scholarly journals Evaluation of variability in high resolution protein structures by global distance scoring

2017 ◽  
Author(s):  
Risa Anzai ◽  
Yoshiki Asami ◽  
Waka Inoue ◽  
Hina Ueno ◽  
Koya Yamada ◽  
...  
Keyword(s):  
High Resolution ◽  
Global Analysis ◽  
Protein Structures ◽  
Data Bank ◽  
Relevant Information ◽  
Systematic Analysis ◽  
Structure Variation ◽  
Model Calculations ◽  
Biologically Relevant ◽  
Global Comparison

AbstractSystematic analysis of statistical and dynamical properties of proteins is critical to understanding cellular events. Extraction of biologically relevant information from a set of high-resolution structures is important because it can provide mechanistic details behind the functional properties of protein families, enabling rational comparison between families. Most of the current structure comparisons are pairwise-based, which hampers the global analysis of increasing contents in the Protein Data Bank. Additionally, pairing of protein structures introduces uncertainty with respect to reproducibility because it frequently accompanies other settings for superimposition. This study introduces intramolecular distance scoring, for the analysis of human proteins, for each of which at least several high-resolution are available. We show that the results are comprehensively used to overview advances at the atomic level exploration of each protein and protein family. This method, and the interpretation based on model calculations, provide new criteria for understanding specific and non-specific structure variation in a protein, enabling global comparison of the dynamics among a vast variety of proteins from different species.

scholarly journals Redetermination of the first unknown protein MicroED structure by high resolution X-ray diffraction

2021 ◽  
Author(s):  
Hongyi Xu ◽  
Xiaodong Zou ◽  
Martin Högbom ◽  
Hugo Lebrette
Keyword(s):  
Protein Structures ◽  
Real Life ◽  
Relevant Information ◽  
Binding Pocket ◽  
X Ray Diffraction ◽  
X Ray ◽  
Biologically Relevant ◽  
Atomic Structures ◽  
Unknown Protein ◽  
Wide Range

Microcrystal electron diffraction (MicroED) has the potential to considerably impact the field of structural biology. Indeed, the method can solve atomic structures of a wide range of molecules, beyond the reach of single particle cryo-electron microscopy, exploiting crystals too small for X-ray diffraction (XRD) even using X-ray free-electron lasers. However, until the first unknown protein structure – a R2-like ligand binding oxidase from Sulfolobus acidocaldarius (SaR2lox) – was recently solved at 3.0 Å resolution, MicroED had only been used to study known protein structures previously obtained by XRD. Here, after adapting sample preparation protocols, the structure of the SaR2lox protein originally solved by MicroED was redetermined by XRD at 2.1 Å resolution. In light of the higher resolution XRD data and taking into account experimental differences of the methods, the quality of the MicroED structure is examined. The analysis demonstrates that MicroED provided an overall accurate model, revealing biologically relevant information specific to SaR2lox, such as the absence of an ether cross-link, but did not allow to detect the presence of a ligand visible by XRD in the protein binding pocket. Furthermore, strengths and weaknesses of MicroED compared to XRD are discussed in the perspective of this real-life protein example. The study provides fundaments to help MicroED become a method of choice for solving novel protein structures.

ACMS: a database of alternate conformations found in the atoms of main and side chains of protein structures

2019 ◽  
Vol 52 (4) ◽  
pp. 910-913 ◽  
Author(s):  
R. Santhosh ◽  
P. Chandrasekaran ◽  
Daliah Michael ◽  
K. Rangachari ◽  
Namrata Bankoti ◽  
...  
Keyword(s):  
High Resolution ◽  
Knowledge Base ◽  
Crystal Structures ◽  
Protein Structures ◽  
Protein Molecule ◽  
Data Bank ◽  
Side Chain ◽  
Biological Macromolecules ◽  
Conformational Ensembles ◽  
User Friendly

Proteins are usually dynamic biological macromolecules, thereby exhibiting a large number of conformational ensembles which influence the association with their neighbours and interacting partners. Most of the side-chain atoms and a few main-chain atoms of the high-resolution crystal structures deposited in the Protein Data Bank adopt alternate conformations. This kind of conformational behaviour prompted the authors to explore the relationship, if any, between the alternate conformations and the function of the protein molecule. Thus, a knowledge base of the alternate conformations of the main- and side-chain atoms of protein structures has been developed. It provides a detailed description of the alternate conformations of various residues for more than 60 000 high-resolution crystal structures. The proposed knowledge base is very user friendly and has various flexible options. The knowledge base will be updated periodically and can be accessed at http://iris.physics.iisc.ac.in/acms.

scholarly journals Automated evaluation of quaternary structures from protein crystals

2017 ◽  
Author(s):  
Spencer Bliven ◽  
Aleix Lafita ◽  
Althea Parker ◽  
Guido Capitani ◽  
Jose M Duarte
Keyword(s):  
Protein Data Bank ◽  
Quaternary Structure ◽  
Protein Structures ◽  
Data Bank ◽  
New Method ◽  
Protein Assemblies ◽  
Native Solution ◽  
Biologically Relevant ◽  
Crystal Contacts ◽  
Structure Of Proteins

AbstractA correct assessment of the quaternary structure of proteins is a fundamental prerequisite to understanding their function, physico-chemical properties and mode of interaction with other proteins. Currently about 90% of structures in the Protein Data Bank are crystal structures, in which the correct quaternary structure is embedded in the crystal lattice among a number of crystal contacts. Computational methods are required to 1) classify all protein-protein contacts in crystal lattices as biologically relevant or crystal contacts and 2) provide an assessment of how the biologically relevant interfaces combine into a biological assembly In our previous work we addressed the first problem with our EPPIC (Evolutionary Protein Protein Interface Classifier) method. Here, we present our solution to the second problem with a new method that combines the interface classification results with symmetry and topology considerations. The new algorithm enumerates all possible valid assemblies within the crystal using a graph representation of the lattice and predicts the most probable biological unit based on the pairwise interface scoring. Our method achieves 85% precision on a new dataset of 1,481 biological assemblies with consensus of PDB annotations. Although almost the same precision is achieved by PISA, currently the most popular quaternary structure assignment method, we show that, due to the fundamentally different approach to the problem, the two methods are complementary and could be combined to improve biological assembly assignments. The software for the automatic assessment of protein assemblies (EPPIC version 3) has been made available through a web server at http://www.eppic-web.org.Author summaryX-ray diffraction experiments are the main experimental technique to reveal the detailed atomic 3-dimensional structure of proteins. In these experiments, proteins are packed into crystals, an environment that is far away from their native solution environment. Determining which parts of the structure reflect the protein’s state in the cell rather than being artifacts of the crystal environment can be a difficult task. How the different protein subunits assemble together in solution is known as the quaternary structure. Finding the correct quaternary structure is important both to understand protein oligomerization and for the understanding of protein-protein interactions at large. Here we present a new method to automatically determine the quaternary structure of proteins given their crystal structure. We provide a theoretical basis for properties that correct protein assemblies should possess, and provide a systematic evaluation of all possible assemblies according to these properties. The method provides a guidance to the experimental structural biologist as well as to structural bioinformaticians analyzing protein structures in bulk. Assemblies are provided for all proteins in the Protein Data Bank through a public website and database that is updated weekly as new structures are released.

EDPDB: a multifunctional tool for protein structure analysis

1995 ◽  
Vol 28 (5) ◽  
pp. 624-630 ◽  
Author(s):  
X.-J. Zhang ◽  
B. W. Matthews
Keyword(s):  
Protein Structure ◽  
Solvent Accessibility ◽  
Protein Structures ◽  
Relevant Information ◽  
Fortran Program ◽  
Large Set ◽  
Protein Structure Analysis ◽  
Van Der Waals Volume ◽  
Biologically Relevant

EDPDB is a Fortran program that simplifies the analysis of protein structure and makes it easy to extract various types of geometrical and biologically relevant information for the molecule both in isolation as well as in its crystallographic context. EDPDB offers a large set of functions by which the user can evaluate, select and manipulate the coordinates of protein structures. Types of calculation available include the determination of solvent accessibility, bond lengths and torsion angles, determination of the van der Waals volume of a group of atoms, determination of the best-fit plane through a set of points, evaluation of crystal contacts between a molecule in a crystal and all symmetry-related molecules, and the determination of `hinge-bending' motion between protein domains. It is also possible to compare different structures, to perform coordinate manipulations and to edit coordinate files. The program augments the graphic analysis of protein structure by allowing the user to construct a simple set of commands that will rapidly screen an entire structure. It may also make special purpose analyses feasible without complicated programming.

scholarly journals NoRCE: non-coding RNA sets cis enrichment tool

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Gulden Olgun ◽  
Afshan Nabi ◽  
Oznur Tastan
Keyword(s):  
Expression Patterns ◽  
Target Prediction ◽  
Enrichment Analysis ◽  
Fruit Fly ◽  
Relevant Information ◽  
R Package ◽  
Data Repository ◽  
Biologically Relevant ◽  
Gene Sets ◽  
Data Files

Abstract Background While some non-coding RNAs (ncRNAs) are assigned critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic proximity on the sequence can hint at a functional association. Results We present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. Other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information can be incorporated to conduct a richer enrichment analysis. To this end, NoRCE includes several relevant datasets as part of its data repository, including cell-line specific TAD boundaries, functional gene sets, and expression data for coding & ncRNAs specific to cancer. Additionally, the users can utilize custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast. Conclusions NoRCE is a platform-independent, user-friendly, comprehensive R package that can be used to gain insight into the functional importance of a list of ncRNAs of any type. The tool offers flexibility to conduct the users’ preferred set of analyses by designing their own pipeline of analysis. NoRCE is available in Bioconductor and https://github.com/guldenolgun/NoRCE.

scholarly journals Recent Advances in Unmanned Aerial Vehicle Forest Remote Sensing—A Systematic Review. Part I: A General Framework

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 327 ◽  
Author(s):  
Riccardo Dainelli ◽  
Piero Toscano ◽  
Salvatore Filippo Di Gennaro ◽  
Alessandro Matese
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Relevant Information ◽  
Forest Monitoring ◽  
Machine Learning Techniques ◽  
Planted Forests ◽  
Systematic Analysis ◽  
Aerial Photogrammetry ◽  
Wide Range ◽  
Aerial Vehicle

Natural, semi-natural, and planted forests are a key asset worldwide, providing a broad range of positive externalities. For sustainable forest planning and management, remote sensing (RS) platforms are rapidly going mainstream. In a framework where scientific production is growing exponentially, a systematic analysis of unmanned aerial vehicle (UAV)-based forestry research papers is of paramount importance to understand trends, overlaps and gaps. The present review is organized into two parts (Part I and Part II). Part II inspects specific technical issues regarding the application of UAV-RS in forestry, together with the pros and cons of different UAV solutions and activities where additional effort is needed, such as the technology transfer. Part I systematically analyzes and discusses general aspects of applying UAV in natural, semi-natural and artificial forestry ecosystems in the recent peer-reviewed literature (2018–mid-2020). The specific goals are threefold: (i) create a carefully selected bibliographic dataset that other researchers can draw on for their scientific works; (ii) analyze general and recent trends in RS forest monitoring (iii) reveal gaps in the general research framework where an additional activity is needed. Through double-step filtering of research items found in the Web of Science search engine, the study gathers and analyzes a comprehensive dataset (226 articles). Papers have been categorized into six main topics, and the relevant information has been subsequently extracted. The strong points emerging from this study concern the wide range of topics in the forestry sector and in particular the retrieval of tree inventory parameters often through Digital Aerial Photogrammetry (DAP), RGB sensors, and machine learning techniques. Nevertheless, challenges still exist regarding the promotion of UAV-RS in specific parts of the world, mostly in the tropical and equatorial forests. Much additional research is required for the full exploitation of hyperspectral sensors and for planning long-term monitoring.

scholarly journals Hydrogen atoms in protein structures: high-resolution X-ray diffraction structure of the DFPase

2013 ◽  
Vol 6 (1) ◽  
pp. 308 ◽  
Author(s):  
Mikael Elias ◽  
Dorothee Liebschner ◽  
Jurgen Koepke ◽  
Claude Lecomte ◽  
Benoit Guillot ◽  
...  
Keyword(s):  
High Resolution ◽  
Protein Structures ◽  
X Ray Diffraction ◽  
Hydrogen Atoms ◽  
X Ray ◽  
Diffraction Structure

scholarly journals Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 320
Author(s):  
Emilio Guirado ◽  
Javier Blanco-Sacristán ◽  
Emilio Rodríguez-Caballero ◽  
Siham Tabik ◽  
Domingo Alcaraz-Segura ◽  
...  
Keyword(s):  
High Resolution ◽  
Optical Sensors ◽  
Biotic Interactions ◽  
Relevant Information ◽  
Vegetation Monitoring ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Segmentation Methods ◽  
High Resolution Images ◽  
Very High

Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. For this reason, improving segmentation methods and understanding the effect of spatial resolution on segmentation results is key to improve dryland vegetation monitoring. We explored and analyzed the accuracy of Object-Based Image Analysis (OBIA) and Mask Region-based Convolutional Neural Networks (Mask R-CNN) and the fusion of both methods in the segmentation of scattered vegetation in a dryland ecosystem. As a case study, we mapped Ziziphus lotus, the dominant shrub of a habitat of conservation priority in one of the driest areas of Europe. Our results show for the first time that the fusion of the results from OBIA and Mask R-CNN increases the accuracy of the segmentation of scattered shrubs up to 25% compared to both methods separately. Hence, by fusing OBIA and Mask R-CNNs on very high-resolution images, the improved segmentation accuracy of vegetation mapping would lead to more precise and sensitive monitoring of changes in biodiversity and ecosystem services in drylands.

scholarly journals The Impact of Forcing Datasets on the High-Resolution Simulation of Tropical Storm Ivan (2004) in the Southern Appalachians

2012 ◽  
Vol 140 (10) ◽  
pp. 3300-3326 ◽  
Author(s):  
Xiaoming Sun ◽  
Ana P. Barros
Keyword(s):  
High Resolution ◽  
Global Analysis ◽  
Surface Wind ◽  
Tropical Storm ◽  
Southern Appalachians ◽  
Low Level ◽  
Simulated Precipitation ◽  
Forced Simulation ◽  
The Impact ◽  
Resolution Simulation

Abstract The influence of large-scale forcing on the high-resolution simulation of Tropical Storm Ivan (2004) in the southern Appalachians was investigated using the Weather Research and Forecasting model (WRF). Two forcing datasets were employed: the North American Regional Reanalysis (NARR; 32 km × 32 km) and the NCEP Final Operational Global Analysis (NCEP FNL; 1° × 1°). Simulated fields were evaluated against rain gauge, radar, and satellite data; sounding observations; and the best track from the National Hurricane Center (NHC). Overall, the NCEP FNL forced simulation (WRF_FNL) captures storm structure and evolution more accurately than the NARR forced simulation (WRF_NARR), benefiting from the hurricane initialization scheme in the NCEP FNL. Further, the performance of WRF_NARR is also negatively affected by a previously documented low-level warm bias in NARR. These factors lead to excessive precipitation in the Piedmont region, delayed rainfall in Alabama, as well as spatially displaced and unrealistically extreme rainbands during its passage over the southern Appalachians. Spatial filtering of the simulated precipitation fields confirms that the storm characteristics inherited from the forcing are critical to capture the storm’s impact at local places. Compared with the NHC observations, the storm is weaker in both NARR and NCEP FNL (up to Δp ~ 5 hPa), yet it is persistently deeper in all WRF simulations forced by either dataset. The surface wind fields are largely overestimated. This is attributed to the underestimation of surface roughness length over land, leading to underestimation of surface drag, reducing low-level convergence, and weakening the dissipation of the simulated cyclone.

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