Identification of potential modulator of<i>Anopheles gambiae</i>odorant binding protein 1 by hierarchical virtual screening and molecular dynamics

Bomfim, Mayra Ramos do; Araújo, Janay Stefany Carneiro; Macêdo, Williams Jorge da Cruz; Santos, Cleydson Breno Rodrigues dos; Leite, Franco Henrique Andrade

doi:10.1080/07391102.2020.1796807

Cited by 5 publications

(4 citation statements)

References 50 publications

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“…The same method was used to analyze and reveal the molecular chemical mechanism of AlepPBP2 and AlepPBP3 in Athetis lepigone and the phoxim, and key amino acids have been identified, which would be of great help in the development of highly effective insecticides [48]. In contrast, when studying the mechanism of AgamOBP1-ligand interaction, the hierarchical virtual screening was used to screen potential ligands on a large scale and prioritize them [49]. For Anopheles gambiae, ligands are recognized by the AgamOBPs homodimer and AgamOBPs can bind to multiple ligands.…”

Section: Discussionmentioning

confidence: 99%

Interaction Analysis of Odorant-Binding Protein 12 from Sirex noctilio and Volatiles from Host Plants and Symbiotic Fungi Based on Molecule Dynamics Simulation

Rong

Hao

et al. 2022

Agronomy

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As a quarantine pest of conifer, Sirex noctilio has caused widespread harm around the world. It is expected that the molecular mechanism of protein–ligand binding can be elucidated to carry out the pest control. Through studies of SnocOBP12–ligand hydrophobic binding and dynamics and responsible amino acid residues identification, we got some promising results. SnocOBP12 had a general and excellent affinity for host plant volatiles, and may be a key protein for S. noctilio to find host plants. Among the many odor molecules that are bound to SnocOBP12, (−)-α-cedrene and (E)-β-farnesene from host plants and (−)-globuol from the symbiotic fungi of Sirex noctilio stood out and formed highly stable complexes with SnocOBP12. By the molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) method, the calculated free binding energy of the three complexes was −30.572 ± 0.101 kcal/mol, −28.349 ± 0.119 kcal/mol and −25.244 ± 0.152 kcal/mol, respectively. It was found that the van der Waals energy contributed to the stability of the complexes. Some key amino acid residues were also found: LEU74 and TYR109 were very important for SnocOBP12 to stably bind (−)-α-cedrene, while for (E)-β-farnesene, ILE6, MET10, and LEU74 were very important for the stable binding system. We discovered three potential ligands and analyzed the interaction pattern of the protein with them, this paper provides a favorable molecular basis for optimizing the attractant formulation. Investigation of the binding characteristics in the olfactory system at the molecular level is helpful to understand the behavior of S. noctilio and develop new methods for more effective and environmentally friendly pest control.

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Section: Discussionmentioning

confidence: 99%

Interaction Analysis of Odorant-Binding Protein 12 from Sirex noctilio and Volatiles from Host Plants and Symbiotic Fungi Based on Molecule Dynamics Simulation

Rong

Hao

et al. 2022

Agronomy

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“…The original structure files were dewatered and other ions were removed. Molecular docking was performed using AutoDock Vina 1.2.5 [38]. We used Gaussian 09 (https://gaussian.com, accessed on 15 December 2023) to perform quantum chemistry calculations [39] for the above molecules, and Multiwfn to visualize the LUMO and HOMO orbitals.…”

Section: Molecular Docking and Quantum Chemistry Calculationsmentioning

confidence: 99%

Mlp4green: A Binary Classification Approach Specifically for Green Odor

Yang,

Qian,

et al. 2024

IJMS

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Fresh green leaves give off a smell known as “green odor.” It has antibacterial qualities and can be used to attract or repel insects. However, a common method for evaluating green odor molecules has never existed. Machine learning techniques are widely used in research to forecast molecular attributes for binary classification. In this work, the green odor molecules were first trained and learned using machine learning methods, and then clustering analysis and molecular docking were performed to further explore their molecular characteristics and mechanisms of action. For comparison, four algorithmic models were employed, MLP performed the best in all metrics, including Accuracy, Precision, Average Precision, Matthews coefficient, and Area under curve. We determined by difference analysis that, in comparison to non-green odor molecules, green odor molecules have a lower molecular mass and fewer electrons. Based on the MLP algorithm, we constructed a binary classification prediction website for green odors. The first application of deep learning techniques to the study of green odor molecules can be seen as a signal of a new era in which green odor research has advanced into intelligence and standardization.

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“…Moreover, MS has been successfully used to guide the designing of odor molecules. In olfactory pathways, the odorant binding protein 1 (OBP1) is the main receptor for odor recognition on the malarial vector; thus, it can be used to modulate mosquito behavior and develop new attractants or repellents . Using MS and hierarchical virtual screening, Bomfim et al.…”

Section: Screening and Designing Of Flavor Molecules Based On Computa...mentioning

confidence: 99%

“…Using MS and hierarchical virtual screening, Bomfim et al. successfully identified a modulator for Anopheles gambiae OBP1, indicating the potential application of MS in molecular screening and designing …”

Section: Screening and Designing Of Flavor Molecules Based On Computa...mentioning

confidence: 99%

Data-Driven Elucidation of Flavor Chemistry

Kou

Shi

Gao

et al. 2023

J. Agric. Food Chem.

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Flavor molecules are commonly used in the food industry to enhance product quality and consumer experiences but are associated with potential human health risks, highlighting the need for safer alternatives. To address these health-associated challenges and promote reasonable application, several databases for flavor molecules have been constructed. However, no existing studies have comprehensively summarized these data resources according to quality, focused fields, and potential gaps. Here, we systematically summarized 25 flavor molecule databases published within the last 20 years and revealed that data inaccessibility, untimely updates, and nonstandard flavor descriptions are the main limitations of current studies. We examined the development of computational approaches (e.g., machine learning and molecular simulation) for the identification of novel flavor molecules and discussed their major challenges regarding throughput, model interpretability, and the lack of gold-standard data sets for equitable model evaluation. Additionally, we discussed future strategies for the mining and designing of novel flavor molecules based on multi-omics and artificial intelligence to provide a new foundation for flavor science research.

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Identification of potential modulator ofAnopheles gambiaeodorant binding protein 1 by hierarchical virtual screening and molecular dynamics

Cited by 5 publications

References 50 publications

Interaction Analysis of Odorant-Binding Protein 12 from Sirex noctilio and Volatiles from Host Plants and Symbiotic Fungi Based on Molecule Dynamics Simulation

Interaction Analysis of Odorant-Binding Protein 12 from Sirex noctilio and Volatiles from Host Plants and Symbiotic Fungi Based on Molecule Dynamics Simulation

Mlp4green: A Binary Classification Approach Specifically for Green Odor

Data-Driven Elucidation of Flavor Chemistry

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