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.

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.

Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence.

Peptides modulate many processes of human physiology targeting ion channels, protein receptors, or enzymes. They represent valuable starting points for the development of new biologics against communicable and non-communicable disorders. However, turning native peptide ligands into druggable materials requires high selectivity and efficacy, predictable metabolism, and good safety profiles.

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.

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.

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.

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.

Natural product drug discovery in the artificial intelligence era.

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas.

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.

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.

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.

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.

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 β-turn between residues Glu9-Phe12 and a kinked alpha helix between Val16-Gly37.

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.

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 (GSK3β). 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).

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 3β (GSK3β) 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.

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.

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.

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.