scholarly journals Worldwide Protein Data Bank (wwPDB): A virtual treasure for research in biotechnology

2021 ◽  
Keyword(s):  
Protein Data Bank ◽  
3D Structure ◽  
Deep Understanding ◽  
Data Bank ◽  
Biological Data ◽  
Digital Data ◽  
Internet Resource ◽  
Wide Range ◽  
Great Support ◽  
Data Updating

Abstract The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RSCB PDB) provides a wide range of digital data regarding biology and biomedicine. This huge internet resource involves a wide range of important biological data, obtained from experiments around the globe by different scientists. The Worldwide Protein Data Bank (wwPDB) represents a brilliant collection of 3D structure data associated with important and vital biomolecules including nucleic acids (RNAs and DNAs) and proteins. Moreover, this database accumulates knowledge regarding function and evolution of biomacromolecules which supports different disciplines such as biotechnology. 3D structure, functional characteristics and phylogenetic properties of biomacromolecules give a deep understanding of the biomolecules’ characteristics. An important advantage of the wwPDB database is the data updating time, which is done every week. This updating process helps users to have the newest data and information for their projects. The data and information in wwPDB can be a great support to have an accurate imagination and illustrations of the biomacromolecules in biotechnology. As demonstrated by the SARS-CoV-2 pandemic, rapidly reliable and accessible biological data for microbiology, immunology, vaccinology, and drug development are critical to address many healthcare-related challenges that are facing humanity. The aim of this paper is to introduce the readers to wwPDB, and to highlight the importance of this database in biotechnology, with the expectation that the number of scientists interested in the utilization of Protein Data Bank’s resources will increase substantially in the coming years.

scholarly journals RCSB Protein Data Bank tools for 3D structure-guided cancer research: human papillomavirus (HPV) case study

Oncogene ◽  
2020 ◽  
Vol 39 (43) ◽  
pp. 6623-6632
Author(s):  
David S. Goodsell ◽  
Stephen K. Burley
Keyword(s):  
Human Papillomavirus ◽  
Protein Data Bank ◽  
3D Structure ◽  
Healthcare Providers ◽  
Data Bank ◽  
Data Repository ◽  
Biological Macromolecules ◽  
Biologic Drugs ◽  
Related Proteins

Abstract Atomic-level three-dimensional (3D) structure data for biological macromolecules often prove critical to dissecting and understanding the precise mechanisms of action of cancer-related proteins and their diverse roles in oncogenic transformation, proliferation, and metastasis. They are also used extensively to identify potentially druggable targets and facilitate discovery and development of both small-molecule and biologic drugs that are today benefiting individuals diagnosed with cancer around the world. 3D structures of biomolecules (including proteins, DNA, RNA, and their complexes with one another, drugs, and other small molecules) are freely distributed by the open-access Protein Data Bank (PDB). This global data repository is used by millions of scientists and educators working in the areas of drug discovery, vaccine design, and biomedical and biotechnology research. The US Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) provides an integrated portal to the PDB archive that streamlines access for millions of worldwide PDB data consumers worldwide. Herein, we review online resources made available free of charge by the RCSB PDB to basic and applied researchers, healthcare providers, educators and their students, patients and their families, and the curious public. We exemplify the value of understanding cancer-related proteins in 3D with a case study focused on human papillomavirus.

scholarly journals RCSB Protein Data Bank: Sustaining A Living Digital Data Resource That Enables Breakthroughs In Scientific Research And Biomedical Education

2018 ◽  
Vol 32 (S1) ◽  
Author(s):  
Stephen K. Burley ◽  
Helen M. Berman ◽  
Cole Christie ◽  
Jose M. Duarte ◽  
Zukang Feng ◽  
...  
Keyword(s):  
Protein Data Bank ◽  
Scientific Research ◽  
Data Bank ◽  
Digital Data ◽  
Data Resource ◽  
Biomedical Education

scholarly journals Disordered Residues and Patterns in the Protein Data Bank

Molecules ◽  
2020 ◽  
Vol 25 (7) ◽  
pp. 1522 ◽  
Author(s):  
Mikhail Yu. Lobanov ◽  
Ilya V. Likhachev ◽  
Oxana V. Galzitskaya
Keyword(s):  
Amino Acids ◽  
Statistical Analysis ◽  
Protein Data Bank ◽  
Protein Structures ◽  
3D Structure ◽  
Data Bank ◽  
Disordered Regions

We created a new library of disordered patterns and disordered residues in the Protein Data Bank (PDB). To obtain such datasets, we clustered the PDB and obtained the groups of chains with different identities and marked disordered residues. We elaborated a new procedure for finding disordered patterns and created a new version of the library. This library includes three sets of patterns: unique patterns, patterns consisting of two kinds of amino acids, and homo-repeats. Using this database, the user can: (1) find homologues in the entire Protein Data Bank; (2) perform a statistical analysis of disordered residues in protein structures; (3) search for disordered patterns and homo-repeats; (4) search for disordered regions in different chains of the same protein; (5) download clusters of protein chains with different identity from our database and library of disordered patterns; and (6) observe 3D structure interactively using MView. A new library of disordered patterns will help improve the accuracy of predictions for residues that will be structured or unstructured in a given region.

scholarly journals RCSB Protein Data Bank: Sustaining a living digital data resource that enables breakthroughs in scientific research and biomedical education

2017 ◽  
Vol 27 (1) ◽  
pp. 316-330 ◽  
Author(s):  
Stephen K. Burley ◽  
Helen M. Berman ◽  
Cole Christie ◽  
Jose M. Duarte ◽  
Zukang Feng ◽  
...  
Keyword(s):  
Protein Data Bank ◽  
Scientific Research ◽  
Data Bank ◽  
Digital Data ◽  
Data Resource ◽  
Biomedical Education

scholarly journals RCSB Protein Data Bank: sustaining a living digital data resource that enables breakthroughs in scientific research and biomedical education

2018 ◽  
Vol 74 (a1) ◽  
pp. a118-a118
Author(s):  
Christine Zardecki ◽  
Helen M. Berman ◽  
Cole Christie ◽  
Jose M. Duarte ◽  
Zukang Feng ◽  
...  
Keyword(s):  
Protein Data Bank ◽  
Scientific Research ◽  
Data Bank ◽  
Digital Data ◽  
Data Resource ◽  
Biomedical Education

scholarly journals The accuracy of NMR protein structures in the Protein Data Bank

2021 ◽  
Author(s):  
Nicholas J Fowler ◽  
Adnan Sljoka ◽  
Mike P Williamson
Keyword(s):  
Protein Data Bank ◽  
Chemical Shifts ◽  
Protein Structures ◽  
Data Bank ◽  
Random Coil ◽  
Accuracy Analysis ◽  
Nmr Structures ◽  
Wide Range ◽  
Polypeptide Chains ◽  
Current Accuracy

We recently described a method, ANSURR, for measuring the accuracy of NMR protein structures. It is based on comparing residue-specific measures of rigidity from backbone chemical shifts via the random coil index, and from structures. Here, we report the use of ANSURR to analyse NMR ensembles within the Protein Data Bank (PDB). NMR structures cover a wide range of accuracy, which improved over time until about 2005, since when accuracy has not improved. Most structures have accurate secondary structure, but are too floppy, particularly in loops. There is a need for more experimental restraints in loops. The best current accuracy measures are Ramachandran distribution and number of NOE restraints per residue. The precision of structure ensembles correlates with accuracy, as does the number of hydrogen bond restraints per residue. If a structure contains additional components (such as additional polypeptide chains or ligands), then their inclusion improves accuracy. Analysis of over 7000 PDB NMR ensembles is available via our website ansurr.com.

4, 5-Dihydro-1H-Pyrazol-1-YL Derivatives as MAO-A Inhibitors with Antidepressant Profile: Insilico Design and Docking

2020 ◽  
Vol 8 (1) ◽  
pp. 42-45
Author(s):  
T. Rajkumar ◽  
◽  
S.V. Suresh Kumar ◽  
N. Srinivasan ◽  
◽  
...  
Keyword(s):  
Protein Data Bank ◽  
Amine Oxidase ◽  
Focal Point ◽  
Brain Activity ◽  
Research Work ◽  
Chemical Properties ◽  
Data Bank ◽  
Biological Data ◽  
Monoamine Oxidase A ◽  
Mao A

Mono Amine Oxidase-A will be a key controller for typical brain activity. It is a flavoenzyme which debases amines, for example, dopamine, norepinephrine, and serotonin, by means of oxidative deamination. Main focal point of the existent research work is to design, docking and biological screening of novel Pyrazole derivatives on MAO-A as Antidepressants. In our present study we used software’s like ACD chemsketch and biological data bases like Protein Data Bank (PDB). ACD/Chem sketch (v 14.00) to draw molecules, reactions and schematic diagrams, calculate chemical properties and design professional reports and presentation. Also it can produce SMILES notations to structure. Open babel converts SMILES to PDB file for docking. Receptors like 2z5x, 2z5y, 2bxs 6fvz, 2bxr are downloaded from the Protein Data Bank (PDB). Docking studies were performed on docking Server. In order to explore their binding mode and selectivity behavior, molecular docking in the active site of MAO-A was carried out for these derivatives. Analysis of the docked poses of the compounds showed that they adopt similar conformations to the highly selective MAO-A inhibitor. The docking pose of compound with 2BXR was confirmed by molecular dynamics. By all these in silico data, it can be confirmed that all the designed compounds are having drug like nature and suitable as drug candidates and extremely promising which on further assessments may provoke medicine particles against Monoamine oxidase A. Especially, C, N can be considered as potent and can be used to treat depression and anxiety.

Fault Tolerant Reliable Protocol (FTRP) Performance Evaluation in Wireless Sensor Networks: An Extensitive Study

2019 ◽  
pp. 1-36
Keyword(s):  
Wireless Sensor Networks ◽  
Fault Tolerance ◽  
Sensor Networks ◽  
Fault Tolerant ◽  
Cluster Head ◽  
Biological Data ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Good Performer ◽  
Wide Range

Fault Tolerant Reliable Protocol (FTRP) is proposed as a novel routing protocol designed for Wireless Sensor Networks (WSNs). FTRP offers fault tolerance reliability for packet exchange and support for dynamic network changes. The key concept used is the use of node logical clustering. The protocol delegates the routing ownership to the cluster heads where fault tolerance functionality is implemented. FTRP utilizes cluster head nodes along with cluster head groups to store packets in transient. In addition, FTRP utilizes broadcast, which reduces the message overhead as compared to classical flooding mechanisms. FTRP manipulates Time to Live values for the various routing messages to control message broadcast. FTRP utilizes jitter in messages transmission to reduce the effect of synchronized node states, which in turn reduces collisions. FTRP performance has been extensively through simulations against Ad-hoc On-demand Distance Vector (AODV) and Optimized Link State (OLSR) routing protocols. Packet Delivery Ratio (PDR), Aggregate Throughput and End-to-End delay (E-2-E) had been used as performance metrics. In terms of PDR and aggregate throughput, it is found that FTRP is an excellent performer in all mobility scenarios whether the network is sparse or dense. In stationary scenarios, FTRP performed well in sparse network; however, in dense network FTRP’s performance had degraded yet in an acceptable range. This degradation is attributed to synchronized nodes states. Reliably delivering a message comes to a cost, as in terms of E-2-E. results show that FTRP is considered a good performer in all mobility scenarios where the network is sparse. In sparse stationary scenario, FTRP is considered good performer, however in dense stationary scenarios FTRP’s E-2-E is not acceptable. There are times when receiving a network message is more important than other costs such as energy or delay. That makes FTRP suitable for wide range of WSNs applications, such as military applications by monitoring soldiers’ biological data and supplies while in battlefield and battle damage assessment. FTRP can also be used in health applications in addition to wide range of geo-fencing, environmental monitoring, resource monitoring, production lines monitoring, agriculture and animals tracking. FTRP should be avoided in dense stationary deployments such as, but not limited to, scenarios where high application response is critical and life endangering such as biohazards detection or within intensive care units.

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