IMRPS: Inserted and Modified Residues in Protein Structures. A database

2020 ◽  
Vol 53 (2) ◽  
pp. 569-573
Author(s):  
R. Santhosh ◽  
Namrata Bankoti ◽  
M. Gurudarshan ◽  
J. Jeyakanthan ◽  
K. Sekar
Keyword(s):  
User Interface ◽  
Protein Structure ◽  
Protein Data Bank ◽  
Graphical User Interface ◽  
Protein Structures ◽  
Quality Criteria ◽  
Data Bank ◽  
Biological Processes ◽  
Online Database ◽  
Post Translational Modifications

Modified residues present in proteins are the result of post-translational modifications (PTMs). These PTMs increase the functional diversity of the proteome and influence various biological processes and diseased conditions. Therefore, identification and understanding of PTMs in various protein structures is of great significance. In view of this, an online database, Inserted and Modified Residues in Protein Structures (IMRPS), has been developed. IMRPS is a derived database that furnishes information on the residues modified and inserted in the protein structures available in the Protein Data Bank (PDB). The database is equipped with a graphical user interface and has an option to view the data for non-redundant protein structures (25 and 90%) as well. A quality criteria cutoff has been incorporated to assist in displaying the specific set of PDB codes. The entire protein structure along with the inserted or modified residues can be visualized in JSmol. This database will be updated regularly (presently, every three months) and can be accessed through the URL http://cluster.physics.iisc.ac.in/imrps/.

TOP: a new method for protein structure comparisons and similarity searches

2000 ◽  
Vol 33 (1) ◽  
pp. 176-183 ◽  
Author(s):  
Guoguang Lu
Keyword(s):  
User Interface ◽  
Protein Structure ◽  
Protein Structures ◽  
Three Dimensional ◽  
Data Bank ◽  
Structure Alignment ◽  
Dimensional Structure ◽  
Protein Structure Alignment ◽  
Protein Structure Analysis ◽  
Structure Comparison

In order to facilitate the three-dimensional structure comparison of proteins, software for making comparisons and searching for similarities to protein structures in databases has been developed. The program identifies the residues that share similar positions of both main-chain and side-chain atoms between two proteins. The unique functions of the software also include database processingviaInternet- and Web-based servers for different types of users. The developed method and its friendly user interface copes with many of the problems that frequently occur in protein structure comparisons, such as detecting structurally equivalent residues, misalignment caused by coincident match of Cαatoms, circular sequence permutations, tedious repetition of access, maintenance of the most recent database, and inconvenience of user interface. The program is also designed to cooperate with other tools in structural bioinformatics, such as the 3DB Browser software [Prilusky (1998).Protein Data Bank Q. Newslett.84, 3–4] and the SCOP database [Murzin, Brenner, Hubbard & Chothia (1995).J. Mol. Biol.247, 536–540], for convenient molecular modelling and protein structure analysis. A similarity ranking score of `structure diversity' is proposed in order to estimate the evolutionary distance between proteins based on the comparisons of their three-dimensional structures. The function of the program has been utilized as a part of an automated program for multiple protein structure alignment. In this paper, the algorithm of the program and results of systematic tests are presented and discussed.

scholarly journals Iditis: Protein Structure Database

1998 ◽  
Vol 54 (6) ◽  
pp. 1071-1077 ◽  
Author(s):  
Stephen Gardner ◽  
Janet Thornton
Keyword(s):  
User Interface ◽  
Protein Structure ◽  
Experimental Design ◽  
Graphical User Interface ◽  
Molecular Interactions ◽  
Protein Structures ◽  
Structure Database ◽  
Wide Range ◽  
Database Engine

The validation, enrichment and organization of the data stored in PDB files is essential for those data to be used accurately and efficiently for modelling, experimental design and the determination of molecular interactions. TheIditisprotein structure database has been designed to allow the widest possible range of queries to be performed across all available protein structures. TheIditisdatabase is the most comprehensive protein structure resource currently available, and contains over 500 fields of information describing all publicly deposited protein structures. A custom-written database engine and graphical user interface provide a natural and simple environment for the construction of searches for complex sequence- and structure-based motifs. Extensions and specialized interfaces allow the data generated by the database to used in conjunction with a wide range of applications.

scholarly journals Enriched Conformational Sampling of DNA and Proteins with a Hybrid Hamiltonian Derived from the Protein Data Bank

2018 ◽  
Vol 19 (11) ◽  
pp. 3405 ◽  
Author(s):  
Emanuel Peter ◽  
Jiří Černý
Keyword(s):  
Partition Function ◽  
Protein Data Bank ◽  
Protein Structures ◽  
Data Bank ◽  
Weighting Factor ◽  
Potential Of Mean Force ◽  
Conformational Space ◽  
Dynamics Simulation ◽  
Conformational Sampling ◽  
Speed Increase

In this article, we present a method for the enhanced molecular dynamics simulation of protein and DNA systems called potential of mean force (PMF)-enriched sampling. The method uses partitions derived from the potentials of mean force, which we determined from DNA and protein structures in the Protein Data Bank (PDB). We define a partition function from a set of PDB-derived PMFs, which efficiently compensates for the error introduced by the assumption of a homogeneous partition function from the PDB datasets. The bias based on the PDB-derived partitions is added in the form of a hybrid Hamiltonian using a renormalization method, which adds the PMF-enriched gradient to the system depending on a linear weighting factor and the underlying force field. We validated the method using simulations of dialanine, the folding of TrpCage, and the conformational sampling of the Dickerson–Drew DNA dodecamer. Our results show the potential for the PMF-enriched simulation technique to enrich the conformational space of biomolecules along their order parameters, while we also observe a considerable speed increase in the sampling by factors ranging from 13.1 to 82. The novel method can effectively be combined with enhanced sampling or coarse-graining methods to enrich conformational sampling with a partition derived from the PDB.

scholarly journals PDBrenum: a webserver and program providing Protein Data Bank files renumbered according to their UniProt sequences

2021 ◽  
Author(s):  
Bulat Faezov ◽  
Roland L. Dunbrack
Keyword(s):  
Protein Data Bank ◽  
Data Bank ◽  
Post Translational Modifications ◽  
X Ray ◽  
X Ray Crystallography ◽  
Link Type ◽  
Binding Partners ◽  
Cryo Electron Microscopy ◽  
Comparative Structure ◽  
In The Beginning

AbstractThe Protein Data Bank (PDB) was established at Brookhaven National Laboratories in 1971 as an archive for biological macromolecular crystal structures. In the beginning the archive held only seven structures but in early 2021, the database has more than 170,000 structures solved by X-ray crystallography, nuclear magnetic resonance, cryo-electron microscopy, and other methods. Many proteins have been studied under different conditions (e.g., binding partners such as ligands, nucleic acids, or other proteins; mutations and post-translational modifications), thus enabling comparative structure-function studies. However, these studies are made more difficult because authors are allowed by the PDB to number the amino acids in each protein sequence in any manner they wish. This results in the same protein being numbered differently in the available PDB entries. In addition to the coordinates, there are many fields that contain information regarding specific residues in the sequence of each protein in the entry. Here we provide a webserver and Python3 application that fixes the PDB sequence numbering problem by replacing the author numbering with numbering derived from the corresponding UniProt sequences. We obtain this correspondence from the SIFTS database from PDBe. The server and program can take a list of PDB entries and provide renumbered files in mmCIF format and the legacy PDB format for both asymmetric unit files and biological assembly files provided by PDBe. The server can also take a list of UniProt identifiers (“P04637” or “P53_HUMAN”) and return the desired files.AvailabilitySource code is freely available at https://github.com/Faezov/PDBrenum. The webserver is located at: http://dunbrack3.fccc.edu/[email protected] or [email protected].

scholarly journals Conformational variability in proteins bound to single-stranded DNA: a new benchmark for new docking perspectives

2021 ◽  
Author(s):  
Dominique MIAS-LUCQUIN ◽  
Isaure Chauvot de Beauchêne
Keyword(s):  
Protein Data Bank ◽  
Conformational Changes ◽  
Molecular Interactions ◽  
Protein Structures ◽  
Data Bank ◽  
Computational Docking ◽  
Ssdna Binding ◽  
Conformational Variability ◽  
High Flexibility ◽  
Docking Benchmark

We explored the Protein Data-Bank (PDB) to collect protein-ssDNA structures and create a multi-conformational docking benchmark including both bound and unbound protein structures. Due to ssDNA high flexibility when not bound, no ssDNA unbound structure is included. For the 143 groups identified as bound-unbound structures of the same protein , we studied the conformational changes in the protein induced by the ssDNA binding. Moreover, based on several bound or unbound protein structures in some groups, we also assessed the intrinsic conformational variability in either bound or unbound conditions, and compared it to the supposedly binding-induced modifications. This benchmark is, to our knowledge, the first attempt made to peruse available structures of protein – ssDNA interactions to such an extent, aiming to improve computational docking tools dedicated to this kind of molecular interactions.

scholarly journals RepeatsDB in 2021: improved data and extended classification for protein tandem repeat structures

2020 ◽  
Vol 49 (D1) ◽  
pp. D452-D457
Author(s):  
Lisanna Paladin ◽  
Martina Bevilacqua ◽  
Sara Errigo ◽  
Damiano Piovesan ◽  
Ivan Mičetić ◽  
...  
Keyword(s):  
Protein Data Bank ◽  
Tandem Repeat ◽  
Tandem Repeats ◽  
Classification Scheme ◽  
Sequence Similarity ◽  
Protein Structures ◽  
Hierarchical Classification ◽  
Structural Similarity ◽  
Data Bank ◽  
Similarity Class

Abstract The RepeatsDB database (URL: https://repeatsdb.org/) provides annotations and classification for protein tandem repeat structures from the Protein Data Bank (PDB). Protein tandem repeats are ubiquitous in all branches of the tree of life. The accumulation of solved repeat structures provides new possibilities for classification and detection, but also increasing the need for annotation. Here we present RepeatsDB 3.0, which addresses these challenges and presents an extended classification scheme. The major conceptual change compared to the previous version is the hierarchical classification combining top levels based solely on structural similarity (Class > Topology > Fold) with two new levels (Clan > Family) requiring sequence similarity and describing repeat motifs in collaboration with Pfam. Data growth has been addressed with improved mechanisms for browsing the classification hierarchy. A new UniProt-centric view unifies the increasingly frequent annotation of structures from identical or similar sequences. This update of RepeatsDB aligns with our commitment to develop a resource that extracts, organizes and distributes specialized information on tandem repeat protein structures.

scholarly journals Accurate Representation of Protein-Ligand Structural Diversity in the Protein Data Bank (PDB)

2020 ◽  
Vol 21 (6) ◽  
pp. 2243
Author(s):  
Nicolas K. Shinada ◽  
Peter Schmidtke ◽  
Alexandre G. de Brevern
Keyword(s):  
Protein Data Bank ◽  
Protein Sequence ◽  
Large Scale ◽  
Protein Structures ◽  
Structural Diversity ◽  
Data Bank ◽  
Protein Distribution ◽  
Research Areas ◽  
Identity Threshold ◽  
Protein Sequence Identity

The number of available protein structures in the Protein Data Bank (PDB) has considerably increased in recent years. Thanks to the growth of structures and complexes, numerous large-scale studies have been done in various research areas, e.g., protein–protein, protein–DNA, or in drug discovery. While protein redundancy was only simply managed using simple protein sequence identity threshold, the similarity of protein-ligand complexes should also be considered from a structural perspective. Hence, the protein-ligand duplicates in the PDB are widely known, but were never quantitatively assessed, as they are quite complex to analyze and compare. Here, we present a specific clustering of protein-ligand structures to avoid bias found in different studies. The methodology is based on binding site superposition, and a combination of weighted Root Mean Square Deviation (RMSD) assessment and hierarchical clustering. Repeated structures of proteins of interest are highlighted and only representative conformations were conserved for a non-biased view of protein distribution. Three types of cases are described based on the number of distinct conformations identified for each complex. Defining these categories decreases by 3.84-fold the number of complexes, and offers more refined results compared to a protein sequence-based method. Widely distinct conformations were analyzed using normalized B-factors. Furthermore, a non-redundant dataset was generated for future molecular interactions analysis or virtual screening studies.

scholarly journals Detection oftrans–cisflips and peptide-plane flips in protein structures

2015 ◽  
Vol 71 (8) ◽  
pp. 1604-1614 ◽  
Author(s):  
Wouter G. Touw ◽  
Robbie P. Joosten ◽  
Gert Vriend
Keyword(s):  
Structure Function ◽  
Web Service ◽  
Protein Data Bank ◽  
Protein Structures ◽  
Data Bank ◽  
Peptide Bond ◽  
Peptide Bonds ◽  
Unknown Peptide ◽  
Peptide Plane ◽  
Service Interface

A coordinate-based method is presented to detect peptide bonds that need correction either by a peptide-plane flip or by atrans–cisinversion of the peptide bond. When applied to the whole Protein Data Bank, the method predicts 4617trans–cisflips and many thousands of hitherto unknown peptide-plane flips. A few examples are highlighted for which a correction of the peptide-plane geometry leads to a correction of the understanding of the structure–function relation. All data, including 1088 manually validated cases, are freely available and the method is available from a web server, a web-service interface and throughWHAT_CHECK.

scholarly journals Real time structural search of the Protein Data Bank

2019 ◽  
Author(s):  
Dmytro Guzenko ◽  
Stephen K. Burley ◽  
Jose M. Duarte
Keyword(s):  
Real Time ◽  
Protein Data Bank ◽  
Electron Density ◽  
Polypeptide Chain ◽  
Protein Structures ◽  
Data Bank ◽  
Zernike Moment ◽  
Search Problem ◽  
Mathematical Tool ◽  
Protein Assemblies

AbstractDetection of protein structure similarity is a central challenge in structural bioinformatics. Comparisons are usually performed at the polypeptide chain level, however the functional form of a protein within the cell is often an oligomer. This fact, together with recent growth of oligomeric structures in the Protein Data Bank (PDB), demands more efficient approaches to oligomeric assembly alignment/retrieval. Traditional methods use atom level information, which can be complicated by the presence of topological permutations within a polypeptide chain and/or subunit rearrangements. These challenges can be overcome by comparing electron density volumes directly. But, brute force alignment of 3D data is a compute intensive search problem. We developed a 3D Zernike moment normalization procedure to orient electron density volumes and assess similarity with unprecedented speed. Similarity searching with this approach enables real-time retrieval of proteins/protein assemblies resembling a target, from PDB or user input, together with resulting alignments (http://shape.rcsb.org).Author SummaryProtein structures possess wildly varied shapes, but patterns at different levels are frequently reused by nature. Finding and classifying these similarities is fundamental to understand evolution. Given the continued growth in the number of known protein structures in the Protein Data Bank, the task of comparing them to find the common patterns is becoming increasingly complicated. This is especially true when considering complete protein assemblies with several polypeptide chains, where the large sizes further complicate the issue. Here we present a novel method that can detect similarity between protein shapes and that works equally fast for any size of proteins or assemblies. The method looks at proteins as volumes of density distribution, departing from what is more usual in the field: similarity assessment based on atomic coordinates and chain connectivity. A volumetric function is amenable to be decomposed with a mathematical tool known as 3D Zernike polynomials, resulting in a compact description as vectors of Zernike moments. The tool was introduced in the 1990s, when it was suggested that the moments could be normalized to be invariant to rotations without losing information. Here we demonstrate that in fact this normalization is possible and that it offers a much more accurate method for assessing similarity between shapes, when compared to previous attempts.

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