Cysteine specific bioconjugation with benzyl isothiocyanates.

Protein labelling has a wide variety of applications in medicinal chemistry and chemical biology. In addition to covalent inhibition, specific labelling of biomolecules with fluorescent dyes is important in both target discovery, validation and diagnostics. Our research was conducted through the fragment-based development of a new benzyl-isothiocyanate-activated fluorescent dye based on the fluorescein scaffold. This molecule was evaluated against fluorescein isothiocyanate, a prevalent labelling agent. The reactivity and selectivity of phenyl- and benzyl isothiocyanate were compared at different pHs, and their activity was tested on several protein targets. Finally, the clinically approved antibody trastuzumab (and it's Fab fragment) were specifically labelled through reaction with free cysteines reductively liberated from their interchain disulfide bonds. The newly developed benzyl-fluorescein isothiocyanate and its optimized labelling protocol stands to be a valuable addition to the tool kit of chemical biology.

Reproducibility of biomarker identifications from mass spectrometry proteomic data in cancer studies.

Reproducibility of disease signatures and clinical biomarkers in multi-omics disease analysis has been a key challenge due to a multitude of factors. The heterogeneity of the limited sample, various biological factors such as environmental confounders, and the inherent experimental and technical noises, compounded with the inadequacy of statistical tools, can lead to the misinterpretation of results, and subsequently very different biology. In this paper, we investigate the biomarker reproducibility issues, potentially caused by differences of statistical methods with varied distribution assumptions or marker selection criteria using Mass Spectrometry proteomic ovarian tumor data. We examine the relationship between effect sizes, p values, Cauchy p values, False Discovery Rate p values, and the rank fractions of identified proteins out of thousands in the limited heterogeneous sample. We compared the markers identified from statistical single features selection approaches with machine learning wrapper methods. The results reveal marked differences when selecting the protein markers from varied methods with potential selection biases and false discoveries, which may be due to the small effects, different distribution assumptions, and p value type criteria versus prediction accuracies. The alternative solutions and other related issues are discussed in supporting the reproducibility of findings for clinical actionable outcomes.

A desirability function-based scoring scheme for selecting fragment-like class A aminergic GPCR ligands.

A physicochemical property-based desirability scoring scheme for fragment-based drug discovery was developed for class A aminergic GPCR targeted fragment libraries. Physicochemical property distributions of known aminergic GPCR-active fragments from the ChEMBL database were examined and used for a desirability function-based score. Property-distributions such as log D (at pH 7.4), PSA, pKa (strongest basic center), number of nitrogen atoms, number of oxygen atoms, and the number of rotatable bonds were combined into a desirability score (FrAGS). The validation of the scoring scheme was carried out using both public and proprietary experimental screening data. The scoring scheme is suitable for the design of aminergic GPCR targeted fragment libraries and might be useful for preprocessing fragments before structure based virtual or wet screening.