{"title":"MiLoPYP: self-supervised molecular pattern mining and particle localization in situ","authors":"Qinwen Huang, Ye Zhou, Alberto Bartesaghi","doi":"10.1038/s41592-024-02403-6","DOIUrl":null,"url":null,"abstract":"Cryo-electron tomography allows the routine visualization of cellular landscapes in three dimensions at nanometer-range resolutions. When combined with single-particle tomography, it is possible to obtain near-atomic resolution structures of frequently occurring macromolecules within their native environment. Two outstanding challenges associated with cryo-electron tomography/single-particle tomography are the automatic identification and localization of proteins, tasks that are hindered by the molecular crowding inside cells, imaging distortions characteristic of cryo-electron tomography tomograms and the sheer size of tomographic datasets. Current methods suffer from low accuracy, demand extensive and time-consuming manual labeling or are limited to the detection of specific types of proteins. Here, we present MiLoPYP, a two-step dataset-specific contrastive learning-based framework that enables fast molecular pattern mining followed by accurate protein localization. MiLoPYP’s ability to effectively detect and localize a wide range of targets including globular and tubular complexes as well as large membrane proteins, will contribute to streamline and broaden the applicability of high-resolution workflows for in situ structure determination. MiLoPYP is a two-step, dataset-specific contrastive learning-based method for fast and accurate detection and localization of a diverse range of target structures in cryo-electron tomography data, enabling improved in situ structural biology.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02403-6.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Methods","FirstCategoryId":"99","ListUrlMain":"https://www.nature.com/articles/s41592-024-02403-6","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Cryo-electron tomography allows the routine visualization of cellular landscapes in three dimensions at nanometer-range resolutions. When combined with single-particle tomography, it is possible to obtain near-atomic resolution structures of frequently occurring macromolecules within their native environment. Two outstanding challenges associated with cryo-electron tomography/single-particle tomography are the automatic identification and localization of proteins, tasks that are hindered by the molecular crowding inside cells, imaging distortions characteristic of cryo-electron tomography tomograms and the sheer size of tomographic datasets. Current methods suffer from low accuracy, demand extensive and time-consuming manual labeling or are limited to the detection of specific types of proteins. Here, we present MiLoPYP, a two-step dataset-specific contrastive learning-based framework that enables fast molecular pattern mining followed by accurate protein localization. MiLoPYP’s ability to effectively detect and localize a wide range of targets including globular and tubular complexes as well as large membrane proteins, will contribute to streamline and broaden the applicability of high-resolution workflows for in situ structure determination. MiLoPYP is a two-step, dataset-specific contrastive learning-based method for fast and accurate detection and localization of a diverse range of target structures in cryo-electron tomography data, enabling improved in situ structural biology.
期刊介绍:
Nature Methods is a monthly journal that focuses on publishing innovative methods and substantial enhancements to fundamental life sciences research techniques. Geared towards a diverse, interdisciplinary readership of researchers in academia and industry engaged in laboratory work, the journal offers new tools for research and emphasizes the immediate practical significance of the featured work. It publishes primary research papers and reviews recent technical and methodological advancements, with a particular interest in primary methods papers relevant to the biological and biomedical sciences. This includes methods rooted in chemistry with practical applications for studying biological problems.