[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123322-en":3,"doc-seo-123322-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123322,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Lipid discovery enabled by sequence statistics and machine learning","Lipid discovery is enabled by sequence statistics and machine learning to classify lysine lipid specificity. The study builds an aligned protein-sequence dataset and trains a restricted Boltzmann machine to learn hidden unit representations constrained by the sequence data. Hidden units are examined and interpreted to identify sequence configurations that support enzyme classification, then used to score and filter datasets for additional model training. Experimental validation is shown for lipid synthesis patterns driven by mprF from multiple Streptococcus species.","RESEARCH ARTICLE  \nFigures and figure supplements  \nLipid discovery enabled by sequence statistics and machine learning  \nPriya M Christensen and Jonathan Martin et al.  \nChristensen, Martin et al. eLife 2024;13:RP94929. DOI: [https://doi.org/10.7554/eLife.94929](https://doi.org/10.7554/eLife.94929) 1 of 13  \n Research article Computational and Systems Biology | Microbiology and Infectious Disease  \nLys-Glc-DAG    \nFigure 1. Chemical structures of lysine lipids. Lys-PG, lysyl-phosphatidylglycerol; Lys-Glc-DAG, lysyl-glucosyl-diacylglycerol; Lys-Glc2-DAG, lysyldiglucosyl-diacylglycerol.  \nChristensen, Martin et al. eLife 2024;13:RP94929. DOI: [https://doi.org/10.7554/eLife.94929](https://doi.org/10.7554/eLife.94929) 2 of 13  \n Research article Computational and Systems Biology | Microbiology and Infectious Disease  \nFigure 2. Synthesis of lysine lipids (Lys-PG and Lys-Glc-DAG) in S. mitis expressing mprFs from S. agalactiae, S. salivarus, and S. ferus. (a) S. mitis NCTC12261 with empty vector control (pABG5) lacks lysine lipids; (b) S. agalactiae mprF (pGBSMprF) produces both Lys-PG and Lys-Glc-DAG; (c) S. salivarius mprF produces only Lys-PG; (d) S. ferus mprF produces only Lys-Glc-DAG. Left panels: total ion chromatograms (TIC); middle panels: mass spectra of retention time 19.5–21.5 min showing Lys-PG and PC; right panels: mass spectra of retention time 26–30 min showing Lys-Glc-DAG. Note:‘*’ is an extraction artifact due to chloroform used. DAG, diacylglycerol; MHDAG, monohexosyldiacylglycerol; DHDAG, dihexosyldiacylglycerol; PG, phosphatidylglycerol; Lys-PG, lysyl-phosphatidylglycerol; Lys-Glc-DAG, lysyl-glucosyl-diacylglycerol; PC, phosphatidylcholine.  \nChristensen, Martin et al. eLife 2024;13:RP94929. DOI: [https://doi.org/10.7554/eLife.94929](https://doi.org/10.7554/eLife.94929) 3 of 13  \n Research article Computational and Systems Biology | Microbiology and Infectious Disease  \nSequence Selection  \nClassify  \nInterpret  \n...  \nAligned Sequences Aligned Sequences  \nFigure 3. Schematic of the restricted Boltzmann machine (RBM) methodology. An aligned set of protein sequences is first used to learn a hidden unit representation that best describes the statistics of the sequence dataset given restrictions on the hidden unit representation. Then, the individual hidden units can be studied to find particular units which allow useful enzyme classification, and additionally, these weights can be meaningfully interpreted as statistically covarying sequence configurations. Additionally, the classification can be used to create filtered datasets to train more models.  \nChristensen, Martin et al. eLife 2024;13:RP94929. DOI: [https://doi.org/10.7554/eLife.94929](https://doi.org/10.7554/eLife.94929) 4 of 13  \n Research article Computational and Systems Biology | Microbiology and Infectious Disease  \n\n|  | \u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>b\u003Cbr>\u003Cbr>c\u003Cbr>\u003Cbr>\u003Cbr> | \u003Cbr>d\u003Cbr>\u003Cbr> |\n| --- | --- | --- |\n\nFigure 4. Example of hidden unit analysis and usage. (a) The structure of PDB:7DUW, with the red colored region being the transmembrane flippase domain and the yellow boxed region the cytosolic domain which we focus on. (b) The activations produced by inputting a sequence into a hidden unit, producing a single number as output which corresponds to a summation of negatively and positively weighted residues. Performed on entire training set (histogram in blue), highlighting sequences corresponding to predominantly positive weighted residues. (c) Hidden unit from a restricted Boltzmann machine (RBM) trained on the Pfam DUF2156 domain. The MSA positions 152 and 212 correspond to residues S684 and R742, respectively. (d) Residues (in yellow) in the MprF cytosolic domain which form the binding pocket for Lys-tRNALys (the ligand analogue L-lysine amide shown in green), from PDB:4V36 . LYN, L-lysine amide.  \nChristensen, Martin et al. eLife 2024;13:RP94929. DOI: [https://doi.org/10.7554/eLife.94929](https://doi.org/10.7554/eLife.94929) 5 of 13  \n Rese","cbCaibXyXGj0cm5q","https://ap.wps.com/l/cbCaibXyXGj0cm5q","pdf",7320339,1,13,"English","en",105,"# Introduction\n## Lysine lipid targets and chemical structures\n## Protein sequence alignment and dataset preparation\n## Restricted Boltzmann machine classification framework\n## Hidden unit interpretation and lipid specificity\n## Experimental validation and synthesis results","[{\"question\":\"What computational approach is used to discover lipid specificity from sequences?\",\"answer\":\"A restricted Boltzmann machine is trained on an aligned set of protein sequences using sequence-statistical constraints to learn hidden unit representations relevant to classification.\"},{\"question\":\"How do hidden units help interpret enzyme-lipid specificity?\",\"answer\":\"Individual hidden units are analyzed to find units that support enzyme classification, and their learned weights are interpreted as statistically covarying sequence configurations linked to lipid specificity.\"},{\"question\":\"How are lysine lipid findings validated experimentally?\",\"answer\":\"Lysine lipid synthesis is examined using mprF expression in different Streptococcus species, showing distinct lipid product profiles captured by chemical structure analysis and mass-spectrometry readouts.\"}]","Lipid discovery enabled by sequence statistics and machine learning | 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computational approach is used to discover lipid specificity from sequences?","Question",{"text":75,"@type":76},"A restricted Boltzmann machine is trained on an aligned set of protein sequences using sequence-statistical constraints to learn hidden unit representations relevant to classification.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do hidden units help interpret enzyme-lipid specificity?",{"text":80,"@type":76},"Individual hidden units are analyzed to find units that support enzyme classification, and their learned weights are interpreted as statistically covarying sequence configurations linked to lipid specificity.",{"name":82,"@type":73,"acceptedAnswer":83},"How are lysine lipid findings validated experimentally?",{"text":84,"@type":76},"Lysine lipid synthesis is examined using mprF expression in different Streptococcus species, showing distinct lipid product profiles captured by chemical structure analysis and mass-spectrometry 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