Iron-sulfur (Fe-S) clusters are critical cofactors in metalloproteins, essential for cellular processes such as energy production, DNA repair, enzymatic catalysis, and metabolic regulation. While Fe-S Show more
Iron-sulfur (Fe-S) clusters are critical cofactors in metalloproteins, essential for cellular processes such as energy production, DNA repair, enzymatic catalysis, and metabolic regulation. While Fe-S cluster functions are intimately linked to their redox properties, their precise roles in many proteins remain unclear. In this study, we present a regression model based on experimental redox potential (E m ) data, utilizing only two features: the Fe-S cluster's total charge and the Fe atoms' average valence. This model achieves a high correlation with experimental data (R 2 = 0.82) and an average prediction error of 0.12 V. Applying this model across the Protein Data Bank, we predict E m values for all cataloged Fe-S clusters, uncovering redox potential trends across diverse cluster classes. The computed redox potentials showed strong agreement with experimental values, achieving an overall accuracy of 88%. This streamlined, computationally accessible approach enhances the annotation and mechanistic understanding of Fe-S proteins, offering new insights into the redox variability of electron transport proteins. Our model holds promise for advancing studies of metalloprotein function and facilitating the design of bioinspired redox systems. Show less
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potenti Show more
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation â and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration. Show less
Summary The treatment of colorectal cancer (CRC) with FOLFOX shows some efficacy, but these tumors quickly develop resistance to this treatment. We have observed increased phosphorylation of AKT1/mTO Show more
Summary The treatment of colorectal cancer (CRC) with FOLFOX shows some efficacy, but these tumors quickly develop resistance to this treatment. We have observed increased phosphorylation of AKT1/mTOR/4EBP1 and levels of p21 in FOLFOX-resistant CRC cells. We have identified a small molecule, NSC49L, that stimulates protein phosphatase 2A (PP2A) activity, downregulates the AKT1/mTOR/4EBP1-axis, and inhibits p21 translation. We have provided evidence that NSC49L- and TRAIL-mediated sensitization is synergistically induced in p21-knockdown CRC cells, which is reversed in p21-overexpressing cells. p21 binds with procaspase 3 and prevents the activation of caspase 3. We have shown that TRAIL induces apoptosis through the activation of caspase 3 by NSC49L-mediated downregulation of p21 translation, and thereby cleavage of procaspase 3 into caspase 3. NSC49L does not affect global protein synthesis. These studies provide a mechanistic understanding of NSC49L as a PP2A agonist, and how its combination with TRAIL sensitizes FOLFOX-resistant CRC cells. Show less