scholarly journals MultiFit: a web server for fitting multiple protein structures into their electron microscopy density map

2011 ◽  
Vol 39 (suppl) ◽  
pp. W167-W170 ◽  
Author(s):  
E. Tjioe ◽  
K. Lasker ◽  
B. Webb ◽  
H. J. Wolfson ◽  
A. Sali
Keyword(s):  
Electron Microscopy ◽  
Protein Structures ◽  
Web Server ◽  
Multiple Protein ◽  
Density Map

scholarly journals Deep Learning for Validating and Estimating Resolution of Cryo-Electron Microscopy Density Maps †

Molecules ◽  
2019 ◽  
Vol 24 (6) ◽  
pp. 1181 ◽  
Author(s):  
Todor Avramov ◽  
Dan Vyenielo ◽  
Josue Gomez-Blanco ◽  
Swathi Adinarayanan ◽  
Javier Vargas ◽  
...  
Keyword(s):  
Neural Network ◽  
Electron Microscopy ◽  
Deep Learning ◽  
Protein Structures ◽  
Imaging Method ◽  
Classification Models ◽  
Cryo Electron Microscopy ◽  
Density Maps ◽  
Density Map ◽  
3D Cnn

Cryo-electron microscopy (cryo-EM) is becoming the imaging method of choice for determining protein structures. Many atomic structures have been resolved based on an exponentially growing number of published three-dimensional (3D) high resolution cryo-EM density maps. However, the resolution value claimed for the reconstructed 3D density map has been the topic of scientific debate for many years. The Fourier Shell Correlation (FSC) is the currently accepted cryo-EM resolution measure, but it can be subjective, manipulated, and has its own limitations. In this study, we first propose supervised deep learning methods to extract representative 3D features at high, medium and low resolutions from simulated protein density maps and build classification models that objectively validate resolutions of experimental 3D cryo-EM maps. Specifically, we build classification models based on dense artificial neural network (DNN) and 3D convolutional neural network (3D CNN) architectures. The trained models can classify a given 3D cryo-EM density map into one of three resolution levels: high, medium, low. The preliminary DNN and 3D CNN models achieved 92.73% accuracy and 99.75% accuracy on simulated test maps, respectively. Applying the DNN and 3D CNN models to thirty experimental cryo-EM maps achieved an agreement of 60.0% and 56.7%, respectively, with the author published resolution value of the density maps. We further augment these previous techniques and present preliminary results of a 3D U-Net model for local resolution classification. The model was trained to perform voxel-wise classification of 3D cryo-EM density maps into one of ten resolution classes, instead of a single global resolution value. The U-Net model achieved 88.3% and 94.7% accuracy when evaluated on experimental maps with local resolutions determined by MonoRes and ResMap methods, respectively. Our results suggest deep learning can potentially improve the resolution evaluation process of experimental cryo-EM maps.

The visualCMAT: A web-server to select and interpret correlated mutations/co-evolving residues in protein families

2018 ◽  
Vol 16 (02) ◽  
pp. 1840005 ◽  
Author(s):  
Dmitry Suplatov ◽  
Yana Sharapova ◽  
Daria Timonina ◽  
Kirill Kopylov ◽  
Vytas Švedas
Keyword(s):  
Rational Design ◽  
Visual Analysis ◽  
Structural Information ◽  
Protein Structures ◽  
Web Server ◽  
Physical Contact ◽  
Protein Families ◽  
Sequence Alignments ◽  
Homologous Proteins ◽  
Correlated Mutations

The visualCMAT web-server was designed to assist experimental research in the fields of protein/enzyme biochemistry, protein engineering, and drug discovery by providing an intuitive and easy-to-use interface to the analysis of correlated mutations/co-evolving residues. Sequence and structural information describing homologous proteins are used to predict correlated substitutions by the Mutual information-based CMAT approach, classify them into spatially close co-evolving pairs, which either form a direct physical contact or interact with the same ligand (e.g. a substrate or a crystallographic water molecule), and long-range correlations, annotate and rank binding sites on the protein surface by the presence of statistically significant co-evolving positions. The results of the visualCMAT are organized for a convenient visual analysis and can be downloaded to a local computer as a content-rich all-in-one PyMol session file with multiple layers of annotation corresponding to bioinformatic, statistical and structural analyses of the predicted co-evolution, or further studied online using the built-in interactive analysis tools. The online interactivity is implemented in HTML5 and therefore neither plugins nor Java are required. The visualCMAT web-server is integrated with the Mustguseal web-server capable of constructing large structure-guided sequence alignments of protein families and superfamilies using all available information about their structures and sequences in public databases. The visualCMAT web-server can be used to understand the relationship between structure and function in proteins, implemented at selecting hotspots and compensatory mutations for rational design and directed evolution experiments to produce novel enzymes with improved properties, and employed at studying the mechanism of selective ligand’s binding and allosteric communication between topologically independent sites in protein structures. The web-server is freely available at https://biokinet.belozersky.msu.ru/visualcmat and there are no login requirements.

Homology modeling using SWISS-Model for Biochemistry I v1

2021 ◽  
Author(s):  
Michael Friedman ◽  
Chris Berndsen
Keyword(s):  
Nucleic Acids ◽  
Homology Modeling ◽  
Homology Modelling ◽  
Protein Structures ◽  
Web Server ◽  
James Madison

Protocol for homology modeling proteins for use in Biochemistry I at James Madison University. Protocol guides students to use the SWISS-Model web server (citations below). Citations for servers: Waterhouse, A., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., Heer, F. T., de Beer, T. A. P., Rempfer, C., Bordoli, L., Lepore, R., and Schwede, T. (2018) SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 46, W296–W303.

scholarly journals Fibril structure of amyloid-β(1–42) by cryo–electron microscopy

Science ◽  
2017 ◽  
Vol 358 (6359) ◽  
pp. 116-119 ◽  
Author(s):  
Lothar Gremer ◽  
Daniel Schölzel ◽  
Carla Schenk ◽  
Elke Reinartz ◽  
Jörg Labahn ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Senile Plaques ◽  
Amyloid Β ◽  
Amyloid Β Protein ◽  
Dimer Interface ◽  
Cryo Electron Microscopy ◽  
Fibril Structure ◽  
Β Protein ◽  
Density Map ◽  
Main Component

Amyloids are implicated in neurodegenerative diseases. Fibrillar aggregates of the amyloid-β protein (Aβ) are the main component of the senile plaques found in brains of Alzheimer’s disease patients. We present the structure of an Aβ(1–42) fibril composed of two intertwined protofilaments determined by cryo–electron microscopy (cryo-EM) to 4.0-angstrom resolution, complemented by solid-state nuclear magnetic resonance experiments. The backbone of all 42 residues and nearly all side chains are well resolved in the EM density map, including the entire N terminus, which is part of the cross-β structure resulting in an overall “LS”-shaped topology of individual subunits. The dimer interface protects the hydrophobic C termini from the solvent. The characteristic staggering of the nonplanar subunits results in markedly different fibril ends, termed “groove” and “ridge,” leading to different binding pathways on both fibril ends, which has implications for fibril growth.

scholarly journals PROMALS web server for accurate multiple protein sequence alignments

2007 ◽  
Vol 35 (Web Server) ◽  
pp. W649-W652 ◽  
Author(s):  
J. Pei ◽  
B.-H. Kim ◽  
M. Tang ◽  
N. V. Grishin
Keyword(s):  
Protein Sequence ◽  
Web Server ◽  
Sequence Alignments ◽  
Multiple Protein

scholarly journals Visualizing viral protein structures in cells using genetic probes for correlated light and electron microscopy

Methods ◽  
2015 ◽  
Vol 90 ◽  
pp. 39-48 ◽  
Author(s):  
Horng D. Ou ◽  
Thomas J. Deerinck ◽  
Eric Bushong ◽  
Mark H. Ellisman ◽  
Clodagh C. O’Shea
Keyword(s):  
Electron Microscopy ◽  
Viral Protein ◽  
Protein Structures ◽  
Light And Electron Microscopy ◽  
Genetic Probes ◽  
In Cells

scholarly journals HORI: a web server to compute Higher Order Residue Interactions in protein structures

2010 ◽  
Vol 11 (Suppl 1) ◽  
pp. S24 ◽  
Author(s):  
Pandurangan Sundaramurthy ◽  
Khader Shameer ◽  
Raashi Sreenivasan ◽  
Sunita Gakkhar ◽  
Ramanathan Sowdhamini
Keyword(s):  
Protein Structures ◽  
Web Server ◽  
Higher Order
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