Integration of Residue Attributes for Sequence Diversity Characterization of Terpenoid Enzymes

Kibinge, Nelson; Ikeda, Shun; Ono, Naoaki; Altaf‐Ul‐Amin, Md.; Kanaya, Shigehiko

doi:10.1155/2014/753428

Cited by 3 publications

(4 citation statements)

References 33 publications

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“…9-1, and Fig. 9-2; Zamyatnin, 1972; Kibinge et al, 2014). We calculated an additional parameter, end-to-end residue length, using a 6-31G* basis set Hartree–Fock calculation in SPARTAN ’14 (Wavefunction).…”

Section: Resultsmentioning

confidence: 94%

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Menthol Stereoisomers Exhibit Different Effects on α4β2 nAChR Upregulation and Dopamine Neuron Spontaneous Firing

Henderson

Grant

Chu

et al. 2018

eNeuro

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Menthol contributes to poor cessation rates among smokers, in part because menthol enhances nicotine reward and reinforcement. Mentholated tobacco products contain (−)-menthol and (+)-menthol, in varying proportions. We examined these two menthol stereoisomers for their ability to upregulate α4β2 nAChRs and to alter dopamine neuron firing frequency using long-term, low-dose (≤500 nm) exposure that is pharmacologically relevant to smoking. We found that (−)-menthol upregulates α4β2 nAChRs while (+)-menthol does not. We also found that (−)-menthol decreases dopamine neuron baseline firing and dopamine neuron excitability, while (+)-menthol exhibits no effect. We then examined both stereoisomers for their ability to inhibit α4β2 nAChR function at higher concentrations (>10 µm) using the Xenopus oocyte expression system. To probe for the potential binding site of menthol, we conducted flooding simulations and site-directed mutagenesis. We found that menthol likely binds to the 9´ position on the TM2 (transmembrane M2) helix. We found that menthol inhibition is dependent on the end-to-end distance of the side chain at the 9´ residue. Additionally, we have found that (−)-menthol is only modestly (∼25%) more potent than (+)-menthol at inhibiting wild-type α4β2 nAChRs and a series of L9´ mutant nAChRs. These data reveal that menthol exhibits a stereoselective effect on nAChRs and that the stereochemical effect is much greater for long-term, submicromolar exposure in mice than for acute, higher-level exposure. We hypothesize that of the two menthol stereoisomers, only (−)-menthol plays a role in enhancing nicotine reward through nAChRs on dopamine neurons.

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Section: Resultsmentioning

confidence: 94%

“…Additionally, we fitted the IC 40 or IC 30 values for (−)-menthol and (+)-menthol, respectively, against the reduced AA index (Kibinge et al, 2014). The reduced AA index is a set of eight indices describing the variability of amino acids based on experimental results.…”

Section: Resultsmentioning

confidence: 99%

Menthol Stereoisomers Exhibit Different Effects on α4β2 nAChR Upregulation and Dopamine Neuron Spontaneous Firing

Henderson

Grant

Chu

et al. 2018

eNeuro

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“…The key properties identified by the CLN-MLEM2 method are provided in Supplementary Table S2 . These properties were selected from the rAAindex ( 56 ), a reduced subset of the AAindex focused on hydropathy. We initiated the machine learning training by providing literature data to set up the initial inhibition descriptions for rough set participation to address selected pathogens ( Supplementary Table S1 ).…”

Section: Resultsmentioning

confidence: 99%

“…The second enhancement for targeting was introduced by focusing on the key physicochemical property features. We integrated eight of indices proposed by a recent study as reduced AAindex (rAAindex) obtained from a subset of original 544 original indices in the amino acid index database ( 56 ). Kibinge et al, applied a random forest (RF) algorithm for property reduction and maximizing metadata capturing.…”

Section: Methodsmentioning

confidence: 99%

Machine learning-enabled design features of antimicrobial peptides selectively targeting peri-implant disease progression

Boone,

Tjokro,

Chu

et al. 2024

Front. Dent. Med

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Peri-implantitis is a complex infectious disease that manifests as progressive loss of alveolar bone around the dental implants and hyper-inflammation associated with microbial dysbiosis. Using antibiotics in treating peri-implantitis is controversial because of antibiotic resistance threats, the non-selective suppression of pathogens and commensals within the microbial community, and potentially serious systemic sequelae. Therefore, conventional treatment for peri-implantitis comprises mechanical debridement by nonsurgical or surgical approaches with adjunct local microbicidal agents. Consequently, current treatment options may not prevent relapses, as the pathogens either remain unaffected or quickly re-emerge after treatment. Successful mitigation of disease progression in peri-implantitis requires a specific mode of treatment capable of targeting keystone pathogens and restoring bacterial community balance toward commensal species. Antimicrobial peptides (AMPs) hold promise as alternative therapeutics through their bacterial specificity and targeted inhibitory activity. However, peptide sequence space exhibits complex relationships such as sparse vector encoding of sequences, including combinatorial and discrete functions describing peptide antimicrobial activity. In this paper, we generated a transparent Machine Learning (ML) model that identifies sequence-function relationships based on rough set theory using simple summaries of the hydropathic features of AMPs. Comparing the hydropathic features of peptides according to their differential activity for different classes of bacteria empowered predictability of antimicrobial targeting. Enriching the sequence diversity by a genetic algorithm, we generated numerous candidate AMPs designed for selectively targeting pathogens and predicted their activity using classifying rough sets. Empirical growth inhibition data is iteratively fed back into our ML training to generate new peptides, resulting in increasingly more rigorous rules for which peptides match targeted inhibition levels for specific bacterial strains. The subsequent top scoring candidates were empirically tested for their inhibition against keystone and accessory peri-implantitis pathogens as well as an oral commensal bacterium. A novel peptide, VL-13, was confirmed to be selectively active against a keystone pathogen. Considering the continually increasing number of oral implants placed each year and the complexity of the disease progression, prevalence of peri-implant diseases continues to rise. Our approach offers transparent ML-enabled paths towards developing antimicrobial peptide-based therapies targeting the changes in the microbial communities that can beneficially impact disease progression.

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Korean Text Classification Using Randomforest and XGBoost Focusing on Seoul Metropolitan Civil Complaint Data

Eun¹,

신현철²,

Lee³

2017

The Korea Journal of BigData

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Integration of Residue Attributes for Sequence Diversity Characterization of Terpenoid Enzymes

Cited by 3 publications

References 33 publications

Menthol Stereoisomers Exhibit Different Effects on α4β2 nAChR Upregulation and Dopamine Neuron Spontaneous Firing

Menthol Stereoisomers Exhibit Different Effects on α4β2 nAChR Upregulation and Dopamine Neuron Spontaneous Firing

Machine learning-enabled design features of antimicrobial peptides selectively targeting peri-implant disease progression

Korean Text Classification Using Randomforest and XGBoost Focusing on Seoul Metropolitan Civil Complaint Data

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