Tracing Beta Strands Using StrandTwister from Cryo-EM Density Maps at Medium Resolutions

Abstract

Major secondary structure elements such as α helices and β sheets can be computationally detected from cryoelectron microscopy (cryo-EM) density maps with medium resolutions of 5-10 Å. However, a critical piece of information for modeling atomic structures is missing, because there are no tools to detect β strands from cryo-EM maps at medium resolutions. We propose a method, StrandTwister, to detect the traces of β strands through the analysis of twist, an intrinsic nature of a β sheet. StrandTwister has been tested using 100 β sheets simulated at 10 Å resolution and 39 β sheets computationally detected from cryo-EM density maps at 4.4-7.4 Å resolutions. Although experimentally derived cryo-EM maps contain errors, StrandTwister's best detections over 39 cases were able to detect 81.87% of the β strands, with an overall 1.66 Å two-way distance between the detected and observed β traces. StrandTwister appears to detect the traces of β strands on major β sheets quite accurately, particularly at the central area of a β sheet.

Detection of Protein Secondary Structure Patterns from 3D Cryo-TEM Maps at Medium Resolution - Combining the Best of SSETracer and VolTrac

“…(A) The density region of EMD-1780 (5.5Å resolution) corresponding to chain K of PDB ID 3IZ6 is superimposed with the helices (colored cylinders) and β-sheet (blue) detected by SSETracer [4]. (B) The atomic structure of PDB ID 3IZ6 chain K (ribbon) is superimposed with the detected helices (thick cylinders) and β-strand traces (thin sticks) using StrandTwister [5]. The detected helices and strands are colored from N-terminal to C-terminal using rainbow colors.…”

“…Although the backbone of a protein structure is often not visible at medium resolution, secondary structures, such as α-helices and β-sheets, can be computationally identified [1][2][3][4]. In addition, the location of β-strands can be derived from the density region of a β-sheet once it is segmented out from the surrounding density [5].Over the years, various pattern recognition methods have been developed for the identification of α-helices [1-4, 6, 7] and β-sheets [1,2,4,8]. More recently, machine learning techniques, such as Support Vector Machine (SVM) and Convolutional Neural Networks (CNN), have been applied to this problem [2,9].…”

“…Although the backbone of a protein structure is often not visible at medium resolution, secondary structures, such as α-helices and β-sheets, can be computationally identified [1][2][3][4]. In addition, the location of β-strands can be derived from the density region of a β-sheet once it is segmented out from the surrounding density [5].…”

Deriving Protein Backbone Using Traces Extracted from Density Maps at Medium Resolutions

“…Most of the major β-sheets can also be detected using various methods such as SheetTracer, SSEhunter, SSELearner and SSETracer [29][30][31][32]. By analyzing the twist of β-sheet density, the position of β-strands can be predicted [31]. A detected helix/β-strand (Fig.…”

“…It relies on the detection of secondary structure positions and the connection patterns encoded in the skeleton of the density map. A number of computational methods have been developed to detect α-helices from the density maps [25][26][27][28][29][30][31]. Most helices longer than two turns can be detected.…”