Large Number Of Peptides Research Articles

Deep2Pep: A deep learning method in multi-label classification of bioactive peptide

Functional peptides are easy to absorb and have low side effects, which has attracted increasing interest from pharmaceutical scientists. However, due to the limitations in the laboratory funding and human resources, it is difficult to screen the functional peptides from a large number of peptides with unknown functions. With the development of machine learning and Deep learning, the combination of computational methods and biological information provides an effective method for identifying peptide functions. To explore the value of multi-functional active peptides, a new deep learning method named Deep2Pep (Deep learning to Peptides) was constructed, which was based on sequence encoding, embedding, and language tokenizer. It can achieve predictions of peptides on antimicrobial, antihypertensive, antioxidant and antihyperglycemic by converting sequence information into digital vectors, combined BiLSTM, attention-residual algorithm, and BERT Encoder. The results showed that Deep2Pep had a Hamming Loss of 0.095, subset Accuracy of 0.737, and Macro F1-Score of 0.734. which outperformed other models. BiLSTM played a primary role in Deep2Pep, which BERT encoder was in an auxiliary position. Deep learning algorithms was used in this study to accurately predict the four active functions of peptides, and it was expected to provide effective references for predicting multi-functional peptides.

Antimicrobial peptides recognition using weighted physicochemical property encoding.

Antimicrobial resistance is a major public health concern. Antimicrobial peptides (AMPs) are one of the host defense mechanisms responding efficiently against multidrug-resistant microbes. Since the process of screening AMPs from a large number of peptides is still high-priced and time-consuming, the development of a precise and rapid computer-aided tool is essential for preliminary AMPs selection ahead of laboratory experiments. In this study, we proposed AMPs recognition models using a new peptide encoding method called amino acid index weight (AAIW). Four AMPs recognition models including antimicrobial, antibacterial, antiviral, and antifungal were trained based on datasets combined from the DRAMP and other published databases. These models achieved high performance compared to the preceding AMPs recognition models when evaluated on two independent test sets. All four models yielded over 93% in accuracy and 0.87 in Matthew's correlation coefficient (MCC). An online AMPs recognition server is accessible at https://amppred-aaiw.com.

Bioactive peptides from scorpion venoms: therapeutic scaffolds and pharmacological tools.

Evolution and natural selection have endowed animal venoms, including scorpion venoms, with a wide range of pharmacological properties. Consequently, scorpions, their venoms, and/or their body parts have been used since time immemorial in traditional medicines, especially in Africa and Asia. With respect to their pharmacological potential, bioactive peptides from scorpion venoms have become an important source of scientific research. With the rapid increase in the characterization of various components from scorpion venoms, a large number of peptides are identified with an aim of combating a myriad of emerging global health problems. Moreover, some scorpion venom-derived peptides have been established as potential scaffolds helpful for drug development. In this review, we summarize the promising scorpion venoms-derived peptides as drug candidates. Accordingly, we highlight the data and knowledge needed for continuous characterization and development of additional natural peptides from scorpion venoms, as potential drugs that can treat related diseases.

Overview of research and development of polypeptide drugs and traditional Chinese medicine-peptides

Peptide is a compound consisting of 2-50 amino acids, which is intermediate between small molecule and protein. It is characterized by a variety of biological activities, easy absorption, strong specific targeting, and few side effects and has become one of the hotspots in biomedical research in recent years. Chinese medicine contains a large number of peptides. The traditional processing methods such as decocting and boiling can effectively boost peptides to exert their due biological activities. At present, however, the research on Chinese medicinal components in laboratory generally employs high-concentration alcohol extraction method, which may cause the peptides to be ignored in many natural Chinese medicines. Substantial studies have revealed that the peptides in Chinese medicine are important material basis responsible for the traditional efficacy. Based on years of research and literature retrieval, this study put forward the concept of "traditional Chinese medicine(TCM)-peptides", referring to the components consisting of two or more amino acids with molecular weight between small molecules and proteins that can express the efficacy of Chinese medicine. Furthermore, this study also summarized the extraction and separation of TCM-peptides, and structure determination methods and routes, predicted the research prospect of modern research methods of TCM-peptides based on "holistic view" and big data. The artificial intelligence prediction was combined with high-throughput screening technology to improve the discovery efficiency and accuracy of TCM-peptides, and holographic images between TCM-peptides and biological targets were established to provide references for the innovative drug design and related health product development of TCM-peptides based on TCM theories.

Antirid peptides in cosmeceutical formula

Perception of beauty is tied to the idea of youth. This is why a large number of peptides proposed in the field of cosmetics are used as anti-aging/anti-wrinkle products. Some peptides have been proven in their efficacy through clinical skin trials. This paper describes some peptides that are most frequently used in cosmeceutical products and their potential benefits and practical use in dermatology.

Neuromodulation Can Be Simple: Myoinhibitory Peptide, Contained in Dedicated Regulatory Pathways, Is the Only Neurally-Mediated Peptide Modulator of Stick Insect Leg Muscle.

In the best studied cases (Aplysia feeding, crustacean stomatogastric system), peptidergic modulation is mediated by large numbers of peptides. Furthermore, in Aplysia, excitatory motor neurons release the peptides, obligatorily coupling target activation and modulator release. Vertebrate nervous systems typically contain about a hundred peptide modulators. These data have created a belief that modulation is, in general, complex. The stick insect leg is a well-studied locomotory model system, and the complete stick insect neuropeptide inventory was recently described. We used multiple techniques to comprehensively examine stick insect leg peptidergic modulation. Single-cell mass spectrometry (MS) and immunohistochemistry showed that myoinhibitory peptide (MIP) is the only neuronal (as opposed to hemolymph-borne) peptide modulator of all leg muscles. Leg muscle excitatory motor neurons contained no neuropeptides. Only the common inhibitor (CI) and dorsal unpaired median (DUM) neuron groups, each neuron of which innervates a group of functionally-related leg muscles, contained MIP. We described MIP transport to, and receptor presence in, one leg muscle, the extensor tibiae (ExtTi). MIP application reduced ExtTi slow fiber force and shortening by about half, increasing the muscle's ability to contract and relax rapidly. These data show neuromodulation does not need to be complex. Excitation and modulation do not need to be obligatorily coupled (Aplysia feeding). Modulation does not need to involve large numbers of peptides, with the attendant possibility of combinatorial explosion (stomatogastric system). Modulation can be simple, mediated by dedicated regulatory neurons, each innervating a single group of functionally-related targets, and all using the same neuropeptide.SIGNIFICANCE STATEMENT Vertebrate and invertebrate nervous systems contain large numbers (around a hundred in human brain) of peptide neurotransmitters. In prior work, neuropeptide modulation has been complex, either obligatorily coupling postsynaptic excitation and modulation, or large numbers of peptides modulating individual neural networks. The complete stick insect neuropeptide inventory was recently described. We comprehensively describe here peptidergic modulation in the stick insect leg. Surprisingly, out of the large number of potential peptide transmitters, only myoinhibitory peptide (MIP) was present in neurons innervating leg muscles. Furthermore, the peptide was present only in dedicated regulatory neurons, not in leg excitatory motor neurons. Peptidergic modulation can thus be simple, neither obligatorily coupling target activation and modulation nor involving so many peptides that combinatorial explosion can occur.

Gas-Phase Fragmentation of ADP-Ribosylated Peptides: Arginine-Specific Side-Chain Losses and Their Implication in Database Searches.

ADP-ribosylation is a reversible post-translational modification of proteins that has been linked to many biological processes. The identification of ADP-ribosylated proteins and particularly of their acceptor amino acids remains a major challenge. The attachment sites of the modification are difficult to localize by mass spectrometry (MS) because of the labile nature of the linkage and the complex fragmentation pattern of the ADP-ribose in MS/MS experiments. In this study we performed a comprehensive analysis of higher-energy collisional dissociation (HCD) spectra acquired from ADP-ribosylated peptides which were modified on arginine, serine, glutamic acid, aspartic acid, tyrosine, or lysine residues. In addition to the fragmentation of the peptide backbone, various cleavages of the ADP-ribosylated amino acid side chains were investigated. We focused on gas-phase fragmentations that were specific either to ADP-ribosylated arginine or to ADP-ribosylated serine and other O-linked ADP-ribosylations. The O-glycosidic linkage between ADP-ribose and serine, glutamic acid, or aspartic acid was the major cleavage site, making localization of these modification sites difficult. In contrast, the bond between ADP-ribose and arginine was relatively stable. The main cleavage site was the inner bond of the guanidine group, which resulted in the formation of ADP-ribosylated carbodiimide and of ornithine in place of modified arginine. Taking peptide fragment ions resulting from this specific cleavage into account, a considerably larger number of peptides containing ADP-ribosylated arginine were identified in database searches. Furthermore, the presence of diagnostic ions and of losses of fragments from peptide ions allowed us, in most cases, to distinguish between ADP-ribosylated arginine and serine residues.

Sense and Nonsense of Elevated Column Temperature in Proteomic Bottom-up LC-MS Analyses.

Elevated column temperature represents a simple means for improving chromatographic separation of peptides. Here, we demonstrated the advantages of the column temperature in peptide separation using state-of-the-art columns. More importantly, we also determined how temperature can impair proteomic bottom-up analyses. We found that an elevated temperature in combination with the acidic pH of the mobile phase induced in-column peptide hydrolysis with high specificity to Asp and accelerated five modification reactions of amino acids. The positive effects of temperature dominated in the 30 min long gradients since the column operated at 90 °C provided the largest number of identified peptides and proteins. However, the adverse effects of temperature on peptide integrity in longer liquid chromatography-mass spectrometry (LC-MS) analyses required its reduction to obtain optimum results. The largest number of peptides was identified using the column maintained at 75 °C in 60 min long gradients, at 60 °C in 120 min long gradients, and at 45 °C in 240 min long gradients. Our results indicate that no universal column temperature exists for bottom-up LC-MS analyses. Quite the contrary, the temperature setting must be selected rationally to exploit the full capabilities of the state-of-the-art mass spectrometers in proteomic LC-MS analyses, with the gradient time being a critical factor.

Bioactive peptides derived from human milk proteins: an update.

Human milk contains a variety of bioactive proteins, and some of the bioactivities are exerted only after proteins are digested in the gastrointestinal tract. This review aims to overview recent studies on bioactive peptides in human milk and gastric digesta of breast-fed infants. Milk protein-derived peptides are endogenously present in human milk, and some of them have been reported to be bioactive peptides, such as a homologue of caseinophosphopeptide, an antimicrobial peptide, and an immunomodulatory peptide. A larger number of peptides are identified in gastric aspirates from breast-fed infants, and bioactive peptides such as angiotensin I-converting enzyme-inhibitory peptides, an antioxidative peptide, opioid agonist peptides are only found in the digesta of human milk but not in human milk per se. Casein is the greatest source of released bioactive peptides. Technological advances have considerably increased our capability to identify and characterize peptides derived from human milk proteins. However, their physiological significance and the roles of these bioactive peptides in growth and development of breast-fed infants have not yet been sufficiently elucidated, and further in-vivo experiments and clinical trials are warranted.

Abstract 4536: Applying immunopeptidomics and machine learning to improve neoantigen prediction for therapeutic and diagnostic use

Abstract Background: Neoantigens are increasingly critical in immuno-oncology as therapeutic targets for neoantigen-based personalized cancer vaccines (PCVs) and as potential biomarkers for immunotherapy response. However, identifying which neoepitopes are more likely to provoke an immune response remains an important challenge for improving the effectiveness of PCVs and enabling neoantigens as a biomarker in immunotherapy. In recent years, Immuno-peptidomics has greatly improved in sensitivity and specificity, providing large number of peptides bound to MHC class I alleles in vivo. These advances make it possible to identify processed cell surface MHC bound peptides in an in vivo setting, providing accurate and representative presented peptide data for development of an improved neoantigen prediction pipeline. Methods: We generated high quality allele-specific training data for development of an accurate predictive algorithm. Mono-allelic HLA class I cell lines were generated by transfecting individual class I HLA alleles into the HLA class I null cell line K562, prioritizing alleles which will ultimately allow for development of a pan-class-I-allele prediction algorithm. Cell surface bound MHC class I peptides were identified for each transfected allele using immuno-peptidomics. We then developed and trained neural networks to predict MHC class I presentation for each assayed HLA allele. The predictive accuracy for each allele was comprehensively validated using immuno-peptidomic results derived from three sources: mono-allelic cell lines, deconvoluted cell lines, and patient derived tumor samples. Results: We applied immuno-peptidomics to develop a large and highly representative profile of MHC class I peptidomics across 30 HLA class I alleles. We then utilized this dataset to develop a highly accurate HLA class I presentation neural network. Through our work, we have identified thousands of HLA class I peptides bound to each of 30 unique HLA class I alleles, greatly expanding the known mono-allelic space. Our neoantigen prediction algorithm has been extensively validated, consistently achieving a higher overall accuracy across alleles (precision 0.88) than other publicly available tools (precision less than 0.7) based on both in vitro binding data and immuno-peptidomics, when tested on a broad set of peptide sources: mono-allelic cell lines, deconvoluted cell lines, and patient-derived tumor samples. Conclusions: Effective neoantigen identification can be greatly improved through application of immuno-peptidomics. We have generated extensive mono-allelic HLA class I cell lines and extensively characterized their class I ligandomes. We have used this data to develop and train a novel presentation neural network. Finally, we have extensively validated this tool using multiple newly-derived in vitro and in vivo sources, demonstrating very strong accuracy. Citation Format: Datta Mellacheruvu, Nick Phillips, Gabor Bartha, Jason Harris, Robert Power, Rena McClory, John West, Richard Chen, Sean Michael Boyle. Applying immunopeptidomics and machine learning to improve neoantigen prediction for therapeutic and diagnostic use [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4536.

Proteolysis in Danish blue cheese during ripening

Proteolysis in Danish blue cheese was studied during 9 weeks' ripening. Levels of pH 4.6-soluble N as a percentage of total N increased from 7.2% to 25%, indicating extensive proteolysis. Urea-polyacrylamide gel electrophoretograms confirmed the extent of proteolysis through chymosin and plasmin action early in ripening, but later the action of Penicillium roqueforti proteinases became apparent. The proteolytic specificity of Penicillium roqueforti PR-R proteinases on αS1- and β-casein was determined in a model system. Regions most susceptible to proteinase action in αS1-casein were 6–40, 69–99, 124–147 and 155–199, with a total of 91 cleavage sites identified; regions in β-casein susceptible to proteolysis were 43–87, 101–119, 161–185 and 192–209 with a total of 118 cleavage sites identified. A large number of peptides was identified cheese extracts during 9 weeks ripening, principally from αS1-casein regions 1–40, 105–136 and 150–176 and β-casein regions 6–14, 46–68, 101–140 and 193–209.

Insights Into the Complexity of Yeast Extract Peptides and Their Utilization by Streptococcus thermophilus.

Streptococcus thermophilus, an extensively used lactic starter, is generally produced in yeast extract-based media containing a complex mixture of peptides whose exact composition remains elusive. In this work, we aimed at investigating the peptide content of a commercial yeast extract (YE) and identifying dynamics of peptide utilization during the growth of the industrial S. thermophilus N4L strain, cultivated in 1 l bioreactors under pH-regulation. To reach that goal, we set up a complete analytical workflow based on mass spectrometry (peptidomics). About 4,600 different oligopeptides ranging from 6 to more than 30 amino acids in length were identified during the time-course of the experiment. Due to the low spectral abundance of individual peptides, we performed a clustering approach to decipher the rules of peptide utilization during fermentation. The physicochemical characteristics of consumed peptides perfectly matched the known affinities of the oligopeptide transport system of S. thermophilus. Moreover, by analyzing such a large number of peptides, we were able to establish that peptide net charge is the major factor for oligopeptide transport in S. thermophilus N4L.

EBprotV2: A Perseus Plugin for Differential Protein Abundance Analysis of Labeling-Based Quantitative Proteomics Data.

We present EBprotV2, a Perseus plugin for peptide-ratio-based differential protein abundance analysis in labeling-based proteomics experiments. The original version of EBprot models the distribution of log-transformed peptide-level ratios as a Gaussian mixture of differentially abundant proteins and nondifferentially abundant proteins and computes the probability score of differential abundance for each protein based on the reproducible magnitude of peptide ratios. However, the fully parametric model can be inflexible, and its R implementation is time-consuming for data sets containing a large number of peptides (e.g., >100 000). The new tool built in the C++ language is not only faster in computation time but also equipped with a flexible semiparametric model that handles skewed ratio distributions better. We have also developed a Perseus plugin for EBprotV2 for easy access to the tool. In addition, the tool now offers a new submodule (MakeGrpData) to transform label-free peptide intensity data into peptide ratio data for group comparisons and performs differential abundance analysis using mixture modeling. This approach is especially useful when the label-free data have many missing peptide intensity data points.

Discovery of a Novel Nav1.7 Inhibitor From Cyriopagopus albostriatus Venom With Potent Analgesic Efficacy

Spider venoms contain a vast array of bioactive peptides targeting ion channels. A large number of peptides have high potency and selectivity toward sodium channels. Nav1.7 contributes to action potential generation and propagation and participates in pain signaling pathway. In this study, we describe the identification of μ-TRTX-Ca2a (Ca2a), a novel 35-residue peptide from the venom of Vietnam spider Cyriopagopus albostriatus (C. albostriatus) that potently inhibits Nav1.7 (IC50 = 98.1 ± 3.3 nM) with high selectivity against skeletal muscle isoform Nav1.4 (IC50 > 10 μM) and cardiac muscle isoform Nav1.5 (IC50 > 10 μM). Ca2a did not significantly alter the voltage-dependent activation or fast inactivation of Nav1.7, but it hyperpolarized the slow inactivation. Site-directed mutagenesis analysis indicated that Ca2a bound with Nav1.7 at the extracellular S3–S4 linker of domain II. Meanwhile, Ca2a dose-dependently attenuated pain behaviors in rodent models of formalin-induced paw licking, hot plate test, and acetic acid-induced writhing. This study indicates that Ca2a is a potential lead molecule for drug development of novel analgesics.

Natural Peptides in Drug Discovery Targeting Acetylcholinesterase.

Acetylcholinesterase-inhibitory peptide has gained much importance since it can inhibit acetylcholinesterase (AChE) and increase the availability of acetylcholine in cholinergic synapses, enhancing cholinergic transmission in pharmacological treatment of Alzheimer’s disease (AD). Natural peptides have received considerable attention as biologically important substances as a source of AChE inhibitors. These natural peptides have high potential pharmaceutical and medicinal values due to their bioactivities as neuroprotective and neurodegenerative treatment activities. These peptides have attracted great interest in the pharmaceutical industries, in order to design potential peptides for use in the prophylactic and therapy purposes. Some natural peptides and their derivatives have high commercial values and have succeeded in reaching the pharmaceutical market. A large number of peptides are already in preclinical and clinical pipelines for treatment of various diseases. This review highlights the recent researches on the various natural peptides and future prospects for AD management.

Abstract 4678: High-throughput single-cell linking of antigen specificities with T cell receptor sequences using de novo generated DNA-linked MHC tetramers

Abstract Identification of correlated antigens recognized by T cell and its T cell receptor (TCR) sequences at single cell level is extremely useful to the understanding and treating immune-related diseases. Although mass cytometry, CyTOF, has pushed the number of antigenic peptides that can be interrogated at once to around 100, the destructive nature of CyTOF prohibits linking pMHCs recognized by a T cell with its TCR receptor sequences. In addition, the high cost associated with peptide synthesis process prevents the quick generation of a peptide library that can be tailored to any pathogens or diseases. Here we describe a new technology, TetATCR-Seq for Tetramer Associated TCR sequencing, that is capable of de novo generating a large amount of peptides and mapping antigen recognition with single cell TCR sequences by DNA barcoded tetramers. It evolves three major steps: 1) de novo peptide generation, 2) DNA barcoded tetramer generation, and 3) single cell sequencing of tetramer DNA barcode and TCR sequences. By spiking in 5 altered peptide ligands (APL) that are known to bind to a HCV T cell clone with other 90 irrelevant peptides, we showed that the binding of all these 5 APLs can be faithfully detected by sequencing while non-specific binding is minimum. Using a published set of cancer neo-antigen sequences, we showed that these peptides and their wild type counterparts can be quickly generated. Resulted tetramers can be used to clearly identify a population of neo-antigen only or wild type antigen only or double positive T cells. Subsequent single cell TCR sequencing showed that TCR paired α and β chain sequences can be obtained in more than 80% of single sorted T cells. Simultaneous tetramer DNA barcode sequencing showed that many T cells are found to cross-reactive to both neo-antigen and its wild type counterpart, suggesting the necessity of strict screening for neo-antigen only TCR in adoptive cell transfer therapy. In summary, the TetaTCR-seq tool we developed here enables one to quickly survey antigen repertoire again TCR repertoire at single cell level for a large number of peptides. This is an extremely useful tool in profiling T cell repertoire reactivity in infection and vaccination. It is also a useful tool to screen for T cell cross-reactivity and select neo-antigen specific TCR for cancer immunotherapy. Citation Format: Ning Jiang, Shu-qi Zhang, Chenfeng He, Eric Sun, Mingliang Zhang, Weiping Jia. High-throughput single-cell linking of antigen specificities with T cell receptor sequences using de novo generated DNA-linked MHC tetramers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4678.

Ultrafiltration performance and recovery of bioactive peptides after fractionation of tryptic hydrolysate generated from pressure-treated β-lactoglobulin

High hydrostatic pressure-assisted enzymatic digestion of β-LG accelerated protein digestion but drastically modified the resulting peptide profile, which may affect the performance of ultrafiltration (UF) which is used to fractionate the hydrolysates. Consequently, the aim of this work was to evaluate the change in UF performance of tryptic hydrolysates generated after β-LG pre-pressurization at 0.1 (control), 400 and 600 MPa. Compared to the other conditions, high peptide relative abundance, including abundance of several bioactive peptides, was observed for the 400 MPa hydrolysate. During total recirculation and concentration mode, the permeate flux of the 400 MPa hydrolysate was lower than for other conditions. After peptide desorption from the membrane, ALPHMIR, an antihypertensive peptide, was identified as the main fouling material. For the 400 MPa condition, a larger number of peptides, mainly negatively charged and with higher relative abundance in the hydrolysate, were identified on the membrane surface compared to other conditions. While the repulsion phenomenon should occur between these peptides and the membrane material, both negatively charged, their detection at membrane surface is due to a size effect and hydrophobic interaction rather than a charge mechanism. Consequently, even if pressure treatment of β-LG improved the production of bioactive peptides, it is necessary to optimize hydrodynamic conditions or membrane material during filtration to minimize loss of UF performance.

PROHIBITIN3 Forms Complexes with ISOCHORISMATE SYNTHASE1 to Regulate Stress-Induced Salicylic Acid Biosynthesis in Arabidopsis.

Salicylic acid (SA) is a major defense signal in plants. In Arabidopsis (Arabidopsis thaliana), the chloroplast-localized isochorismate pathway is the main source of SA biosynthesis during abiotic stress or pathogen infections. In the first step of the pathway, the enzyme ISOCHORISMATE SYNTHASE1 (ICS1) converts chorismate to isochorismate. An unknown enzyme subsequently converts isochorismate to SA. Here, we show that ICS1 protein levels increase during UV-C stress. To identify proteins that may play roles in SA production by regulating ICS1, we analyzed proteins that coimmunoprecipitated with ICS1 via mass spectrometry. The ICS1 complexes contained a large number of peptides from the PROHIBITIN (PHB) protein family, with PHB3 the most abundant. PHB proteins have diverse biological functions that include acting as scaffolds for protein complex formation and stabilization. PHB3 was reported previously to localize to mitochondria. Using fractionation, protease protection, and live imaging, we show that PHB3 also localizes to chloroplasts, where ICS1 resides. Notably, loss of PHB3 function led to decreased ICS1 protein levels in response to UV-C stress. However, ICS1 transcript levels remain unchanged, indicating that ICS1 is regulated posttranscriptionally. The phb3 mutant displayed reduced levels of SA, the SA-regulated protein PR1, and hypersensitive cell death in response to UV-C and avirulent strains of Pseudomonas syringae and, correspondingly, supported increased growth of P. syringae The expression of a PHB3 transgene in the phb3 mutant complemented all of these phenotypes. We suggest a model in which the formation of PHB3-ICS1 complexes stabilizes ICS1 to promote SA production in response to stress.

Integrin-Targeted Peptide- and Peptidomimetic-Drug Conjugates for the Treatment of Tumors

Integrins are heterodimeric cell surface receptors that mediate cell-cell and cell-extracellular matrix adhesion. These molecules play a role in processes such as cell growth and proliferation, differentiation, migration, cell trafficking, besides contributing to angiogenesis and tumor development. Given their biological role, integrins have been proposed as amenable targets in medicinal chemistry. In particular, αvβ3, αvβ5, αvβ6 and α5β1, integrins involved in tumor angiogenesis and metastasis, have been the subject of studies aimed at the discovery of novel cancer therapeutics. A large number of peptides and peptidomimetics based on the RGD (Arg-Gly-Asp) recognition sequence were developed in the past two decades as integrin ligands. Though such ligands have not been satisfactory as anti-angiogenic agents, their use as tools to achieve selective tumor targeting of anticancer drugs has been explored. In this review, we summarize recent literature and patent applications in which integrin peptidic and peptidomimetic ligands were conjugated to chemotherapeutic agents both with stable or cleavable bonds to achieve tumor targeted drug delivery. Relevant recent patents and literature in this field have been considered spanning the search from 2000 to 2016. Literature and patents were examined according to the different classes of cytotoxic drug targeted to integrins. In spite of the promising features of the conjugates, none of them has entered clinical trials. New efforts are focused on innovative approaches in the field such as the synthesis of new integrin ligands able to target a single integrin type or the employment of nanoparticles based drug delivery systems.

Deamidation post-translational modification in naturally generated peptides in Spanish dry-cured ham.

A large number of peptides are generated during the processing of dry-cured ham that can be affected by post-translational modifications (PTM). One of the most studied PTMs is the oxidation of susceptible residues but other modifications such as deamidation have been scarcely reported in the literature. This work has been focused on the impact of deamidation in those peptides generated in 12-months dry-cured hams. The results obtained in Biceps femoris and Semimembranosus muscles showed that 52% and 48% of the identified peptides, respectively, were affected by PTMs, with a total of 277 and 282 sequences showing deamidation in both muscles, respectively. It appears that deamidation is not influenced by the type of muscle but is probably favored by characteristic processing conditions like temperature, pH, and salting of hams. The knowledge of the affected sequences provides novel information for a better characterisation of proteolysis phenomena during the processing of dry-cured ham.