Diversification of Simple Arenes into Complex (Amino)cyclitols.

Highly oxygenated cyclohexanes, including (amino)cyclitols, are featured in natural products possessing a notable range of biological activities. As such, these building blocks are valuable tools for medicinal chemistry. While de novo synthetic strategies have provided access to select compounds, challenges including stereochemical density and complexity have hindered the development of a general approach to (amino)cyclitol structures. This work reports the use of arenophile chemistry to access dearomatized intermediates which are amenable to diverse downstream transformations. Practical guidelines were developed for the synthesis of natural and non-natural (amino)cyclitols from simple arenes through a series of strategic functionalization events.

CalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides.

To combat infection by microorganisms host organisms possess a primary arsenal via the innate immune system. Among them are defense peptides with the ability to target a wide range of pathogenic organisms, including bacteria, viruses, parasites, and fungi. Here, we present the development of a novel machine learning model capable of predicting the activity of antimicrobial peptides (AMPs), CalcAMP. AMPs, in particular short ones (<35 amino acids), can become an effective solution to face the multi-drug resistance issue arising worldwide. Whereas finding potent AMPs through classical wet-lab techniques is still a long and expensive process, a machine learning model can be useful to help researchers to rapidly identify whether peptides present potential or not. Our prediction model is based on a new data set constructed from the available public data on AMPs and experimental antimicrobial activities. CalcAMP can predict activity against both Gram-positive and Gram-negative bacteria. Different features either concerning general physicochemical properties or sequence composition have been assessed to retrieve higher prediction accuracy. CalcAMP can be used as an promising prediction asset to identify short AMPs among given peptide sequences.

Transformer Performance for Chemical Reactions: Analysis of Different Predictive and Evaluation Scenarios.

The prediction of chemical reaction pathways has been accelerated by the development of novel machine learning architectures based on the deep learning paradigm. In this context, deep neural networks initially designed for language translation have been used to accurately predict a wide range of chemical reactions. Among models suited for the task of language translation, the recently introduced molecular transformer reached impressive performance in terms of forward-synthesis and retrosynthesis predictions. In this study, we first present an analysis of the performance of transformer models for product, reactant, and reagent prediction tasks under different scenarios of data availability and data augmentation. We find that the impact of data augmentation depends on the prediction task and on the metric used to evaluate the model performance. Second, we probe the contribution of different combinations of input formats, tokenization schemes, and embedding strategies to model performance. We find that less stable input settings generally lead to better performance. Lastly, we validate the superiority of round-trip accuracy over simpler evaluation metrics, such as top-k accuracy, using a committee of human experts and show a strong agreement for predictions that pass the round-trip test. This demonstrates the usefulness of more elaborate metrics in complex predictive scenarios and highlights the limitations of direct comparisons to a predefined database, which may include a limited number of chemical reaction pathways.

Loss of hepatic SMLR1 causes hepatosteatosis and protects against atherosclerosis due to decreased hepatic VLDL secretion.

BACKGROUND AND AIMS: The assembly and secretion of VLDL from the liver, a pathway that affects hepatic and plasma lipids, remains incompletely understood. We set out to identify players in the VLDL biogenesis pathway by identifying genes that are co-expressed with the MTTP gene that encodes for microsomal triglyceride transfer protein, key to the lipidation of apolipoprotein B, the core protein of VLDL. Using human and murine transcriptomic data sets, we identified small leucine-rich protein 1 ( SMLR1 ), encoding for small leucine-rich protein 1, a protein of unknown function that is exclusively expressed in liver and small intestine. APPROACH AND RESULTS: To assess the role of SMLR1 in the liver, we used somatic CRISPR/CRISPR-associated protein 9 gene editing to silence murine Smlr1 in hepatocytes ( Smlr1 -LKO). When fed a chow diet, male and female mice show hepatic steatosis, reduced plasma apolipoprotein B and triglycerides, and reduced VLDL secretion without affecting microsomal triglyceride transfer protein activity. Immunofluorescence studies show that SMLR1 is in the endoplasmic reticulum and Cis-Golgi complex. The loss of hepatic SMLR1 in female mice protects against diet-induced hyperlipidemia and atherosclerosis but causes NASH. On a high-fat, high-cholesterol diet, insulin and glucose tolerance tests did not reveal differences in male Smlr1 -LKO mice versus controls. CONCLUSIONS: We propose a role for SMLR1 in the trafficking of VLDL from the endoplasmic reticulum to the Cis-Golgi complex. While this study uncovers SMLR1 as a player in the VLDL assembly, trafficking, and secretion pathway, it also shows that NASH can occur with undisturbed glucose homeostasis and atheroprotection.

Comparative Assessment of Protein Kinase Inhibitors in Public Databases and in PKIDB.

Since the first approval of a protein kinase inhibitor (PKI) by the Food and Drug Administration (FDA) in 2001, 55 new PKIs have reached the market, and many inhibitors are currently being evaluated in clinical trials. This is a clear indication that protein kinases still represent major drug targets for the pharmaceutical industry. In a previous work, we have introduced PKIDB, a publicly available database, gathering PKIs that have already been approved (Phase 4), as well as those currently in clinical trials (Phases 0 to 3). This database is updated frequently, and an analysis of the new data is presented here. In addition, we compared the set of PKIs present in PKIDB with the PKIs in early preclinical studies found in ChEMBL, the largest publicly available chemical database. For each dataset, the distribution of physicochemical descriptors related to drug-likeness is presented. From these results, updated guidelines to prioritize compounds for targeting protein kinases are proposed. The results of a principal component analysis (PCA) show that the PKIDB dataset is fully encompassed within all PKIs found in the public database. This observation is reinforced by a principal moments of inertia (PMI) analysis of all molecules. Interestingly, we notice that PKIs in clinical trials tend to explore new 3D chemical space. While a great majority of PKIs is located on the area of "flatland", we find few compounds exploring the 3D structural space. Finally, a scaffold diversity analysis of the two datasets, based on frequency counts was performed. The results give insight into the chemical space of PKIs, and can guide researchers to reach out new unexplored areas. PKIDB is freely accessible from the following website: http://www.icoa.fr/pkidb.