{"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/retrieval-enhanced-mutation-mastery","title":"Retrieval-Enhanced Mutation Mastery: Augmenting Zero-Shot Prediction of Protein Language Model","arxiv_id":"2410.21127","date":"2024-10-28","proceeding":null,"authors":["Yang Tan","Ruilin Wang","Banghao Wu","Liang Hong","Bingxin Zhou"],"abstract":"Enzyme engineering enables the modification of wild-type proteins to meet industrial and research demands by enhancing catalytic activity, stability, binding affinities, and other properties. The emergence of deep learning methods for protein modeling has demonstrated superior results at lower costs compared to traditional approaches such as directed evolution and rational design. In mutation effect prediction, the key to pre-training deep learning models lies in accurately interpreting the complex relationships among protein sequence, structure, and function. This study introduces a retrieval-enhanced protein language model for comprehensive analysis of native properties from sequence and local structural interactions, as well as evolutionary properties from retrieved homologous sequences. The state-of-the-art performance of the proposed ProtREM is validated on over 2 million mutants across 217 assays from an open benchmark (ProteinGym). We also conducted post-hoc analyses of the model's ability to improve the stability and binding affinity of a VHH antibody. Additionally, we designed 10 new mutants on a DNA polymerase and conducted wet-lab experiments to evaluate their enhanced activity at higher temperatures. Both in silico and experimental evaluations confirmed that our method provides reliable predictions of mutation effects, offering an auxiliary tool for biologists aiming to evolve existing enzymes. The implementation is publicly available at https://github.com/tyang816/ProtREM.","url_abs":"https://arxiv.org/abs/2410.21127v1","url_pdf":"https://arxiv.org/pdf/2410.21127v1.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":"retrieval-enhanced-mutation-mastery","repo_url":"https://github.com/tyang816/protrem","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"protein-language-model","task_name":"Protein Language Model"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2410.21127","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.21127"}},"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/tyang816/protrem","reach":{"status":"ok","spdx":"NOASSERTION"}}],"summary":{"ran_draft_wrong":1,"unverified":4},"by_repo_kind":{"official":{"samples":5,"ran":1,"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":5,"samples":[{"code_sha256_prefix":"38735f4bca7f8220","entry":"tuple_index","repo":"tyang816/protrem","repo_kind":"official","path":"src/structure/encoder/layer.py","file_url":"https://github.com/tyang816/protrem/blob/HEAD/src/structure/encoder/layer.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":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"38735f4bca7f8220"}},{"code_sha256_prefix":"43fcef6d96df4137","entry":"count_matrix_from_residue_alignment","repo":"tyang816/protrem","repo_kind":"official","path":"compute_fitness.py","file_url":"https://github.com/tyang816/protrem/blob/HEAD/compute_fitness.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"43fcef6d96df4137"}},{"code_sha256_prefix":"6434f74231dba8f6","entry":"generate_pos_subgraph","repo":"tyang816/protrem","repo_kind":"official","path":"src/structure/build_subgraph.py","file_url":"https://github.com/tyang816/protrem/blob/HEAD/src/structure/build_subgraph.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"6434f74231dba8f6"}},{"code_sha256_prefix":"2a0d035332e0e312","entry":"randn","repo":"tyang816/protrem","repo_kind":"official","path":"src/structure/encoder/layer.py","file_url":"https://github.com/tyang816/protrem/blob/HEAD/src/structure/encoder/layer.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"2a0d035332e0e312"}},{"code_sha256_prefix":"5ce6809d0b113bcb","entry":"read_multi_fasta","repo":"tyang816/protrem","repo_kind":"official","path":"compute_fitness.py","file_url":"https://github.com/tyang816/protrem/blob/HEAD/compute_fitness.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"5ce6809d0b113bcb"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}