EnzyKR and DeepLasso illustrate how task-specific neural architectures, grounded in physical constraints (activation barriers, topology) and trained on structure- or library-scale data, can deliver actionable predictions for enzyme selectivity and RiPP bioactivity in real design workflows.<\/p>\n
EnzyKR<\/em><\/span>. EnzyKR is a chirality-aware deep learning framework for stereoselective biocatalysis. It uses a classifier\u2013regressor pipeline that first identifies the reactive binding conformer of a substrate\u2013hydrolase complex and then predicts its activation free energy, with a structure-based encoding that captures chiral interactions. Trained on 204 docked substrate\u2013enzyme complexes and tested on 20 held-out complexes, EnzyKR achieved R = 0.72 and MAE = 1.54 kcal\u00b7mol\u207b\u00b9 for barrier prediction; across 28 hydrolase-catalyzed kinetic-resolution reactions, it correctly predicted the favored enantiomer and outperformed DLKcat in 18\/28 cases (64%). These results position EnzyKR as a practical model for selecting hydrolases that deliver high enantioselectivity in kinetic resolution.<\/p>\n DeepLasso<\/span><\/em>. DeepLasso leverages massive sequence\u2013activity data to predict the RNAP-inhibitory activity of lasso-peptide variants. Built from a >90,000-member ubonodin library, it combines a sequence encoder (CNN + BiLSTM + attention) with a topology encoder that tags ring, loop, and tail regions, feeding a classifier\u2013regressor architecture to handle dropout vs. measurable variants and to score enrichment values. In 5-fold cross-validation, the classifier achieved 73%\/63% hit rates on dropout\/nondropout identification, and the regressor reached Pearson R = 0.80 (Spearman R = 0.77) on held-out variants. The screen also uncovered variants with sub-micromolar MICs against Burkholderia cenocepacia, underscoring the model\u2019s utility for prioritizing potent designs across large mutational spaces.<\/p>\n Both works were led by Xinchun Ran.<\/p>\n The software code<\/strong>:<\/p>\n https:\/\/github.com\/ChemBioHTP\/LassoPred<\/p>\n Publications<\/strong>:<\/p>\n https:\/\/pubs.rsc.org\/en\/content\/articlelanding\/2023\/sc\/d3sc02752j<\/p>\n https:\/\/pubs.acs.org\/doi\/full\/10.1021\/acscentsci.2c01487<\/p>\n","protected":false},"excerpt":{"rendered":" EnzyKR and DeepLasso illustrate how task-specific neural architectures, grounded in physical constraints (activation barriers, topology) and trained on structure- or library-scale data, can deliver actionable predictions for enzyme selectivity and RiPP bioactivity in real design workflows. EnzyKR. EnzyKR is a chirality-aware deep learning framework for stereoselective biocatalysis. It uses a classifier\u2013regressor pipeline that first identifies…<\/p>\n","protected":false},"author":253,"featured_media":659,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[9],"tags":[],"class_list":["post-656","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-software"],"acf":[],"_links":{"self":[{"href":"https:\/\/lab.vanderbilt.edu\/zyang-lab\/wp-json\/wp\/v2\/posts\/656","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lab.vanderbilt.edu\/zyang-lab\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lab.vanderbilt.edu\/zyang-lab\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lab.vanderbilt.edu\/zyang-lab\/wp-json\/wp\/v2\/users\/253"}],"replies":[{"embeddable":true,"href":"https:\/\/lab.vanderbilt.edu\/zyang-lab\/wp-json\/wp\/v2\/comments?post=656"}],"version-history":[{"count":1,"href":"https:\/\/lab.vanderbilt.edu\/zyang-lab\/wp-json\/wp\/v2\/posts\/656\/revisions"}],"predecessor-version":[{"id":658,"href":"https:\/\/lab.vanderbilt.edu\/zyang-lab\/wp-json\/wp\/v2\/posts\/656\/revisions\/658"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/lab.vanderbilt.edu\/zyang-lab\/wp-json\/wp\/v2\/media\/659"}],"wp:attachment":[{"href":"https:\/\/lab.vanderbilt.edu\/zyang-lab\/wp-json\/wp\/v2\/media?parent=656"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lab.vanderbilt.edu\/zyang-lab\/wp-json\/wp\/v2\/categories?post=656"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lab.vanderbilt.edu\/zyang-lab\/wp-json\/wp\/v2\/tags?post=656"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}