experiments. Then, we utilized $\pi$-HelixNovo to de novo gut metaproteome peptides for the first time. The results show $\pi$-HelixNovo increases the identification coverage and accuracy of gut metaproteome and enhances the taxonomic resolution of gut metaproteome. We finally trained a powerful $\pi$-HelixNovo utilizing a larger training dataset, and as expected, $\pi$-HelixNovo achieves unprecedented performance, even for peptide-spectrum matches with never-before-seen peptide sequences. We also use the powerful $\pi$-HelixNovo to identify antibody peptides and multi-enzyme cleavage peptides, and $\pi$-HelixNovo is highly robust in these applications. Our results demonstrate the effectivity of the complementary spectrum and take a significant step forward in de novo peptide sequencing.