National Institutes of Health/National Institute of General Medical Sciences (NIH/NIGMS)
R01GM080139
米国
引用
ジャーナル: Nat Methods / 年: 2021 タイトル: Deep learning-based mixed-dimensional Gaussian mixture model for characterizing variability in cryo-EM. 著者: Muyuan Chen / Steven J Ludtke / 要旨: Structural flexibility and/or dynamic interactions with other molecules is a critical aspect of protein function. Cryogenic electron microscopy (cryo-EM) provides direct visualization of individual ...Structural flexibility and/or dynamic interactions with other molecules is a critical aspect of protein function. Cryogenic electron microscopy (cryo-EM) provides direct visualization of individual macromolecules sampling different conformational and compositional states. While numerous methods are available for computational classification of discrete states, characterization of continuous conformational changes or large numbers of discrete state without human supervision remains challenging. Here we present e2gmm, a machine learning algorithm to determine a conformational landscape for proteins or complexes using a three-dimensional Gaussian mixture model mapped onto two-dimensional particle images in known orientations. Using a deep neural network architecture, e2gmm can automatically resolve the structural heterogeneity within the protein complex and map particles onto a small latent space describing conformational and compositional changes. This system presents a more intuitive and flexible representation than other manifold methods currently in use. We demonstrate this method on both simulated data and three biological systems to explore compositional and conformational changes at a range of scales. The software is distributed as part of EMAN2.
名称: Pre-catalytic spliceosome / タイプ: complex / ID: 1 / 親要素: 0 詳細: Re-processing of the dataset EMPIAR-10180 to resolve the continuous movement of the system. Five frames of the first motion mode are attached as additional map files.
由来(天然)
生物種: Saccharomyces cerevisiae (パン酵母)
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実験情報
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構造解析
手法
クライオ電子顕微鏡法
解析
単粒子再構成法
試料の集合状態
particle
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試料調製
緩衝液
pH: 8
凍結
凍結剤: ETHANE
詳細
Re-processing of EMPIAR-10180.
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電子顕微鏡法
顕微鏡
FEI TITAN KRIOS
撮影
フィルム・検出器のモデル: GATAN K2 SUMMIT (4k x 4k) 平均電子線量: 56.0 e/Å2
解像度のタイプ: BY AUTHOR / 解像度: 20.0 Å / 解像度の算出法: OTHER / ソフトウェア - 名称: EMAN (ver. 2.91) 詳細: Each 3D frame of the continuous movement movie includes 4000 particles. The maps are filtered to 20A for visualization. 使用した粒子像数: 4000
初期 角度割当
タイプ: NOT APPLICABLE
最終 角度割当
タイプ: PROJECTION MATCHING / ソフトウェア - 名称: EMAN (ver. 2.91)
最終 3次元分類
ソフトウェア - 名称: EMAN (ver. 2.91) / 詳細: Deep learning based manifold embedding