scholarly journals Solving the first novel protein structure by 3D micro-crystal electron diffraction

2019 ◽  
Author(s):  
H. Xu ◽  
H. Lebrette ◽  
M.T.B. Clabbers ◽  
J. Zhao ◽  
J.J. Griese ◽  
...  
Keyword(s):  
Protein Structure ◽  
Electron Diffraction ◽  
Model Building ◽  
Protein Structures ◽  
X Ray ◽  
X Ray Crystallography ◽  
Unknown Protein ◽  
Potential Map ◽  
Crystal Electron ◽  
And Function

AbstractMicro-crystal electron diffraction (MicroED) has recently shown potential for structural biology. It enables studying biomolecules from micron-sized 3D crystals that are too small to be studied by conventional X-ray crystallography. However, to the best of our knowledge, MicroED has only been applied to re-determine protein structures that had already been solved previously by X-ray diffraction. Here we present the first unknown protein structure – an R2lox enzyme – solved using MicroED. The structure was phased by molecular replacement using a search model of 35% sequence identity. The resulting electrostatic scattering potential map at 3.0 Å resolution was of sufficient quality to allow accurate model building and refinement. Our results demonstrate that MicroED has the potential to become a widely applicable tool for revealing novel insights into protein structure and function, opening up new opportunities for structural biologists.

scholarly journals Solving a new R2lox protein structure by microcrystal electron diffraction

2019 ◽  
Vol 5 (8) ◽  
pp. eaax4621 ◽  
Author(s):  
Hongyi Xu ◽  
Hugo Lebrette ◽  
Max T. B. Clabbers ◽  
Jingjing Zhao ◽  
Julia J. Griese ◽  
...  
Keyword(s):  
Protein Structure ◽  
Electron Diffraction ◽  
Model Building ◽  
Protein Structures ◽  
X Ray Diffraction ◽  
X Ray ◽  
X Ray Crystallography ◽  
Potential Map ◽  
Metal Cofactor ◽  
And Function

Microcrystal electron diffraction (MicroED) has recently shown potential for structural biology. It enables the study of biomolecules from micrometer-sized 3D crystals that are too small to be studied by conventional x-ray crystallography. However, to date, MicroED has only been applied to redetermine protein structures that had already been solved previously by x-ray diffraction. Here, we present the first new protein structure—an R2lox enzyme—solved using MicroED. The structure was phased by molecular replacement using a search model of 35% sequence identity. The resulting electrostatic scattering potential map at 3.0-Å resolution was of sufficient quality to allow accurate model building and refinement. The dinuclear metal cofactor could be located in the map and was modeled as a heterodinuclear Mn/Fe center based on previous studies. Our results demonstrate that MicroED has the potential to become a widely applicable tool for revealing novel insights into protein structure and function.

scholarly journals Structure and function of cement proteins in human adenovirus

2014 ◽  
Vol 70 (a1) ◽  
pp. C1603-C1603
Author(s):  
Vijay Reddy ◽  
Glen Nemerow
Keyword(s):  
Protein Structures ◽  
Human Adenovirus ◽  
Icosahedral Symmetry ◽  
Virion Assembly ◽  
X Ray ◽  
X Ray Crystallography ◽  
Capsid Shell ◽  
Multiple Copies ◽  
And Function ◽  
Cement Protein

Human adenoviruses (HAdVs) are large (~150nm in diameter, 150MDa) nonenveloped double-stranded DNA (dsDNA) viruses that cause respiratory, ocular, and enteric diseases. The capsid shell of adenovirus (Ad) comprises multiple copies of three major capsid proteins (MCP: hexon, penton base and fiber) and four minor/cement proteins (IIIa, VI, VIII and IX) that are organized with pseudo T=25 icosahedral symmetry. In addition, six other proteins (V, VII, μ, IVa2, terminal protein and protease) are encapsidated along with the 36Kb dsDNA genome inside the capsid. The crystal structures of all three MCPs are known and so is their organization in the capsid from prior X-ray crystallography and cryoEM analyses. However structures and locations of various cement proteins are of considerable debate. We have determined and refined the structure of an entire human adenovirus employing X-ray crystallpgraphic methods at 3.8Å resolution. Adenovirus cement proteins play crucial roles in virion assembly, disassembly, cell entry and infection. Based on the refined crystal structure of adenovirus, we have determined the structure of the cement protein VI, a key membrane-lytic molecule and its associations with proteins V and VIII, which together glue peripentonal hexons beneath vertex region and connect them to rest of the capsid. Following virion maturation, the cleaved N-terminal pro-peptide of VI is observed deep in the peripentonal hexon cavity, detached from the membrane-lytic domain. Furthermore, we have significantly revised the recent cryoEM models for proteins IIIa and IX and both are located on the capsid exterior. Together, the cement proteins exclusively stabilize the hexon shell, thus rendering penton vertices the weakest links of the adenovirus capsid. Adenovirus cement protein structures reveal the molecular basis of the maturation cleavage of VI that is needed for endosome rupture and delivery of the virion into cytoplasm.

scholarly journals Identification of unknown proteins in X-ray crystallography and cryo-EM

2021 ◽  
Author(s):  
Grzegorz Chojnowski ◽  
Adam J. Simpkin ◽  
Diego A. Leonardo ◽  
Wolfram Seifert-Davila ◽  
Dan E. Vivas-Ruiz ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Protein Structures ◽  
Protein Sequences ◽  
Protein Structure Determination ◽  
Natural Sources ◽  
X Ray ◽  
X Ray Crystallography ◽  
Unknown Protein ◽  
Automated Pipeline ◽  
Unknown Sequence

AbstractAlthough experimental protein structure determination usually targets known proteins, chains of unknown sequence are often encountered. They can be purified from natural sources, appear as an unexpected fragment of a well characterized protein or as a contaminant. Regardless of the source of the problem, the unknown protein always requires tedious characterization. Here we present an automated pipeline for the identification of protein sequences from cryo-EM reconstructions and crystallographic data. We present the method’s application to characterize the crystal structure of an unknown protein purified from a snake venom. We also show that the approach can be successfully applied to the identification of protein sequences and validation of sequence assignments in cryo-EM protein structures.

Structure determination using convergent-beam electron diffraction

1992 ◽  
Vol 50 (2) ◽  
pp. 1162-1163
Author(s):  
J W Steeds ◽  
R Vincent
Keyword(s):  
Electron Diffraction ◽  
Model Building ◽  
Diffraction Theory ◽  
Bloch Wave ◽  
X Ray ◽  
X Ray Crystallography ◽  
Local Perturbations ◽  
Fitting Procedures ◽  
Convergent Beam ◽  
Kinematical Approach

There are many different approaches in quantitative electron diffraction which are being vigorously pursued at present. The approach we adopt is based on the insights provided by the Bloch-wave formulation of dynamical electron diffraction theory into the physics of dynamical scattering. This insight is used to select diffraction situations where a pseudo-kinematical approximation may be made. A forwards route is then possible directly from the experimental observations to the structural implications. This contrasts with the model-building, multi-parameter fitting procedures used in many other approaches where a problem of uniqueness inevitably arises.Because the pseudo-kinematical approach ignores many of the detailed dynamical interactions which occur locally over small angular ranges we do not attempt to make accurate measurements, and wherever possible average (visually at least) along Bragg lines to eliminate local perturbations. In a sense the work resembles early X-ray crystallography where reflections were put in one of six or so classes from very weak to very strong.

scholarly journals PROBABILISTIC ENSEMBLES FOR IMPROVED INFERENCE IN PROTEIN-STRUCTURE DETERMINATION

2012 ◽  
Vol 10 (01) ◽  
pp. 1240009 ◽  
Author(s):  
AMEET SONI ◽  
JUDE SHAVLIK
Keyword(s):  
Protein Structure ◽  
Structure Determination ◽  
Protein Structures ◽  
Three Dimensional ◽  
Protein Structure Determination ◽  
Complex Problem ◽  
Approximate Inference ◽  
X Ray ◽  
X Ray Crystallography ◽  
Inference Methods

Protein X-ray crystallography — the most popular method for determining protein structures — remains a laborious process requiring a great deal of manual crystallographer effort to interpret low-quality protein images. Automating this process is critical in creating a high-throughput protein-structure determination pipeline. Previously, our group developed ACMI, a probabilistic framework for producing protein-structure models from electron-density maps produced via X-ray crystallography. ACMI uses a Markov Random Field to model the three-dimensional (3D) location of each non-hydrogen atom in a protein. Calculating the best structure in this model is intractable, so ACMI uses approximate inference methods to estimate the optimal structure. While previous results have shown ACMI to be the state-of-the-art method on this task, its approximate inference algorithm remains computationally expensive and susceptible to errors. In this work, we develop Probabilistic Ensembles in ACMI (PEA), a framework for leveraging multiple, independent runs of approximate inference to produce estimates of protein structures. Our results show statistically significant improvements in the accuracy of inference resulting in more complete and accurate protein structures. In addition, PEA provides a general framework for advanced approximate inference methods in complex problem domains.

scholarly journals findMySequence: a neural-network-based approach for identification of unknown proteins in X-ray crystallography and cryo-EM

IUCrJ ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Grzegorz Chojnowski ◽  
Adam J. Simpkin ◽  
Diego A. Leonardo ◽  
Wolfram Seifert-Davila ◽  
Dan E. Vivas-Ruiz ◽  
...  
Keyword(s):  
Neural Network ◽  
Protein Structures ◽  
Protein Sequences ◽  
Protein Structure Determination ◽  
Natural Sources ◽  
X Ray ◽  
X Ray Crystallography ◽  
Unknown Protein ◽  
Automated Pipeline ◽  
Unknown Sequence

Although experimental protein-structure determination usually targets known proteins, chains of unknown sequence are often encountered. They can be purified from natural sources, appear as an unexpected fragment of a well characterized protein or appear as a contaminant. Regardless of the source of the problem, the unknown protein always requires characterization. Here, an automated pipeline is presented for the identification of protein sequences from cryo-EM reconstructions and crystallographic data. The method's application to characterize the crystal structure of an unknown protein purified from a snake venom is presented. It is also shown that the approach can be successfully applied to the identification of protein sequences and validation of sequence assignments in cryo-EM protein structures.

scholarly journals Training data composition affects performance of protein structure analysis algorithms

2021 ◽  
Author(s):  
Alexander Derry ◽  
Kristy A. Carpenter ◽  
Russ B. Altman
Keyword(s):  
Protein Structure ◽  
Molecular Mechanisms ◽  
Protein Structures ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Training Set ◽  
Protein Structure Analysis ◽  
X Ray ◽  
X Ray Crystallography ◽  
Test Sets

The three-dimensional structures of proteins are crucial for understanding their molecular mechanisms and interactions. Machine learning algorithms that are able to learn accurate representations of protein structures are therefore poised to play a key role in protein engineering and drug development. The accuracy of such models in deployment is directly influenced by training data quality. The use of different experimental methods for protein structure determination may introduce bias into the training data. In this work, we evaluate the magnitude of this effect across three distinct tasks: estimation of model accuracy, protein sequence design, and catalytic residue prediction. Most protein structures are derived from X-ray crystallography, nuclear magnetic resonance (NMR), or cryo-electron microscopy (cryo-EM); we trained each model on datasets consisting of either all three structure types or of only X-ray data. We find that across these tasks, models consistently perform worse on test sets derived from NMR and cryo-EM than they do on test sets of structures derived from X-ray crystallography, but that the difference can be mitigated when NMR and cryo-EM structures are included in the training set. Importantly, we show that including all three types of structures in the training set does not degrade test performance on X-ray structures, and in some cases even increases it. Finally, we examine the relationship between model performance and the biophysical properties of each method, and recommend that the biochemistry of the task of interest should be considered when composing training sets.

scholarly journals Heavy Atom Detergent/Lipid Combined X-ray Crystallography for Elucidating the Structure-Function Relationships of Membrane Proteins

Membranes ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 823
Author(s):  
Shinya Hanashima ◽  
Takanori Nakane ◽  
Eiichi Mizohata
Keyword(s):  
Membrane Proteins ◽  
Structure Function ◽  
New Technologies ◽  
Heavy Atom ◽  
Protein Structures ◽  
X Ray ◽  
X Ray Crystallography ◽  
Function Relationship ◽  
And Function ◽  
Relationship Of

Membrane proteins reside in the lipid bilayer of biomembranes and the structure and function of these proteins are closely related to their interactions with lipid molecules. Structural analyses of interactions between membrane proteins and lipids or detergents that constitute biological or artificial model membranes are important for understanding the functions and physicochemical properties of membrane proteins and biomembranes. Determination of membrane protein structures is much more difficult when compared with that of soluble proteins, but the development of various new technologies has accelerated the elucidation of the structure-function relationship of membrane proteins. This review summarizes the development of heavy atom derivative detergents and lipids that can be used for structural analysis of membrane proteins and their interactions with detergents/lipids, including their application with X-ray free-electron laser crystallography.

scholarly journals Homology-based loop modelling yields more complete crystallographic protein structures

2018 ◽  
Author(s):  
Bart van Beusekom ◽  
Krista Joosten ◽  
Maarten L. Hekkelman ◽  
Robbie P. Joosten ◽  
Anastassis Perrakis
Keyword(s):  
Protein Structure ◽  
Protein Function ◽  
Model Building ◽  
Protein Structures ◽  
Data Bank ◽  
X Ray ◽  
New Methods ◽  
Density Maps ◽  
Complete Protein ◽  
Automated Procedures

AbstractInherent protein flexibility, poor or low-resolution diffraction data, or poor electron density maps, often inhibit building complete structural models during X-ray structure determination. However, advances in crystallographic refinement and model building nowadays often allow to complete previously missing parts. Here, we present algorithms that identify regions missing in a certain model but present in homologous structures in the Protein Data Bank (PDB), and “graft” these regions of interest. These new regions are refined and validated in a fully automated procedure. Including these developments in our PDB-REDO pipeline, allowed to build 24,962 missing loops in the PDB. The models and the automated procedures are publically available through the PDB-REDO databank and web server (https://pdb-redo.eu). More complete protein structure models enable a higher quality public archive, but also a better understanding of protein function, better comparison between homologous structures, and more complete data mining in structural bioinformatics projects.SynopsisThousands of missing regions in existing protein structure models are completed using new methods based on homology.

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