Deep Learning for Segmentation Of 3D Cryo-EM Images

Cryo-electron microscopy (cryo-EM) is an emerging biophysical technique for structural determination of protein complexes. However, accurate detection of secondary structures is still challenging when cryo-EM density maps are at medium resolutions (5-10 Å). Most existing methods are image processing methods that do not fully utilize available images in the cryo-EM database. In this paper, we present a deep learning approach to segment secondary structure elements as helices and β-sheets from medium- resolution density maps. The proposed 3D convolutional neural network is shown to detect secondary structure locations with an F1 score between 0.79 and 0.88 for six simulated test cases. The architecture was also applied to experimentally-derived cryo- EM density regions of 571 protein chains. . The average F1 score for helix detection is 0.747 and 0.674 for β-sheets in a test involving seven cryo-EM density regions. Additionally, we extend an arc-length association method to β -strands and show that this method for measuring error is superior to many popular methods. An interactive tool is also presented that can visualize the results of this arc-length association method

Computational Detection of Protein Structure Components from 3-Dimensional Images Obtained from Cryo-Electron Microscopy

Cryo-electron microscopy is an emerging biophysical technique for structural determination of large protein complexes. While more atomic structures are being determined using this technique, it is still challenging to derive atomic structures from density maps produced at medium resolution when no suitable templates are available. A critical step in structure determination is how a protein chain threads through the 3- dimensional density map. When the central axis of a helix is detectable in a cryo-EM density map, it is possible to quantify the agreement between this central axis and a central axis calculated from the atomic model or structure. We propose a novel arc-length association method to compare the two axes reliably. The results show that our proposed method sensitively distinguishes lateral and longitudinal discrepancies between the two axes, which makes the method particularly suitable for the systematic investigation of cryoEM map-model pairs

Challenges in Matching Secondary Structures in Cryo-EM – An Exploration

Cryo-electron microscopy is a fast emerging biophysical technique for structural determination of large protein complexes. While more atomic structures are being determined using this technique, it is still challenging to derive atomic structures from density maps produced at medium resolution when no suitable templates are available. A critical step in structure determination is how a protein chain threads through the 3- dimensional density map. A dynamic programming method was previously developed to generate K best matches of secondary structures between the density map and its protein sequence using shortest paths in a related weighted graph. We discuss challenges associated with the creation of the weighted graph and explore heuristic methods to solve the problem of matching secondary structures

Comparing an Atomic Model or Structure to a Corresponding Cryo-EM Image at the Central Axis of a Helix

Three-dimensional density maps of biological specimens from cryo-electron microscopy (cryo- EM) can be interpreted in the form of atomic models that are modeled into the density, or they can be compared to known atomic structures. When the central axis of a helix is detectable in a cryo-EM density map, it is possible to quantify the agreement between this central axis and a central axis calculated from the atomic model or structure. We propose a novel arc-length association method to compare the two axes reliably. This method was applied to 79 helices in simulated density maps and six case studies using cryo-EM maps at 6.4-7.7Å resolution. The arclength association method is then compared to three existing measures that evaluate the separation of two helical axes: a two-way distance between point sets, the length difference between two axes, and the individual amino acid detection accuracy. The results show that our proposed method sensitively distinguishes lateral and longitudinal discrepancies between the two axes, which makes the method particularly suitable for the systematic investigation of cryo- EM map-model pairs

Molecular diversity in phenolic and polyphenolic precursors of tannin-inspired nanocoatings

The strong interfacial properties of selected plant polyphenols were recently exploited in forming functionally versatile nanocoatings via dip-coating. Here, we screened a library of ~20 natural and synthetic phenols and polyphenols, identifying eight catechol-, gallol- and resorcinol-rich precursors capable of forming coatings. Several newly identified compounds expand the molecular diversity of tannin-inspired coatings