{"doi":"10.1101/2020.08.07.242347","title":"Ig-VAE: Generative Modeling of Protein Structure by Direct 3D Coordinate Generation","abstract":"A bstract While deep learning models have seen increasing applications in protein science, few have been implemented for protein backbone generation—an important task in structure-based problems such as active site and interface design. We present a new approach to building class-specific backbones, using a variational auto-encoder to directly generate the 3D coordinates of immunoglobulins. Our model is torsion- and distance-aware, learns a high-resolution embedding of the dataset, and generates novel, high-quality structures compatible with existing design tools. We show that the Ig-VAE can be used to create a computational model of a SARS-CoV2-RBD binder via latent space sampling. We further demonstrate that the model’s generative prior is a powerful tool for guiding computational protein design, motivating a new paradigm under which backbone design is solved as constrained optimization problem in the latent space of a generative model.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2020,"id":118770,"datarank":0.5641800173540344,"base_score":3.7612001156935624,"endowment":3.7612001156935624,"self_citation_contribution":0.5641800173540344,"citation_network_contribution":0.0,"self_endowment_contribution":0.5641800173540344,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":42,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9431,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":552054,"name":"Christian A. Choe","orcid":"0000-0001-8871-9682","position":1,"is_corresponding":false},{"id":317333,"name":"Po‐Ssu Huang","orcid":"0000-0002-7948-2895","position":2,"is_corresponding":false},{"id":551923,"name":"Raphael R. Eguchi","orcid":"0000-0002-2704-8464","position":0,"is_corresponding":true}],"reference_count":58,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:14:03.409507Z","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":[]}