scholarly journals Automatic annotation of Cryo-EM maps with the convolutional neural network Haruspex

2019 ◽  
Author(s):  
Philipp Mostosi ◽  
Hermann Schindelin ◽  
Philip Kollmannsberger ◽  
Andrea Thorn
Keyword(s):  
Model Building ◽  
De Novo ◽  
Three Dimensional ◽  
Protein Secondary Structure ◽  
Map Interpretation ◽  
Density Maps ◽  
Starting Point ◽  
Support Domain ◽  
Particle Images ◽  
Annotate Protein

AbstractIn recent years, three-dimensional density maps reconstructed from single particle images obtained by electron cryo-microscopy (Cryo-EM) have reached unprecedented resolution. However, map interpretation can be challenging, in particular if the constituting structures require de-novo model building or are very mobile. Here, we demonstrate the potential of convolutional neural networks for the annotation of Cryo-EM maps: our network Haruspex has been trained on a carefully curated set of 293 experimentally derived reconstruction maps to automatically annotate protein secondary structure elements as well as RNA/DNA. It can be straightforwardly applied to annotate newly reconstructed maps to support domain placement or to supply a starting point for main-chain placement. Due to its high recall and precision rates of 95.1% and 80.3%, respectively, on an independent test set of 122 maps, it can also be used for validation during model building. The trained network will be available as part of the CCP-EM suite.

scholarly journals Building brain network in electron density map determined by micro-CT

2014 ◽  
Vol 70 (a1) ◽  
pp. C1752-C1752
Author(s):  
Rino Saiga ◽  
Susumu Takekoshi ◽  
Naoya Nakamura ◽  
Akihisa Takeuchi ◽  
Kentaro Uesugi ◽  
...  
Keyword(s):  
Density Distribution ◽  
Electron Density ◽  
Electron Density Distribution ◽  
Model Building ◽  
Three Dimensional ◽  
Brain Network ◽  
X Ray ◽  
Density Maps ◽  
Density Map ◽  
Electron Density Map

In macromolecular crystallography, an electron density distribution is traced to build a model of the target molecule. We applied this method to model building for electron density maps of a brain network. Human cerebral tissue was stained with heavy atoms [1]. The sample was then analyzed at the BL20XU beamline of SPring-8 to obtain a three-dimensional map of X-ray attenuation coefficients representing the electron density distribution. Skeletonized wire models were built by placing and connecting nodes in the map [2], as shown in the figure below. The model-building procedures were similar to those reported for crystallographic analyses of macromolecular structures, while the neuronal network was automatically traced by using a Sobel filter. Neuronal circuits were then analytically resolved from the skeletonized models. We suggest that X-ray microtomography along with model building in the electron density map has potential as a method for understanding three-dimensional microstructures relevant to biological functions.

scholarly journals A mosaic bulk-solvent model improves density maps and the fit between model and data

2021 ◽  
Author(s):  
Pavel V. Afonine ◽  
Paul D. Adams ◽  
Oleg V Sobolev ◽  
Alexandre Urzhumtsev
Keyword(s):  
Protein Data Bank ◽  
Model Building ◽  
Data Bank ◽  
Crystallographic Data ◽  
Software Packages ◽  
Map Interpretation ◽  
Density Maps ◽  
Solvent Model ◽  
Modeling Power ◽  
R Factors

Bulk solvent is a major component of bio-macromolecular crystals and therefore contributes significantly to diffraction intensities. Accurate modeling of the bulk-solvent region has been recognized as important for many crystallographic calculations, from computing of R-factors and density maps to model building and refinement. Owing to its simplicity and computational and modeling power, the flat (mask-based) bulk-solvent model introduced by Jiang & Brunger (1994) is used by most modern crystallographic software packages to account for disordered solvent. In this manuscript we describe further developments of the mask-based model that improves the fit between the model and the data and aids in map interpretation. The new algorithm, here referred to as mosaic bulk-solvent model, considers solvent variation across the unit cell. The mosaic model is implemented in the computational crystallography toolbox and can be used in Phenix in most contexts where accounting for bulk-solvent is required. It has been optimized and validated using a sufficiently large subset of the Protein Data Bank entries that have crystallographic data available.

scholarly journals Peppy

2020 ◽  
Vol 2 (1) ◽  
pp. 49-50
Author(s):  
David Doak ◽  
Gareth Denyer ◽  
Juliet Gerrard ◽  
Joel Mackay ◽  
Jane Allison
Keyword(s):  
Virtual Reality ◽  
Secondary Structure ◽  
Degrees Of Freedom ◽  
Model Building ◽  
Three Dimensional ◽  
Protein Secondary Structure ◽  
Peptide Backbone ◽  
Molecular Forces ◽  
And Function ◽  
The University

Science students are traditionally taught protein structure and function through textbook pictures and/or physical model building. This is not effective for most students because conceiving large, complex three-dimensional chemicals structure and dynamic molecular interactions requires a very high degree of abstract thought, imagination and extrapolation. It is intuitively reasonable to believe that a virtual reality approach would aid appreciation of nanoscale molecular structure, function and dynamics. I will describe the Virtual Reality (VR) tool, “Peppy” (1), that we have developed for exploring the molecular forces which drive protein secondary structure. Peppy allows students to build, visualise and manipulate polypeptides within the six degrees of freedom that characterises the VR environment. Peppy not only recreates traditional secondary structures dependent on hydrogen- bonding in a generic peptide backbone, it also permits students to insert any and all of the 20 amino acids and to examine the effect of the shapes and electrostatic forces of these on secondary structure. The highly extrapolative environment created by Peppy is extended with features that encourage student engagement, such as a selfie camera, interactive Ramachandran plot, and even features to emphasise the dynamics of a vibrant macromolecular structure. Being able to physically and directly grab and manipulate the atoms and angles with the virtual hand enhances the connection of students with the molecules and results in an exploration experience unmatched by traditional 3D visualisation software. I will also describe the testing and iterative improvement of Peppy during deployment to large undergraduate classes at the University of Sydney, which boasts the Immersive Learning Lab, with 26 VR (Oculus Rift) headsets. Remarkably, even students with no prior VR experience are able to interact with Peppy in an engaged and meaningful way within just 10 minutes and, after less than an hour many are able to build highly complex multi-peptide structures such as β-barrels or experiment with long peptides containing a variety of side chains and disulphide bonds. The experience resonates with the students well after the session, as evidenced by their reflections and follow-up questions regarding the physics of the simulation and ideas for extension of the software.

scholarly journals How far are we from automatic crystal structure solution via molecular-replacement techniques?

2020 ◽  
Vol 76 (1) ◽  
pp. 9-18 ◽  
Author(s):  
Maria Cristina Burla ◽  
Benedetta Carrozzini ◽  
Giovanni Luca Cascarano ◽  
Carmelo Giacovazzo ◽  
Giampiero Polidori
Keyword(s):  
Prior Information ◽  
Model Building ◽  
Probability Distributions ◽  
Probabilistic Approach ◽  
Three Dimensional ◽  
Joint Probability ◽  
Distribution Functions ◽  
Molecular Replacement ◽  
Starting Point ◽  
Building Program

Although the success of molecular-replacement techniques requires the solution of a six-dimensional problem, this is often subdivided into two three-dimensional problems. REMO09 is one of the programs which have adopted this approach. It has been revisited in the light of a new probabilistic approach which is able to directly derive conditional distribution functions without passing through a previous calculation of the joint probability distributions. The conditional distributions take into account various types of prior information: in the rotation step the prior information may concern a non-oriented model molecule alone or together with one or more located model molecules. The formulae thus obtained are used to derive figures of merit for recognizing the correct orientation in the rotation step and the correct location in the translation step. The phases obtained by this new version of REMO09 are used as a starting point for a pipeline which in its first step extends and refines the molecular-replacement phases, and in its second step creates the final electron-density map which is automatically interpreted by CAB, an automatic model-building program for proteins and DNA/RNA structures.

scholarly journals Catalytic Foldamers: When the Structure Guides the Function

Catalysts ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 700 ◽  
Author(s):  
Baptiste Legrand ◽  
Julie Aguesseau-Kondrotas ◽  
Matthieu Simon ◽  
Ludovic Maillard
Keyword(s):  
De Novo ◽  
Three Dimensional ◽  
Protein Secondary Structure ◽  
Limited Range ◽  
De Novo Design ◽  
Biological Processes ◽  
Natural Evolution ◽  
Reaction Conditions ◽  
Intrinsic Nature ◽  
Recent Developments

Enzymes are predominantly proteins able to effectively and selectively catalyze highly complex biochemical reactions in mild reaction conditions. Nevertheless, they are limited to the arsenal of reactions that have emerged during natural evolution in compliance with their intrinsic nature, three-dimensional structures and dynamics. They optimally work in physiological conditions for a limited range of reactions, and thus exhibit a low tolerance for solvent and temperature conditions. The de novo design of synthetic highly stable enzymes able to catalyze a broad range of chemical reactions in variable conditions is a great challenge, which requires the development of programmable and finely tunable artificial tools. Interestingly, over the last two decades, chemists developed protein secondary structure mimics to achieve some desirable features of proteins, which are able to interfere with the biological processes. Such non-natural oligomers, so called foldamers, can adopt highly stable and predictable architectures and have extensively demonstrated their attractiveness for widespread applications in fields from biomedical to material science. Foldamer science was more recently considered to provide original solutions to the de novo design of artificial enzymes. This review covers recent developments related to peptidomimetic foldamers with catalytic properties and the principles that have guided their design.

scholarly journals Automatic building of protein atomic models from cryo-EM density maps using residue co-evolution

2020 ◽  
Author(s):  
Guillaume Bouvier ◽  
Benjamin Bardiaux ◽  
Riccardo Pellarin ◽  
Chiara Rapisarda ◽  
Michael Nilges
Keyword(s):  
Conformational Changes ◽  
Minimum Spanning Tree ◽  
De Novo ◽  
Three Dimensional ◽  
Weighted Graph ◽  
Atomic Resolution ◽  
Side Chain ◽  
Evolutionary Information ◽  
Atomic Models ◽  
Density Maps

AbstractElectron cryo-microscopy (cryo-EM) has emerged as a powerful method to obtain three-dimensional (3D) structures of macromolecular complexes at atomic or near-atomic resolution. However, de novo building of atomic models from near-atomic resolution (3-5 Å) cryo-EM density maps is a challenging task, in particular since poorly resolved side-chain densities hamper sequence assignment by automatic procedures at a lower resolution. Furthermore, segmentation of EM density maps into individual subunits remains a difficult problem when no three-dimensional structures of these subunits exist, or when significant conformational changes occur between the isolated and complexed form of the subunits. To tackle these issues, we have developed a graph-based method to thread most of the C-α trace of the protein backbone into the EM density map. The EM density is described as a weighted graph such that the resulting minimum spanning tree encompasses the high-density regions of the map. A pruning algorithm cleans the tree and finds the most probable positions of the C-α atoms, using side-chain density when available, as a collection of C-α trace fragments. By complementing experimental EM maps with contact predictions from sequence co-evolutionary information, we demonstrate that our approach can correctly segment EM maps into individual subunits and assign amino acids sequence to backbone traces to generate full-atom models.

scholarly journals Improving experimental phases for strong reflections prior to density modification

2013 ◽  
Vol 69 (10) ◽  
pp. 2039-2049 ◽  
Author(s):  
Monarin Uervirojnangkoorn ◽  
Rolf Hilgenfeld ◽  
Thomas C. Terwilliger ◽  
Randy J. Read
Keyword(s):  
Electron Density ◽  
Probability Distributions ◽  
Target Function ◽  
Weighted Average ◽  
Acta Cryst ◽  
Map Interpretation ◽  
Density Maps ◽  
Starting Point ◽  
Experimental Phasing ◽  
The Impact

Experimental phasing of diffraction data from macromolecular crystals involves deriving phase probability distributions. These distributions are often bimodal, making their weighted average, the centroid phase, improbable, so that electron-density maps computed using centroid phases are often non-interpretable. Density modification brings in information about the characteristics of electron density in protein crystals. In successful cases, this allows a choice between the modes in the phase probability distributions, and the maps can cross the borderline between non-interpretable and interpretable. Based on the suggestions by Vekhter [Vekhter (2005),Acta Cryst.D61, 899–902], the impact of identifying optimized phases for a small number of strong reflections prior to the density-modification process was investigated while using the centroid phase as a starting point for the remaining reflections. A genetic algorithm was developed that optimizes the quality of such phases using the skewness of the density map as a target function. Phases optimized in this way are then used in density modification. In most of the tests, the resulting maps were of higher quality than maps generated from the original centroid phases. In one of the test cases, the new method sufficiently improved a marginal set of experimental SAD phases to enable successful map interpretation. A computer program,SISA, has been developed to apply this method for phase improvement in macromolecular crystallography.

High Resolution Microscopy of Multi-Layered Biological Crystals

1982 ◽  
Vol 40 ◽  
pp. 80-83
Author(s):  
H.A. Cohen ◽  
T.W. Jeng ◽  
W. Chiu
Keyword(s):  
High Resolution ◽  
Low Dose ◽  
Three Dimensional ◽  
Data Interpretation ◽  
Two Dimensional ◽  
Biological Objects ◽  
Density Maps ◽  
Density Map ◽  
Complex Crystal ◽  
High Resolution Microscopy

This tutorial will discuss the methodology of low dose electron diffraction and imaging of crystalline biological objects, the problems of data interpretation for two-dimensional projected density maps of glucose embedded protein crystals, the factors to be considered in combining tilt data from three-dimensional crystals, and finally, the prospects of achieving a high resolution three-dimensional density map of a biological crystal. This methodology will be illustrated using two proteins under investigation in our laboratory, the T4 DNA helix destabilizing protein gp32*I and the crotoxin complex crystal.

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