Classification of RNA backbone conformations into rotamers using <sup>13</sup>C′ chemical shifts: exploring how far we can go

Icazatti, Alejandro A.; Loyola, Juan Martín; Szleifer, Igal; Vila, Jorge A.; Martin, Osvaldo A.

doi:10.7717/peerj.7904

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…S3). The absence of water molecules in the SCAL model reveals a few biases where, for example, the syn conformation is more populated than expected as compared with the experimental or the ideal values collected from the experimental structures of nucleic acids (63, 64). The SCALW model is also biased, and the STDW but to a lesser extent; only the FULLW model is exempted.…”

Section: Resultsmentioning

confidence: 92%

MCSS-based Predictions of Binding Mode and Selectivity of Nucleotide Ligands

González-Alemán

Chevrollier

Simões³

et al. 2019

Preprint

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Computational fragment-based approaches have been widely used in drug design and drug discovery. One of the limitations for their application is the lack of performance of the scoring functions. With the emergence of new fragment-based approaches for single-stranded RNA ligands, we propose an analysis of the docking power of an MCSS-based approach evaluated on nucleotide binding sites. Combined with a clustering of MCSS-generated poses and some state-of-the-art scoring functions, the results suggest that it could be used in the design of oligonucleotides. MCSS | ligand binding | docking | scoring | protein-nucleotide interactions | FBDCorrespondence: fabrice.leclerc@i2bc.paris-saclay.fr

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Section: Resultsmentioning

confidence: 92%

MCSS-based Predictions of Binding Mode and Selectivity of Nucleotide Ligands

González-Alemán

Chevrollier

Simões³

et al. 2019

Preprint

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“…The absence of water molecules in the SCAL model reveals a few biases where, for example, the syn conformation is more populated than expected as compared with the experimental or the ideal values collected from the experimental structures of nucleic acids. 75,76 The SCALW model is also biased, and the STDW but to a lesser extent; only the FULLW model is exempted. Another common bias in models (except for the FULLW model) is the over-representation of the C2'-endo conformation for the ribose while the initial conformation is always a C3'-endo conformation.…”

Section: Docking Powermentioning

confidence: 99%

MCSS-Based Predictions of Binding Mode and Selectivity of Nucleotide Ligands

González-Alemán

Chevrollier

Simões³

et al. 2021

J. Chem. Theory Comput.

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Computational fragment-based approaches are widely used in drug design and discovery. One of their limitations is the lack of performance of docking methods, mainly the scoring functions. With the emergence of fragment-based approaches for singlestranded RNA ligands, we analyze the performance in docking and screening powers of an MCSS-based approach. The performance is evaluated on a benchmark of proteinnucleotide complexes where the four RNA residues are used as fragments. The screening power can be considered the major limiting factor for the fragment-based modeling or design of sequence-selective oligonucleotides. We show that the MCSS sampling is efficient even for such large and flexible fragments. Hybrid solvent models based on some partial explicit representation improve both the docking and screening powers.

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“…Ref. [323] uses a range of ML methods to model both DFT and experimental data in order to classify RNA conformations. Another common usage of DFT is the prediction of nuclear magnetic resonance shifts (NMR) in order to support experimental diagnostics.…”

Section: Magnetic Thermal and Energetic Propertiesmentioning

confidence: 99%

A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry

Fiedler¹,

Shah²,

Bußmann³

et al. 2021

Preprint

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With the growth of computational resources, the scope of electronic structure simulations has increased greatly. Artificial intelligence and robust data analysis hold the promise to accelerate large-scale simulations and their analysis to hitherto unattainable scales. Machine learning is a rapidly growing field for the processing of such complex datasets. It has recently gained traction in the domain of electronic structure simulations, where density functional theory takes the prominent role of the most widely used electronic structure method. Thus, DFT calculations represent one of the largest loads on academic high-performance computing systems across the world. Accelerating these with machine learning can reduce the resources required and enables simulations of larger systems. Hence, the combination of density functional theory and machine learning has the potential to rapidly advance electronic structure applications such as in-silico materials discovery and the search for new chemical reaction pathways. We provide the theoretical background of both density functional theory and machine learning on a generally accessible level. This serves as the basis of our comprehensive review including research articles up to December 2020 in chemistry and materials science that employ machine-learning techniques. In our analysis, we categorize the body of research into main threads and extract impactful results. We conclude our review with an outlook on exciting research directions in terms of a citation analysis.

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Classification of RNA backbone conformations into rotamers using ¹³C′ chemical shifts: exploring how far we can go

Cited by 5 publications

References 22 publications

MCSS-based Predictions of Binding Mode and Selectivity of Nucleotide Ligands

MCSS-based Predictions of Binding Mode and Selectivity of Nucleotide Ligands

MCSS-Based Predictions of Binding Mode and Selectivity of Nucleotide Ligands

A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry

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Classification of RNA backbone conformations into rotamers using 13C′ chemical shifts: exploring how far we can go

Cited by 5 publications

References 22 publications

MCSS-based Predictions of Binding Mode and Selectivity of Nucleotide Ligands

MCSS-based Predictions of Binding Mode and Selectivity of Nucleotide Ligands

MCSS-Based Predictions of Binding Mode and Selectivity of Nucleotide Ligands

A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry

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Product

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Classification of RNA backbone conformations into rotamers using ¹³C′ chemical shifts: exploring how far we can go