Mechanisms for cardiac calcium pump activation by its substrate and a synthetic allosteric modulator using fluorescence lifetime imaging.

The discovery of allosteric modulators is an emerging paradigm in drug discovery, and signal transduction is a subtle and dynamic process that is challenging to characterize. We developed a time-correlated single photon-counting imaging approach to investigate the structural mechanisms for small-molecule activation of the cardiac sarcoplasmic reticulum Ca2+-ATPase, a pharmacologically important pump that transports Ca2+ at the expense of adenosine triphosphate (ATP) hydrolysis. We first tested whether the dissociation of sarcoplasmic reticulum Ca2+-ATPase from its regulatory protein phospholamban is required for small-molecule activation. We found that CDN1163, a validated sarcoplasmic reticulum Ca2+-ATPase activator, does not have significant effects on the stability of the sarcoplasmic reticulum Ca2+-ATPase-phospholamban complex. Time-correlated single photon-counting imaging experiments using the nonhydrolyzable ATP analog ß,γ-Methyleneadenosine 5'-triphosphate (AMP-PCP) showed ATP is an allosteric modulator of sarcoplasmic reticulum Ca2+-ATPase, increasing the fraction of catalytically competent structures at physiologically relevant Ca2+ concentrations. Unlike ATP, CDN1163 alone has no significant effects on the Ca2+-dependent shifts in the structural populations of sarcoplasmic reticulum Ca2+-ATPase, and it does not increase the pump's affinity for Ca2+ ions. However, we found that CDN1163 enhances the ATP-mediated modulatory effects to increase the population of catalytically competent sarcoplasmic reticulum Ca2+-ATPase structures. Importantly, this structural shift occurs within the physiological window of Ca2+ concentrations at which sarcoplasmic reticulum Ca2+-ATPase operates. We demonstrated that ATP is both a substrate and modulator of sarcoplasmic reticulum Ca2+-ATPase and showed that CDN1163 and ATP act synergistically to populate sarcoplasmic reticulum Ca2+-ATPase structures that are primed for phosphorylation. This study provides novel insights into the structural mechanisms for sarcoplasmic reticulum Ca2+-ATPase activation by its substrate and a synthetic allosteric modulator.

A novel machine learning-based screening identifies statins as inhibitors of the calcium pump SERCA.

We report a novel small-molecule screening approach that combines data augmentation and machine learning to identify Food and Drug Administration (FDA)-approved drugs interacting with the calcium pump (Sarcoplasmic reticulum Ca2+-ATPase, SERCA) from skeletal (SERCA1a) and cardiac (SERCA2a) muscle. This approach uses information about small-molecule effectors to map and probe the chemical space of pharmacological targets, thus allowing to screen with high precision large databases of small molecules, including approved and investigational drugs. We chose SERCA because it plays a major role in the excitation-contraction-relaxation cycle in muscle and it represents a major target in both skeletal and cardiac muscle. The machine learning model predicted that SERCA1a and SERCA2a are pharmacological targets for seven statins, a group of FDA-approved 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors used in the clinic as lipid-lowering medications. We validated the machine learning predictions by using in vitro ATPase assays to show that several FDA-approved statins are partial inhibitors of SERCA1a and SERCA2a. Complementary atomistic simulations predict that these drugs bind to two different allosteric sites of the pump. Our findings suggest that SERCA-mediated Ca2+ transport may be targeted by some statins (e.g., atorvastatin), thus providing a molecular pathway to explain statin-associated toxicity reported in the literature. These studies show the applicability of data augmentation and machine learning-based screening as a general platform for the identification of off-target interactions and the applicability of this approach extends to drug discovery.