Antimicrobial peptides in livestock: a review with a one health approach.

Antimicrobial peptides (AMPs), often referred to as nature's antibiotics, are ubiquitous in living organisms, spanning from bacteria to humans. Their potency, versatility, and unique mechanisms of action have garnered significant research attention. Unlike conventional antibiotics, peptides are biodegradable, adding to their appeal as potential candidates to address bacterial resistance in livestock farming-a challenge that has been under scrutiny for decades. This issue is complex and multifactorial, influenced by a variety of components. The World Health Organization (WHO) has proposed a comprehensive approach known as One Health, emphasizing the interconnectedness of human-animal-environment relationships in tackling such challenges. This review explores the application of AMPs in livestock farming and how they can mitigate the impact of this practice within the One Health framework.

Structure-aware machine learning strategies for antimicrobial peptide discovery.

Machine learning models are revolutionizing our approaches to discovering and designing bioactive peptides. These models often need protein structure awareness, as they heavily rely on sequential data. The models excel at identifying sequences of a particular biological nature or activity, but they frequently fail to comprehend their intricate mechanism(s) of action. To solve two problems at once, we studied the mechanisms of action and structural landscape of antimicrobial peptides as (i) membrane-disrupting peptides, (ii) membrane-penetrating peptides, and (iii) protein-binding peptides. By analyzing critical features such as dipeptides and physicochemical descriptors, we developed models with high accuracy (86-88%) in predicting these categories. However, our initial models (1.0 and 2.0) exhibited a bias towards α-helical and coiled structures, influencing predictions. To address this structural bias, we implemented subset selection and data reduction strategies. The former gave three structure-specific models for peptides likely to fold into α-helices (models 1.1 and 2.1), coils (1.3 and 2.3), or mixed structures (1.4 and 2.4). The latter depleted over-represented structures, leading to structure-agnostic predictors 1.5 and 2.5. Additionally, our research highlights the sensitivity of important features to different structure classes across models.

Deconstructing the Potency and Cell-Line Selectivity of Membranolytic Anticancer Peptides.

Current cancer treatments damage healthy cells and tissues, causing short-term and long-term side effects. New treatments are desired that show greater selectivity toward cancer cells and evade the common mechanisms of multidrug resistance. Membranolytic anticancer peptides (mACPs) hold promise against cancer and multidrug resistance. Amphipathicity, hydrophobicity, and net charge of mACPs participate in their respective interactions with cell membranes and their overall inhibition of cancer cells. To support the design of cell-line selective mACPs, we investigated the relationships that amino acid composition, physicochemical properties, sequence motifs, and sequence homology could have with their potency and selectivity towards several healthy and cancer cell lines. Sequence length and net charge are known to affect the selectivity of mACPs between cancer and healthy cell lines. Our study reveals that increasing the net charge or flexibility (i. e., small and aliphatic residues) influences their selectivity between cancer cell lines with comparable lipid compositions.

Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds.

We report the main conclusions of the first Chemoinformatics and Artificial Intelligence Colloquium, Mexico City, June 15-17, 2022. Fifteen lectures were presented during a virtual public event with speakers from industry, academia, and non-for-profit organizations. Twelve hundred and ninety students and academics from more than 60 countries. During the meeting, applications, challenges, and opportunities in drug discovery, de novo drug design, ADME-Tox (absorption, distribution, metabolism, excretion and toxicity) property predictions, organic chemistry, peptides, and antibiotic resistance were discussed. The program along with the recordings of all sessions are freely available at https://www.difacquim.com/english/events/2022-colloquium/ .

Profiling hymenopteran venom toxins: Protein families, structural landscape, biological activities, and pharmacological benefits.

Hymenopterans are an untapped source of venom secretions. Their recent proteo-transcriptomic studies have revealed an extraordinary pool of toxins that participate in various biological processes, including pain, paralysis, allergic reactions, and antimicrobial activities. Comprehensive and clade-specific campaigns to collect hymenopteran venoms are therefore needed. We consider that data-driven bioprospecting may help prioritise sampling and alleviate associated costs. This work established the current protein landscape from hymenopteran venoms to evaluate possible sample bias by studying their origins, sequence diversity, known structures, and biological functions. We collected all 282 reported hymenopteran toxins (peptides and proteins) from the UniProt database that we clustered into 21 protein families from the three studied clades - wasps, bees, and ants. We identified 119 biological targets of hymenopteran toxins ranging from pathogen membranes to eukaryotic proteases, ion channels and protein receptors. Our systematic study further extended to hymenopteran toxins' therapeutic and biotechnological values, where we revealed promising applications in crop pests, human infections, autoimmune diseases, and neurodegenerative disorders.

Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity.

Peptides have positively impacted the pharmaceutical industry as drugs, biomarkers, or diagnostic tools of high therapeutic value. However, only a handful have progressed to the market. Toxicity is one of the main obstacles to translating peptides into clinics. Hemolysis or hemotoxicity, the principal source of toxicity, is a natural or disease-induced event leading to the death of vital red blood cells. Initial screenings for toxicity have been widely evaluated using erythrocytes as the gold standard. More recently, many online databases filled with peptide sequences and their biological meta-data have paved the way toward hemolysis prediction using user-friendly, fast-access machine learning-driven programs. This review details the growing contributions of in silico approaches developed in the last decade for the large-scale prediction of erythrocyte lysis induced by peptides. After an overview of the pharmaceutical landscape of peptide therapeutics, we highlighted the relevance of early hemolysis studies in drug development. We emphasized the computational models and algorithms used to this end in light of historical and recent findings in this promising field. We benchmarked seven predictors using peptides from different data sets, having 7-35 amino acids in length. According to our predictions, the models have scored an accuracy over 50.42% and a minimal Matthew's correlation coefficient over 0.11. The maximum values for these statistical parameters achieved 100.0% and 1.00, respectively. Finally, strategies for optimizing peptide selectivity were described, as well as prospects for future investigations. The development of in silico predictive approaches to peptide toxicity has just started, but their important contributions clearly demonstrate their potential for peptide science and computer-aided drug design. Methodology refinement and increasing use will motivate the timely and accurate in silico identification of selective, non-toxic peptide therapeutics.

Phylogenomics and population genomics of SARS-CoV-2 in Mexico during the pre-vaccination stage reveals variants of interest B.1.1.28.4 and B.1.1.222 or B.1.1.519 and the nucleocapsid mutation S194L associated with symptoms.

Understanding the evolution of the SARS-CoV-2 virus in various regions of the world during the Covid-19 pandemic is essential to help mitigate the effects of this devastating disease. We describe the phylogenomic and population genetic patterns of the virus in Mexico during the pre-vaccination stage, including asymptomatic carriers. A real-time quantitative PCR screening and phylogenomic reconstructions directed at sequence/structure analysis of the spike glycoprotein revealed mutation of concern E484K in genomes from central Mexico, in addition to the nationwide prevalence of the imported variant 20C/S:452R (B.1.427/9). Overall, the detected variants in Mexico show spike protein mutations in the N-terminal domain (i.e. R190M), in the receptor-binding motif (i.e. T478K, E484K), within the S1-S2 subdomains (i.e. P681R/H, T732A), and at the basis of the protein, V1176F, raising concerns about the lack of phenotypic and clinical data available for the variants of interest we postulate: 20B/478K.V1 (B.1.1.222 or B.1.1.519) and 20B/P.4 (B.1.1.28.4). Moreover, the population patterns of single nucleotide variants from symptomatic and asymptomatic carriers obtained with a self-sampling scheme confirmed the presence of several fixed variants, and differences in allelic frequencies among localities. We identified the mutation N:S194L of the nucleocapsid protein associated with symptomatic patients. Phylogenetically, this mutation is frequent in Mexican sub-clades. Our results highlight the dual and complementary role of spike and nucleocapsid proteins in adaptive evolution of SARS-CoV-2 to their hosts and provide a baseline for specific follow-up of mutations of concern during the vaccination stage.

Cysteine-Rich Peptides: Hyperstable Scaffolds for Protein Engineering.

Cysteine-rich peptides (CRPs) are small proteins of less than 100 amino acids in length characterized by the presence of disulfide bridges and common end-to-end macrocyclization. These properties confer hyperstability against high temperatures, salt concentration, serum presence, and protease degradation to CRPs. Moreover, their intercysteine domains (loops) are susceptible to residue hypervariability. CRPs have been successfully applied as stable scaffolds for molecular grafting, a protein engineering process in which cysteine-rich structures provide higher thermodynamic and metabolic stability to an epitope and acquire new biological function(s). This review describes the successes and limitations of seven cysteine-rich scaffolds, their bioactive epitopes, and the resulting grafted peptides.

Machine learning-guided discovery and design of non-hemolytic peptides.

Reducing hurdles to clinical trials without compromising the therapeutic promises of peptide candidates becomes an essential step in peptide-based drug design. Machine-learning models are cost-effective and time-saving strategies used to predict biological activities from primary sequences. Their limitations lie in the diversity of peptide sequences and biological information within these models. Additional outlier detection methods are needed to set the boundaries for reliable predictions; the applicability domain. Antimicrobial peptides (AMPs) constitute an extensive library of peptides offering promising avenues against antibiotic-resistant infections. Most AMPs present in clinical trials are administrated topically due to their hemolytic toxicity. Here we developed machine learning models and outlier detection methods that ensure robust predictions for the discovery of AMPs and the design of novel peptides with reduced hemolytic activity. Our best models, gradient boosting classifiers, predicted the hemolytic nature from any peptide sequence with 95-97% accuracy. Nearly 70% of AMPs were predicted as hemolytic peptides. Applying multivariate outlier detection models, we found that 273 AMPs (~ 9%) could not be predicted reliably. Our combined approach led to the discovery of 34 high-confidence non-hemolytic natural AMPs, the de novo design of 507 non-hemolytic peptides, and the guidelines for non-hemolytic peptide design.

Predicting Blood⁻Brain Barrier Permeability of Marine-Derived Kinase Inhibitors Using Ensemble Classifiers Reveals Potential Hits for Neurodegenerative Disorders.

The recent success of small-molecule kinase inhibitors as anticancer drugs has generated significant interest in their application to other clinical areas, such as disorders of the central nervous system (CNS). However, most kinase inhibitor drug candidates investigated to date have been ineffective at treating CNS disorders, mainly due to poor blood⁻brain barrier (BBB) permeability. It is, therefore, imperative to evaluate new chemical entities for both kinase inhibition and BBB permeability. Over the last 35 years, marine biodiscovery has yielded 471 natural products reported as kinase inhibitors, yet very few have been evaluated for BBB permeability. In this study, we revisited these marine natural products and predicted their ability to cross the BBB by applying freely available open-source chemoinformatics and machine learning algorithms to a training set of 332 previously reported CNS-penetrant small molecules. We evaluated several regression and classification models, and found that our optimised classifiers (random forest, gradient boosting, and logistic regression) outperformed other models, with overall cross-validated model accuracies of 80%⁻82% and 78%⁻80% on external testing. All 3 binary classifiers predicted 13 marine-derived kinase inhibitors with appropriate physicochemical characteristics for BBB permeability.

Helixconstraints and amino acid substitution in GLP-1 increase cAMP and insulin secretion but not beta-arrestin 2 signaling.

Glucagon-like peptide (GLP-1) is an endogenous hormone that induces insulin secretion from pancreatic islets and modified forms are used to treat diabetes mellitus type 2. Understanding how GLP-1 interacts with its receptor (GLP-1R) can potentially lead to more effective drugs. Modeling and NMR studies of the N-terminus of GLP-1 suggest a ß-turn between residues Glu9-Phe12 and a kinked alpha helix between Val16-Gly37. N-terminal turn constraints attenuated binding affinity and activity (compounds 1-8). Lys-Asp (i, i+4) crosslinks in the middle and at the C-terminus increased alpha helicity and cAMP stimulation without much effect on binding affinity or beta-arrestin 2 recruitment (compounds 9-18). Strategic positioning of helix-inducing constraints and amino acid substitutions (Tyr16, Ala22) increased peptide helicity and produced ten-fold higher cAMP potency (compounds 19-28) over GLP-1(7-37)-NH2. The most potent cAMP activator (compound 23) was also the most potent inducer of insulin secretion.

Helix Nucleation by the Smallest Known α-Helix in Water.

Cyclic pentapeptides (e.g. Ac-(cyclo-1,5)-[KAXAD]-NH2 ; X=Ala, 1; Arg, 2) in water adopt one α-helical turn defined by three hydrogen bonds. NMR structure analysis reveals a slight distortion from α-helicity at the C-terminal aspartate caused by torsional restraints imposed by the K(i)-D(i+4) lactam bridge. To investigate this effect on helix nucleation, the more water-soluble 2 was appended to N-, C-, or both termini of a palindromic peptide ARAARAARA (≤5 % helicity), resulting in 67, 92, or 100 % relative α-helicity, as calculated from CD spectra. From the C-terminus of peptides, 2 can nucleate at least six α-helical turns. From the N-terminus, imperfect alignment of the Asp5 backbone amide in 2 reduces helix nucleation, but is corrected by a second unit of 2 separated by 0-9 residues from the first. These cyclic peptides are extremely versatile helix nucleators that can be placed anywhere in 5-25 residue peptides, which correspond to most helix lengths in protein-protein interactions.

Callyspongisines A-D: bromopyrrole alkaloids from an Australian marine sponge, Callyspongia sp.

An extract of the Great Australian Bight marine sponge Callyspongia sp. (CMB-01152) displayed inhibitory activity against the neurodegenerative disease kinase targets casein kinase 1 (CK1), cyclin-dependent kinase 5 (CDK5) and glycogen synthase kinase 3 (GSK3ß). Chemical investigation, employing HPLC-DAD-MS single ion extraction protocols, facilitated identification of the new bromopyrrole alkaloids, callyspongisines A-D (1-4), and two known co-metabolites, hymenialdisine (5) and 2-bromoaldisine (6). Structure elucidation of 1-6 was supported by detailed spectroscopic analysis and chemical interconversion, as well as biosynthetic and synthetic considerations. Callyspongisine A (1) is only the second reported example of a natural imino-oxazoline, and the first to feature a spiro heterocyclic framework, while callyspongisines B-D (2-4) were speculated to be storage and handling artefacts of 1. The kinase inhibitory activity detected in Callyspongia sp. (CMB-01152) was attributed to 5.

Lamellarins as inhibitors of P-glycoprotein-mediated multidrug resistance in a human colon cancer cell line.

Chemical analysis of a Didemnum sp. (CMB-01656) collected during scientific Scuba operations off Wasp Island, New South Wales, yielded five new lamellarins A1 (1), A2 (2), A3 (3), A4 (4) and A5 (5) and eight known lamellarins C (6), E (7), K (8), M (9), S (10), T (11), X (12) and χ (13). Analysis of a second Didemnum sp. (CMB-02127) collected during scientific trawling operations along the Northern Rottnest Shelf, Western Australia, yielded the new lamellarin A6 (14) and two known lamellarins G (15) and Z (16). Structures were assigned to 1-16 on the basis of detailed spectroscopic analysis with comparison to literature data and authentic samples. Access to this unique library of natural lamellarins (1-16) provided a rare opportunity for structure-activity relationship (SAR) investigations, probing interactions between lamellarins and the ABC transporter efflux pump P-glycoprotein (P-gp) with a view to reversing multidrug resistance in a human colon cancer cell line (SW620 Ad300). These SAR studies, which were expanded to include the permethylated lamellarin derivative (17) and a series of lamellarin-inspired synthetic coumarins (19-24) and isoquinolines (25-26), successfully revealed 17 as a promising new non-cytotoxic P-gp inhibitor pharmacophore.

Kinase inhibitor scaffolds against neurodegenerative diseases from a Southern Australian ascidian, Didemnum sp.

Screening a library of Southern Australian and Antarctic marine invertebrates and algae for inhibitors of neurodegenerative disease kinase targets casein kinase 1 (CK1δ), cyclin-dependent kinase 5 (CDK5) and glycogen synthase kinase 3ß (GSK3ß) identified a Western Australian Didemnum species (CMB-02127) as a high-priority specimen. Chemical fractionation returned the known aromatic alkaloids ningalins B-D as the major metabolites, together with six minor metabolites, the new ningalins E-G and the known hexacyclic pyrrole alkaloids lamellarins Z, G and A6. All structures were assigned by detailed spectroscopic analysis and literature comparisons, and the structural assignments were supported by biosynthetic considerations. The ningalins showed potent and broad inhibition across the three kinases, while the lamellarins were generally more selective for CDK5. Docking studies using published X-ray crystal structures of CDK5 revealed both scaffolds target the ATP binding pocket.

Proteasome inhibitors from Neoboutonia melleri.

Thirty new cycloartane derivatives (1-3, 5-12, 14-32) have been isolated from the leaves of Neoboutonia melleri. Their novelty stems from the loss of one of the C-4 methyl groups (1-3, 5-12, 14-25, and 32) and from the presence of an "extra" carbon atom in the side chain (1-3, 5-12, 14-20, 26-29, and 30-32). Furthermore, compound 32 possesses a rare triterpene skeleton with the cyclopropane ring fused onto C-1 and C-10, instead of C-9 and C-10. The structures were determined by spectrometric means, chemical correlations, and X-ray crystallography of derivative 1c. The substitution pattern in ring A, with a cyclopropyl ring conjugated with an α,ß-unsaturated carbonyl moiety, confers to the molecule a particular reactivity, giving rise to a formal inversion of the stereochemistry of the cyclopropane ring under UV irradiation. These compounds showed an interesting level of activity on the proteasome pathway, thus motivating their evaluation as possible anticancer agents. The large number of isolated compounds permitted a structure-activity relationship analysis, which showed that the presence of the two enone functions was a requirement for the activity.

Semisynthetic neoboutomellerone derivatives as ubiquitin-proteasome pathway inhibitors.

The interesting pharmacological properties of neoboutomellerones 1 and 2 were the basis for the assembly of a small library of analogues consisting of natural products isolated from the plant Neoboutonia melleri and of semisynthetic derivatives. As the two enone systems (C23-C24a and C1-C3) and the two hydroxyls groups (C22 and C26) of neoboutomellerones are required for activity, modifications were focused on these functional groups. Biological evaluation by using a cellular assay for proteasome activity provided clues regarding the mechanism of action of these natural products and synthetic derivatives. Certain neoboutomellerone derivatives inhibited the proliferation of human WM-266-4 melanoma tumor cells at submicromolar concentration and warrant evaluation as anticancer agents.