Calcium-dependent cardiac muscle contraction is regulated by the protein complex troponin (cTn) and specifically by the regulatory N-terminal domain (N-cTnC) which contains one active Ca 2+ binding site (site II). It has been previously shown that cardiac muscle contractility and functionality is affected by mutations in N-cTnC which alter calcium binding affinity. Here, we describe the application of adaptive steered molecular dynamics to characterize the influence of N-cTnC mutations on site II calcium binding affinity. We observed the correct trends for all of the studied calcium sensitizing and desensitizing mutants, in conjunction with loop II perturbations. Additionally, the potential of mean force accuracy was shown to increase substantially with increasingly slower speeds and using fewer trajectories. This study presents a novel approach to computationally estimate the Ca 2+ binding affinity of N-cTnC structures and is a valuable potential tool to support the design and characterization of novel mutations with potential therapeutic benefits.

Computer-aided drug design, an important component of the early stages of the drug discovery pipeline, routinely identifies large numbers of false positive hits that are subsequently confirmed to be experimentally inactive compounds. We have developed a methodology to improve true positive prediction rates in structure-based drug design and have successfully applied the protocol to twenty target systems and identified the top three performing conformers for each of the targets. Receptor performance was evaluated based on the area under the curve of the receiver operating characteristic curve for two independent sets of known actives. For a subset of five diverse cancer-related disease targets, we validated our approach through experimental testing of the top 50 compounds from a blind screening of a small molecule library containing hundreds of thousands of compounds. Our methods of receptor and compound selection resulted in the identification of 22 novel inhibitors in the low μM−nM range, with the most potent being an EGFR inhibitor with an IC 50 value of 7.96 nM. Additionally, for a subset of five independent target systems, we demonstrated the utility of Gaussian accelerated molecular dynamics to thoroughly explore a target system's potential energy surface and generate highly predictive receptor conformations.

Cardiac troponin C (cTnC) is the calcium (Ca2+) sensing component of the troponin complex. Binding of Ca2+ to cTnC triggers a cascade of myofilament conformational changes that culminate in force production. Mutations in cTnC linked to hypertrophic myocardial myopathy (HCM) induce a greater degree and duration of Ca2+ binding, which may underly the hypertrophic phenotype. Recent evidence from our laboratories demonstrated novel modifications of cTnC Ca2+ binding by cellular magnesium (Mg2+) that we hypothesize may be of significance in promoting HCM.Regulation of contraction has long been thought to occur exclusively through Ca2+ binding to site II of cTnC. However, abundant cellular Mg2+ is a potential competitor for binding to the same sites; work by several groups also suggests this is possible. We have used isothermal titration calorimetry (ITC) to explore the thermodynamic properties associated with the interaction between Ca2+/Mg2+ and site II of cTnC; these experiments demonstrated that physiological concentrations of Mg2+ may compete with Ca2+ to bind site II of cTnC.In experiments reported here, we studied a series of mutations in cTnC thought to be causal in HCM. Three mutants (A8V, L29Q, and A31S) slightly elevated the affinity for both Ca2+ and Mg2+, whereas other mutants (L48Q, Q50R, and C84Y), that are closer to the C-terminal domain and surrounding the EF hand binding motif of site II had a more significant effect on affinity and the thermodynamics of the binding interaction.

Machine learning (ML) has revolutionised the field of structure-based drug design (SBDD) in recent years. During the training stage, ML techniques typically analyse large amounts of experimentally determined data to create predictive models in order to inform the drug discovery process. Deep learning (DL) is a subfield of ML, that relies on multiple layers of a neural network to extract significantly more complex patterns from experimental data, and has recently become a popular choice in SBDD. This review provides a thorough summary of the recent DL trends in SBDD with a particular focus on de novo drug design, binding site prediction, and binding affinity prediction of small molecules.

1H NMR spectroscopy is a molecular characterization technique that is ubiquitously used in research and teaching laboratories alike. Strategic deuteration of molecules is a commonly employed technique in the deconvolution of 1H NMR signals. Here this approach is applied in a second-year chemistry course in which students synthesize 4-methoxychalcone using the Claisen–Schmidt variant of the foundational aldol reaction. The 1H NMR spectrum is complicated by a crowded aromatic and alkene region due in part to extensive conjugation through the two phenyl groups surrounding an α,β-unsaturated ketone. This report demonstrates that students gain competence and confidence in making 1H NMR assignments when presented with a curated set of selectively deuterated chalcones rather than the corresponding spectrum of the fully protiated chalcone. While deuteration does not resolve all of the challenges associated with interpreting this spectrum, it is an invaluable tool to complement chemical shifts (including the predictive nature of resonance structures), integration, and spin–spin splitting. A proposed lecture is presented that is timed to correlate with the performance of the chalcone synthesis lab. Assessment data collected before and after the lecture and then again on the final exam point to the effectiveness of this approach.

Despite large investments from academia and industry, heart failure, which results from a disruption of the contractile apparatus, remains a leading cause of death. Cardiac muscle contraction is a calcium-dependent mechanism, which is regulated by the troponin protein complex (cTn) and specifically by the N-terminal domain of its calcium binding subunit (cNTnC). There is an increasing need for the development of small molecules that increase calcium sensitivity without altering systolic calcium concentration, thereby strengthening cardiac function. Here, we examined the effect of our previously identified calcium sensitizing small molecule, ChemBridge compound 7930079, in the context of several homologous muscle systems. The effect of this molecule on force generation in isolated cardiac trabeculae and slow skeletal muscle fibers was measured. Furthermore, we explored the use of Gaussian accelerated molecular dynamics in sampling highly predictive receptor conformations based on NMR derived starting structures. Additionally, we took a rational computational approach for lead optimization based on lipophilic diphenyl moieties. This led to the identification of three novel low affinity binders, which had similar binding affinities to known positive inotrope trifluoperazine. The most potent identified calcium sensitizer was compound 16 with an apparent affinity of 117±17 μM.

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Despite large investments from academia and industry, heart failure, which results from a disruption of the contractile apparatus, remains a leading cause of death. Cardiac muscle contraction is a calcium-dependent mechanism, which is regulated by the troponin protein complex (cTn) and specifically by the N-terminal domain of its calcium-binding subunit (cNTnC). There is an increasing need for the development of small molecules that increase calcium sensitivity without altering the systolic calcium concentration, thereby strengthening the cardiac function. Here, we examined the effect of our previously identified calcium-sensitizing small molecule, ChemBridge compound 7930079, in the context of several homologous muscle systems. The effect of this molecule on force generation in isolated cardiac trabeculae and slow skeletal muscle fibers was measured. Furthermore, we explored the use of Gaussian accelerated molecular dynamics in sampling highly predictive receptor conformations based on NMR-derived starting structures. Additionally, we took a rational computational approach for lead optimization based on lipophilic diphenyl moieties. This integrated structural− biochemical−physiological approach led to the identification of three novel low-affinity binders, which had similar binding affinities to the known positive inotrope trifluoperazine. The most potent identified calcium sensitizer was compound 16 with an apparent affinity of 117 ± 17 μM.