Ulrich Wolfgang Arndt. 23 April 1924 — 24 March 2006

2014 ◽  
Vol 60 ◽  
pp. 39-55
Author(s):  
R. A. Crowther ◽  
A. G. W. Leslie
Keyword(s):  
Protein Structures ◽  
Three Dimensional ◽  
Direct Methods ◽  
Biological Molecules ◽  
Data Generation ◽  
Data Set ◽  
X Ray ◽  
X Ray Crystallography ◽  
Whole Process ◽  
Wide Range

Ulrich (Uli) Arndt was a physicist and engineer whose contributions to the development of a wide range of instrumentation for X-ray crystallography played an important part in our ability to solve the atomic structure of large biological molecules. Such detailed information about protein structures has for the past 50 years underpinned the huge advances in the field of molecular biology. His innovations spanned all aspects of data generation and collection, from improvements in X-ray tubes, through novel designs for diffractometers and cameras to film scanners and more direct methods of X-ray detection. When he started in the field, the intensities of individual X-ray reflections were often estimated by eye from films. By the end of his career the whole process of collecting from a crystal a three-dimensional data set, possibly comprising hundreds of thousands of measurements, was fully automated and very rapid.

3-D reconstruction of molecular shape from microcrystal thin sections

1983 ◽  
Vol 41 ◽  
pp. 748-749
Author(s):  
S. Cusack ◽  
J.-C. Jésior
Keyword(s):  
Three Dimensional ◽  
Molecular Shape ◽  
Biological Molecules ◽  
Fourier Space ◽  
Single Particles ◽  
Thin Sections ◽  
Standard Methods ◽  
X Ray ◽  
X Ray Crystallography ◽  
Dimensional Reconstruction

Three-dimensional reconstruction techniques using electron microscopy have been principally developed for application to 2-D arrays (i.e. monolayers) of biological molecules and symmetrical single particles (e.g. helical viruses). However many biological molecules that crystallise form multilayered microcrystals which are unsuitable for study by either the standard methods of 3-D reconstruction or, because of their size, by X-ray crystallography. The grid sectioning technique enables a number of different projections of such microcrystals to be obtained in well defined directions (e.g. parallel to crystal axes) and poses the problem of how best these projections can be used to reconstruct the packing and shape of the molecules forming the microcrystal.Given sufficient projections there may be enough information to do a crystallographic reconstruction in Fourier space. We however have considered the situation where only a limited number of projections are available, as for example in the case of catalase platelets where three orthogonal and two diagonal projections have been obtained (Fig. 1).

scholarly journals Role of Computational Methods in Going beyond X-ray Crystallography to Explore Protein Structure and Dynamics

2018 ◽  
Vol 19 (11) ◽  
pp. 3401 ◽  
Author(s):  
Ashutosh Srivastava ◽  
Tetsuro Nagai ◽  
Arpita Srivastava ◽  
Osamu Miyashita ◽  
Florence Tama
Keyword(s):  
Protein Dynamics ◽  
Computational Methods ◽  
Protein Structures ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
X Ray ◽  
X Ray Crystallography ◽  
Insight Into

Protein structural biology came a long way since the determination of the first three-dimensional structure of myoglobin about six decades ago. Across this period, X-ray crystallography was the most important experimental method for gaining atomic-resolution insight into protein structures. However, as the role of dynamics gained importance in the function of proteins, the limitations of X-ray crystallography in not being able to capture dynamics came to the forefront. Computational methods proved to be immensely successful in understanding protein dynamics in solution, and they continue to improve in terms of both the scale and the types of systems that can be studied. In this review, we briefly discuss the limitations of X-ray crystallography in studying protein dynamics, and then provide an overview of different computational methods that are instrumental in understanding the dynamics of proteins and biomacromolecular complexes.

scholarly journals Whole-pattern fitting technique in serial femtosecond nanocrystallography

IUCrJ ◽  
2016 ◽  
Vol 3 (2) ◽  
pp. 127-138 ◽  
Author(s):  
Ruben A. Dilanian ◽  
Sophie R. Williams ◽  
Andrew V. Martin ◽  
Victor A. Streltsov ◽  
Harry M. Quiney
Keyword(s):  
Powder Diffraction ◽  
Diffraction Data ◽  
Three Dimensional ◽  
Monte Carlo Integration ◽  
Data Set ◽  
X Ray ◽  
X Ray Crystallography ◽  
Small Crystals ◽  
Crystallite Structure ◽  
Serial Crystallography

Serial femtosecond X-ray crystallography (SFX) has created new opportunities in the field of structural analysis of protein nanocrystals. The intensity and timescale characteristics of the X-ray free-electron laser sources used in SFX experiments necessitate the analysis of a large collection of individual crystals of variable shape and quality to ultimately solve a single, average crystal structure. Ensembles of crystals are commonly encountered in powder diffraction, but serial crystallography is different because each crystal is measured individually and can be orientedviaindexing and merged into a three-dimensional data set, as is done for conventional crystallography data. In this way, serial femtosecond crystallography data lie in between conventional crystallography data and powder diffraction data, sharing features of both. The extremely small sizes of nanocrystals, as well as the possible imperfections of their crystallite structure, significantly affect the diffraction pattern and raise the question of how best to extract accurate structure-factor moduli from serial crystallography data. Here it is demonstrated that whole-pattern fitting techniques established for one-dimensional powder diffraction analysis can be feasibly extended to higher dimensions for the analysis of merged SFX diffraction data. It is shown that for very small crystals, whole-pattern fitting methods are more accurate than Monte Carlo integration methods that are currently used.

Artificial intelligence to solve the X-ray crystallography phase problem: a case study report.

2021 ◽  
Author(s):  
Irene Barbarin-Bocahu ◽  
Marc GRAILLE
Keyword(s):  
Structure Prediction ◽  
Three Dimensional ◽  
Structural Similarity ◽  
Molecular Replacement ◽  
Learning Approaches ◽  
Phase Problem ◽  
Data Set ◽  
X Ray ◽  
X Ray Crystallography ◽  
Eukaryotic Mrnas

The determination of three dimensional structures of macromolecules is one of the actual challenge in biology with the ultimate objective of understanding their function. So far, X-ray crystallography is the most popular method to solve structure, but this technique relies on the generation of diffracting crystals. Once a correct data set has been obtained, the calculation of electron density maps requires to solve the so-called phase problem using different approaches. The most frequently used technique is molecular replacement, which relies on the availability of the structure of a protein sharing strong structural similarity with the studied protein. Its success rate is directly correlated with the quality of the models used for the molecular replacement trials. The availability of models as accurate as possible is then definitely critical. Very recently, a breakthrough step has been made in the field of protein structure prediction thanks to the use of machine learning approaches as implemented in the AlphaFold or RoseTTAFold structure prediction programs. Here, we describe how these recent improvements helped us to solve the crystal structure of a protein involved in the nonsense-mediated mRNA decay pathway (NMD), an mRNA quality control pathway dedicated to the elimination of eukaryotic mRNAs harboring premature stop codons.

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.

Membrane protein crystallography in the era of modern structural biology

2020 ◽  
Vol 48 (6) ◽  
pp. 2505-2524
Author(s):  
Tristan O. C. Kwan ◽  
Danny Axford ◽  
Isabel Moraes
Keyword(s):  
Structural Biology ◽  
Molecular Level ◽  
Protein Structures ◽  
Three Dimensional ◽  
Biological Macromolecules ◽  
X Ray ◽  
X Ray Crystallography ◽  
Cellular Processes ◽  
Biochemical Level

The aim of structural biology has been always the study of biological macromolecules structures and their mechanistic behaviour at molecular level. To achieve its goal, multiple biophysical methods and approaches have become part of the structural biology toolbox. Considered as one of the pillars of structural biology, X-ray crystallography has been the most successful method for solving three-dimensional protein structures at atomic level to date. It is however limited by the success in obtaining well-ordered protein crystals that diffract at high resolution. This is especially true for challenging targets such as membrane proteins (MPs). Understanding structure-function relationships of MPs at the biochemical level is vital for medicine and drug discovery as they play critical roles in many cellular processes. Though difficult, structure determination of MPs by X-ray crystallography has significantly improved in the last two decades, mainly due to many relevant technological and methodological developments. Today, numerous MP crystal structures have been solved, revealing many of their mechanisms of action. Yet the field of structural biology has also been through significant technological breakthroughs in recent years, particularly in the fields of single particle electron microscopy (cryo-EM) and X-ray free electron lasers (XFELs). Here we summarise the most important advancements in the field of MP crystallography and the significance of these developments in the present era of modern structural biology.

scholarly journals Covering complete proteomes with X-ray structures: a current snapshot

2014 ◽  
Vol 70 (11) ◽  
pp. 2781-2793 ◽  
Author(s):  
Marcin J. Mizianty ◽  
Xiao Fan ◽  
Jing Yan ◽  
Eric Chalmers ◽  
Christopher Woloschuk ◽  
...  
Keyword(s):  
Homology Modeling ◽  
Structure Determination ◽  
Large Scale ◽  
Structural Model ◽  
Target Selection ◽  
Three Dimensional ◽  
X Ray ◽  
X Ray Crystallography ◽  
Knowledge Based ◽  
Wide Range

Structural genomics programs have developed and applied structure-determination pipelines to a wide range of protein targets, facilitating the visualization of macromolecular interactions and the understanding of their molecular and biochemical functions. The fundamental question of whether three-dimensional structures of all proteins and all functional annotations can be determined using X-ray crystallography is investigated. A first-of-its-kind large-scale analysis of crystallization propensity for all proteins encoded in 1953 fully sequenced genomes was performed. It is shown that current X-ray crystallographic knowhow combined with homology modeling can provide structures for 25% of modeling families (protein clusters for which structural models can be obtained through homology modeling), with at least one structural model produced for each Gene Ontology functional annotation. The coverage varies between superkingdoms, with 19% for eukaryotes, 35% for bacteria and 49% for archaea, and with those of viruses following the coverage values of their hosts. It is shown that the crystallization propensities of proteomes from the taxonomic superkingdoms are distinct. The use of knowledge-based target selection is shown to substantially increase the ability to produce X-ray structures. It is demonstrated that the human proteome has one of the highest attainable coverage values among eukaryotes, and GPCR membrane proteins suitable for X-ray structure determination were determined.

X-ray crystallographic studies of protein–ligand interactions

2003 ◽  
Vol 31 (5) ◽  
pp. 973-979 ◽  
Author(s):  
R.A. Palmer ◽  
H. Niwa
Keyword(s):  
Binding Site ◽  
Protein Structures ◽  
Three Dimensions ◽  
Small Samples ◽  
Mistletoe Lectin ◽  
Surface Binding ◽  
X Ray ◽  
X Ray Crystallography ◽  
Sugar Binding ◽  
Wide Range

X-ray crystallography enables details of covalent and non-covalent interactions to be analysed quantitatively in three dimensions, thus providing the basis for the understanding of binding of ligands to proteins as well as modes of action such as cell-surface binding. This article is concerned with current methods employed for the X-ray analysis of protein structures complexed with ligands. It deals mainly with ‘what can be done’ in current research, rather than providing details of ‘how to do it’. In recent years significant advances have been made in a variety of techniques: growing protein crystals from very small samples by scanning a wide range of conditions; X-ray intensity data collection and measurement through the use of charge-coupled devices and high-intensity, versatile synchrotron sources; cryo-crystallography which both stabilizes the crystals and provides improved data; methods for analysing and interpreting the structures, dependent, at least in part, on both structural and sequence databases; and improvements in hardware and software. To illustrate the type of results achievable two examples involving protein–sugar interactions are discussed: (i) SNAII (the lectin Sambucus nigra agglutinin-II from elder) N-terminal sugar-binding site where terminal sugar units in a glycosylation chain from a symmetry-related molecule bind and (ii) MLI (mistletoe lectin I) C-terminal sugar-binding site with lactose.

scholarly journals In-silico study of Hesperidin, Epigallocatechin (EGCG ), Kaempferol and Quercetin

2021 ◽  
Author(s):  
Soham Chatterjee ◽  
Smrajit Maiti ◽  
Amrita Bannerjee ◽  
Mehak Kanwar
Keyword(s):  
Spatial Data ◽  
Chemical Structure ◽  
Three Dimensional ◽  
Rna Dependent Rna Polymerase ◽  
X Ray ◽  
X Ray Crystallography ◽  
Wide Range ◽  
In Silico Study ◽  
3Cl Protease ◽  
Conclusive Data

Abstract SARS-Cov-2 or COVID-19 has caused a global disaster and catastrophe which has consequently led to a pandemic for the last two decades, the world has faced coronaviruses similar to SARS-Cov-2 such as SARS-Cov and Mers-Cov. In this study, a wide range of proteins such as Plpro (Papain like Protease), Rdrp (RNA-Dependent-RNA-Polymerase), Mpro or 3cl Protease and Spike Protein. The selected proteins were listed retrieved from RCSB PDB(https://www.rcsb.org/) And Zhang lab (https://www.zhanglab.ccmb.med.umich.edu/COVID-19/) with their corresponding and respective PDB-ID. The 3d Structures or 2D Structures of these molecules were selected on the sole basis of resolved resolution (in Å)of the structures during the X-ray crystallography and Electron Microscopy. Structures were retrieved in .pdb format. The three dimensional ligand molecules were retrieved from PubChem chemical structure In Spatial Data Base (.SDF) format. The respective ligand molecules are; Hesperidin, Kaempferol, Quercetin, Epigallocatechin. This molecular docking shows significant data of polyphenols, flavonoids and bioflavonoids inhibiting SARS-Cov-2 proteins which could lead to conclusive data for treatment of polyphenols, flavonoids and bioflavonoids against SARS-Cov-2.

Difference Fourier Analysis of Glucose Embedded and Frozen Hydrated Purple Membrane

1982 ◽  
Vol 40 ◽  
pp. 74-75
Author(s):  
Jules S. Jaffe ◽  
Robert M. Glaeser
Keyword(s):  
Purple Membrane ◽  
Data Set ◽  
High Resolution Data ◽  
X Ray ◽  
X Ray Crystallography ◽  
Fourier Techniques ◽  
Versus Protein ◽  
The Difference ◽  
Difference Fourier ◽  
Ideal Method

Although difference Fourier techniques are standard in X-ray crystallography it has only been very recently that electron crystallographers have been able to take advantage of this method. We have combined a high resolution data set for frozen glucose embedded Purple Membrane (PM) with a data set collected from PM prepared in the frozen hydrated state in order to visualize any differences in structure due to the different methods of preparation. The increased contrast between protein-ice versus protein-glucose may prove to be an advantage of the frozen hydrated technique for visualizing those parts of bacteriorhodopsin that are embedded in glucose. In addition, surface groups of the protein may be disordered in glucose and ordered in the frozen state. The sensitivity of the difference Fourier technique to small changes in structure provides an ideal method for testing this hypothesis.

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