Machine learning-guided engineering of genetically encoded fluorescent calcium indicators.

Here we used machine learning to engineer genetically encoded fluorescent indicators, protein-based sensors critical for real-time monitoring of biological activity. We used machine learning to predict the outcomes of sensor mutagenesis by analyzing established libraries that link sensor sequences to functions. Using the GCaMP calcium indicator as a scaffold, we developed an ensemble of three regression models trained on experimentally derived GCaMP mutation libraries. The trained ensemble performed an in silico functional screen on 1,423 novel, uncharacterized GCaMP variants. As a result, we identified the ensemble-derived GCaMP (eGCaMP) variants, eGCaMP and eGCaMP+, which achieve both faster kinetics and larger ∆F/F0 responses upon stimulation than previously published fast variants. Furthermore, we identified a combinatorial mutation with extraordinary dynamic range, eGCaMP2+, which outperforms the tested sixth-, seventh- and eighth-generation GCaMPs. These findings demonstrate the value of machine learning as a tool to facilitate the efficient engineering of proteins for desired biophysical characteristics.

Optogenetic Microwell Array Screening System: A High-Throughput Engineering Platform for Genetically Encoded Fluorescent Indicators.

Genetically encoded fluorescent indicators (GEFIs) are protein-based optogenetic tools that change their fluorescence intensity when binding specific ligands in cells and tissues. GEFI encoding DNA can be expressed in cell subtypes while monitoring cellular physiological responses. However, engineering GEFIs with physiological sensitivity and pharmacological specificity often requires iterative optimization through trial-and-error mutagenesis while assessing their biophysical function in vitro one by one. Here, the vast mutational landscape of proteins constitutes a significant obstacle that slows GEFI development, particularly for sensors that rely on mammalian host systems for testing. To overcome these obstacles, we developed a multiplexed high-throughput engineering platform called the optogenetic microwell array screening system (Opto-MASS) that functionally tests thousands of GEFI variants in parallel in mammalian cells. Opto-MASS represents the next step for engineering optogenetic tools as it can screen large variant libraries orders of magnitude faster than current methods. We showcase this system by testing over 13,000 dopamine and 21,000 opioid sensor variants. We generated a new dopamine sensor, dMASS1, with a >6-fold signal increase to 100 nM dopamine exposure compared to its parent construct. Our new opioid sensor, µMASS1, has a ∼4.6-fold signal increase over its parent scaffold's response to 500 nM DAMGO. Thus, Opto-MASS can rapidly engineer new sensors while significantly shortening the optimization time for new sensors with distinct biophysical properties.

Insight into the Mode of Action of 8-Hydroxyquinoline-Based Blockers on the Histamine Receptor 2.

Histamine receptor 2 (HRH2) blockers are used to treat peptic ulcers and gastric reflux. Chlorquinaldol and chloroxine, which contain an 8-hydroxyquinoline (8HQ) core, have recently been identified as blocking HRH2. To gain insight into the mode of action of 8HQ-based blockers, here, we leverage an HRH2-based sensor in yeast to evaluate the role of key residues in the HRH2 active site on histamine and 8HQ-based blocker binding. We find that the HRH2 mutations D98A, F254A, Y182A, and Y250A render the receptor inactive in the presence of histamine, while HRH2:D186A and HRH2:T190A retain residual activity. Based on molecular docking studies, this outcome correlates with the ability of the pharmacologically relevant histamine tautomers to interact with D98 via the charged amine. Docking studies also suggest that, unlike established HRH2 blockers that interact with both ends of the HRH2 binding site, 8HQ-based blockers interact with only one end, either the end framed by D98/Y250 or T190/D186. Experimentally, we find that chlorquinaldol and chloroxine still inactivate HRH2:D186A by shifting their engagement from D98 to Y250 in the case of chlorquinaldol and D186 to Y182 in the case of chloroxine. Importantly, the tyrosine interactions are supported by the intramolecular hydrogen bonding of the 8HQ-based blockers. The insight gained in this work will aid in the development of improved HRH2 therapeutics. More generally, this work demonstrates that Gprotein-coupled receptor (GPCR)-based sensors in yeast can help elucidate the mode of action of novel ligands for GPCRs, a family of receptors that bind 30% of FDA therapeutics.

Discovery of 8-Hydroxyquinoline as a Histamine Receptor 2 Blocker Scaffold.

Histamine receptor 2 (HRH2) activation in the stomach results in gastric acid secretion, and HRH2 blockers are used for the treatment of peptidic ulcers and acid reflux. Over-the-counter HRH2 blockers carry a five-membered aromatic heterocycle, with two of them additionally carrying a tertiary amine that decomposes to N-nitrosodimethylamine, a human carcinogen. To discover a novel HRH2 blocker scaffold to serve in the development of next-generation HRH2 blockers, we developed an HRH2-based sensor in yeast by linking human HRH2 activation to cell luminescence. We used the HRH2-based sensor to screen a 403-member anti-infection chemical library and identified three HRH2 blockers, chlorquinaldol, chloroxine, and broxyquinoline, all sharing an 8-hydroxyquinoline scaffold, which is not found among known HRH2 antagonists. Critically, we validate their HRH2-blocking ability in mammalian cells. Molecular docking suggests that the HRH2 blockers bind the histamine binding pocket and structure-activity data point toward these blockers acting as competitive antagonists. Chloroxine and broxyquinoline are antimicrobials that can be found in the gastrointestinal tract at concentrations that would block HRH2, thus likely modulating gastric acid secretion. Taken together, this work demonstrates the utility of GPCR-based sensors for rapid drug discovery applications, identifies a novel HRH2 blocker scaffold, and provides further evidence that antimicrobials not only target the human microbiota but also the human host.