The PRIDE server for protein three-dimensional similarity

2002 ◽  
Vol 35 (5) ◽  
pp. 648-649 ◽  
Author(s):  
Kristian Vlahovicek ◽  
Oliviero Carugo ◽  
Sándor Pongor
Keyword(s):  
Similarity Search ◽  
Three Dimensional ◽  
Pairwise Comparison ◽  
Distance Matrix ◽  
Data Bank ◽  
Protein Domain ◽  
Distance Distributions ◽  
Protein Domain Structure ◽  
Structural Clustering ◽  
Structure Query

The PRIDE server is an implementation of thePRIDEalgorithm that compares protein three-dimensional structures in terms of their Cαdistance distributions. In response to queries presented as single or concatenated Protein Data Bank (PDB) files, the server can carry out (i) a pairwise comparison of two protein three-dimensional structures, (ii) a structural clustering of protein three-dimensional structures, providing a distance matrix and a dendrogram as an output; and (iii) a similarity search with a protein domain structure query against the CATH database.

scholarly journals A computer-based approach for developing linamarase inhibitory agents

2020 ◽  
Vol 5 (7) ◽  
Author(s):  
Lucas Paul ◽  
Celestin N. Mudogo ◽  
Kelvin M. Mtei ◽  
Revocatus L. Machunda ◽  
Fidele Ntie-Kang
Keyword(s):  
Structure Elucidation ◽  
Three Dimensional ◽  
Data Bank ◽  
Competitive Inhibitor ◽  
Industrial Applications ◽  
Dimensional Structure ◽  
Full Understanding ◽  
The Poor ◽  
Computer Based ◽  
Inhibitory Agents

AbstractCassava is a strategic crop, especially for developing countries. However, the presence of cyanogenic compounds in cassava products limits the proper nutrients utilization. Due to the poor availability of structure discovery and elucidation in the Protein Data Bank is limiting the full understanding of the enzyme, how to inhibit it and applications in different fields. There is a need to solve the three-dimensional structure (3-D) of linamarase from cassava. The structural elucidation will allow the development of a competitive inhibitor and various industrial applications of the enzyme. The goal of this review is to summarize and present the available 3-D modeling structure of linamarase enzyme using different computational strategies. This approach could help in determining the structure of linamarase and later guide the structure elucidation in silico and experimentally.

scholarly journals BanLec-eGFP Chimera as a Tool for Evaluation of Lectin Binding to High-Mannose Glycans on Microorganisms

Biomolecules ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 180
Author(s):  
Zorana Lopandić ◽  
Luka Dragačević ◽  
Dragan Popović ◽  
Uros Andjelković ◽  
Rajna Minić ◽  
...  
Keyword(s):  
Fluorescent Protein ◽  
Lectin Binding ◽  
Three Dimensional ◽  
Data Bank ◽  
Salmonella Typhi ◽  
Excitation Wavelength ◽  
Protein Data Bank Entry ◽  
High Mannose

Fluorescently labeled lectins are useful tools for in vivo and in vitro studies of the structure and function of tissues and various pathogens such as viruses, bacteria, and fungi. For the evaluation of high-mannose glycans present on various glycoproteins, a three-dimensional (3D) model of the chimera was designed from the crystal structures of recombinant banana lectin (BanLec, Protein Data Bank entry (PDB): 5EXG) and an enhanced green fluorescent protein (eGFP, PDB 4EUL) by applying molecular modeling and molecular mechanics and expressed in Escherichia coli. BanLec-eGFP, produced as a soluble cytosolic protein of about 42 kDa, revealed β-sheets (41%) as the predominant secondary structures, with the emission peak maximum detected at 509 nm (excitation wavelength 488 nm). More than 65% of the primary structure was confirmed by mass spectrometry. Competitive BanLec-eGFP binding to high mannose glycans of the influenza vaccine (Vaxigrip®) was shown in a fluorescence-linked lectin sorbent assay (FLLSA) with monosaccharides (mannose and glucose) and wild type BanLec and H84T BanLec mutant. BanLec-eGFP exhibited binding to mannose residues on different strains of Salmonella in flow cytometry, with especially pronounced binding to a Salmonella Typhi clinical isolate. BanLec-eGFP can be a useful tool for screening high-mannose glycosylation sites on different microorganisms.

scholarly journals Protein dynamics revealed by NMR relaxation methods

2018 ◽  
Vol 2 (1) ◽  
pp. 93-105 ◽  
Author(s):  
Fa-An Chao ◽  
R. Andrew Byrd
Keyword(s):  
Protein Dynamics ◽  
Structural Biology ◽  
Dynamic Models ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Data Bank ◽  
Biological Macromolecules ◽  
Relaxation Methods ◽  
Genomic Databases ◽  
Wide Range

Structural biology often focuses primarily on three-dimensional structures of biological macromolecules, deposited in the Protein Data Bank (PDB). This resource is a remarkable entity for the worldwide scientific and medical communities, as well as the general public, as it is a growing translation into three-dimensional space of the vast information in genomic databases, e.g. GENBANK. There is, however, significantly more to understanding biological function than the three-dimensional co-ordinate space for ground-state structures of biomolecules. The vast array of biomolecules experiences natural dynamics, interconversion between multiple conformational states, and molecular recognition and allosteric events that play out on timescales ranging from picoseconds to seconds. This wide range of timescales demands ingenious and sophisticated experimental tools to sample and interpret these motions, thus enabling clearer insights into functional annotation of the PDB. NMR spectroscopy is unique in its ability to sample this range of timescales at atomic resolution and in physiologically relevant conditions using spin relaxation methods. The field is constantly expanding to provide new creative experiments, to yield more detailed coverage of timescales, and to broaden the power of interpretation and analysis methods. This review highlights the current state of the methodology and examines the extension of analysis tools for more complex experiments and dynamic models. The future for understanding protein dynamics is bright, and these extended tools bring greater compatibility with developments in computational molecular dynamics, all of which will further our understanding of biological molecular functions. These facets place NMR as a key component in integrated structural biology.

scholarly journals EnTAP: Bringing Faster and Smarter Functional Annotation to Non-Model Eukaryotic Transcriptomes

2018 ◽  
Author(s):  
Alexander J. Hart ◽  
Samuel Ginzburg ◽  
Muyang (Sam) Xu ◽  
Cera R. Fisher ◽  
Nasim Rahmatpour ◽  
...  
Keyword(s):  
Similarity Search ◽  
De Novo ◽  
Gene Annotation ◽  
Enrichment Analysis ◽  
Orthologous Gene ◽  
Protein Domain ◽  
Family Assessment ◽  
Ontology Term ◽  
Protein Coding ◽  
Functional Gene Annotation

ABSTRACTEnTAP (Eukaryotic Non-Model Transcriptome Annotation Pipeline) was designed to improve the accuracy, speed, and flexibility of functional gene annotation for de novo assembled transcriptomes in non-model eukaryotes. This software package addresses the fragmentation and related assembly issues that result in inflated transcript estimates and poor annotation rates, while focusing primarily on protein-coding transcripts. Following filters applied through assessment of true expression and frame selection, open-source tools are leveraged to functionally annotate the translated proteins. Downstream features include fast similarity search across three repositories, protein domain assignment, orthologous gene family assessment, and Gene Ontology term assignment. The final annotation integrates across multiple databases and selects an optimal assignment from a combination of weighted metrics describing similarity search score, taxonomic relationship, and informativeness. Researchers have the option to include additional filters to identify and remove contaminants, identify associated pathways, and prepare the transcripts for enrichment analysis. This fully featured pipeline is easy to install, configure, and runs significantly faster than comparable annotation packages. EnTAP is optimized to generate extensive functional information for the gene space of organisms with limited or poorly characterized genomic resources.

[29] Protein data bank archives of three-dimensional macromolecular structures

1997 ◽  
pp. 556-571 ◽  
Author(s):  
Enrique E. Abola ◽  
Joel L. Sussman ◽  
Jaime Prilusky ◽  
Nancy O. Manning
Keyword(s):  
Protein Data Bank ◽  
Three Dimensional ◽  
Data Bank

MRPC (Missing Regions in Polypeptide Chains): a knowledgebase

2019 ◽  
Vol 52 (6) ◽  
pp. 1422-1426
Author(s):  
Rajendran Santhosh ◽  
Namrata Bankoti ◽  
Adgonda Malgonnavar Padmashri ◽  
Daliah Michael ◽  
Jeyaraman Jeyakanthan ◽  
...  
Keyword(s):  
Protein Structures ◽  
Three Dimensional ◽  
Protein Molecule ◽  
Data Bank ◽  
Protein Crystal ◽  
Dimensional Structure ◽  
Protein Structure Analysis ◽  
Three Dimensional Structure ◽  
X Ray Crystallography ◽  
Polypeptide Chains

Missing regions in protein crystal structures are those regions that cannot be resolved, mainly owing to poor electron density (if the three-dimensional structure was solved using X-ray crystallography). These missing regions are known to have high B factors and could represent loops with a possibility of being part of an active site of the protein molecule. Thus, they are likely to provide valuable information and play a crucial role in the design of inhibitors and drugs and in protein structure analysis. In view of this, an online database, Missing Regions in Polypeptide Chains (MRPC), has been developed which provides information about the missing regions in protein structures available in the Protein Data Bank. In addition, the new database has an option for users to obtain the above data for non-homologous protein structures (25 and 90%). A user-friendly graphical interface with various options has been incorporated, with a provision to view the three-dimensional structure of the protein along with the missing regions using JSmol. The MRPC database is updated regularly (currently once every three months) and can be accessed freely at the URL http://cluster.physics.iisc.ac.in/mrpc.

scholarly journals Artificial intelligence and big data facilitated targeted drug discovery

2019 ◽  
Vol 4 (4) ◽  
pp. 206-213 ◽  
Author(s):  
Benquan Liu ◽  
Huiqin He ◽  
Hongyi Luo ◽  
Tingting Zhang ◽  
Jingwei Jiang
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Drug Discovery ◽  
Three Dimensional ◽  
Data Bank ◽  
The Cancer Genome Atlas ◽  
Biological Databases ◽  
Cancer Genome Atlas ◽  
Targeted Drug ◽  
Targeted Drug Discovery

Different kinds of biological databases publicly available nowadays provide us a goldmine of multidiscipline big data. The Cancer Genome Atlas is a cancer database including detailed information of many patients with cancer. DrugBank is a database including detailed information of approved, investigational and withdrawn drugs, as well as other nutraceutical and metabolite structures. PubChem is a chemical compound database including all commercially available compounds as well as other synthesisable compounds. Protein Data Bank is a crystal structure database including X-ray, cryo-EM and nuclear magnetic resonance protein three-dimensional structures as well as their ligands. On the other hand, artificial intelligence (AI) is playing an important role in the drug discovery progress. The integration of such big data and AI is making a great difference in the discovery of novel targeted drug. In this review, we focus on the currently available advanced methods for the discovery of highly effective lead compounds with great absorption, distribution, metabolism, excretion and toxicity properties.

scholarly journals BLASTing away preconceptions in crystallization trials

2019 ◽  
Vol 75 (3) ◽  
pp. 184-192 ◽  
Author(s):  
Gabriel Jan Abrahams ◽  
Janet Newman
Keyword(s):  
Protein Data Bank ◽  
Sequence Similarity ◽  
Three Dimensional ◽  
Protein Sequences ◽  
Protein Molecule ◽  
Data Bank ◽  
Dimensional Structure ◽  
Critical Step ◽  
Quantitative Verification ◽  
Better Than

Crystallization is in many cases a critical step for solving the three-dimensional structure of a protein molecule. Determining which set of chemicals to use in the initial screen is typically agnostic of the protein under investigation; however, crystallization efficiency could potentially be improved if this were not the case. Previous work has assumed that sequence similarity may provide useful information about appropriate crystallization cocktails; however, the authors are not aware of any quantitative verification of this assumption. This research investigates whether, given current information, one can detect any correlation between sequence similarity and crystallization cocktails. BLAST was used to quantitate the similarity between protein sequences in the Protein Data Bank, and this was compared with three estimations of the chemical similarities of the respective crystallization cocktails. No correlation was detected between proteins of similar (but not identical) sequence and their crystallization cocktails, suggesting that methods of determining screens based on this assumption are unlikely to result in screens that are better than those currently in use.

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