Recent developments in molecular modeling tools and applications related to pharmaceutical and biomedical research

Peluso, Paola; Chankvetadze, Bezhan

doi:10.1016/j.jpba.2023.115836

Cited by 7 publications

(4 citation statements)

References 98 publications

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“…Therefore, the substrate binding and catalytic residues cannot be identified from the available structural information. Fortunately, computational biology approaches, such as structural modeling, molecular docking, and molecular dynamic simulation, provide powerful tools for predicting substrate or coenzyme binding sites [2,8,9,29,30]. In this work, the three-dimensional structure of NgBDH was first modeled with AlphaFold2, which has an overall structure very similar to those of other members of the MDR family.…”

Section: Discussionmentioning

confidence: 99%

The critical role of residues Phe120 and Val161 of (2 R,3 R)‑2,3‑butanediol dehydrogenase from Neisseria gonorrhoeae as probed by molecular docking and site‐directed mutagenesis

Dong,

Zhang,

Gui

et al. 2024

J Basic Microbiol

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NAD+‐dependent (2 R,3 R)‑2,3‑butanediol dehydrogenase (BDH) from Neisseria gonorrhoeae (NgBDH) is a representative member of the medium‐chain dehydrogenase/reductase (MDR) superfamily. To date, little information is available on the substrate binding sites and catalytic residues of BDHs from this superfamily. In this work, according to molecular docking studies, we found that conserved residues Phe120 and Val161 form strong hydrophobic interactions with both (2 R,3 R)‑2,3‑butanediol (RR‐BD) and meso‐2,3‑butanediol (meso‐BD) and that mutations of these residues to alanine or threonine impair substrate binding. To further evaluate the roles of these two residues, Phe120 and Val161 were mutated to alanine or threonine. Kinetic analysis revealed that, relative to those of wild type, the apparent KM values of the Phe120Ala mutant for RR‐BD and meso‐BD increased 36‐ and 369‐fold, respectively; the catalytic efficiencies of this mutant with RR‐BD and meso‐BD decreased approximately 586‐ and 3528‐fold, respectively; and the apparent KM values of the Val161Ala mutant for RR‐BD and meso‐BD increased 4‐ and 37‐fold, respectively, the catalytic efficiencies of this mutant with RR‐BD and meso‐BD decreased approximately 3‐ and 28‐fold, respectively. Additionally, the Val161Thr mutant slightly decreased catalytic efficiencies (twofold with RR‐BD; 7.3‐fold with meso‐BD) due to an increase in KM (sixfold for RR‐BD; 24‐fold for meso‐BD) and a slight increase (2.8‐fold with RR‐BD; 3.3‐fold with meso‐BD) in kcat. These findings validate the critical roles of Phe120 and Val161 of NgBDH in substrate binding and catalysis. Overall, the current study provides a better understanding of the substrate binding and catalysis of BDHs within the MDR superfamily.

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Section: Discussionmentioning

confidence: 99%

The critical role of residues Phe120 and Val161 of (2 R,3 R)‑2,3‑butanediol dehydrogenase from Neisseria gonorrhoeae as probed by molecular docking and site‐directed mutagenesis

Dong,

Zhang,

Gui

et al. 2024

J Basic Microbiol

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“…The accurate prediction of partial atomic charges in organic molecules is a cornerstone in the realm of computational chemistry (1)(2)(3)(4)(5)(6), supporting a wide array of applications from molecular dynamics simulations to drug discovery (7)(8)(9)(10)(11). Traditional methodologies, such as Density Functional Theory (DFT) (7)(8)(9)(10)(11)(12)(13)(14), the Mulliken approach (6), and semi-empirical methods such as MOPAC (15) and Gaussian (16), have established a foundational understanding of molecular interactions and properties. However, these methods are signi cantly constrained by their computational demands, especially when dealing with the complexity of large molecular systems (17).…”

Section: Introductionmentioning

confidence: 99%

Bridging the Computational Gap: Sliding Window Technique Meets GCNN for Enhanced Molecular Charge Predictions

Domínguez-Arca

2024

Preprint

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In the quest for advancing computational tools capable of accurately calculating, estimating, or predicting partial atomic charges in organic molecules, this work introduces a pioneering Machine Learning-based tool designed to transcend the limitations of traditional methods like DFT, Mulliken, and semi-empirical approaches such as MOPAC and Gaussian. Recognizing the crucial role of partial atomic charges in molecular dynamics simulations for studying solvation, protein interactions, substrate interactions, and membrane permeability, we aim to introduce a tool that not only offers enhanced computational efficiency but also extends the predictive capabilities to molecules larger than those in the QM9 dataset, traditionally analyzed using Mulliken charges. Employing a novel neural network architecture adept at learning graph properties and, by extension, the characteristics of organic molecules, this study presents a "sliding window" technique. This method segments larger molecules into smaller, manageable substructures for charge prediction, significantly reducing computational demands and processing times. Our results highlight the model's predictive accuracy for unseen molecules from the QM9 database and its successful application to the resveratrol molecule, providing insights into the hydrogen-donating capabilities of CH groups in aromatic rings—a feature not predicted by existing tools like CGenFF or ATB but supported by literature. This breakthrough not only presents a novel alternative for determining partial atomic charges in computational chemistry but also underscores the potential of convolutional neural networks to discern molecular features based on stoichiometry and geometric configuration. Such advancements hint at the future possibility of designing molecules with desired charge sequences, promising a transformative impact on drug discovery.

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“…Much less applied spectroscopic techniques that have been utilized include ultraviolet spectroscopy, fluorimetry, Fourier transform and attenuated total reflectance IR spectroscopy, or electronic and vibrational circular dichroism. Furthermore, molecular modeling methods have been applied to obtain information about binding thermodynamics and the selector-selectand complex structures [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies on the elucidation of chiral recognition mechanisms in separation sciences were compiled in a monograph [12], book chapters [13][14][15][16] or review papers such as [17][18][19][20][21][22][23][24][25]. The application of NMR spectroscopy for this purpose has been summarized in [3][4][5][6] and reviews on molecular modeling approaches for separation sciences can be found in [6][7][8][9][10][11]. Within the reviewed period of time, the progress in the development of chiral stationary phases (CSPs) for highperformance liquid chromatography (HPLC) [26][27][28] and gas chromatography [29] was provided and an overview of chiral mobile phase additives in chromatography was published [30].…”

Section: Introductionmentioning

confidence: 99%

Update on chiral recognition mechanisms in separation science

Scriba

2024

J of Separation Science

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The stereospecific analysis of chiral molecules is an important issue in many scientific fields. In separation sciences, this is achieved via the formation of transient diastereomeric complexes between a chiral selector and the selectand enantiomers driven by molecular interactions including electrostatic, ion‐dipole, dipole‐dipole, van der Waals or π‐π interactions as well as hydrogen or halogen bonds depending on the nature of selector and selectand. Nuclear magnetic resonance spectroscopy and molecular modeling methods are currently the most frequently applied techniques to understand the selector‐selectand interactions at a molecular level and to draw conclusions on the chiral separation mechanism. The present short review summarizes some of the recent achievements for the understanding of the chiral recognition of the most important chiral selectors combining separation techniques with molecular modeling and/or spectroscopic techniques dating between 2020 and early 2024. The selectors include polysaccharide derivatives, cyclodextrins, macrocyclic glycopeptides, proteins, donor‐acceptor type selectors, ion‐exchangers, crown ethers, and molecular micelles. The application of chiral ionic liquids and chiral deep eutectic solvents, as well as further selectors, are also briefly addressed. A compilation of all published literature on chiral selectors has not been attempted.

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Recent developments in molecular modeling tools and applications related to pharmaceutical and biomedical research

Cited by 7 publications

References 98 publications

The critical role of residues Phe120 and Val161 of (2 R,3 R)‑2,3‑butanediol dehydrogenase from Neisseria gonorrhoeae as probed by molecular docking and site‐directed mutagenesis

The critical role of residues Phe120 and Val161 of (2 R,3 R)‑2,3‑butanediol dehydrogenase from Neisseria gonorrhoeae as probed by molecular docking and site‐directed mutagenesis

Bridging the Computational Gap: Sliding Window Technique Meets GCNN for Enhanced Molecular Charge Predictions

Update on chiral recognition mechanisms in separation science

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