BiopLib and BiopTools—a C programming library and toolset for manipulating protein structure

Porter, Craig T.; Martin, Andrew C.R.

doi:10.1093/bioinformatics/btv482

Cited by 14 publications

(12 citation statements)

References 4 publications

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“…In order to calculate the properties of subsets of a protein surface, it has to be divided into fragments. The program from the BiopTools tool set ( Porter and Martin, 2015 ) was used to form overlapping surface patches from the protein surface.…”

Section: Methodsmentioning

confidence: 99%

IntPred: a structure-based predictor of protein–protein interaction sites

2017

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MotivationProtein–protein interactions are vital for protein function with the average protein having between three and ten interacting partners. Knowledge of precise protein–protein interfaces comes from crystal structures deposited in the Protein Data Bank (PDB), but only 50% of structures in the PDB are complexes. There is therefore a need to predict protein–protein interfaces in silico and various methods for this purpose. Here we explore the use of a predictor based on structural features and which exploits random forest machine learning, comparing its performance with a number of popular established methods.ResultsOn an independent test set of obligate and transient complexes, our IntPred predictor performs well (MCC = 0.370, ACC = 0.811, SPEC = 0.916, SENS = 0.411) and compares favourably with other methods. Overall, IntPred ranks second of six methods tested with SPPIDER having slightly better overall performance (MCC = 0.410, ACC = 0.759, SPEC = 0.783, SENS = 0.676), but considerably worse specificity than IntPred. As with SPPIDER, using an independent test set of obligate complexes enhanced performance (MCC = 0.381) while performance is somewhat reduced on a dataset of transient complexes (MCC = 0.303). The trade-off between sensitivity and specificity compared with SPPIDER suggests that the choice of the appropriate tool is application-dependent.Availability and implementationIntPred is implemented in Perl and may be downloaded for local use or run via a web server at www.bioinf.org.uk/intpred/.Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Methodsmentioning

confidence: 99%

IntPred: a structure-based predictor of protein–protein interaction sites

2017

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“…Thus, when a query structure with metal ion is submitted to STRIDER to access the accuracy of the modeled metal coordination, it reports the metal coordination number and distance profile of metal with its coordinating partners in the binding pocket. Many online and offline tools such as Cytoscape (Shannon et al, 2003), RING2.0 (Piovesan et al, 2016), RINalyzer (Doncheva et al, 2011), PLIP (Salentin et al, 2015), Arpeggio (Jubb et al, 2017), Ligdig (Fuller et al, 2015), GIANT (Kasahara and Kinoshita, 2014), PyMOL (www.pymol.org), Joy (Mizuguchi et al, 1998), Bioptools (Porter and Martin, 2015), PIC (Tina et al, 2007), CheckMyMetal (Zheng et al, 2017) and LIGPLOT+ (Laskowski and Swindells, 2011) are available to characterize favorable and unfavorable interactions of biomolecules, but, each with their own advantages and disadvantages. STRIDER can be a good addition to these resources by providing pairwise unfavorable contacts and metal interaction profiles for a range of conformers in a user-friendly interactive manner.…”

Section: Introductionmentioning

confidence: 99%

STRIDER: Steric hindrance estimator

Patro

Rathinavelan

2020

Preprint

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In silico modeling plays a vital role in the de novo designing and docking of biomacromolecules as well as in exploring their conformational dynamics. Additionally, it has a major role in acquiring the structural insights from the parameters derived from the experimental techniques such as cryo-electron microscopy. Steric hindrance is one of the important measures to validate the accuracy of the constructed model. A web user interface (WUI) namely, STRIDER (steric hindrance estimator) (www.iith.ac.in/strider/) can estimate and report pairwise inter-and intra-molecular steric hindrances using the van der Waals radius of 117 elements through a user interactive interface. STRIDER also identifies and reports the coordination number of 64 metals along with their interacting pattern in an interactive mode. STRIDER can analyze an ensemble of conformers, wherein, multiple conformers are used to circumvent sampling issue in flexible docking, understand protein folding and facilitate structure based virtual screening. Further, it generates a pymol session file that can be used for offline analysis. As STRIDER simply requires the Cartesian coordinates of the given molecule in protein data bank format, any chemical structure can be an input. AvailabilityIt can be freely accessible through: www.iith.ac.in/strider/ without any registration.

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“…BALL [9] is another extensive library for algorithms. Bio-pLib is a C programming library for loading and manipulating macromolecular structures [10]. Finally, most of the parsers focus on the coordinate data explicitly and none describe handling of the header information especially REMARK section present in the .pdb file.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, most of the parsers focus on the coordinate data explicitly and none describe handling of the header information especially REMARK section present in the .pdb file. However BiopLib [10] does provide substantial parsing of the header information which is generally neglected by other parsers.…”

Section: Introductionmentioning

confidence: 99%

Parsers, Data Structures and Algorithms for Macromolecular Analysis Toolkit (MAT): Design and Implementation

Kalyan

Junghare

et al. 2019

Preprint

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The structural information of biological macromolecules are stored in .pdb, .mmcif and lately mmtf files and thus it requires accurate and efficient biological tools for various utilities. Here, we describe Macromolecular Analysis Toolkit (MAT) that parses .pdb, .mmcif and .mmtf files; and builds data structures from the input. This original program is written in C++ programming language to ensure efficiency and consistency to organize structural information in an integral way. The novelty of the program lies in the addition of new structure-based biological algorithms and applications. This package also stands out from other similar libraries by being 1) faster and 2) accurate. We also provide detailed comparison of available parsers on the whole PDB database. The parser of MAT is designed in such a way that it allows quick extraction and organized loading of the core data structure. The same data structure is extended to accommodate information from the .mmcif and .mmtf file parsers. Tokenization of the data allows the extraction of information from disordered text, making it compatible for accurate identification of the entities present in the .pdb file. Additionally, we add a new approach of performance optimization by creating a few derived data structures, namely kD-Tree, Octree and graphs, for certain applications that need spatial coordinate calculations. MAT provides advanced data structure which is time efficient and is designed to avail reusability and consistency in * Corresponding a systematic framework. MAT parser can be accessed online through bitbucket at https://bitbucket.org/gazalk/pdb_parser/.

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BiopLib and BiopTools—a C programming library and toolset for manipulating protein structure

Cited by 14 publications

References 4 publications

IntPred: a structure-based predictor of protein–protein interaction sites

IntPred: a structure-based predictor of protein–protein interaction sites

STRIDER: Steric hindrance estimator

Parsers, Data Structures and Algorithms for Macromolecular Analysis Toolkit (MAT): Design and Implementation

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