Synthesis of aromatic glycoconjugates as anti-fungal agents against Candida spp. and assessment of their covalent crosslinking capabilities.

Covalent drugs are becoming increasingly attractive in drug discovery, as they can enhance potency and selectivity for their molecular targets. Covalent inhibitors have been investigated for several therapeutic applications, including anti-cancer and anti-infection agents. However, there are only a few examples of covalent inhibitors targeting fungal pathogens.

Specific isoforms of the ubiquitin ligase gene WWP2 are targets of osteoarthritis genetic risk via a differentially methylated DNA sequence.

Background: Transitioning from a genetic association signal to an effector gene and a targetable molecular mechanism requires the application of functional fine-mapping tools such as reporter assays and genome editing. In this report, we undertook such studies on the osteoarthritis (OA) risk that is marked by single nucleotide polymorphism (SNP) rs34195470 (A > G). The OA risk-conferring G allele of this SNP associates with increased DNA methylation (DNAm) at two CpG dinucleotides within WWP2.

4-Thiaproline accelerates the slow folding phase of proteins containing cis prolines in the native state by two orders of magnitude.

The cis/trans isomerization of peptidyl-prolyl peptide bonds is often the bottleneck of the refolding reaction for proteins containing cis proline residues in the native state. Proline (Pro) analogues, especially C4-substituted fluoroprolines, have been widely used in protein engineering to enhance the thermodynamic stability of peptides and proteins and to investigate folding kinetics. 4-thiaproline (Thp) has been shown to bias the ring pucker of Pro, to increase the cis population percentage of model peptides in comparison to Pro, and to diminish the activation energy barrier for the cis/trans isomerization reaction.

Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials: the IMI-APPROACH study.

Objectives: To efficiently assess the disease-modifying potential of new osteoarthritis treatments, clinical trials need progression-enriched patient populations. To assess whether the application of machine learning results in patient selection enrichment, we developed a machine learning recruitment strategy targeting progressive patients and validated it in the IMI-APPROACH knee osteoarthritis prospective study.

Design: We designed a two-stage recruitment process supported by machine learning models trained to rank candidates by the likelihood of progression.