High-throughput computational pipeline for rapid development of photocaged peptide tools

The National Institute of Neurological Disorders and Stroke (NINDS) awarded the University of Utah $616,000 on August 1, 2026, under the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative (Assistance Listing 93.372) to develop a high-throughput computational pipeline for rapid design of photocaged neuropeptides.

The project addresses the NIH BRAIN Initiative goal to enable precise modulation of neural circuit components by creating a generalizable, open-source machine learning-based computational pipeline for rational design of photocaged neuropeptides that can activate specific neuropeptide-GPCR signaling pathways with light. Current optogenetic approaches simultaneously activate multiple neurotransmitters, obscuring neuropeptide-specific roles; photocaged neuropeptides overcome this limitation but remain challenging and resource-intensive to develop. The pipeline integrates solvent-accessibility mapping, receptor docking, and molecular dynamics simulations, leveraging experimentally resolved protein structures and AI-predicted AlphaFold2 models. The project focuses on 40 neuropeptide-GPCR pairs with experimentally solved structures and known roles in neurological diseases including autism spectrum disorders, postpartum depression, chronic pain, and metabolic diseases. Aim 1 generates computational models predicting optimal photocaging residues that prevent GPCR binding in darkness but restore ligand activity after targeted photolysis. Aim 2 experimentally validates predictions and creates a library of analogs; the top three analogs per neuropeptide are synthesized via solid-phase chemistry and screened in high-throughput GPCR assays to ensure minimal dark-state activity.

Work is performed in Salt Lake City, Utah, with a period of performance from August 1, 2026, through July 31, 2029.

Abstract

Neuropeptides are abundant and diverse neuromodulators that orchestrate fundamental neural circuits underlying complex behaviors and physiological functions through activation of G protein-coupled receptors (GPCRS) expressed on neurons. Dysregulated neuropeptide signaling contributes significantly to autism spectrum disorders, postpartum depression, chronic pain, and metabolic diseases. Yet tools for the precise manipulation of neuropeptides remain limited. For example, current optogenetic approaches activate multiple neurotransmitters simultaneously, obscuring neuropeptide-specific roles. Photocaged neuropeptides overcome this limitation, providing targeted activation via light, but their development is challenging and resource intensive. To address the NIH brain initiative goal to enable precise modulation of neural circuit components, we propose to develop a generalizable, open-source computational machine learning based pipeline for rapid, rational design of photocaged neuropeptides. Our innovative pipeline will integrate solvent-accessibility mapping, receptor docking, and molecular dynamics simulations, leveraging experimentally resolved structures and AI-predicted ALPHAFOLD2 models for robust predictions. We selected 40 neuropeptide-gpcr pairs that have experimentally solved structures and have known critical roles in normal neuronal communication and in neurological diseases. Aim 1 will generate a computational model that predicts optimal photocaging residues in these 40 neuropeptides that prevent gpcr binding in darkness but restore ligand activity after targeted photolysis. Aim 2 experimentally validates these predictions and provides benchmarks and a library of analogs. The top 3 analogs per neuropeptide will be synthesized via solid-phase chemistry and screened in high-throughput gpcr assays in vitro to ensure minimal dark-state activity and potent activation by light. These experiments will benchmark our predicted analogs against existing caged peptides, which will be used to further train the model generated in aim 1. We will characterize the effects of top-performing photocaged neuropeptide analogs on neuronal circuit activity using patch-clamp recordings and two-photon calcium imaging in EX vivo brain slices. Success criteria include ≤5 % residual receptor activity in the dark state and restoration of potency (ec₅₀) to within twofold of the native neuropeptide following photolysis. This innovative pipeline is projected to cut peptide design-to-testing timelines from six months to under three weeks, significantly reducing development costs by prioritizing high-confidence candidates. All computational tools, models, and experimental protocols will be freely disseminated and published under open access, democratizing access to advanced neuropeptide technologies. Immediate impacts include breakthroughs in the understanding of social behavior, pain processing, stress regulation, and reproductive physiology. Long-term, this work will accelerate discovery and therapeutic development, aligning closely with the NIH vision of cells and circuits toward cures.