The Protein Data Bank (PDB) is the largest database of experimentally determined protein structures, containing more than 230 000 experimentally determined structures. The chemical reactivity of proteins is based on the electron density distribution, which is usually approximated by partial atomic charges. However, because of the size and high variability, there is not yet a universal and accurate tool for calculating the partial atomic charges of these structures. For this reason, we introduce the web application PDBCharges: a tool for quick calculation of partial atomic charges for protein structures from PDB. The charges are calculated using the recent semi-empirical quantum-mechanical method GFN1-xTB, which reproduces PBE0/TZVP/CM5 charges. The computed partial atomic charges can be downloaded in common data formats or visualized online via the powerful Mol* Viewer. The PDBCharges application is freely available at https://pdbcharges.biodata.ceitec.cz and has no login requirement.

• MeSH
• Databases, Protein * MeSH
• Internet MeSH
• Protein Conformation MeSH
• Quantum Theory MeSH
• Proteins * chemistry   MeSH
• Software * MeSH
• Static Electricity MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Proteins * MeSH

The AlphaFold2 prediction algorithm opened up the possibility of exploring proteins' structural space at an unprecedented scale. Currently, >200 million protein structures predicted by this approach are deposited in AlphaFoldDB, covering entire proteomes of multiple organisms, including humans. Predicted structures are, however, stored without detailed functional annotations describing their chemical behaviour. Partial atomic charges, which map electron distribution over a molecule and provide a clue to its chemical reactivity, are an important example of such data. We introduce the web application αCharges: a tool for the quick calculation of partial atomic charges for protein structures from AlphaFoldDB. The charges are calculated by the recent empirical method SQE+qp, parameterised for this class of molecules using robust quantum mechanics charges (B3LYP/6-31G*/NPA) on PROPKA3 protonated structures. The computed partial atomic charges can be downloaded in common data formats or visualised via the powerful Mol* viewer. The αCharges application is freely available at https://alphacharges.ncbr.muni.cz with no login requirement.

• MeSH
• Algorithms MeSH
• Protein Conformation MeSH
• Humans MeSH
• Proteins * chemistry   MeSH
• Proteome MeSH
• Software * MeSH
• Computational Biology * instrumentation   methods   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Proteins * MeSH
• Proteome MeSH

BACKGROUND: Partial atomic charges find many applications in computational chemistry, chemoinformatics, bioinformatics, and nanoscience. Currently, frequently used methods for charge calculation are the Electronegativity Equalization Method (EEM), Charge Equilibration method (QEq), and Extended QEq (EQeq). They all are fast, even for large molecules, but require empirical parameters. However, even these advanced methods have limitations-e.g., their application for peptides, proteins, and other macromolecules is problematic. An empirical charge calculation method that is promising for peptides and other macromolecular systems is the Split-charge Equilibration method (SQE) and its extension SQE+q0. Unfortunately, only one parameter set is available for these methods, and their implementation is not easily accessible. RESULTS: In this article, we present for the first time an optimized guided minimization method (optGM) for the fast parameterization of empirical charge calculation methods and compare it with the currently available guided minimization (GDMIN) method. Then, we introduce a further extension to SQE, SQE+qp, adapted for peptide datasets, and compare it with the common approaches EEM, QEq EQeq, SQE, and SQE+q0. Finally, we integrate SQE and SQE+qp into the web application Atomic Charge Calculator II (ACC II), including several parameter sets. CONCLUSION: The main contribution of the article is that it makes SQE methods with their parameters accessible to the users via the ACC II web application ( https://acc2.ncbr.muni.cz ) and also via a command-line application. Furthermore, our improvement, SQE+qp, provides an excellent solution for peptide datasets. Additionally, optGM provides comparable parameters to GDMIN in a markedly shorter time. Therefore, optGM allows us to perform parameterizations for charge calculation methods with more parameters (e.g., SQE and its extensions) using large datasets.

• Keywords
• Empirical methods, Parameterization, Partial atomic charges, Web service,
• Publication type
• Journal Article MeSH

Biomacromolecular structural data make up a vital and crucial scientific resource that has grown not only in terms of its amount but also in its size and complexity. Furthermore, these data are accompanied by large and increasing amounts of experimental data. Additionally, the macromolecular data are enriched with value-added annotations describing their biological, physicochemical and structural properties. Today, the scientific community requires fast and fully interactive web visualization to exploit this complex structural information. This article provides a survey of the available cutting-edge web services that address this challenge. Specifically, it focuses on data-delivery problems, discusses the visualization of a single structure, including experimental data and annotations, and concludes with a focus on the results of molecular-dynamics simulations and the visualization of structural ensembles.

• Keywords
• browser-based, data delivery, macromolecules, visualization, web-based,
• MeSH
• Internet * MeSH
• Macromolecular Substances chemistry   MeSH
• Computer Graphics * MeSH
• Software * MeSH
• User-Computer Interface * MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Macromolecular Substances MeSH