{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/guided-generative-protein-design-using","title":"ReLSO: A Transformer-based Model for Latent Space Optimization and Generation of Proteins","arxiv_id":"2201.09948","date":"2022-01-24","proceeding":null,"authors":["Egbert Castro","Abhinav Godavarthi","Julian Rubinfien","Kevin B. Givechian","Dhananjay Bhaskar","Smita Krishnaswamy"],"abstract":"The development of powerful natural language models have increased the ability to learn meaningful representations of protein sequences. In addition, advances in high-throughput mutagenesis, directed evolution, and next-generation sequencing have allowed for the accumulation of large amounts of labeled fitness data. Leveraging these two trends, we introduce Regularized Latent Space Optimization (ReLSO), a deep transformer-based autoencoder which features a highly structured latent space that is trained to jointly generate sequences as well as predict fitness. Through regularized prediction heads, ReLSO introduces a powerful protein sequence encoder and novel approach for efficient fitness landscape traversal. Using ReLSO, we explicitly model the sequence-function landscape of large labeled datasets and generate new molecules by optimizing within the latent space using gradient-based methods. We evaluate this approach on several publicly-available protein datasets, including variant sets of anti-ranibizumab and GFP. We observe a greater sequence optimization efficiency (increase in fitness per optimization step) by ReLSO compared to other approaches, where ReLSO more robustly generates high-fitness sequences. Furthermore, the attention-based relationships learned by the jointly-trained ReLSO models provides a potential avenue towards sequence-level fitness attribution information.","url_abs":"https://arxiv.org/abs/2201.09948v2","url_pdf":"https://arxiv.org/pdf/2201.09948v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"guided-generative-protein-design-using","repo_url":"https://github.com/KrishnaswamyLab/ReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2201.09948","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.09948"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/KrishnaswamyLab/ReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"ran_draft_wrong":1,"ran_violates":1,"unverified":6},"by_repo_kind":{"official":{"samples":8,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"af78970db657f767","entry":"str2auxnetwork","repo":"KrishnaswamyLab/ReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers","repo_kind":"official","path":"relso/nn/auxnetwork.py","file_url":"https://github.com/KrishnaswamyLab/ReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers/blob/HEAD/relso/nn/auxnetwork.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"af78970db657f767"}},{"code_sha256_prefix":"7c508037b40522af","entry":"str2bool","repo":"KrishnaswamyLab/ReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers","repo_kind":"official","path":"train_relso.py","file_url":"https://github.com/KrishnaswamyLab/ReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers/blob/HEAD/train_relso.py","link_basis":"harvester_set","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"7c508037b40522af"}},{"code_sha256_prefix":"bb42954f765973e6","entry":"directed_evolution_sequence","repo":"KrishnaswamyLab/ReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers","repo_kind":"official","path":"relso/optim/optim_algs.py","file_url":"https://github.com/KrishnaswamyLab/ReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers/blob/HEAD/relso/optim/optim_algs.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"bb42954f765973e6"}},{"code_sha256_prefix":"a378f08b0159841b","entry":"embed_path_from_json","repo":"KrishnaswamyLab/ReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers","repo_kind":"official","path":"relso/utils/model_utils.py","file_url":"https://github.com/KrishnaswamyLab/ReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers/blob/HEAD/relso/utils/model_utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"a378f08b0159841b"}},{"code_sha256_prefix":"8dad5b8f2f73383e","entry":"eval_oracle","repo":"KrishnaswamyLab/ReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers","repo_kind":"official","path":"relso/optim/optim_algs.py","file_url":"https://github.com/KrishnaswamyLab/ReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers/blob/HEAD/relso/optim/optim_algs.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"8dad5b8f2f73383e"}},{"code_sha256_prefix":"64c1e0f93d084ae0","entry":"model_predict","repo":"KrishnaswamyLab/ReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers","repo_kind":"official","path":"relso/optim/optim_algs.py","file_url":"https://github.com/KrishnaswamyLab/ReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers/blob/HEAD/relso/optim/optim_algs.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"64c1e0f93d084ae0"}},{"code_sha256_prefix":"e844fff14b289cce","entry":"scaled_dot_product","repo":"KrishnaswamyLab/ReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers","repo_kind":"official","path":"relso/nn/transformers.py","file_url":"https://github.com/KrishnaswamyLab/ReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers/blob/HEAD/relso/nn/transformers.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"e844fff14b289cce"}},{"code_sha256_prefix":"d1761f68e78db5bb","entry":"weight_path_from_json","repo":"KrishnaswamyLab/ReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers","repo_kind":"official","path":"relso/utils/model_utils.py","file_url":"https://github.com/KrishnaswamyLab/ReLSO-Guided-Generative-Protein-Design-using-Regularized-Transformers/blob/HEAD/relso/utils/model_utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"d1761f68e78db5bb"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}