Large-Scale G Protein-Coupled Olfactory Receptor-Ligand Pairing.
Large-Scale G Protein-Coupled Olfactory Receptor-Ligand Pairing.
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DOI：
10.1021/acscentsci.1c01495
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发表时间：
2022-03-23
影响因子：
18.2
通讯作者：
Golebiowski J
中科院分区：
文献类型：
--
作者：
Cong X;Ren W;Pacalon J;Xu R;Xu L;Li X;de March CA;Matsunami H;Yu H;Yu Y;Golebiowski J
G蛋白偶联受体（gpcr）具有共同的结构折叠和激活机制，但其配体光谱和功能高度多样化。这项工作研究了嗅觉受体（ORs）的氨基酸序列是如何编码对各种配体的多样化反应的。我们建立了一个基于OR序列相似性和配体物理化学特征的蛋白质化学计量学（PCM）模型，利用监督机器学习预测OR对气味剂的反应。通过定点诱变、体外功能测定和分子模拟构建PCM模型。我们发现，ORs的配体选择性主要编码在正畸袋周围8 Å以下的残基上。随后使用随机森林（RF）的预测显示，通过对不同支架的111种ORs和7种气味剂的体外功能分析，准确率高达58%。发现了64对新的or气味对，其中25对or被去孤儿化。最佳模型的去孤儿率为56%。PCM-RF方法将加速OR -气味映射和OR去孤儿化。使用序列和化学特征对蛋白质-配体对进行机器学习预测：选择关键残基是一种直观的知识驱动方法，可降低维数并提高性能。
G protein-coupled receptors (GPCRs) conserve common structural folds and activation mechanisms, yet their ligand spectra and functions are highly diverse. This work investigated how the amino-acid sequences of olfactory receptors (ORs)—the largest GPCR family—encode diversified responses to various ligands. We established a proteochemometric (PCM) model based on OR sequence similarities and ligand physicochemical features to predict OR responses to odorants using supervised machine learning. The PCM model was constructed with the aid of site-directed mutagenesis, in vitro functional assays, and molecular simulations. We found that the ligand selectivity of the ORs is mostly encoded in the residues up to 8 Å around the orthosteric pocket. Subsequent predictions using Random Forest (RF) showed a hit rate of up to 58%, as assessed by in vitro functional assays of 111 ORs and 7 odorants of distinct scaffolds. Sixty-four new OR–odorant pairs were discovered, and 25 ORs were deorphanized here. The best model demonstrated a 56% deorphanization rate. The PCM-RF approach will accelerate OR–odorant mapping and OR deorphanization. Machine learning prediction of protein−ligand pairs using sequence and chemical features: Selecting key residues is an intuitive knowledge-driven method to reduce dimensionality and boost performance.
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影响因子：
4.6
作者：
Caballero-Vidal, Gabriela;Bouysset, Cedric;Jacquin-Joly, Emmanuelle
通讯作者：
Jacquin-Joly, Emmanuelle
DOI：
10.1021/acs.jpclett.8b00633
发表时间：
2018-05-03
期刊：
The journal of physical chemistry letters
影响因子：
--
作者：
Bushdid C;de March CA;Fiorucci S;Matsunami H;Golebiowski J
通讯作者：
Golebiowski J
影响因子：
15
作者：
de March CA;Yu Y;Ni MJ;Adipietro KA;Matsunami H;Ma M;Golebiowski J
通讯作者：
Golebiowski J
影响因子：
8
作者：
Man, O;Gilad, Y;Lancet, D
通讯作者：
Lancet, D
影响因子：
2.4
作者：
Launay, Guillaume;Teletchea, Stephane;Sanz, Guenhael
通讯作者：
Sanz, Guenhael