A chemical interpretation of protein electron density maps in the worldwide protein data bank

Abstract

High-quality three-dimensional structural data is of great value for the functional interpretation of biomacromolecules, especially proteins; however, structural quality varies greatly across the entries in the worldwide Protein Data Bank (wwPDB). Since 2008, the wwPDB has required the inclusion of structure factors with the deposition of x-ray crystallographic structures to support the independent evaluation of structures with respect to the underlying experimental data used to derive those structures. However, interpreting the discrepancies between the structural model and its underlying electron density data is difficult, since derived electron density maps use arbitrary electron density units which are inconsistent between maps from different wwPDB entries. Therefore, we have developed a method that converts electron density values into units of electrons. With this conversion, we have developed new methods that can evaluate specific regions of an x-ray crystallographic structure with respect to a physicochemical interpretation of its corresponding electron 2 density map. We have systematically compared all deposited x-ray crystallographic protein models in the wwPDB with their underlying electron density maps, if available, and characterized the electron density in terms of expected numbers of electrons based on the structural model. The methods generated coherent evaluation metrics throughout all PDB entries with associated electron density data, which are consistent with visualization software that would normally be used for manual quality assessment. To our knowledge, this is the first attempt to derive units of electrons directly from electron density maps without the aid of the underlying structure factors. These new metrics are biochemically-informative and can be extremely useful for filtering out low-quality structural regions from inclusion into systematic analyses that span large numbers of PDB entries. Furthermore, these new metrics will improve the ability of non-crystallographers to evaluate regions of interest within PDB entries, since only the PDB structure and the associated electron density maps are needed. These new methods are available as a well-documented Python package on GitHub and the Python Package Index under a modified Clear BSD open source license. Author summaryElectron density maps are very useful for validating the x-ray structure models in the Protein Data Bank (PDB). However, it is often daunting for non-crystallographers to use electron density maps, as it requires a lot of prior knowledge. This study provides methods that can infer chemical information solely from the electron density maps available from the PDB to interpret the electron density and electron density discrepancy values in terms of units of electrons. It also provides methods to evaluate regions of interest in terms of the number of missing or excessing electrons, so that a broader audience, such as biologists or bioinformaticians, can also make better use of the electron density information available in the ...

Finding High-Quality Metal Ion-Centric Regions Across the Worldwide Protein Data Bank

“…Their electron density data, if available, were acquired from the PDBe website. We used version 1.0 of the pdb-eda Python package [19] to analyze all downloaded PDB entries and matching electron density maps. Metal ions were detected across these PDB entries and filtered against four major quality control criteria: Electron density resolution less than or equal to 2.5 Å;Atom occupancy greater than or equal to 0.9;No symmetry atoms within 3.5 Å;The sum of discrepant electrons within a 3.5 Å region surrounding the metal ion point position is less than the data-derived cutoff.…”

“…Still, this electron density evaluation of regional structural quality has been a tedious process done by manual visual inspection, without objective metrics of quality. To alleviate these shortcomings in electron density evaluation, we have developed new analysis and evaluation methods in a Python package called pdb-eda [19], which facilitate the systematic quality control of protein structural regions of interest across large numbers of wwPDB entries and their corresponding electron density maps. In this study, we apply pdb-eda to a systematic electron density analysis of all metal binding sites containing a bound metal ion.…”

Finding high-quality metal ion-centric regions across the worldwide Protein Data Bank

“…Their electron density data, if available, was acquired from the PDBe website. We used version 1.0 of the pdb-eda Python package [18] to analyze all downloaded PDB entries and matching electron density maps. Metal ions were detected across these PDB entries and filtered against four major quality control criteria:…”

“…But this electron density evaluation of regional structural quality has been a tedious process done by manual visual inspection, without objective metrics of quality. To alleviate these shortcomings in electron density evaluation, we have developed new analysis and evaluation methods in a Python package called pdb-eda [18] that facilitate the systematic quality control of protein structural regions of interest across large numbers of wwPDB entries and their corresponding electron density maps. In this study, we apply pdb-eda to a systematic electron density analysis of all metal binding sites containing a bound metal ion.…”

Deep learning enables the design of functionalde novoantimicrobial proteins

“…Deep-learning (DL) strategies are neural networks with internal processing layers that can be trained to recognize patterns in large and complex data. DL strategies have been used for various protein applications, including the prediction of protein secondary structure and subcellular localization 19,20 ; the prediction of protein contact maps, homology and stability 20 ; protein design, such as the prediction of protein sequences based on protein structures 21 and the design of metalloproteins 22 ; and the prediction of protein folding 23,24 , among several other applications [25][26][27][28][29][30] . It is therefore of great interest to develop new tools that can accurately predict new protein sequence-structure relationships.…”