Biology subjects

Palafox, M. F.

Publications and source records attributed to Palafox, M. F..

3 recordsLinked to original sources

bioRxiv2024

An activation-based high throughput screen identifies caspase-10 inhibitors

Caspases are a family of highly homologous cysteine proteases that play critical roles in inflammation and apoptosis. Small molecule inhibitors are useful tools for studying caspase biology, complementary to genetic approaches. However, achieving inhibitor selectivity for individual members of this highly homologous enzyme family remains a major challenge in developing such tool compounds. Prior studies have revealed that one strategy to tackle this selectivity gap is to target the precursor or zymogen forms of individual caspases, which share reduced structural homology when compared to active proteases. To establish a screening assay that favors the discovery of zymogen-directed caspase-10 selective inhibitors, we engineered a low-background and high-activity tobacco etch virus (TEV)--activated caspase-10 protein. We then subjected this turn-on protease to a high-throughput screen of approximately 100,000 compounds, with an average Z value of 0.58 across all plates analyzed. Counter screening, including against TEV protease, delineated bona fide procaspase-10 inhibitors. Confirmatory studies identified a class of low-pH activatable caspase-10 inhibitors. In parallel, mode-of-action studies revealed that pifithrin-{micro} (PFT{micro}), a reported TP53 inhibitor, also functions as a promiscuous caspase inhibitor. Both inhibitor classes showed preferential zymogen inhibition. Given the generalized utility of activation assays, we expect our screening platform to have widespread applications in identifying state-specific protease inhibitors.

biochemistry↗

bioRxiv2024

Plasma Proteomics of Genetic Brain Arteriosclerosis and Dementia Syndrome Identifies Signatures of Fibrosis, Angiogenesis, and Metabolic Alterations

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is the most common monogenic form of vascular cognitive impairment and dementia. A genetic arteriolosclerotic disease, the molecular mechanisms driving vascular brain degeneration and decline remain unclear. With the goal of driving discovery of disease-relevant biological perturbations in CADASIL, we used machine learning approaches to extract proteomic disease signatures from large-scale proteomics generated from plasma collected from three distinct cohorts in US and Colombia: CADASIL-Early (N = 53), CADASIL-Late (N = 45), and CADASIL-Colombia (N = 71). We extracted molecular signatures with high predictive value for early and late-stage CADASIL and performed robust cross- and external-validation. We examined the biological and clinical relevance of our findings through pathway enrichment analysis and testing of associations with clinical outcomes. Our study represents a model for unbiased discovery of molecular signatures and disease biomarkers, combining non-invasive plasma proteomics with clinical data. We report on novel disease-associated molecular signatures for CADASIL, derived from the accessible plasma proteome, with relevance to vascular cognitive impairment and dementia.

neuroscience↗

bioRxiv2020

From Chemoproteomic-Detected Amino Acids to Genomic Coordinates: Insights into Precise Multi-omic Data Integration

The integration of proteomic, transcriptomic, and genetic-variant annotation data will improve our understanding genotype-phenotype associations. Due, in part, to challenges associated with accurate inter-database mapping, such multi-omic studies have not extended to chemoproteomics, a method that measure the intrinsic reactivity and potential druggability of nucleophilic amino acid side chains. Here, we evaluated two mapping approaches to match chemoproteomic-detected cysteine and lysine residues with their genetic coordinates. Our analysis reveals that databases update cycles and reliance on stable identifiers can lead to pervasive misidentification of labeled residues. Enabled by this examination of mapping strategies, we then integrated our chemoproteomic data with in silico generated predictions of genetic variant pathogenicity, which revealed that codons of highly reactive cysteines are enriched for genetic variants that are predicted to be more deleterious. Our study provides a roadmap for more precise inter-database comparisons and points to untapped opportunities to improve the predictive power of pathogenicity scores and to advance prioritization of putative druggable sites through integration of predictions of pathogenicity with chemoproteomic datasets.

systems biology↗