深度學習助力冷凍電鏡技術
作者:
小柯機器人發布時間:2019/9/1 17:23:44
美國普渡大學Daisuke Kihara研究團隊利用深度學習技術,開發出能夠在中等解析度冷凍電鏡圖譜中檢測蛋白質二級結構的方法。該研究於2019年9月發表於國際一流學術期刊《自然—方法學》上。
研究人員報導了一種叫做Emap2sec的計算方法,它在解析度為5到10埃的EM圖中識別蛋白質的二級結構(α-螺旋、β-摺疊和其他結構)。Emap2sec使用三維深度卷積神經網絡為EM映射中的每個網格點分配二級結構。研究人員在解析度為6.0和10.0埃的34個結構模擬的EM圖上測試了Emap2sec,並在5.0到9.5埃解析度下通過實驗確定的43個圖上測試了Emap2sec。Emap2sec能夠在許多測試的圖譜中清楚地識別二級結構,並且表現出比現有方法更好的性能。
據了解,儘管現在通過冷凍電鏡(cryo-EM)可以常規地報導以近原子解析度的確定結構,但是仍以中等解析度確定許多密度圖,並且從這些圖提取結構信息仍然是一個挑戰。
附:英文原文
Title: Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning
Author: Sai Raghavendra Maddhuri Venkata Subramaniya, Genki Terashi, Daisuke Kihara
Issue&Volume: Volume 16 Issue 9
Abstract: Although structures determined at near-atomic resolution are now routinely reported by cryo-electron microscopy (cryo-EM), many density maps are determined at an intermediate resolution, and extracting structure information from these maps is still a challenge. We report a computational method, Emap2sec, that identifies the secondary structures of proteins (α-helices, β-sheets and other structures) in EM maps at resolutions of between 5 and 10 Å. Emap2sec uses a three-dimensional deep convolutional neural network to assign secondary structure to each grid point in an EM map. We tested Emap2sec on EM maps simulated from 34 structures at resolutions of 6.0 and 10.0 Å, as well as on 43 maps determined experimentally at resolutions of between 5.0 and 9.5 Å. Emap2sec was able to clearly identify the secondary structures in many maps tested, and showed substantially better performance than existing methods.
DOI: 10.1038/s41592-019-0500-1
Source:https://www.nature.com/articles/s41592-019-0500-1