Interactions of Potential Endocrine-Disrupting Chemicals with Whole Human Proteome Predicted by AlphaFold2 Using an In Silico Approach

生物信息学 计算生物学 生化工程 蛋白质组 人类蛋白质组计划 内分泌系统 化学 计算机科学 环境化学 生物 生物信息学 蛋白质组学 工程类 生物化学 激素 基因
作者
Fan Zhang,Yawen Tian,Yitao Pan,Nan Sheng,Jiayin Dai
出处
期刊:Environmental Science & Technology [American Chemical Society]
被引量:5
标识
DOI:10.1021/acs.est.4c03774
摘要

Binding with proteins is a critical molecular initiating event through which environmental pollutants exert toxic effects in humans. Previous studies have been limited by the availability of three-dimensional (3D) protein structures and have focused on only a small set of environmental contaminants. Using the highly accurate 3D protein structure predicted by AlphaFold2, this study explored over 60 million interactions obtained through molecular docking between 20,503 human proteins and 1251 potential endocrine-disrupting chemicals. A total of 66,613,773 docking results were obtained, 1.2% of which were considered to be high binding, as their docking scores were lower than -7. Monocyte to macrophage differentiation factor 2 (MMD2) was predicted to interact with the highest number of environmental pollutants (526), with polychlorinated biphenyls and polychlorinated dibenzofurans accounting for a significant proportion. Dimension reduction and clustering analysis revealed distinct protein profiles characterized by high binding affinities for perfluoroalkyl and polyfluoroalkyl substances (PFAS), phthalate-like chemicals, and other pollutants, consistent with their uniquely enriched pathways. Further structural analysis indicated that binding pockets with a high proportion of charged amino acid residues, relatively low α-helix content, and high β-sheet content were more likely to bind to PFAS than others. This study provides insights into the toxicity pathways of various pollutants impacting human health and offers novel perspectives for the establishment and expansion of adverse outcome pathway-based models.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

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
含糊的代丝完成签到 ,获得积分10
刚刚
朴素的紫安完成签到 ,获得积分10
1秒前
yyj完成签到,获得积分10
2秒前
3秒前
3秒前
量子星尘发布了新的文献求助10
3秒前
君临完成签到,获得积分10
3秒前
林早上完成签到,获得积分20
3秒前
xiu完成签到 ,获得积分10
4秒前
栗爷完成签到,获得积分0
4秒前
深年完成签到,获得积分10
5秒前
求知若渴完成签到,获得积分0
5秒前
所所应助科研通管家采纳,获得10
5秒前
情怀应助科研通管家采纳,获得10
5秒前
华仔应助科研通管家采纳,获得30
5秒前
李爱国应助科研通管家采纳,获得10
6秒前
Orange应助科研通管家采纳,获得10
6秒前
我是老大应助科研通管家采纳,获得10
6秒前
华仔应助科研通管家采纳,获得10
6秒前
SciGPT应助科研通管家采纳,获得10
6秒前
6秒前
一团小煤球完成签到,获得积分10
6秒前
6秒前
NexusExplorer应助科研通管家采纳,获得10
6秒前
6秒前
6秒前
卡乐瑞咩吹可完成签到,获得积分10
6秒前
田様应助科研通管家采纳,获得10
6秒前
苦咖啡行僧完成签到 ,获得积分10
6秒前
鹤鸣完成签到,获得积分10
7秒前
守望阳光1完成签到,获得积分10
7秒前
正直天空发布了新的文献求助10
7秒前
9秒前
YU发布了新的文献求助10
9秒前
大方元风完成签到 ,获得积分10
9秒前
隐形曼青应助自觉寒梦采纳,获得10
10秒前
ntxlks完成签到,获得积分10
10秒前
祝雲完成签到,获得积分10
10秒前
Spice完成签到 ,获得积分10
11秒前
John完成签到,获得积分20
11秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
Research on Disturbance Rejection Control Algorithm for Aerial Operation Robots 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038388
求助须知:如何正确求助?哪些是违规求助? 3576106
关于积分的说明 11374447
捐赠科研通 3305798
什么是DOI,文献DOI怎么找? 1819322
邀请新用户注册赠送积分活动 892672
科研通“疑难数据库(出版商)”最低求助积分说明 815029