BioShell 3.0: Library for Processing Structural Biology Data

Macnar, Joanna M.; Szulc, Natalia A; Krys, Justyna; Badaczewska-Dawid, Aleksandra E.; Gront, Dominik

doi:10.3390/biom10030461

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…PDB-formatted text input should be provided by a user, for which the server returns a predicted secondary structure as a single string in a 3-letter code. BioShell has included Python bindings since its last version, 3.0 [ 39 ]. Therefore, the Flask framework was used on the server-side to call the appropriate Python functions bound to the C++ library.…”

Section: Methodsmentioning

confidence: 99%

Automated Protein Secondary Structure Assignment from Cα Positions Using Neural Networks

2022

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The assignment of secondary structure elements in protein conformations is necessary to interpret a protein model that has been established by computational methods. The process essentially involves labeling the amino acid residues with H (Helix), E (Strand), or C (Coil, also known as Loop). When particular atoms are absent from an input protein structure, the procedure becomes more complicated, especially when only the alpha carbon locations are known. Various techniques have been tested and applied to this problem during the last forty years. The application of machine learning techniques is the most recent trend. This contribution presents the HECA classifier, which uses neural networks to assign protein secondary structure types. The technique exclusively employs Cα coordinates. The Keras (TensorFlow) library was used to implement and train the neural network model. The BioShell toolkit was used to calculate the neural network input features from raw coordinates. The study’s findings show that neural network-based methods may be successfully used to take on structure assignment challenges when only Cα trace is available. Thanks to the careful selection of input features, our approach’s accuracy (above 97%) exceeded that of the existing methods.

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Section: Methodsmentioning

confidence: 99%

Automated Protein Secondary Structure Assignment from Cα Positions Using Neural Networks

2022

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“…HEPES and MES molecules, together with a vicinity of 5 Å radius, were automatically extracted from PDB models released up to that day. The core::chemical module from BioShell 3.0 software package 29 has been used for this task. All structures solved using the PanDDA 30 method were rejected.…”

Section: Methodsmentioning

confidence: 99%

Analysis of protein structures containing HEPES and MES molecules

et al. 2022

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X‐ray crystallography is the main experimental method behind ligand–macromolecule complexes found in the Protein Data Bank (PDB). Applying bioinformatics methods to such structural data can fuel drug discovery, albeit under the condition that the information is correct. Regrettably, a small number of structures in the PDB are of suboptimal quality due to incorrectly identified and modeled ligands in protein–ligand complexes. In this paper, we combine a theoretical‐graph approach, nuclear density estimates, bioinformatics methods, and prior chemical knowledge to analyze two non‐physiological ligands, HEPES and MES, that are frequent components of crystallization and purifications buffers. Our analysis includes quantum mechanics calculations and Cambridge Structure Database (CSD) queries to define the ideal conformation of these ligands, geometry analysis of PDB deposits regarding several quality factors, and a search for homologous structures to identify other small molecules that could bind in place of the parasitic ligand. Our results highlight the need for careful refinement of macromolecule–ligand complexes and better validation tools that integrate results from all relevant resources.

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“…This is an example of analysis that can be performed for an ensemble of similar structures. Both maps were calculated using BioShell (Macnar et al, 2020).…”

Section: Case Study 1: Comparative Analysis Of Sars-cov-2 Plpro Structuresmentioning

confidence: 99%

Rapid response to emerging biomedical challenges and threats

Grabowski

Macnar

Cymborowski

et al. 2021

Int Union Crystallogr J

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As part of the global mobilization to combat the present pandemic, almost 100 000 COVID-19-related papers have been published and nearly a thousand models of macromolecules encoded by SARS-CoV-2 have been deposited in the Protein Data Bank within less than a year. The avalanche of new structural data has given rise to multiple resources dedicated to assessing the correctness and quality of structural data and models. Here, an approach to evaluate the massive amounts of such data using the resource https://covid19.bioreproducibility.org is described, which offers a template that could be used in large-scale initiatives undertaken in response to future biomedical crises. Broader use of the described methodology could considerably curtail information noise and significantly improve the reproducibility of biomedical research.

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BioShell 3.0: Library for Processing Structural Biology Data

Cited by 7 publications

References 16 publications

Automated Protein Secondary Structure Assignment from Cα Positions Using Neural Networks

Automated Protein Secondary Structure Assignment from Cα Positions Using Neural Networks

Analysis of protein structures containing HEPES and MES molecules

Rapid response to emerging biomedical challenges and threats

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