Automatic recognition of ligands in electron density by machine learning

Kowiel, Marcin; Brzeziński, Dariusz; Porebski, P.J.; Shabalin, I.G.; Jaskólski, Mariusz; Minor, W.

doi:10.1093/bioinformatics/bty626

Cited by 27 publications

(32 citation statements)

References 41 publications

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“…Artificial intelligence or machine learning technologies could certainly help to disentangle these difficult ion assignment issues provided that their algorithms are nurtured by sound data (Kowiel et al 2018). At least, such techniques may help to recognize that current structural databases are far from an error free state and suggest reprocessing some of the underlying experimental data.…”

Section: Summary and Concluding Remarksmentioning

confidence: 99%

Nucleobase carbonyl groups are poor Mg²⁺inner-sphere binders but excellent monovalent ion binders—a critical PDB survey

Leonarski

D’Ascenzo

Auffinger

2018

RNA

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Precise knowledge of Mg 2+ inner-sphere binding site properties is vital for understanding the structure and function of nucleic acid systems. Unfortunately, the PDB, which represents the main source of Mg 2+ binding sites, contains a substantial number of assignment issues that blur our understanding of the functions of these ions. Here, following a previous study devoted to Mg 2+ binding to nucleobase nitrogens, we surveyed nucleic acid X-ray structures from the PDB with resolutions ≤2.9 Å to classify the Mg 2+ inner-sphere binding patterns to nucleotide carbonyl, ribose hydroxyl, cyclic ether, and phosphodiester oxygen atoms. From this classification, we derived a set of "prior-knowledge" nucleobase Mg 2+ binding sites. We report that crystallographic examples of trustworthy nucleobase Mg 2+ binding sites are fewer than expected since many of those are associated with misidentified Na + or K +. We also emphasize that binding of Na + and K + to nucleic acids is much more frequent than anticipated. Overall, we provide evidence derived from X-ray structures that nucleobases are poor inner-sphere binders for Mg 2+ but good binders for monovalent ions. Based on strict stereochemical criteria, we propose an extended set of guidelines designed to help in the assignment and validation of ions directly contacting nucleobase and ribose atoms. These guidelines should help in the interpretation of X-ray and cryo-EM solvent density maps. When borderline Mg 2+ stereochemistry is observed, alternative placement of Na + , K + , or Ca 2+ must be considered. We also critically examine the use of lanthanides (Yb 3+ , Tb 3+) as Mg 2+ substitutes in crystallography experiments.

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Section: Summary and Concluding Remarksmentioning

confidence: 99%

Nucleobase carbonyl groups are poor Mg²⁺inner-sphere binders but excellent monovalent ion binders—a critical PDB survey

Leonarski

D’Ascenzo

Auffinger

2018

RNA

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“…The proposed P Q1 (t,d) and P Q1 (t) measures manifest the overall attitude of authors toward the quality of the PDB: Each next deposited structure should be better than the average previous deposit. Each new quality metric [20], visualization technique [31], set of restraints [32], validation algorithm [33], hardware improvement [34], or software update [35] makes it easier to produce good quality structures and to avoid simple mistakes. In an effort to promote constant improvement of overall PDB quality, it would be a desirable ideal to expect that newly added models are above the current average.…”

Section: Discussionmentioning

confidence: 99%

On the evolution of the quality of macromolecular models in the PDB

et al. 2020

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Crystallographic models of biological macromolecules have been ranked using the quality criteria associated with them in the Protein Data Bank (PDB). The outcomes of this quality analysis have been correlated with time and with the journals that published papers based on those models. The results show that the overall quality of PDB structures has substantially improved over the last ten years, but this period of progress was preceded by several years of stagnation or even depression. Moreover, the study shows that the historically observed negative correlation between journal impact and the quality of structural models presented therein seems to disappear as time progresses.

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“…Similarly, a software tool for automatic recognition of ligands in electron density by machine learning 32 has revealed multiple structures with incorrectly modeled ligands. We have rerefined four sample structures, discussed the changes with the original authors, and deposited all four corrected structures in the PDB to replace the original entries.…”

Section: Usage Of Diffraction Data From Public Repositories In Improvmentioning

confidence: 99%

The Integrated Resource for Reproducibility in Macromolecular Crystallography: Experiences of the first four years

Grabowski

Cymborowski

Porebski

et al. 2019

Structural Dynamics

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It has been increasingly recognized that preservation and public accessibility of primary experimental data are cornerstones necessary for the reproducibility of empirical sciences. In the field of molecular crystallography, many journals now recommend that authors of manuscripts presenting a new crystal structure should deposit their primary experimental data (X-ray diffraction images) to one of the dedicated resources created in recent years. Here, we describe our experiences developing the Integrated Resource for Reproducibility in Molecular Crystallography (IRRMC) and describe several examples of a crucial role that diffraction data can play in improving previously determined protein structures. In its first four years, several hundred crystallographers have deposited data from over 5200 diffraction experiments performed at over 60 different synchrotron beamlines or home sources all over the world. In addition to improving the resource and curating submitted data, we have been building a pipeline for extraction or, in some cases, reconstruction of the metadata necessary for seamless automated processing. Preliminary analysis indicates that about 95% of the archived data can be automatically reprocessed. A high rate of reprocessing success shows the feasibility of using the automated metadata extraction and automated processing as a validation step for the deposition of raw diffraction images. The IRRMC is guided by the Findable, Accessible, Interoperable, and Reusable data management principles.

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Automatic recognition of ligands in electron density by machine learning

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 27 publications

References 41 publications

Nucleobase carbonyl groups are poor Mg²⁺inner-sphere binders but excellent monovalent ion binders—a critical PDB survey

Nucleobase carbonyl groups are poor Mg²⁺inner-sphere binders but excellent monovalent ion binders—a critical PDB survey

On the evolution of the quality of macromolecular models in the PDB

The Integrated Resource for Reproducibility in Macromolecular Crystallography: Experiences of the first four years

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Automatic recognition of ligands in electron density by machine learning

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 27 publications

References 41 publications

Nucleobase carbonyl groups are poor Mg2+inner-sphere binders but excellent monovalent ion binders—a critical PDB survey

Nucleobase carbonyl groups are poor Mg2+inner-sphere binders but excellent monovalent ion binders—a critical PDB survey

On the evolution of the quality of macromolecular models in the PDB

The Integrated Resource for Reproducibility in Macromolecular Crystallography: Experiences of the first four years

Contact Info

Product

Resources

About

Nucleobase carbonyl groups are poor Mg²⁺inner-sphere binders but excellent monovalent ion binders—a critical PDB survey

Nucleobase carbonyl groups are poor Mg²⁺inner-sphere binders but excellent monovalent ion binders—a critical PDB survey