{"doi":"10.1101/2021.04.16.440193","title":"Generation of photocaged nanobodies for <i>in vivo</i> applications using genetic code expansion and computationally guided protein engineering","abstract":"Abstract Nanobodies are becoming increasingly popular as tools for manipulating and visualising proteins in vivo . The ability to control nanobody/antigen interactions using light could provide precise spatiotemporal control over protein function. We develop a general approach to engineer photo-activatable nanobodies using photocaged amino acids that are introduced into the target binding interface by genetic code expansion. Guided by computational alanine scanning and molecular-dynamics simulations, we tune nanobody/target binding affinity to eliminate binding before uncaging. Upon photo-activation, binding is restored. We use this approach to generate improved photocaged variants of two anti-GFP nanobodies. These variants exhibit photo-activatable binding triggered by illumination with 365nm light. We demonstrate that the photocaged nanobodies we have created are highly robust and function in a complex cellular environment. We apply them to control subcellular protein localisation in the nematode worm C. elegans . Our approach provides a rare example of computationally designed proteins being directly applied in living animals and demonstrates the importance of accounting for in vivo effects on protein-protein interactions.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2021,"id":218620,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9457,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":563814,"name":"Angeliki Goutou","orcid":null,"position":1,"is_corresponding":false},{"id":816744,"name":"Cyrus R. Sethna","orcid":null,"position":2,"is_corresponding":false},{"id":816405,"name":"Christopher W. Wood","orcid":"0000-0003-1243-3105","position":3,"is_corresponding":false},{"id":562785,"name":"Sebastian Greiss","orcid":"0000-0001-9130-0831","position":4,"is_corresponding":false},{"id":328033,"name":"Jack M. O’Shea","orcid":"0000-0002-9694-7340","position":0,"is_corresponding":true}],"reference_count":43,"raw_metadata":null,"created_at":"2026-07-18T23:53:29.626149Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}