An intrinsically disordered antimicrobial peptide dendrimer from stereorandomized virtual screening.

Membrane-disruptive amphiphilic antimicrobial peptides behave as intrinsically disordered proteins by being unordered in water and becoming α-helical in contact with biological membranes. We recently discovered that synthesizing the α-helical antimicrobial peptide dendrimer L- ((KL)(KL)(LL) KLL) using racemic amino acids to form stereorandomized -, an analytically pure mixture of all possible diastereoisomers of L-, preserved antibacterial activity but abolished hemolysis and cytotoxicity, pointing to an intrinsically disordered antibacterial conformation and an α-helical cytotoxic conformation. In this study, to identify non-toxic intrinsically disordered homochiral antimicrobial peptide dendrimers (AMPDs), we surveyed sixty-three -analogs of - selected by virtual screening.

Medium-Chain Lipid Conjugation Facilitates Cell-Permeability and Bioactivity.

The majority of bioactive molecules act on membrane proteins or intracellular targets and therefore needs to partition into or cross biological membranes. Natural products often exhibit lipid modifications to facilitate critical molecule-membrane interactions, and in many cases their bioactivity is markedly reduced upon removal of a lipid group. However, despite its importance in nature, lipid-conjugation of small molecules is not commonly used in chemical biology and medicinal chemistry, and the effect of such conjugation has not been systematically studied.

Machine Learning Guided Discovery of Non-Hemolytic Membrane Disruptive Anticancer Peptides.

Most antimicrobial peptides (AMPs) and anticancer peptides (ACPs) fold into membrane disruptive cationic amphiphilic α-helices, many of which are however also unpredictably hemolytic and toxic. Here we exploited the ability of recurrent neural networks (RNN) to distinguish active from inactive and non-hemolytic from hemolytic AMPs and ACPs to discover new non-hemolytic ACPs. Our discovery pipeline involved: 1) sequence generation using either a generative RNN or a genetic algorithm, 2) RNN classification for activity and hemolysis, 3) selection for sequence novelty, helicity and amphiphilicity, and 4) synthesis and testing.

Classifying natural products from plants, fungi or bacteria using the COCONUT database and machine learning.

Natural products (NPs) represent one of the most important resources for discovering new drugs. Here we asked whether NP origin can be assigned from their molecular structure in a subset of 60,171 NPs in the recently reported Collection of Open Natural Products (COCONUT) database assigned to plants, fungi, or bacteria. Visualizing this subset in an interactive tree-map (TMAP) calculated using MAP4 (MinHashed atom pair fingerprint) clustered NPs according to their assigned origin ( https://tm.

An Immunomodulatory Peptide Dendrimer Inspired from Glatiramer Acetate.

Glatiramer acetate (GA) is a random polypeptide drug used to treat multiple sclerosis (MS), a chronic autoimmune disease. With the aim of identifying a precisely defined alternative to GA, we synthesized a library of peptide dendrimers with an amino acid composition similar to GA. We then challenged the dendrimers to trigger the release of the anti-inflammatory cytokine interleukin-1 receptor antagonist (IL-1Ra) from human monocytes, which is one of the effects of GA on immune cells.

Machine learning designs non-hemolytic antimicrobial peptides.

Machine learning (ML) consists of the recognition of patterns from training data and offers the opportunity to exploit large structure-activity databases for drug design. In the area of peptide drugs, ML is mostly being tested to design antimicrobial peptides (AMPs), a class of biomolecules potentially useful to fight multidrug-resistant bacteria. ML models have successfully identified membrane disruptive amphiphilic AMPs, however mostly without addressing the associated toxicity to human red blood cells.

One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome.

Background: Molecular fingerprints are essential cheminformatics tools for virtual screening and mapping chemical space. Among the different types of fingerprints, substructure fingerprints perform best for small molecules such as drugs, while atom-pair fingerprints are preferable for large molecules such as peptides. However, no available fingerprint achieves good performance on both classes of molecules.

Perspectives on protein biopolymers: miniaturized flow field-flow fractionation-assisted characterization of a single-cysteine mutated phaseolin expressed in transplastomic tobacco plants.

The development of plant-based protein polymers to employ in biofilm production represents the promising intersection between material science and sustainability, and allows to obtain biodegradable materials that also possess excellent physicochemical properties. A possible candidate for protein biopolymer production is phaseolin, a storage protein highly abundant in P Vulgaris beans. We previously showed that transformed tobacco chloroplasts could be employed to express a mutated phaseolin carrying a signal peptide (directing it into the thylakoids) also enriched of a cysteine residue added to its C-terminal region.

Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning.

Microbial natural products (NPs) are an important source of drugs, however, their structural diversity remains poorly understood. Here we used our recently reported MinHashed Atom Pair fingerprint with diameter of four bonds (MAP4), a fingerprint suitable for molecules across very different sizes, to analyze the Natural Products Atlas (NPAtlas), a database of 25,523 NPs of bacterial or fungal origin. To visualize NPAtlas by MAP4 similarity, we used the dimensionality reduction method tree map (TMAP).

Populating Chemical Space with Peptides Using a Genetic Algorithm.

In drug discovery, one uses chemical space as a concept to organize molecules according to their structures and properties. One often would like to generate new possible molecules at a specific location in the chemical space marked by a molecule of interest. Herein, we report the peptide design genetic algorithm (PDGA, code available at https://github.

PubChem and ChEMBL beyond Lipinski.

Seven million of the currently 94 million entries in the PubChem database break at least one of the four Lipinski constraints for oral bioavailability, 183,185 of which are also found in the ChEMBL database. These non-Lipinski PubChem (NLP) and ChEMBL (NLC) subsets are interesting because they contain new modalities that can display biological properties not accessible to small molecule drugs. Unfortunately, the current search tools in PubChem and ChEMBL are designed for small molecules and are not well suited to explore these subsets, which therefore remain poorly appreciated.

Optimizing Antimicrobial Peptide Dendrimers in Chemical Space.

We used nearest-neighbor searches in chemical space to improve the activity of the antimicrobial peptide dendrimer (AMPD) G3KL and identified dendrimer T7, which has an expanded activity range against Gram-negative pathogenic bacteria including Klebsiellae pneumoniae, increased serum stability, and promising activity in an in vivo infection model against a multidrug-resistant strain of Acinetobacter baumannii. Imaging, spectroscopic studies, and a structural model from molecular dynamics simulations suggest that T7 acts through membrane disruption. These experiments provide the first example of using virtual screening in the field of dendrimers and show that dendrimer size does not limit the activity of AMPDs.

An antimicrobial bicyclic peptide from chemical space against multidrug resistant Gram-negative bacteria.

We used the concept of chemical space to explore a virtual library of bicyclic peptides formed by double thioether cyclization of a precursor linear peptide, and identified an antimicrobial bicyclic peptide (AMBP) with remarkable activity against several MDR strains of Acinetobacter baumannii and Pseudomonas aeruginosa.

Development and validation of a docking-based virtual screening platform for the identification of new lactate dehydrogenase inhibitors.

The human muscle isoform of lactate dehydrogenase (hLDH5) is one of the key enzymes of the glycolytic process. It is overexpressed in metastatic cancer cells and is linked to the vitality of tumors in hypoxic conditions. With the aim of identifying new hLDH5 inhibitors, a fully automated docking-based virtual screening platform was developed by considering different protein conformations and the consensus docking strategy.