Exploring the Molecular Interactions between Volatile Compounds in Coconut Shell Liquid Smoke and Human Bitter Taste TAS2R46 Based on the Molecular Docking and Molecular Dynamics

分子动力学 化学 对接(动物) 分子模型 苦味 分子识别 蛋白质数据库 计算化学 品味 立体化学 生物化学 分子 有机化学 医学 护理部
作者
Hamidah Rahman,Muhamad Ilham Bintang,Aiyi Asnawi,Ellin Febrina
出处
期刊:Tropical Journal of Natural Product Research [University of Bern]
卷期号:7 (12) 被引量:2
标识
DOI:10.26538/tjnpr/v7i12.31
摘要

TAS2R46, a bitter taste receptor, is crucial for detecting harmful substances. Understanding its molecular interactions with bitter compounds could help develop bitter taste modulators for the food and pharmaceutical industries. However, such interactions had remained underexplored. A computational method was utilized in this investigation to examine the binding interactions between TAS2R46 and the bitter components of liquid smoke. By utilizing molecular docking and molecular dynamics simulations, one may analyze the modes of binding, the stability of these interactions, and the essential residues at the binding site. The human TAS2R46 protein (PDB ID 7XP6) had been selected for this study. Molecular docking was employed to predict the binding modes and affinity of the liquid smoke’s ligands to the TAS2R46 receptor. Subsequently, molecular dynamics simulations were conducted to analyze the stability and dynamics of the TAS2R46-liquid smoke ligand complexes over a 100 ns timeframe. Our computational findings revealed that the nine teen-reported compounds of liquid smoke could indeed bind to TAS2R46. Delta energy component calculations had indicated the stability of these ligand-receptor complexes, with 1-(2,4,6-trihydroxyphenyl)-ethanone showing the most favorable binding energy. These results provided crucial insights into the molecular basis of bitter taste perception and may have implications for the food industry and drug development. In conclusion, this research bridged a critical knowledge gap by providing a molecular-level understanding of how TAS2R46 interacted with bitter compounds in liquid smoke.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
华仔应助黄羊采纳,获得10
1秒前
Yuxing完成签到,获得积分10
1秒前
1秒前
2秒前
英俊的铭应助xcy采纳,获得10
3秒前
果冻完成签到,获得积分10
3秒前
4秒前
lsl完成签到,获得积分10
4秒前
星辰大海应助风中雨筠采纳,获得10
4秒前
Kizi2021发布了新的文献求助30
5秒前
YUZ完成签到,获得积分10
7秒前
7秒前
学术牛马完成签到,获得积分10
8秒前
甜美的大白菜真实的钥匙完成签到 ,获得积分10
9秒前
李小丫关注了科研通微信公众号
9秒前
11秒前
故意的黄豆豆完成签到,获得积分10
12秒前
YUZ发布了新的文献求助10
12秒前
无极微光应助科研小民工采纳,获得20
13秒前
14秒前
科研通AI6.2应助11采纳,获得10
15秒前
15秒前
15秒前
夏纤凝完成签到,获得积分10
17秒前
20秒前
xcy发布了新的文献求助10
20秒前
21秒前
发酱完成签到,获得积分10
22秒前
23秒前
王二八完成签到,获得积分20
24秒前
激昂的可乐完成签到,获得积分10
24秒前
wjh完成签到,获得积分20
25秒前
开心幻丝完成签到,获得积分10
26秒前
26秒前
黎明森发布了新的文献求助10
27秒前
28秒前
开心幻丝发布了新的文献求助20
28秒前
cpli完成签到,获得积分10
29秒前
cdercder应助令狐姝采纳,获得10
30秒前
成就糖豆发布了新的文献求助10
31秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Principles of town planning: translating concepts to applications 1000
2016 Venous Blood Study (VBS) (Final V3.0) 510
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
The Effective Clinical Neurologist 3ed 500
The Great Hymn to Šamaš 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7701907
求助须知:如何正确求助?哪些是违规求助? 9260661
关于积分的说明 20027767
捐赠科研通 7277483
什么是DOI,文献DOI怎么找? 3294025
关于科研通互助平台的介绍 2449557
邀请新用户注册赠送积分活动 2300642