PTGL: extension to graph-based topologies of cryo-EM data for large protein structures

2020 ◽  
Author(s):  
Jan Niclas Wolf ◽  
Marcus Keßler ◽  
Jörg Ackermann ◽  
Ina Koch
Keyword(s):  
Quaternary Structure ◽  
Protein Complexes ◽  
Protein Structures ◽  
Supplementary Information ◽  
Large Protein ◽  
Protein Topology ◽  
Large Structures ◽  
Structure Level ◽  
File Parser ◽  
Topology Graph

Abstract Summary We provide a software to describe the topology of large protein complexes based mainly on cryo-EM data and stored as macromolecular Crystallographic Information Files (mmCIFs) in the PDB. The software extends the Protein Topology Graph Library and implements an efficient file parser to analyze mmCIFs. The extended Protein Topology Graph Library includes a graph-based representation of the topology of protein complexes on the supersecondary and quaternary structure level. The library holds topology graphs of 151 837 PDB files; 921 of them are large structures. The abstraction of protein structure complexes to undirected labeled graphs enables classification and comparison of large protein complexes on quaternary structure level. Availability and implementation Online access at http://ptgl.uni-frankfurt.de. Source code in Java under GNU public license 2.0 at https://github.com/MolBIFFM/vplg. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Protein Structures

2020 ◽  
Author(s):  
Arian R. Jamasb ◽  
Pietro Lió ◽  
Tom L. Blundell
Keyword(s):  
Deep Learning ◽  
Learning Community ◽  
Protein Complexes ◽  
Protein Structures ◽  
Surface Mesh ◽  
Large Protein ◽  
Graph Theoretic ◽  
Experimental Structure ◽  
New Protein ◽  
High Throughput Manner

AbstractGraphein is a python library for constructing graph and surface-mesh representations of protein structures for computational analysis. The library interfaces with popular geometric deep learning libraries: DGL, PyTorch Geometric and PyTorch3D. Geometric deep learning is emerging as a popular methodology in computational structural biology. As feature engineering is a vital step in a machine learning project, the library is designed to be highly flexible, allowing the user to parameterise the graph construction, scaleable to facilitate working with large protein complexes, and containing useful pre-processing tools for preparing experimental structure files. Graphein is also designed to facilitate network-based and graph-theoretic analyses of protein structures in a high-throughput manner. As example workflows, we make available two new protein structure-related datasets, previously unused by the geometric deep learning community.Availability and implementationGraphein is written in python. Source code, example usage and datasets, and documentation are made freely available under a MIT License at the following URL: https://github.com/a-r-j/graphein

The emergence of protein complexes: quaternary structure, dynamics and allostery

2012 ◽  
Vol 40 (3) ◽  
pp. 475-491 ◽  
Author(s):  
Tina Perica ◽  
Joseph A. Marsh ◽  
Filipa L. Sousa ◽  
Eviatar Natan ◽  
Lucy J. Colwell ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Protein Function ◽  
Quaternary Structure ◽  
Protein Complexes ◽  
Protein Structures ◽  
Allosteric Communication ◽  
Physical Interactions ◽  
Correlated Mutations ◽  
Analytical Approaches

All proteins require physical interactions with other proteins in order to perform their functions. Most of them oligomerize into homomers, and a vast majority of these homomers interact with other proteins, at least part of the time, forming transient or obligate heteromers. In the present paper, we review the structural, biophysical and evolutionary aspects of these protein interactions. We discuss how protein function and stability benefit from oligomerization, as well as evolutionary pathways by which oligomers emerge, mostly from the perspective of homomers. Finally, we emphasize the specificities of heteromeric complexes and their structure and evolution. We also discuss two analytical approaches increasingly being used to study protein structures as well as their interactions. First, we review the use of the biological networks and graph theory for analysis of protein interactions and structure. Secondly, we discuss recent advances in techniques for detecting correlated mutations, with the emphasis on their role in identifying pathways of allosteric communication.

scholarly journals DeepTracer: Predicting Backbone Atomic Structure from High Resolution Cryo-EM Density Maps of Protein Complexes

2020 ◽  
Author(s):  
Jonas Pfab ◽  
Dong Si
Keyword(s):  
Amino Acids ◽  
Deep Learning ◽  
Protein Structure ◽  
Amino Acid ◽  
Protein Complexes ◽  
Protein Structures ◽  
Prediction Method ◽  
Large Protein ◽  
Structure Information ◽  
Density Maps

AbstractMotivationAccurately determining the atomic structure of proteins represents a fundamental problem in the field of structural bioinformatics. A solution would be significant as protein structure information could be utilized in the medical field, e.g. in the development of vaccines for new viruses. This paper focuses on predicting the protein structure based on 3D images of the proteins captured through cryogenic electron microscopes (cryo-EM). A fully automated computationally efficient protein structure prediction method would be particularly beneficial in the field of cryo-EM as the technology allows researchers to photograph multiple large protein complexes in a single study, which means that a fast prediction method could allow for a high throughput of derived protein structures. We present a deep learning approach, DeepTracer, for predicting locations of the backbone atoms, secondary structure elements, and the amino acid types. In order to connect the predicted amino acids into chains, we applied a modified traveling salesman algorithm.ResultsWe trained our deep learning model on experimental cryo-EM density maps and tested it on a set of 50 density maps. We found that our new approach predicted protein structures with an average RMSD value of 1.18 and a coverage of 87.5%. Furthermore, we detected secondary structure information for 87.2% of amino acids correctly. We also showed preliminarily that 25.2% of amino acid types could be predicted directly from the 3D cryo-EM density map, considering 20 different types in total. Finally, we noted that the prediction runtime of DeepTracer is significantly improved compared to other methods. It predicts a large protein complex structure of more than 30,000 amino acids in only 2 hours.AvailabilityThe repository of this project will be [email protected] informationSupplementary data will be available at Bioinformatics online.

scholarly journals Tafazzins from Drosophila and mammalian cells assemble in large protein complexes with a short half-life

Mitochondrion ◽  
2015 ◽  
Vol 21 ◽  
pp. 27-32 ◽  
Author(s):  
Yang Xu ◽  
Ashim Malhotra ◽  
Steven M. Claypool ◽  
Mindong Ren ◽  
Michael Schlame
Keyword(s):  
Half Life ◽  
Mammalian Cells ◽  
Protein Complexes ◽  
Large Protein ◽  
Short Half Life

Inter-residue interactions in alpha-helical transmembrane proteins

2018 ◽  
Vol 35 (15) ◽  
pp. 2578-2584 ◽  
Author(s):  
Eduardo Mayol ◽  
Mercedes Campillo ◽  
Arnau Cordomí ◽  
Mireia Olivella
Keyword(s):  
Membrane Proteins ◽  
Present Report ◽  
Protein Structures ◽  
Transmembrane Proteins ◽  
Globular Proteins ◽  
Supplementary Information ◽  
Backbone Carbonyl ◽  
Composition Characteristic ◽  
Almost All ◽  
Residue Composition

Abstract Motivation The number of available membrane protein structures has markedly increased in the last years and, in parallel, the reliability of the methods to detect transmembrane (TM) segments. In the present report, we characterized inter-residue interactions in α-helical membrane proteins using a dataset of 3462 TM helices from 430 proteins. This is by far the largest analysis published to date. Results Our analysis of residue–residue interactions in TM segments of membrane proteins shows that almost all interactions involve aliphatic residues and Phe. There is lack of polar–polar, polar–charged and charged–charged interactions except for those between Thr or Ser sidechains and the backbone carbonyl of aliphatic and Phe residues. The results are discussed in the context of the preferences of amino acids to be in the protein core or exposed to the lipid bilayer and to occupy specific positions along the TM segment. Comparison to datasets of β-barrel membrane proteins and of α-helical globular proteins unveils the specific patterns of interactions and residue composition characteristic of α-helical membrane proteins that are the clue to understanding their structure. Availability and implementation Results data and datasets used are available at http://lmc.uab.cat/TMalphaDB/interactions.php. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Computational design of symmetrical eight-bladed β-propeller proteins

IUCrJ ◽  
2019 ◽  
Vol 6 (1) ◽  
pp. 46-55 ◽  
Author(s):  
Hiroki Noguchi ◽  
Christine Addy ◽  
David Simoncini ◽  
Staf Wouters ◽  
Bram Mylemans ◽  
...  
Keyword(s):  
Enzymatic Activity ◽  
Evolutionary History ◽  
Protein Complexes ◽  
Protein A ◽  
Protein Structures ◽  
Computational Design ◽  
Computational Approach ◽  
Modular Architecture ◽  
Cellular Processes ◽  
History Of

β-Propeller proteins form one of the largest families of protein structures, with a pseudo-symmetrical fold made up of subdomains called blades. They are not only abundant but are also involved in a wide variety of cellular processes, often by acting as a platform for the assembly of protein complexes. WD40 proteins are a subfamily of propeller proteins with no intrinsic enzymatic activity, but their stable, modular architecture and versatile surface have allowed evolution to adapt them to many vital roles. By computationally reverse-engineering the duplication, fusion and diversification events in the evolutionary history of a WD40 protein, a perfectly symmetrical homologue called Tako8 was made. If two or four blades of Tako8 are expressed as single polypeptides, they do not self-assemble to complete the eight-bladed architecture, which may be owing to the closely spaced negative charges inside the ring. A different computational approach was employed to redesign Tako8 to create Ika8, a fourfold-symmetrical protein in which neighbouring blades carry compensating charges. Ika2 and Ika4, carrying two or four blades per subunit, respectively, were found to assemble spontaneously into a complete eight-bladed ring in solution. These artificial eight-bladed rings may find applications in bionanotechnology and as models to study the folding and evolution of WD40 proteins.

scholarly journals The right place at the right time: Aurora B kinase localization to centromeres and kinetochores

2020 ◽  
Vol 64 (2) ◽  
pp. 299-311 ◽  
Author(s):  
Amanda J. Broad ◽  
Jennifer G. DeLuca
Keyword(s):  
Spindle Assembly Checkpoint ◽  
Protein Complexes ◽  
Centromeric Heterochromatin ◽  
Aurora B ◽  
Mitotic Chromosomes ◽  
Large Protein ◽  
Aurora B Kinase ◽  
Centromeric Protein ◽  
Spindle Microtubules ◽  
The Right

Abstract The fidelity of chromosome segregation during mitosis is intimately linked to the function of kinetochores, which are large protein complexes assembled at sites of centromeric heterochromatin on mitotic chromosomes. These key “orchestrators” of mitosis physically connect chromosomes to spindle microtubules and transduce forces through these connections to congress chromosomes and silence the spindle assembly checkpoint. Kinetochore-microtubule attachments are highly regulated to ensure that incorrect attachments are not prematurely stabilized, but instead released and corrected. The kinase activity of the centromeric protein Aurora B is required for kinetochore-microtubule destabilization during mitosis, but how the kinase acts on outer kinetochore substrates to selectively destabilize immature and erroneous attachments remains debated. Here, we review recent literature that sheds light on how Aurora B kinase is recruited to both centromeres and kinetochores and discuss possible mechanisms for how kinase interactions with substrates at distinct regions of mitotic chromosomes are regulated.

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