Measurement and Reduction of Radiation Damage in Frozen Hydrated Crystalline Specimens

1978 ◽  
Vol 36 (3) ◽  
pp. 70-77 ◽  
Author(s):  
R.M. Glaeser ◽  
S.B. Hayward
Keyword(s):  
Diffraction Pattern ◽  
Structural Information ◽  
Structural Disorder ◽  
Structural Features ◽  
Biological Macromolecules ◽  
Diffraction Intensity ◽  
Object Structure ◽  
Specimen Material ◽  
Unit Cells ◽  
Identical Unit

Highly ordered or crystalline biological macromolecules become severely damaged and structurally disordered after a brief electron exposure. Evidence that damage and structural disorder are occurring is clearly given by the fading and eventual disappearance of the specimen's electron diffraction pattern. The fading and disappearance of sharp diffraction spots implies a corresponding disappearance of periodic structural features in the specimen. By the same token, there is a oneto- one correspondence between the disappearance of the crystalline diffraction pattern and the disappearance of reproducible structural information that can be observed in the images of identical unit cells of the object structure. The electron exposures that result in a significant decrease in the diffraction intensity will depend somewhat upon the resolution (Bragg spacing) involved, and can vary considerably with the chemical makeup and composition of the specimen material.

Measurement and reduction of damage in frozen hydrated crystalline specimens

1978 ◽  
Vol 36 (2) ◽  
pp. 10-11
Author(s):  
Robert M. Glaeser ◽  
Steven B. Hayward
Keyword(s):  
High Resolution ◽  
Electron Diffraction ◽  
Diffraction Pattern ◽  
Electron Diffraction Pattern ◽  
Cell Membranes ◽  
Structural Information ◽  
Protein Crystals ◽  
Biological Macromolecules ◽  
Diffraction Intensity ◽  
Specimen Material

Highly ordered or crystalline biological macromolecules become severely damaged and disordered after a brief electron exposure, as may be seen by observing the fading and loss of the specimen's electron diffraction pattern. Loss of the diffraction pattern intensity has, in turn, a one-to-one relationship with a loss of the possibility to see structural information in the image. The actual electron exposure that results in a significant decrease in the diffraction intensity will depend first of all upon the resolution (Bragg spacing) involved, and in some cases upon the chemical make-up and composition of the specimen material. For high resolution features (in the range 3Å to 5Å resolution) of specimens such as protein crystals and cell membranes, the structure can become damaged and disordered after an exposure of about 1 electron/Å2 or less. Roughly speaking, this exposure is about 104 times lower than that which is required to produce a statistically defined image at high resolution.

scholarly journals NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning

2018 ◽  
Author(s):  
Michael Schantz Klausen ◽  
Martin Closter Jespersen ◽  
Henrik Nielsen ◽  
Kamilla Kjærgaard Jensen ◽  
Vanessa Isabell Jurtz ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Short Term Memory ◽  
Structural Information ◽  
Solvent Accessibility ◽  
Protein Structures ◽  
Structural Disorder ◽  
Structural Features ◽  
Sequencing Data ◽  
Long Short Term Memory ◽  
Improved Accuracy

ABSTRACTThe ability to predict local structural features of a protein from the primary sequence is of paramount importance for unravelling its function in absence of experimental structural information. Two main factors affect the utility of potential prediction tools: their accuracy must enable extraction of reliable structural information on the proteins of interest, and their runtime must be low to keep pace with sequencing data being generated at a constantly increasing speed.Here, we present an updated and extended version of the NetSurfP tool (http://www.cbs.dtu.dk/services/NetSurfP-2.0/), that can predict the most important local structural features with unprecedented accuracy and runtime. NetSurfP-2.0 is sequence-based and uses an architecture composed of convolutional and long short-term memory neural networks trained on solved protein structures. Using a single integrated model, NetSurfP-2.0 predicts solvent accessibility, secondary structure, structural disorder, and backbone dihedral angles for each residue of the input sequences.We assessed the accuracy of NetSurfP-2.0 on several independent test datasets and found it to consistently produce state-of-the-art predictions for each of its output features. We observe a correlation of 80% between predictions and experimental data for solvent accessibility, and a precision of 85% on secondary structure 3-class predictions. In addition to improved accuracy, the processing time has been optimized to allow predicting more than 1,000 proteins in less than 2 hours, and complete proteomes in less than 1 day.

Analysis of electron-diffraction intensity profiles using a photodiode-array detection system

1983 ◽  
Vol 41 ◽  
pp. 322-323
Author(s):  
J. B. Warren
Keyword(s):  
Electron Diffraction ◽  
Diffraction Pattern ◽  
Detection System ◽  
High Energy ◽  
Photographic Emulsion ◽  
Diffraction Intensity ◽  
Silicon Photodiode ◽  
Photodiode Array Detection ◽  
Analog To Digital ◽  
Photodiode Array

Electron diffraction intensity profiles have been used extensively in studies of polycrystalline and amorphous thin films. In previous work, diffraction intensity profiles were quantitized either by mechanically scanning the photographic emulsion with a densitometer or by using deflection coils to scan the diffraction pattern over a stationary detector. Such methods tend to be slow, and the intensities must still be converted from analog to digital form for quantitative analysis. The Instrumentation Division at Brookhaven has designed and constructed a electron diffractometer, based on a silicon photodiode array, that overcomes these disadvantages. The instrument is compact (Fig. 1), can be used with any unmodified electron microscope, and acquires the data in a form immediately accessible by microcomputer.Major components include a RETICON 1024 element photodiode array for the de tector, an Analog Devices MAS-1202 analog digital converter and a Digital Equipment LSI 11/2 microcomputer. The photodiode array cannot detect high energy electrons without damage so an f/1.4 lens is used to focus the phosphor screen image of the diffraction pattern on to the photodiode array.

High-Resolution Cryo-Electron Microscopy of Biological Macromolecules

1990 ◽  
Vol 48 (1) ◽  
pp. 126-127
Author(s):  
Yoshinori Fujiyoshi
Keyword(s):  
Electron Microscopy ◽  
High Resolution ◽  
Diffraction Pattern ◽  
Superfluid Helium ◽  
Copper Phthalocyanine ◽  
Optical Diffraction ◽  
Irradiation Damage ◽  
Biological Macromolecules ◽  
Cross Sectional ◽  
Instrumental Resolution

The resolution of direct images of biological macromolecules is normally restricted to far less than 0.3 nm. This is not due instrumental resolution, but irradiation damage. The damage to biological macromolecules may expect to be reduced when they are cooled to a very low temperature. We started to develop a new cryo-stage for a high resolution electron microscopy in 1983, and successfully constructed a superfluid helium stage for a 400 kV microscope by 1986, whereby chlorinated copper-phthalocyanine could be photographed to a resolution of 0.26 nm at a stage temperature of 1.5 K. We are continuing to develop the cryo-microscope and have developed a cryo-microscope equipped with a superfluid helium stage and new cryo-transfer device.The New cryo-microscope achieves not only improved resolution but also increased operational ease. The construction of the new super-fluid helium stage is shown in Fig. 1, where the cross sectional structure is shown parallel to an electron beam path. The capacities of LN2 tank, LHe tank and the pot are 1400 ml, 1200 ml and 3 ml, respectively. Their surfaces are placed with gold to minimize thermal radiation. Consumption rates of liquid nitrogen and liquid helium are 170 ml/hour and 140 ml/hour, respectively. The working time of this stage is more than 7 hours starting from full LN2 and LHe tanks. Instrumental resolution of our cryo-stage cooled to 4.2 K was confirmed to be 0.20 nm by an optical diffraction pattern from the image of a chlorinated copper-phthalocyanine crystal. The image and the optical diffraction pattern are shown in Fig. 2 a, b, respectively.

HAZARD: An Expert System for Risk Assessment of Environmental Chemicals

1987 ◽  
Vol 26 (01) ◽  
pp. 13-23 ◽  
Author(s):  
H. W. Gottinger
Keyword(s):  
Expert System ◽  
Structural Information ◽  
Chemical Carcinogenesis ◽  
Structural Features ◽  
Environmental Chemicals ◽  
Chemical Carcinogens ◽  
Carcinogenic Activity ◽  
Current State ◽  
Structure Activity ◽  
Carcinogenic Potential

AbstractThe purpose of this paper is to report on an expert system in design that screens for potential hazards from environmental chemicals on the basis of structure-activity relationships in the study of chemical carcinogenesis, particularly with respect to analyzing the current state of known structural information about chemical carcinogens and predicting the possible carcinogenicity of untested chemicals. The structure-activity tree serves as an index of known chemical structure features associated with carcinogenic activity. The basic units of the tree are the principal recognized classes of chemical carcinogens that are subdivided into subclasses known as nodes according to specific structural features that may reflect differences in carcinogenic potential among chemicals in the class. An analysis of a computerized data base of known carcinogens (knowledge base) is proposed using the structure-activity tree in order to test the validity of the tree as a classification scheme (inference engine).

Rapid Online Buffer Exchange: A Method for Screening of Proteins, Protein Complexes, and Cell Lysates by Native Mass Spectrometry

2019 ◽  
Author(s):  
Zachary VanAernum ◽  
Florian Busch ◽  
Benjamin J. Jones ◽  
Mengxuan Jia ◽  
Zibo Chen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Speed ◽  
Structural Information ◽  
Protein Complexes ◽  
High Sensitivity ◽  
Native Mass Spectrometry ◽  
Structural Features ◽  
Consumer Products ◽  
Cell Lysates ◽  
Protein Expression And Purification

It is important to assess the identity and purity of proteins and protein complexes during and after protein purification to ensure that samples are of sufficient quality for further biochemical and structural characterization, as well as for use in consumer products, chemical processes, and therapeutics. Native mass spectrometry (nMS) has become an important tool in protein analysis due to its ability to retain non-covalent interactions during measurements, making it possible to obtain protein structural information with high sensitivity and at high speed. Interferences from the presence of non-volatiles are typically alleviated by offline buffer exchange, which is timeconsuming and difficult to automate. We provide a protocol for rapid online buffer exchange (OBE) nMS to directly screen structural features of pre-purified proteins, protein complexes, or clarified cell lysates. Information obtained by OBE nMS can be used for fast (<5 min) quality control and can further guide protein expression and purification optimization.

Protein Interaction Domains and Post-Translational Modifications: Structural Features and Drug Discovery Applications

2020 ◽  
Vol 27 (37) ◽  
pp. 6306-6355 ◽  
Author(s):  
Marian Vincenzi ◽  
Flavia Anna Mercurio ◽  
Marilisa Leone
Keyword(s):  
Drug Discovery ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Structural Information ◽  
Protein Complexes ◽  
Structural Features ◽  
Protein Protein Interactions ◽  
Modular Architecture ◽  
Post Translational Modifications ◽  
Interaction Domains

Background:: Many pathways regarding healthy cells and/or linked to diseases onset and progression depend on large assemblies including multi-protein complexes. Protein-protein interactions may occur through a vast array of modules known as protein interaction domains (PIDs). Objective:: This review concerns with PIDs recognizing post-translationally modified peptide sequences and intends to provide the scientific community with state of art knowledge on their 3D structures, binding topologies and potential applications in the drug discovery field. Method:: Several databases, such as the Pfam (Protein family), the SMART (Simple Modular Architecture Research Tool) and the PDB (Protein Data Bank), were searched to look for different domain families and gain structural information on protein complexes in which particular PIDs are involved. Recent literature on PIDs and related drug discovery campaigns was retrieved through Pubmed and analyzed. Results and Conclusion:: PIDs are rather versatile as concerning their binding preferences. Many of them recognize specifically only determined amino acid stretches with post-translational modifications, a few others are able to interact with several post-translationally modified sequences or with unmodified ones. Many PIDs can be linked to different diseases including cancer. The tremendous amount of available structural data led to the structure-based design of several molecules targeting protein-protein interactions mediated by PIDs, including peptides, peptidomimetics and small compounds. More studies are needed to fully role out, among different families, PIDs that can be considered reliable therapeutic targets, however, attacking PIDs rather than catalytic domains of a particular protein may represent a route to obtain selective inhibitors.

A Systematic Review on Popularity, Application and Characteristics of Protein Secondary Structure Prediction Tools

2019 ◽  
Vol 16 (2) ◽  
pp. 159-172 ◽  
Author(s):  
Elaheh Kashani-Amin ◽  
Ozra Tabatabaei-Malazy ◽  
Amirhossein Sakhteman ◽  
Bagher Larijani ◽  
Azadeh Ebrahim-Habibi
Keyword(s):  
Systematic Review ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Web Of Science ◽  
Secondary Structure Prediction ◽  
Structural Information ◽  
Protein Secondary Structure ◽  
Structural Features ◽  
Prediction Tools ◽  
Insight Into

Background: Prediction of proteins’ secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored methods and tools, within various contexts. Objective: A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools. Methods: Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of the 209 studies were finally found eligible to extract data. Results: Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields. Conclusion: This study provides a comprehensive insight into the recent usage of SSP tools which could be helpful for selecting a proper tool.

scholarly journals Computational Modeling to Explain Why 5,5-Diarylpentadienamides are TRPV1 Antagonists

Molecules ◽  
2021 ◽  
Vol 26 (6) ◽  
pp. 1765
Author(s):  
Julio Caballero
Keyword(s):  
Transient Receptor Potential ◽  
Structural Information ◽  
Chemical Activation ◽  
Receptor Potential ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Transient Receptor Potential Vanilloid ◽  
Protein Residues ◽  
Topological Features ◽  
Transient Receptor

Several years ago, the crystallographic structures of the transient receptor potential vanilloid 1 (TRPV1) in the presence of agonists and antagonists were reported, providing structural information about its chemical activation and inactivation. TRPV1’s activation increases the transport of calcium and sodium ions, leading to the excitation of sensory neurons and the perception of pain. On the other hand, its antagonistic inactivation has been explored to design analgesic drugs. The interactions between the antagonists 5,5-diarylpentadienamides (DPDAs) and TRPV1 were studied here to explain why they inactivate TRPV1. The present work identified the structural features of TRPV1–DPDA complexes, starting with a consideration of the orientations of the ligands inside the TRPV1 binding site by using molecular docking. After this, a chemometrics analysis was performed (i) to compare the orientations of the antagonists (by using LigRMSD), (ii) to describe the recurrent interactions between the protein residues and ligand groups in the complexes (by using interaction fingerprints), and (iii) to describe the relationship between topological features of the ligands and their differential antagonistic activities (by using a quantitative structure–activity relationship (QSAR) with 2D autocorrelation descriptors). The interactions between the DPDA groups and the residues Y511, S512, T550, R557, and E570 (with a recognized role in the binding of classic ligands), and the occupancy of isoquinoline or 3-hydroxy-3,4-dihydroquinolin-2(1H)-one groups of the DPDAs in the vanilloid pocket of TRPV1 were clearly described. Based on the results, the structural features that explain why DPDAs inactivate TRPV1 were clearly exposed. These features can be considered for the design of novel TRPV1 antagonists.

scholarly journals Detecting Controversial Articles on Citizen Journalism

2018 ◽  
Vol 11 (1) ◽  
pp. 34
Author(s):  
Alfan Farizki Wicaksono ◽  
Sharon Raissa Herdiyana ◽  
Mirna Adriani
Keyword(s):  
Machine Learning ◽  
Structural Information ◽  
Structural Features ◽  
The Body ◽  
Supervised Machine Learning ◽  
Citizen Journalism ◽  
Learning Approach ◽  
Daily News ◽  
Machine Learning Approach ◽  
Controversial Topic

Someone's understanding and stance on a particular controversial topic can be influenced by daily news or articles he consume everyday. Unfortunately, readers usually do not realize that they are reading controversial articles. In this paper, we address the problem of automatically detecting controversial article from citizen journalism media. To solve the problem, we employ a supervised machine learning approach with several hand-crafted features that exploits linguistic information, meta-data of an article, structural information in the commentary section, and sentiment expressed inside the body of an article. The experimental results shows that our proposed method manages to perform the addressed task effectively. The best performance so far is achieved when we use all proposed feature with Logistic Regression as our model (82.89\% in terms of accuracy). Moreover, we found that information from commentary section (structural features) contributes most to the classification task.

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