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Motivation There has been a burgeoning interest in cyclic peptide therapeutics due to their various outstanding advantages and strong potential for drug formation. However, it is undoubtedly costly and inefficient to use traditional wet lab methods to clarify their biological activities. Using artificial intelligence instead is a more energy-efficient and faster approach. MuCoCP aims to build a complete pre-trained model for extracting potential features of cyclic peptides, which can be fine-tuned to accurately predict cyclic peptide bioactivity on various downstream tasks. To maximize its effectiveness, we use a novel data augmentation method based on a priori chemical knowledge and multiple unsupervised training objective functions to greatly improve the information-grabbing ability of the model. Results To assay the efficacy of the model, we conducted validation on the membrane-permeability of cyclic peptides which achieved an accuracy of 0.87 and R-squared of 0.503 on CycPeptMPDB using semi-supervised training and obtained an accuracy of 0.84 and R-squared of 0.384 using a model with frozen parameters on an external dataset. This result has achieved state-of-the-art, which substantiates the stability and generalization capability of MuCoCP. It means that MuCoCP can fully explore the high-dimensional information of cyclic peptides and make accurate predictions on downstream bioactivity tasks, which will serve as a guide for the future de novo design of cyclic peptide drugs and promote the development of cyclic peptide drugs. Availability and implementation All code used in our proposed method can be found at https://github.com/lennonyu11234/MuCoCP. [ABSTRACT FROM AUTHOR]

Currently, cancer is the second leading cause of death worldwide, and potential targeted drugs and molecular pathways for cancer development and progression have been a hot research topic worldwide. In recent years, the importance of the kinase superfamily in diseases has been well demonstrated by studies on various molecular mechanisms of kinases and the successful application of their inhibitors in diseases. Pseudokinases are members of the kinase superfamily, which have been increasingly documented to play a crucial role in cancers year after year. As a member of pseudokinases, tribbles homolog 3 (TRIB3) also exerts diverse functions in different cancers through different interacting proteins and molecular pathways, especially in tumor immunity, stemness, drug resistance, metabolism, and autophagy. In addition, peptide drugs targeting TRIB3 have high specificity in preclinical studies, which shows great promise for TRIB3 application in diseases including cancers. In this review, we dissect diverse functions played by TRIB3 in different cancers, describing the underlying mechanisms in detail. Notably, inhibitors and agonists currently available for TRIB3 are discussed, indicating the potential for TRIB3 as a therapeutic target. [ABSTRACT FROM AUTHOR]

Peptide drugs are becoming star drug agents with high efficiency and selectivity which open up new therapeutic avenues for various diseases. However, the sensitivity to hydrolase and the relatively short half-life have severely hindered their development. In this study, a new generation artificial intelligence-based system for accurate prediction of peptide half-life was proposed, which realized the half-life prediction of both natural and modified peptides and successfully bridged the evaluation possibility between two important species (human, mouse) and two organs (blood, intestine). To achieve this, enzymatic cleavage descriptors were integrated with traditional peptide descriptors to construct a better representation. Then, robust models with accurate performance were established by comparing traditional machine learning and transfer learning, systematically. Results indicated that enzymatic cleavage features could certainly enhance model performance. The deep learning model integrating transfer learning significantly improved predictive accuracy, achieving remarkable R 2 values: 0.84 for natural peptides and 0.90 for modified peptides in human blood, 0.984 for natural peptides and 0.93 for modified peptides in mouse blood, and 0.94 for modified peptides in mouse intestine on the test set, respectively. These models not only successfully composed the above-mentioned system but also improved by approximately 15% in terms of correlation compared to related works. This study is expected to provide powerful solutions for peptide half-life evaluation and boost peptide drug development. [ABSTRACT FROM AUTHOR]

Motivation Peptides are promising agents for the treatment of a variety of diseases due to their specificity and efficacy. However, the development of peptide-based drugs is often hindered by the potential toxicity of peptides, which poses a significant barrier to their clinical application. Traditional experimental methods for evaluating peptide toxicity are time-consuming and costly, making the development process inefficient. Therefore, there is an urgent need for computational tools specifically designed to predict peptide toxicity accurately and rapidly, facilitating the identification of safe peptide candidates for drug development. Results We provide here a novel computational approach, CAPTP, which leverages the power of convolutional and self-attention to enhance the prediction of peptide toxicity from amino acid sequences. CAPTP demonstrates outstanding performance, achieving a Matthews correlation coefficient of approximately 0.82 in both cross-validation settings and on independent test datasets. This performance surpasses that of existing state-of-the-art peptide toxicity predictors. Importantly, CAPTP maintains its robustness and generalizability even when dealing with data imbalances. Further analysis by CAPTP reveals that certain sequential patterns, particularly in the head and central regions of peptides, are crucial in determining their toxicity. This insight can significantly inform and guide the design of safer peptide drugs. Availability and implementation The source code for CAPTP is freely available at https://github.com/jiaoshihu/CAPTP. [ABSTRACT FROM AUTHOR]

Motivation Cyclization is a common strategy to enhance the therapeutic potential of peptides. Many cyclic peptide drugs have been approved for clinical use, in which the disulfide-driven cyclic peptide is one of the most prevalent categories. Molecular docking is a powerful computational method to predict the binding modes of molecules. For protein-cyclic peptide docking, a big challenge is considering the flexibility of peptides with conformers constrained by cyclization. Results Integrating our efficient peptide 3D conformation sampling algorithm MODPEP2.0 and knowledge-based scoring function ITScorePP, we have proposed an extended version of our hierarchical peptide docking algorithm, named HPEPDOCK2.0, to predict the binding modes of the peptide cyclized through a disulfide against a protein. Our HPEPDOCK2.0 approach was extensively evaluated on diverse test sets and compared with the state-of-the-art cyclic peptide docking program AutoDock CrankPep (ADCP). On a benchmark dataset of 18 cyclic peptide-protein complexes, HPEPDOCK2.0 obtained a native contact fraction of above 0.5 for 61% of the cases when the top prediction was considered, compared with 39% for ADCP. On a larger test set of 25 cyclic peptide-protein complexes, HPEPDOCK2.0 yielded a success rate of 44% for the top prediction, compared with 20% for ADCP. In addition, HPEPDOCK2.0 was also validated on two other test sets of 10 and 11 complexes with apo and predicted receptor structures, respectively. HPEPDOCK2.0 is computationally efficient and the average running time for docking a cyclic peptide is about 34 min on a single CPU core, compared with 496 min for ADCP. HPEPDOCK2.0 will facilitate the study of the interaction between cyclic peptides and proteins and the development of therapeutic cyclic peptide drugs. Availability and implementation http://huanglab.phys.hust.edu.cn/hpepdock/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic caused several million deaths worldwide. Development of anti-coronavirus drugs is thus urgent. Unlike conventional non-peptide drugs, antiviral peptide drugs are highly specific, easy to synthesize and modify, and not highly susceptible to drug resistance. To reduce the time and expense involved in screening thousands of peptides and assaying their antiviral activity, computational predictors for identifying anti-coronavirus peptides (ACVPs) are needed. However, few experimentally verified ACVP samples are available, even though a relatively large number of antiviral peptides (AVPs) have been discovered. In this study, we attempted to predict ACVPs using an AVP dataset and a small collection of ACVPs. Using conventional features, a binary profile and a word-embedding word2vec (W2V), we systematically explored five different machine learning methods: Transformer, Convolutional Neural Network, bidirectional Long Short-Term Memory, Random Forest (RF) and Support Vector Machine. Via exhaustive searches, we found that the RF classifier with W2V consistently achieved better performance on different datasets. The two main controlling factors were: (i) the dataset-specific W2V dictionary was generated from the training and independent test datasets instead of the widely used general UniProt proteome and (ii) a systematic search was conducted and determined the optimal k-mer value in W2V, which provides greater discrimination between positive and negative samples. Therefore, our proposed method, named iACVP, consistently provides better prediction performance compared with existing state-of-the-art methods. To assist experimentalists in identifying putative ACVPs, we implemented our model as a web server accessible via the following link: http://kurata35.bio.kyutech.ac.jp/iACVP. [ABSTRACT FROM AUTHOR]

Studies have shown that some peptides and small molecules can induce non IgE-mediated anaphylactoid reactions through mast cell activation. Upon activation, mast cells degranulate and release vasoactive and proinflammatory mediators, from cytoplasmic granules into the extracellular environment which can induce a cascade of severe adverse reactions. This study describes a lead optimization strategy to select NaV1.7 inhibitor peptides that minimize acute mast cell degranulation (MCD) toxicities. Various in vitro, in vivo, and PKPD models were used to screen candidates and guide peptide chemical modifications to mitigate this risk. Anesthetized rats dosed with peptides demonstrated treatment-related decreases in blood pressure and increases in plasma histamine concentrations which were reversible with a mast cell stabilizer, supporting the MCD mechanism. In vitro testing in rat mast cells with NaV1.7 peptides demonstrated a concentration-dependent increase in histamine. Pharmacodynamic modeling facilitated establishing an in vitro to in vivo correlation for histamine as a biomarker for blood pressure decline via the MCD mechanism. These models enabled assessment of structure-activity relationship (SAR) to identify substructures that contribute to peptide-mediated MCD. Peptides with hydrophobic and cationic characteristics were determined to have an elevated risk for MCD, which could be reduced or avoided by incorporating anionic residues into the protoxin II scaffold. Our analyses support that in vitro MCD assessment in combination with PKPD modeling can guide SAR to improve peptide lead optimization and ensure an acceptable early in vivo tolerability profile with reduced resources, cycle time, and animal use. [ABSTRACT FROM AUTHOR]

Viruses represent one of the greatest threats to human health, necessitating the development of new antiviral drug candidates. Antiviral peptides often possess excellent biological activity and a favourable toxicity profile, and therefore represent a promising field of novel antiviral drugs. As the quantity of sequencing data grows annually, the development of an accurate in silico method for the prediction of peptide antiviral activities is important. This study leverages advances in deep learning and cheminformatics to produce a novel sequence-based deep neural network classifier for the prediction of antiviral peptide activity. The method outperforms the existent best-in-class, with an external test accuracy of 93.9%, Matthews correlation coefficient of 0.87 and an Area Under the Curve of 0.93 on the dataset of experimentally validated peptide activities. This cutting-edge classifier is available as an online web server at https://research.timmons.eu/ennavia , facilitating in silico screening and design of peptide antiviral drugs by the wider research community. [ABSTRACT FROM AUTHOR]

The structural description of peptide ligands bound to G protein-coupled receptors (GPCRs) is important for the discovery of new drugs and deeper understanding of the molecular mechanisms of life. Here we describe a three-stage protocol for the molecular docking of peptides to GPCRs using a set of different programs: (1) CABS-dock for docking fully flexible peptides; (2) PD2 method for the reconstruction of atomistic structures from C-alpha traces provided by CABS-dock and (3) Rosetta FlexPepDock for the refinement of protein–peptide complex structures and model scoring. We evaluated the proposed protocol on the set of seven different GPCR–peptide complexes (including one containing a cyclic peptide), for which crystallographic structures are available. We show that CABS-dock produces high resolution models in the sets of top-scored models. These sets of models, after reconstruction to all-atom representation, can be further improved by Rosetta high-resolution refinement and/or minimization, leading in most of the cases to sub-Angstrom accuracy in terms of interface root-mean-square-deviation measure. [ABSTRACT FROM AUTHOR]

Motivation High-throughput sequencing discovers many naturally occurring disulfide-rich peptides or cystine-rich peptides (CRPs) with diversified bioactivities. However, their structure information, which is very important to peptide drug discovery, is still very limited. Results We have developed a CRP-specific structure prediction method called C ystine- Ri ch peptide S tructure P rediction (CRiSP), based on a customized template database with cystine-specific sequence alignment and three machine-learning predictors. The modeling accuracy is significantly better than several popular general-purpose structure modeling methods, and our CRiSP can provide useful model quality estimations. Availability and implementation The CRiSP server is freely available on the website at http://wulab.com.cn/CRISP. Contact wuyd@pkusz.edu.cn or jiangfan@pku.edu.cn Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The ICH S6(R1) recommendations on safety evaluation of biotherapeutics have led to uncertainty in determining what would constitute a cause for concern that would require genotoxicity testing. A Health and Environmental Sciences Institute's Genetic Toxicology Technical Committee Workgroup was formed to review the current practice of genotoxicity assessment of peptide/ protein-related biotherapeutics. There are a number of properties of peptide/protein-related biotherapeutics that distinguish such products from traditional 'small molecule' drugs and need to be taken into consideration when assessing whether genotoxicity testing may be warranted and if so, how to do it appropriately. Case examples were provided by participating companies and decision trees were elaborated to determine whether and when genotoxicity evaluation is needed for peptides containing natural amino acids, non-natural amino acids and other chemical entities and for unconjugated and conjugated proteins. From a scientific point of view, there is no reason for testing peptides containing exclusively natural amino acids irrespective of the manufacturing process. If non-natural amino acids, organic linkers and other non-linker chemical components have already been tested for genotoxicity, there is no need to re-evaluate them when used in different peptide/protein-related biotherapeutics. Unless the peptides have been modified to be able to enter the cells, it is generally more appropriate to evaluate the peptides containing the nonnatural amino acids and other non-linker chemical moieties in vivo where the cleavage products can be formed. For linkers, it is important to determine if exposure to reactive forms are likely to occur and from which origin. When the linkers are anticipated to be potential mutagenic impurities they should be evaluated according to ICH M7. If linkers are expected to be catabolic products, it is recommended to test the entire conjugate in vivo, as this would ensure that the relevant 'free' linker forms stemming from in vivo catabolism are tested. [ABSTRACT FROM AUTHOR]

Background: Low circulating brain derived neurotrophic factor may promote cognitive deterioration, but the effects of neurotrophic and combination drug therapies on serum brain derived neurotrophic factor were not previously investigated in Alzheimer's disease. Methods: We evaluated the effects of Cerebrolysin, donepezil, and the combined therapy on brain derived neurotrophic factor serum levels at week 16 (end of Cerebrolysin treatment) and week 28 (endpoint) in mild-to-moderate Alzheimer's disease patients. Results: Cerebrolysin, but not donepezil, increased serum brain derived neurotrophic factor at week 16, while the combination therapy enhanced it at both week 16 and study endpoint. Brain derived neurotrophic factor responses were significantly higher in the combination therapy group than in donepezil and Cerebrolysin groups at week 16 and week 28, respectively. Brain derived neurotrophic factor increases were greater in apolipoprotein E epsilon-4 allele carriers, and higher brain derived neurotrophic factor levels were associated with better cognitive improvements in apolipoprotein E epsilon-4 allele patients treated with Cerebrolysin and the combined therapy. Conclusion: Our results indicate a synergistic action of Cerebrolysin and donepezil to increase serum brain derived neurotrophic factor and delaying cognitive decline, particularly in Alzheimer's disease cases with apolipoprotein E epsilon-4 allele. [ABSTRACT FROM AUTHOR]

Innate lymphoid cells (ILCs) are a newly identified subset of innate cells that play fundamentally crucial roles for early immune defense at mucosal and non-mucosal sites. ILCs consist of ILC1s, ILC2s and ILC3s, which each have distinct transcription factors controlling their development and function. Interestingly, each of the ILC subsets represents the innate counterparts of CD4+ helper T-cell subsets Th1, Th2 and Th17 on the basis of transcriptional regulation. ILC1s that produce IFN- or TNF-α, ILC2s that produce Th2-type cytokines mainly such as IL-5 or IL-13 and ILC3s have been recently reported and reviewed in terms of IL-22- or IL-17-producing function and cell development. However, in this relatively new field, it remains likely that additional functional and regulatory mechanisms remain to be explored. More recent findings show that ILC3s are regulated by RORt, which plays an important role for the mucosal barrier and surface protection against pathogenic bacterial infection. ILC3s might cooperate with other cells (e.g. T cells or dendritic cells) directly or indirectly, and subsequently ILC3s have impact on tissues with prompt regulation. Especially, ILC3s in mucosal site are well known to protect the intestinal surface barrier through inducible antimicrobial peptides via IL-22. Here, I will summarize and discuss the roles, function and heterogeneity of ILC3s in mucosal tissues. [ABSTRACT FROM AUTHOR]

The defining hallmark of the newly described species, Staphylococcus argenteus, in comparison to its sister species, S. aureus and S. schweitzeri, is the absence of production of the carotenoid pigment, staphyloxanthin. Staphylococcus argenteus lacks the responsible genetic locus crtOPQMN. We examined the impact of carotenoid synthesis in two non-pigmented S. argenteus strains, MSHR1132 and SCC1165. Following complementation with a plasmid containing the carotenoid operon (pTX-crtOPQMN), compared to wild type, both complemented strains showed substantial carotenoid production, with a resultant increase in cell membrane rigidity. Surprisingly, both crtOPQMN-complemented strains exhibited increased susceptibility to the host defense peptides, LL-37 and hNP-1 in vitro, and reduced virulence in an experimental rabbit endocarditis model. [ABSTRACT FROM AUTHOR]

Macrocyclic peptides are potentially a source of powerful drugs, but their de novo discovery remains challenging. Here we describe the discovery of a high-affinity (Kd = 10 nM) peptide macrocycle (M21) against human tumor necrosis factor-alpha (hTNFα), a key drug target in the treatment of inflammatory disorders, directly from diverse semi-synthetic phage peptide repertoires. The bicyclic peptide M21 (ACPPCLWQVLC) comprises two loops covalently anchored to a 2,4,6-trimethyl-mesitylene core and upon binding induces disassembly of the trimeric TNFα cytokine into dimers and monomers. A 2.9 Å crystal structure of the M21/hTNFα complex reveals the peptide bound to a hTNFα dimer at a normally buried epitope in the trimer interface overlapping the binding site of a previously discovered small molecule ligand (SPD304), which also induces TNF trimer dissociation and synergizes with M21 in the inhibition of TNFα cytotoxicity. The discovery of M21 underlines the potential of semi-synthetic bicyclic peptides as ligands for the discovery of cryptic epitopes, some of which are poorly accessible to antibodies. [ABSTRACT FROM AUTHOR]

Objectives To demonstrate the correlation between the nanostructure formation and the long duration of action in vivo of peptides, the morphology of nanostructures of LXT-101, a novel synthetic amphiphilic peptide of gonadotropin-releasing hormone antagonist were observed when dissolved in different solvents, and their long-acting properties in vivo were investigated in this study. Methods The morphology of nanostructures of LXT-101 was observed by transmission electron microscopy when dissolved in different solvents, and the plasma concentrations of LXT-101 and testosterone levels were also assayed for different solutions after intramuscular injection administration in beagle dogs. Key findings TEM data suggest that LXT-101 in pure water can form fibres, while in mannitol, dextrose or sodium chloride solution, they tend to form vesicles. The pharmacokinetic and pharmacodynamic results showed that the plasma concentrations of LXT-101 within 48 h were much higher but descended dramatically with mannitol, dextrose and NaCl solutions structurally composed of vesicles compared with that of pure water structurally composed of fibres. An effectively suppression of testosterone can be achieved only 2 or 3 days with the frontal three solutions, while LXT-101 in pure water maintained over a period of 7 days. Conclusions It may indicate that LXT-101 peptide in pure water forms fibre depot that release monomeric active peptide slowly. The correlation between the nanostructure and duration of action in vivo suggests that the addition of excipients influence self-assembly process of LXT-101 that leads to the formation of different nanostructures and exhibit various behaviours in vivo. [ABSTRACT FROM AUTHOR]

STUDY QUESTION Does antimicrobial peptide, LL-37, inhibit sperm fertilizing ability? SUMMARY ANSWER Our results indicate that LL-37 inhibits mouse and human sperm fertilizing ability. WHAT IS KNOWN ALREADY LL-37, a cationic antimicrobial peptide, exerts its microbicidal effects through the disruption of microbial cytoplasmic membranes following its interaction with microbial surface anionic phospholipids. ALL-38 (an LL-37 close analogue: LL-37 + Ala at the N-terminus) is produced in the vagina 2–6 h post-intercourse from its precursor hCAP-18, a seminal plasma component. At this time, motile sperm have already swum into the uterine cavity, thus unexposed to ALL-38. Since sperm contain a substantial amount of acidic sulfogalactosylglycerolipid (SGG) on their surface, treatment of sperm with LL-37 may cause their membrane disruption in an analogous manner to that occurring on microbial membranes. STUDY DESIGN, SIZE AND DURATION Mouse/human sperm treated (2–30 min) with LL-37 in a physiological concentration range (up to 10.8 µM) were assessed for SGG-dependent LL-37 binding, and parameters relevant to fertilizing ability, namely motility and intactness of the sperm acrosome and plasma membrane. Ability of mouse sperm to fertilize eggs in vitro was also evaluated. Each study was performed with greater than or equal to three different sperm samples. The efficacy of LL-37 to inhibit sperm fertilizing ability in vivo was determined in female mice (n = 26 each for LL-37 treatment and no treatment), using sperm retrieved from 26 males. PARTICIPANTS/MATERIALS, SETTING, METHODS Human sperm samples were donated by fertile men. LL-37 was chemically synthesized and was biotinylated for sperm binding studies. Sperm motility was assessed by videomicroscopy and the acrosomal status by Coomassie blue staining of acrosome-intact mouse sperm or the exposure of CD46, an inner acrosomal membrane protein, of acrosome reacted human sperm. Sperm membrane permeabilization/disruption was assessed by the loss of hypo-osmotic swelling response, an incorporation of Sytox Green (a membrane impermeable fluorescent DNA dye), and electron microscopy. Mouse IVF was scored by the presence of two pronuclei in eggs 6 h post-insemination. Ability of mouse sperm to fertilize eggs in vivo was determined by the pregnancy outcome of female mice injected transcervically with sperm with or without LL-37. MAIN RESULTS AND THE ROLE OF CHANCE Biotinylated LL-37 bound to both mouse and human sperm and the binding was partially dependent on sperm surface SGG. Mouse and human sperm became immotile and underwent a premature acrosome reaction upon treatment with LL-37 at 3.6 and 10.8 µM, respectively. The initial action of LL-37 on both mouse and human sperm appeared to be through permeabilization/disruption of sperm surface membranes evidenced by the loss of hypo-osmotic swelling response, Sytox Green staining and electron microscopy revealing ultrastructural damage. Mouse sperm treated with 3.6 µM LL-37 lost the ability to fertilize eggs both in vitro and in vivo. All 26 female mice inseminated with sperm and LL-37 did not become pregnant. No apparent damage to the reproductive tract was observed as revealed by histological characterization in LL-37-inseminated mice and these females resumed fecundity following mating with fertile males. LIMITATIONS, REASONS FOR CAUTION Direct demonstration that LL-37 treated human sperm fail to fertilize eggs was limited by legal restrictions on obtaining human eggs for such use. WIDER IMPLICATIONS OF THE FINDINGS Our results reveal selective inhibitory effects of LL-37 on sperm fertilizing ability in mice without apparent impairment to the female reproductive tract. LL-37 is therefore a promising candidate to be developed into a vaginal ... [ABSTRACT FROM AUTHOR]

Objectives This in vitro investigation was designed to examine potential neuroprotection by dicaffeoylquinic acids ( diCQAs) extracted from a traditional Chinese medicinal herb herba erigerontis and their effects against the toxicity induced by β-amyloid peptide ( Aβ25-35). Methods The neuroblastoma SH-SY5Y cell line was treated with Aβ or 3, 4- diCQA, 3, 5- diCQA or 4, 5- diCQA. 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide ( MTT) reduction was assayed by spectrophotometrical method, lipid peroxidation (malondialdehyde) on the basis of the level of thiobarbituric acid-reactive substance, the activity of superoxide dismutase by the xanthine oxidase procedure, the frequency of apoptosis by flow cytometry, and the levels of α3 and α7 nicotinic acetylcholine receptor ( nAChR) subunit proteins by Western blotting. Key findings When the cells were exposed to Aβ25-35, MTT reduction declined, oxidative stress and apoptosis were enhanced, and the expression of α3 and α7 nAChR subunit proteins was lowered. Expression of the α7 nAChR subunit protein was increased by all three diCQAs, and the level of α3 was increased by 3, 5- diCQA and 4, 5- diCQA. Significantly, pretreatment with diCQAs attenuated the neurotoxic effects of Aβ25-35, a neuroprotective effect in which the upregulation of α7 and α3 nAChR may be involved. Conclusion The diCQAs exert a protective effect on Aβ-induced neurotoxicity in SH-SY5Y cells and a potential underlying mechanism involving stimulation of nAChRs. [ABSTRACT FROM AUTHOR]

PEGylation of peptide drugs prolongs their circulating lifetimes in plasma. However, PEGylation can produce a decrease in the in vitro bioactivity. Longer poly(ethylene glycol) (PEG) chains are favourable for circulating lifetimes but unfavourable for in vitro bioactivities. In order to circumvent the conflicting effects of PEG length, a hydrophobic peptide, using an antimicrobial peptide as a model, was PEGylated with short PEG chains. The PEGylated peptides self-assembled in aqueous solution into micelles with PEG shell and peptide core. In these micelles, the core peptides were protected by the shell, thus reducing proteolytic degradation. Meanwhile, most of the in vitro antimicrobial activities still remained due to the short PEG chain attached. The stabilities of the PEGylated peptides were much higher than that of the unPEGylated peptides in the presence of chymotrypsin and serum. The antimicrobial activities of the PEGylated peptides in the presence of serum, an ex vivo assay, were much higher than that of the unPEGylated peptide. [ABSTRACT FROM PUBLISHER]

Background. Telavancin is an investigational, rapidly bactericidal lipoglycopeptide with a multifunctionalmechanism of action. Methods. We conducted 2 parallel, randomized, double-blind, active-control, phase 3 studies with a prespecified pooled analysis design. Patients aged ≥18 years who had complicated skin and skin-structure infections caused by suspected or confirmed gram-positive organisms were randomized to receive either telavancin (10 mg/kg intravenously every 24 h) or vancomycin (1 g intravenously every 12 h). Results. A total of 1867 patients were randomized and received ≥1 dose of study medication. In the clinically evaluable population, at 7-14 days after receipt of the last antibiotic dose, success was achieved in 88% and 87% of patients who received telavancin and vancomycin, respectively (95% confidence interval for the difference,-2.1 to 4.6). Methicillin-resistant Staphylococcus aureus was isolated at baseline from samples from 579 clinically evaluable patients. Among these patients with methicillin-resistant S. aureus infection, cure rates were 91% among patients who received telavancin and 86% among patients who received vancomycin (95% confidence interval for the difference, -1.1 to 9.3). Microbiologic eradication among patients infected with methicillin-resistant S. aureus was 90% in the telavancin treatment group and 85% in the vancomycin treatment group (95% confidence interval for the difference, -0.9 to 9.8). Therapy was discontinued because of adverse events in 8% and 6% of patients who received telavancin and vancomycin, respectively. Except for mild taste disturbance, nausea, vomiting, and serum creatinine concentration elevation in the telavancin treatment group and pruritus in the vancomycin treatment group, adverse events were similar between groups with regard to type and severity. Conclusions. Telavancin given once daily is at least as effective as vancomycin for the treatment of patients with complicated skin and skin-structure infections, including those infected with methicillin-resistant S. aureus. [ABSTRACT FROM AUTHOR]

Reports on the launch of human trials to study drugs that will abrogate cell cycle checkpoints. Biotechnology company CanBas' launch of the first human trial of a peptide drug; Exelixis' plans to study an orally bioavailable small-molecule inhibitor of the chk1 and chk2 checkpoint kinases; Efforts to identify and clone mammalian checkpoint genes.

The article discusses a study related to evaluation of bremelanotide (BMT) as an effective treatment for hypoactive sexual desire disorder (HSDD) characterized by lack of desire for sexual activity. Topics discussed include reduction in distress related to low desire showed from Female Sexual Distress Scale-Desire/Arousal/Orgasm (FSDS-DAO) scores, improvement in Female Sexual Function Index (FSFI) and improvements in HSDD core components such as lubrication and arousal.

An abstract of the article "Conversations with Women About Female Sexual Dysfunction (FSD) and Treatment with Bremelanotide." by P. E. Koochaki and colleagues is presented.

The article discusses research on the hemodynamic and pharmacokinetic interactions of bremelanotide and ethanol for the treatment of hypoactive sexual desire disorder (HSDD) in healthy test subjects compared to a placebo group. The study evaluated the safety and tolerability of bremelanotide when co-administered with ethanol, as well as its adverse events including hypotensive or orthostatic hypotensive effects.

The article presents the dosage forms of new drugs such as Liraglutide injection and Meclizine hydrochloride.

Identifying the epitope to which an antibody binds is central for many immunological applications such as drug design and vaccine development. The Pepitope server is a web-based tool that aims at predicting discontinuous epitopes based on a set of peptides that were affinity-selected against a monoclonal antibody of interest. The server implements three different algorithms for epitope mapping: PepSurf, Mapitope, and a combination of the two. The rationale behind these algorithms is that the set of peptides mimics the genuine epitope in terms of physicochemical properties and spatial organization. When the three-dimensional (3D) structure of the antigen is known, the information in these peptides can be used to computationally infer the corresponding epitope. A user-friendly web interface and a graphical tool that allows viewing the predicted epitopes were developed. Pepitope can also be applied for inferring other types of protein–protein interactions beyond the immunological context, and as a general tool for aligning linear sequences to a 3D structure. Availability: http://pepitope.tau.ac.il/ Contact: talp@post.tau.ac.il [ABSTRACT FROM AUTHOR]