Penta- and hexa-coordinate ferric hemoglobins display distinct pH titration profiles measured by Soret peak shifts

Hemoglobins with diverse characteristics have been identified in all kingdoms of life. Their ubiquitous presence indicates that these proteins play important roles in physiology, though function for all hemoglobins are not yet established with certainty. Their physiological role may depend on their ability to bind ligands, which in turn is dictated by their heme chemistry. However, we have an incomplete understanding of the mechanism of ligand binding for these newly discovered hemoglobins and the measurement of their kinetic parameters depend on their coordination at the heme iron. To gain insights into their functional role, it is important to categorize the new hemoglobins into either penta- or hexa-coordinated varieties. We demonstrate that simple pH titration and absorbance measurements can determine the coordination state of heme iron atom in ferric hemoglobins, thus providing unambiguous information about the classification of new globins. This method is rapid, sensitive and requires low concentration of protein. Penta- and hexa-coordinate hemoglobins displayed distinct pH titration profiles as observed in a variety of hemoglobins. The pentacoordinate distal histidine mutant proteins of hexacoordinate hemoglobins and ligand-bound hexacoordinate forms of pentacoordinate hemoglobins reverse the pH titration profiles, thus validating the sensitivity of this spectroscopic technique.     

Identification of a Kinase Profile that Predicts Chromosome Damage Induced by Small Molecule Kinase Inhibitors

Kinases are heavily pursued pharmaceutical targets because of their mechanistic role in many diseases. Small molecule kinase inhibitors (SMKIs) are a compound class that includes marketed drugs and compounds in various stages of drug development. While effective, many SMKIs have been associated with toxicity including chromosomal damage. Screening for kinase-mediated toxicity as early as possible is crucial, as is a better understanding of how off-target kinase inhibition may give rise to chromosomal damage. To that end, we employed a competitive binding assay and an analytical method to predict the toxicity of SMKIs. Specifically, we developed a model based on the binding affinity of SMKIs to a panel of kinases to predict whether a compound tests positive for chromosome damage. As training data, we used the binding affinity of 113 SMKIs against a representative subset of all kinases (290 kinases), yielding a 113×290 data matrix. Additionally, these 113 SMKIs were tested for genotoxicity in an in vitro micronucleus test (MNT). Among a variety of models from our analytical toolbox, we selected using cross-validation a combination of feature selection and pattern recognition techniques: Kolmogorov-Smirnov/T-test hybrid as a univariate filter, followed by Random Forests for feature selection and Support Vector Machines (SVM) for pattern recognition. Feature selection identified 21 kinases predictive of MNT. Using the corresponding binding affinities, the SVM could accurately predict MNT results with 85% accuracy (68% sensitivity, 91% specificity). This indicates that kinase inhibition profiles are predictive of SMKI genotoxicity. While in vitro testing is required for regulatory review, our analysis identified a fast and cost-efficient method for screening out compounds earlier in drug development. Equally important, by identifying a panel of kinases predictive of genotoxicity, we provide medicinal chemists a set of kinases to avoid when designing compounds, thereby providing a basis for rational drug design away from genotoxicity.