In silico binding affinity prediction for metabotropic glutamate receptors using both endpoint free energy methods and a machine learning-based scoring function.
In silico binding affinity prediction for metabotropic glutamate receptors using both endpoint free energy methods and a machine learning-based scoring function.
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DOI：
10.1039/d2cp01727j
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发表时间：
2022-08-03
影响因子：
3.3
通讯作者：
Wang, Junmei
中科院分区：
文献类型：
--
作者：
Zhai, Jingchen;He, Xibing;Sun, Yuchen;Wan, Zhuoya;Ji, Beihong;Liu, Shuhan;Li, Song;Wang, Junmei
代谢型谷氨酸受体（metabotropic glutamate receptor，mGluRs）在调节谷氨酸信号通路中发挥重要作用，参与神经病变和外周稳态的调节。mGluR 4属于III组mGluR，在所有mGluR中最广泛地分布于外周。研究表明，该受体的调节与糖尿病、结直肠癌等多种疾病有关。然而，由于缺乏解析的mGluR 4蛋白结构，基于结构的药物设计用于鉴定调节mGluR 4受体的小分子的应用受到限制。在这项工作中，我们首先基于mGluR 8的晶体结构构建了mGluR 4的同源模型，然后进行分层虚拟筛选（HVS）以鉴定mGluR 4的可能活性配体。HVS协议包括三个层次的过滤器，包括Glide对接，分子动力学（MD）模拟和结合自由能计算。我们成功地优先从一组筛选化合物使用HVS的mGluR 4的活性配体。基于结合亲和力预测的活性配体几乎可以覆盖所有的实验确定的活性配体，只有一个配体遗漏。与Glide对接方法相比，MM-PB/GBSA-WSAS方法的测量和预测结合亲和力之间的相关性显著改善。更重要的是，我们已经确定了配体结合的热点，我们发现SER 157和GLY 158倾向于促进mGluR 4配体的选择性，而ALA 154和ALA 155可以解释mGluR 8的配体选择性。我们还识别出了对配体效力至关重要的其他5个关键残基。mGluR 4和mGluR 8之间结合模式的差异可以指导我们开发更有效和选择性的调节剂。此外，我们评估了IPSF的性能，IPSF是一种通过机器学习算法训练的新型评分函数，用于指导药物先导物优化。交叉验证均方根误差（RMSE）远小于终点方法，相关系数与两种mGLUR的最佳终点方法相当。因此，基于机器学习的IPSF可以应用于指导先导化合物优化，尽管活性/非活性的总数不大，这是药物发现项目中的典型场景。
The metabotropic glutamate receptors (mGluRs) play an important role in regulating glutamate signal pathways, which involves in neuropathy and periphery homeostasis. The mGluR4, which belongs to Group III mGluRs, is most widely distributed in periphery among all the mGluRs. It has been proved that the regulation of this receptor is involved in diabetes, colorectal carcinoma and many other diseases. However, the application of structure-based drug design to identify small molecules to regulate mGluR4 receptor is limited due to the absence of a resolved mGluR4 protein structure. In this work, we first built a homology model of mGluR4 based on a crystal structure of mGluR8, and then conducted hierarchical virtual screening (HVS) to identify possible active ligands for mGluR4. The HVS protocol consists of three hierarchical filters including Glide docking, molecular dynamic (MD) simulation and binding free energy calculation. We successfully prioritized active ligands of mGluR4 from a set of screening compounds using HVS. The predicted active ligands based on binding affinities can almost cover all the experiment-determined active ligands, with only one ligand missed. The correlation between the measured and predicted binding affinities is significantly improved for the MM-PB/GBSA-WSAS methods compared to the Glide docking method. More importantly, we have identified hotspots for ligand binding, and we found that SER157 and GLY158 tend to contribute to the selectivity of mGluR4 ligands, while ALA154 and ALA155 could account for the ligand selectivity to mGluR8. We also recognized other 5 key residues that are critical for ligand potency. The difference of the binding profiles between mGluR4 and mGluR8 can guide us to develop more potent and selective modulators. Moreover, we evaluated the performance of IPSF, a novel type of scoring function trained by a machine learning algorithm on residue-ligand interaction profiles, in guiding drug lead optimization. The cross-validation root-mean-square errors (RMSE) are much smaller than those by the endpoint methods, and the correlation coefficients are comparable to the best endpoint methods for both mGLURs. Thus, machine learning based IPSF can be applied to guide lead optimization, albeit the total number of actives/inactives are not big, a typical scenario in drug discovery project.
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影响因子：
3.5
作者：
Gathiaka, Symon;Liu, Shuai;Gilson, Michael K.
通讯作者：
Gilson, Michael K.
影响因子：
4.4
作者：
JORGENSEN, WL;CHANDRASEKHAR, J;KLEIN, ML
通讯作者：
KLEIN, ML
DOI：
10.1038/nrd.2017.178
发表时间：
2017-12
期刊：
Nature reviews. Drug discovery
影响因子：
--
作者：
Hauser AS;Attwood MM;Rask-Andersen M;Schiöth HB;Gloriam DE
通讯作者：
Gloriam DE
影响因子：
14.9
作者：
Gaulton A;Hersey A;Nowotka M;Bento AP;Chambers J;Mendez D;Mutowo P;Atkinson F;Bellis LJ;Cibrián-Uhalte E;Davies M;Dedman N;Karlsson A;Magariños MP;Overington JP;Papadatos G;Smit I;Leach AR
通讯作者：
Leach AR
影响因子：
14.9
作者：
UniProt Consortium
通讯作者：
UniProt Consortium