Facile and practical enantioselective synthesis of propargylic alcohols by direct addition of alkynes to aldehydes catalyzed by chiral disulfide–oxazolidine ligands

The enantioselective alkynylation reaction of aldehydes with alkynes and diethylzinc, catalyzed by chiral disulfide–oxazolidine ligands, provides a simple, practical and inexpensive method to access chiral propargylic alcohols in good yields and satisfactory ee's.     

An insight into the mechanisms of the phototoxic response induced by cyamemazine in cultured fibroblasts and keratinocytes

The phototoxicity of cyamemazine (CMZ, Tercian), a neuroleptic of the phenothiazine family, has recently been reported in humans. CMZ has an absorbance maximum at 267 nm (molar absorptivity, 25,800 M(-1) cm(-1)) but a weaker molar absorptivity in the ultraviolet A (UV-A) region. CMZ exhibits a fluorescence with maximum emission at 535 nm and a quantum yield of 0.11. CMZ is a powerful photosensitizing agent toward HS 68 human skin fibroblasts and NCTC 2544 keratinocytes. At a UV-A radiation dose of 10 J/cm2, innocuous to cells in the absence of CMZ, the LD50 (lethal dose corresponding to 50% killing) are 0.5 and 1 microM for the fibroblasts and the keratinocytes, respectively, after overnight incubation with the drug. Short incubation times do not significantly alter the LD50. The CMZ-induced phototoxicity is accompanied by lipid membrane peroxidation consistent with the amphiphilic character of this photosensitizer. Keratinocytes are an order of magnitude less sensitive to the photosensitized lipid peroxidation than fibroblasts. Microspectrofluorometry reveals that lysosomal membranes are major sites of CMZ incorporation into the two cell lines because a Forster-type resonance energy transfer process occurs from CMZ to LysoTracker Red DND99 (LTR), a specific fluorescent probe of lysosomal membranes. The CMZ-photosensitized destruction of LTR demonstrates that CMZ retains its photosensitizing capacity after its lysosomal uptake.     

Interaction and lipid-induced conformation of two cecropin-melittin hybrid peptides depend on peptide and membrane composition

The interaction of two hybrid peptides of cecropin A and melittin [CA(1-8)M(1-18) and CA(1-7)M(2-9)] with liposomes was studied by differential scanning calorimetry (DSC), circular dichroism (CD), and quasi-elastic light scattering (QELS). The study was carried out with large unilamellar vesicles (LUVs) of three different lipid compositions: 1,2-dimyristoil-sn-glycero-3-phosphocholine (DMPC), 1,2-dimyristoyl-sn-glycero-3-phospho-rac-(1-glycerol) (DMPG) and a binary mixture of DMPC/DMPG, in a wide range of peptide-to-lipid (P:L) molar ratios (0 to 1:7). DSC results indicate that, for both peptides, the interaction depends on membrane composition, with very different behavior for zwitterionic and anionic membranes. CD data show that, although the two peptides have different secondary structures in buffer (random coil for CA(1-7)M(2-9) and predominantly beta-sheet for CA(1-8)M(1-18)), they both adopt an alpha-helical structure in the presence of the membranes. Overall, results are compatible with a model involving a strong electrostatic surface interaction between the peptides and the negatively charged liposomes, which gives place to aggregation in the gel phase and precipitation after a threshold peptide concentration. In the case of zwitterionic membranes, a progressive surface coverage with peptide molecules destabilizes the membrane, eventually leading to membrane disruption. Moreover, delicate modulations in behavior were observed depending on the peptide.     

Antibody-recruiting protein-catalyzed capture agents to combat antibiotic-resistant bacteria

Antibiotic resistant infections are projected to cause over 10 million deaths by 2050, yet the development of new antibiotics has slowed. This points to an urgent need for methodologies for the rapid development of antibiotics against emerging drug resistant pathogens. We report on a generalizable combined computational and synthetic approach, called antibody-recruiting protein-catalyzed capture agents (AR-PCCs), to address this challenge. We applied the combinatorial protein catalyzed capture agent (PCC) technology to identify macrocyclic peptide ligands against highly conserved surface protein epitopes of carbapenem-resistant Klebsiella pneumoniae, an opportunistic Gram-negative pathogen with drug resistant strains. Multi-omic data combined with bioinformatic analyses identified epitopes of the highly expressed MrkA surface protein of K. pneumoniae for targeting in PCC screens. The top-performing ligand exhibited high-affinity (EC₅₀ ∼50 nM) to full-length MrkA, and selectively bound to MrkA-expressing K. pneumoniae, but not to other pathogenic bacterial species. AR-PCCs that bear a hapten moiety promoted antibody recruitment to K. pneumoniae, leading to enhanced phagocytosis and phagocytic killing by macrophages. The rapid development of this highly targeted antibiotic implies that the integrated computational and synthetic toolkit described here can be used for the accelerated production of antibiotics against drug resistant bacteria.

Antibody-recruiting protein-catalyzed capture agent (AR-PCCs) are a new class of all-synthetic and highly targeted antibiotics that recruit endogenous immune responses to eliminate drug-resistant microbes.     

Beyond generative models: superfast traversal,optimization, novelty,exploration and discovery (STONED) algorithm for molecules using SELFIES

Inverse design allows the generation of molecules with desirable physical quantities using property optimization. Deep generative models have recently been applied to tackle inverse design, as they possess the ability to optimize molecular properties directly through structure modification using gradients. While the ability to carry out direct property optimizations is promising, the use of generative deep learning models to solve practical problems requires large amounts of data and is very time-consuming. In this work, we propose STONED – a simple and efficient algorithm to perform interpolation and exploration in the chemical space, comparable to deep generative models. STONED bypasses the need for large amounts of data and training times by using string modifications in the SELFIES molecular representation. First, we achieve non-trivial performance on typical benchmarks for generative models without any training. Additionally, we demonstrate applications in high-throughput virtual screening for the design of drugs, photovoltaics, and the construction of chemical paths, allowing for both property and structure-based interpolation in the chemical space. Overall, we anticipate our results to be a stepping stone for developing more sophisticated inverse design models and benchmarking tools, ultimately helping generative models achieve wider adoption.

Interpolation and exploration within the chemical space for inverse design.     

Generation and reactivity of 3‐carbethoxy‐5‐phenyl‐5H,7H‐thiazolo[3,4‐c]oxazol‐4‐ium‐1‐olate

3‐Carbethoxy‐5‐phenyl‐5H,7H‐thiazolo[3,4‐c]oxazol‐4‐ium‐1‐olate was generated from (2R,4R)‐N‐ethoxyoxalyl‐2‐phenylthiazolidine‐4‐carboxylic acid and its reactivity studied. This münchnone showed low reactivity as dipole although from the reaction with dimethyl acetylenedicarboxylate the corresponding (3R)‐3‐phenyl‐17H,3H‐pyrrolo[1,2‐c]thiazole‐5,6,7‐tricarboxylate could be isolated. The thermolysis of (2R,4R)‐N‐ethoxyoxalyl‐2‐phenylthiazolidine‐4‐carboxylic acid in refluxing acetic anhydride led to the synthesis of N‐(1‐ethoxycarbonyl‐2‐phenylvinyl)‐2‐phenyl‐4‐thioxo‐1,3‐thiazolidine. The structure of methyl (2R,4R)‐N‐ethoxyoxalyl‐2‐phenylthiazoliddine‐4‐carboxylate was determined by X‐ray crystallography.     

Optimal investment with two-factor uncertainty

This paper presents a real options model to value the option to invest in a project contingent on two stochastic factors. A general sensitivity analysis is conducted highlighting the importance of the variance and correlation between the two variables. A higher correlation is shown to increase always the values of the trigger, the active project and the option. The impact of uncertainty is more complex and depends on the assumption about which variables adjust and the correlation between the variables and the market.