De novo main-chain modeling for EM maps using MAINMAST

Terashi, Genki; Kihara, Daisuke

doi:10.1038/s41467-018-04053-7

Cited by 127 publications

(164 citation statements)

References 33 publications

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“…This RMSD method walks each predicted backbone trace and pairs it with the closest Cα atom in the ground truth structure. This produces lower/better RMSD values than other methods [12] [25] because it allows for Cα skips in the ground truth backbone trace.…”

Section: Resultsmentioning

confidence: 98%

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Cascaded-CNN: Deep Learning to Predict Protein Backbone Structure from High-Resolution Cryo-EM Density Maps

Moritz

Pfab

et al. 2019

Preprint

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Cryo-electron microscopy (cryo-EM) has become a leading technology for determining protein structures. Recent advances in this field have allowed for atomic resolution. However, predicting the backbone trace of a protein has remained a challenge on all but the most pristine density maps (< 2.5Å resolution). Here we introduce a deep learning model that uses a set of cascaded convolutional neural networks (CNNs) to predict Cα atoms along a protein's backbone structure. The cascaded-CNN (C-CNN) is a novel deep learning architecture comprised of multiple CNNs, each predicting a specific aspect of a protein's structure. This model predicts secondary structure elements (SSEs), backbone structure, and Cα atoms, combining the results of each to produce a complete prediction map. The cascaded-CNN is a semantic segmentation image classifier and was trained using thousands of simulated density maps. This method is largely automatic and only requires a recommended threshold value for each evaluated protein. A specialized tabu-search path walking algorithm was used to produce an initial backbone trace with Cα placements. A helix-refinement algorithm made further improvements to the α-helix SSEs of the backbone trace. Finally, a novel quality assessment-based combinatorial algorithm was used to effectively map Cα traces to obtain full-atom protein structures. This method was tested on 50 experimental maps between 2.6Å and 4.4Å resolution. It outperformed several state-of-the-art prediction methods including RosettaES, MAINMAST, and a Phenix based method by producing the most complete prediction models, as measured by percentage of found Cα atoms. This method accurately predicted 88.5% (mean) of the Cα atoms within 3Å of a protein's backbone structure surpassing the 66.8% mark achieved by the leading alternate method (Phenix based fully automatic method) on the same set of density maps. The C-CNN also achieved an average RMSD of 1.23Å for all 50 experimental density maps which is similar to the Phenix based fully automatic method. This model and all code can be downloaded at https://github.com/DrDongSi/Ca-Backbone-Prediction. I. Introduction.Proteins perform a vast array of functions within organisms. From molecule transportation, to mechanical cellular support, to immune protection, proteins are the central building blocks of life in the universe [1]. Despite each protein being composed from a combination of the same 20 naturally occurring amino acids, a protein's functionality is mainly derived from its unique threedimensional (3D) shape. Therefore, learning the details of a protein's 3D structure is a prerequisite to understanding its biological function. A. Cryogenic Electronic Microscopy (Cryo-EM)Currently, one of the leading techniques for determining the atomic structure of proteins is cryoelectron microscopy (cryo-EM). Cryo-EM is a relatively new technique which uses a high-energy electron beam to image vitrified biological specimens. In the past five years, more than 1,000 protein structures have been determined to bet...

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Section: Resultsmentioning

confidence: 98%

“…Another leading backbone prediction model is the MAINMAST algorithm developed by researchers at Purdue University [25]. MAINMAST produces a backbone trace, consisting of a set of Cα atoms, from high density regions of an electron density map.…”

Section: Current Protein Prediction Modelsmentioning

confidence: 99%

Cascaded-CNN: Deep Learning to Predict Protein Backbone Structure from High-Resolution Cryo-EM Density Maps

Moritz

Pfab

et al. 2019

Preprint

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“…De novo modeling tools build a full atom model or a main-chain trace without using a template structure. There are six tools that belong to this category, EM-Fold [70], Gorgon [71], Rosetta [73,89], Pathwalking [72,74], Phenix [75,77], and MAINMAST [76,90]. The methods discussed below are summarized in Table 1.…”

Section: De Novo Modeling Methodsmentioning

confidence: 99%

“…MAINMAST (MAINchin Model trAcing from Spanning Tree) is a de novo modeling method, which was recently developed by our group [76]. MAINMAST provides a set of models with their confidence score.…”

Section: De Novo Modeling Methodsmentioning

confidence: 99%

Advances in Structure Modeling Methods for Cryo-Electron Microscopy Maps

Alnabati

Kihara

2019

Molecules

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Cryo-electron microscopy (cryo-EM) has now become a widely used technique for structure determination of macromolecular complexes. For modeling molecular structures from density maps of different resolutions, many algorithms have been developed. These algorithms can be categorized into rigid fitting, flexible fitting, and de novo modeling methods. It is also observed that machine learning (ML) techniques have been increasingly applied following the rapid progress of the ML field. Here, we review these different categories of macromolecule structure modeling methods and discuss their advances over time.Molecules 2020, 25, 82 2 of 13 methods, in this order. Then, we discuss methods that use machine learning approaches, which are emerging in recent years in the cryo-EM structure modeling field. Figure 1. The number of rigid fitting, flexible fitting, and de novo modeling software published per year. The statistics are based on publication. The plot shows 28 rigid fitting methods [9-36], 33 flexible fitting methods [37-69], and 8 de novo modeling methods [70-77]. Rigid Fitting MethodsIn rigid body fitting, high resolution atomic models which are derived from X-ray crystallography, Nuclear Magnetic Resonance (NMR) spectroscopy, or protein prediction are fitted into a cryo-EM map. One of the earliest rigid fitting methods is EMfit developed by Rossmann et al. in 2001 [12]. In EMfit, a high-resolution structure is placed manually into a specific position in the EM map. Then, a 3-D rotational search is applied to find the best orientation. After that, EMfit optimizes the initial fitting by performing local rotational and translational steps. In general, rigid-body fitting methods search for the best placement of an atomic model in a density map. Search algorithms that have been used for rigid fitting include Fast Fourier transform-based (FFT) [14,32,35], grid-threading Monte Carlo (GTMC) [16], spherical harmonic-based search [20], and geometric hashing [27]. FFT is a fast search scheme that accelerates the 3-D translational search [14]. HermiteFit speeds up the rotation step in the FFT by representing densities as three-dimensional orthogonal Hermite functions and performing rotation in the Hermite space [32]. Fast polar Fourier search is a variation of FFT, which is based on non-uniform SO(3) Fourier Transforms [35]. Its principal advantage is the ability to search efficiently and uniformly over a set of samples of the conformational space. GTMC combines grid search and Monte Carlo sampling [16]. GTMC divides the search space into grid points and uses Monte Carlo to find local maxima near the grid points to identify the global maximum. ADP_EM is a spherical harmonic-based (SH) method which applies exhaustive translational scanning and accelerates the rotational search by representing densities as SH functions [20]. Geometric hashing identifies a set of possible transformations which are stored in a fast-searchable hash map [27]. Later, the set of transformations is searched to find the best fit.While the methods above e...

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“…This averaging, when combined with complications such as image misclassification, highly heterogeneous samples, or a limited number of sample views, typically limits the resolutions that can be attained (Lyumkis, 2019) . This makes map interpretation difficult, and has necessitated the development of a number of tools for model-building and refinement into such cryoEM maps (Bonomi et al, 2019;Chen et al, 2016;Segura et al, 2016;Terashi & Kihara, 2018;Terwilliger et al, 2018;van Zundert et al, 2015) .…”

Section: Introductionmentioning

confidence: 99%

Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM

Farrell

Anishchanka

Shakeel

et al. 2020

Preprint

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Cryo-electron microscopy of protein complexes often leads to moderate resolution maps (4-8 Å), with visible secondary structure elements but poorly resolved loops, making model-building challenging. In the absence of high-resolution structures of homologues, only coarse-grained structural features are typically inferred from these maps, and it is often impossible to assign specific regions of density to individual protein subunits. This paper describes a new method for overcoming these difficulties that integrates predicted residue distance distributions from a deep-learned convolutional neural network, computational protein folding using Rosetta, and automated EM-map-guided complex assembly. We apply this method to a 4.6 Å resolution cryoEM map of Fanconi Anemia core complex (FAcc), an E3 ubiquitin ligase required for DNA interstrand crosslink repair, which was previously challenging to interpret as it is comprised of 6557 residues, only 1897 of which are covered by homology models. In the published structure built from this map, only 387 residues could be assigned to specific subunits. By building and placing into density 42 deep-learning guided models containing 4795 residues not included in the previously published structure, we are able to determine an almost-complete atomic model of FAcc, in which 5182 of the 6557 residues were placed. The resulting model is consistent with previously published biochemical data, and facilitates interpretation of disease related mutational data. We anticipate that our approach will be broadly useful for cryoEM structure determination of large complexes containing many subunits for which there are no homologues of known structure.

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De novo main-chain modeling for EM maps using MAINMAST

Cited by 127 publications

References 33 publications

Cascaded-CNN: Deep Learning to Predict Protein Backbone Structure from High-Resolution Cryo-EM Density Maps

Cascaded-CNN: Deep Learning to Predict Protein Backbone Structure from High-Resolution Cryo-EM Density Maps

Advances in Structure Modeling Methods for Cryo-Electron Microscopy Maps

Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM

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