Virtual screening of drugs targeting PD-L1 protein

Lin Kai-Dong; Lin Xiao-Qian; Lin Xu-Bo

doi:10.7498/aps.72.20231068

Abstract

Monoclonal antibody inhibitors targeting PD-1/PD-L1 immune checkpoints are gradually entering the market and have achieved certain positive effects in the treatments of various types of tumors. However, with the expansion of application, the limitations of antibody drugs and problems such as excessive homogenization of research gradually appear, making small-molecule inhibitors the new focus of researchers. This study aims to use ligand-based and structure-based binding activity prediction methods to achieve virtual screening of small-molecule inhibitors targeting PD-L1, thereby helping to accelerate the development of small molecule drugs. A dataset of PD-L1 small-molecule inhibitory activity from relevant research literature and patents is collected and activity judgment classification models with intensity prediction regression models are constructed based on different molecular featurization methods and machine learning algorithms. The two types of models filter 68 candidate compounds with high PD-L1 inhibitory activity from a large drug-like small molecule screening pool (ZINC15). Ten of these compounds not only have good drug similarities and pharmacokinetics, but also exhibit comparable binding affinities and similar mechanisms of action with previous reported hotspot compounds in molecular docking. This phenomenon is further verified in subsequent molecular dynamics simulation and the estimation of binding free energy. In this study, a virtual screening workflow integrating ligand-based method and structure-based method is developed, and potential PD-L1 small-molecule inhibitors are effectively screened from large compound databases, which is expected to help accelerate the application and expansion of tumor immunotherapy.

Keywords:

Authors and contacts

1.
Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, School of Engineering Medicine, Beihang Unitaversity, Beijing 100191, China
2.
Shen Yuan Honors College, Beihang University, Beijing 100191, China

Corresponding author: Lin Xu-Bo, linxbseu@buaa.edu.cn

Funds: Project supported by the National Nature Science Foundation of China (Grant No. 21903002) and the Excellence Research Fund of Shen Yuan Honors College, Beihang University, China (Grant No. 230121202).

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References

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Cited By

图 1 数据集聚类　(a)分类模型阳性样本骨架的14个簇; (b)回归模型样本骨架的13个簇. 环形树状图的颜色代表不同的分子结构骨架簇, 颜色越接近, 骨架越相似, 一旁的化学结构图是每个簇中最具代表性的骨架

Figure 1. Dataset clustering: (a) 14 clusters of scaffolds of active compounds in the classification models; (b) 13 clusters of scaffolds of compounds in the regression models. The colors of the circular dendrograms represent different clusters of scaffolds, and the closer the colors are, the more similar the scaffolds are. The chemical structure diagrams on the side are the most representative scaffolds of each cluster.

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图 2 分类模型性能表现　(a)灵敏度; (b)特异度; (c)准确度; (d)马修斯相关系数

Figure 2. Performance of binary models for classification tasks: (a) SE; (b) SP; (c) ACC; (d) MCC.

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图 3 分子结构片段在两类样本间的计数差异　(a)活性化合物计数占优的结构片段; (b)非活性化合物计数占优的结构片段. 结构片段的中心原子以蓝色突出显示; 芳香性原子被着色为黄色, 而脂肪烃链原子则被着色为灰色

Figure 3. Count difference of fragments of structures between active and inactive compounds: (a) Fragments in active compounds with a proportion higher than inactive compounds; (b) fragments in active compounds with a proportion lower than inactive compounds. The center atoms of substructure fragments are highlighted in blue; aromaticity atoms are colored in yellow and aliphatic hydrocarbon atoms are colored in gray.

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图 4 回归模型性能表现　(a)平均绝对误差; (b)均方根误差

Figure 4. Performance of continuous models for regression tasks: (a) MAE; (b) RMSE.

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图 5 两种最佳性能回归模型预测结果　(a), (c)样本预测值与标签值分布; (b), (d)样本预测偏差

Figure 5. Prediction results of two best-performing continuous models: (a), (c) Distribution of predicted values and label values; (b), (d) prediction residuals.

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图 6 虚拟筛选流程

Figure 6. Workflow of virtual screening.

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图 7 RMSD　(a) PD-L1二聚体骨架; (b)配体

Figure 7. RMSD: (a) Backbone of PD-L1 dimer; (b) ligand.

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图 8 配体与PD-L1结合模式和关键残基　(a) ZINC000021723762; (b) ZINC000021874692; (c) ZINC000175468610; (d) ZINC000019770413; (e) ZINC000021874694; (f) ZINC000952973550; (g) ZINC000064987401; (h) ZINC000003908573; (i) ZINC000020538424; (j) ZINC000004063088; (k) BMS202; (l) INCB086550

Figure 8. Ligand binding mode with PD-L1 and key residues: (a) ZINC000021723762; (b) ZINC000021874692; (c) ZINC000175468610; (d) ZINC000019770413; (e) ZINC000021874694; (f) ZINC000952973550; (g) ZINC000064987401; (h) ZINC000003908573; (i) ZINC000020538424; (j) ZINC000004063088; (k) BMS202; (l) NCB086550.

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表 1 最低对接评分的10种候选化合物及BMS202和INCB086550的对接结果

Table 1. Docking results for the top 10 hits with BMS202 and INCB086550.

序号	化合物	化学结构式	对接分数/(kcal·mol^–1)
Hit 1	ZINC000021723762		–11.8
Hit 2	ZINC000021874692		–10.7
Hit 3	ZINC000175468610		–10.7
Hit 4	ZINC000019770413		–10.6
Hit 5	ZINC000021874694		–10.6
Hit 6	ZINC000952973550		–10.5
Hit 7	ZINC000064987401		–10.5
Hit 8	ZINC000003908573		–10.4
Hit 9	ZINC000020538424		–10.4
Hit 10	ZINC000004063088		–10.3
Ctr 1	BMS202		–11.1
Ctr 2	INCB086550		–10.0

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表 2 结合自由能计算结果

Table 2. Results of MMPBSA.

化合物	$ {E}_{{\mathrm{v}}{\mathrm{d}}{\mathrm{w}}} $/(kcal·mol^–1)	$ {E}_{{\mathrm{e}}{\mathrm{l}}{\mathrm{e}}} $/(kcal·mol^–1)	$ {E}_{{\mathrm{P}}{\mathrm{B}}} $/(kcal·mol^–1)	$ {E}_{{\mathrm{S}}{\mathrm{A}}} $/(kcal·mol^–1)	$ \Delta G $/(kcal·mol^–1)
ZINC000021723762	–58.86$ \pm 0.12 $	–8.76$ \pm $0.10	35.53$ \pm $0.09	–4.03$ \pm $0.00	–36.12$ \pm $0.12
ZINC000021874692	–50.42$ \pm $0.11	–6.50$ \pm $0.14	35.65$ \pm $0.16	–4.50$ \pm $0.01	–25.78$ \pm 0.12 $
ZINC000175468610	–42.46$ \pm 0.09 $	–14.34$ \pm 0.08 $	36.20$ \pm 0.13 $	–4.02$ \pm $0.00	–24.61$ \pm 0.10 $
ZINC000019770413	–47.31$ \pm 0.08 $	–3.45$ \pm 0.08 $	26.32$ \pm 0.07 $	–3.48$ \pm $0.00	–27.91$ \pm 0.08 $
ZINC000021874694	–55.17$ \pm 0.09 $	–17.87$ \pm 0.13 $	45.67$ \pm 0.14 $	–4.59$ \pm $0.00	–31.97$ \pm 0.12 $
ZINC000952973550	–55.82$ \pm 0.08 $	–13.47$ \pm 0.11 $	45.83$ \pm 0.11 $	–4.19$ \pm $0.00	–27.65$ \pm 0.11 $
ZINC000064987401	–48.50$ \pm 0.08 $	–14.71$ \pm 0.12 $	38.59$ \pm 0.11 $	–4.06$ \pm $0.00	–28.68$ \pm 0.12 $
ZINC000003908573	–44.75$ \pm 0.07 $	4.59$ \pm 0.11 $	20.20$ \pm 0.13 $	–3.74$ \pm $0.00	–23.70$ \pm 0.12 $
ZINC000020538424	–49.50$ \pm 0.09 $	–8.50$ \pm 0.13 $	32.61$ \pm 0.16 $	–4.17$ \pm $0.01	–29.57$ \pm 0.10 $
ZINC000004063088	–64.14$ \pm 0.09 $	–5.38$ \pm 0.11 $	36.75$ \pm 0.10 $	–4.21$ \pm $0.00	–36.98$ \pm 0.10 $
BMS202	–40.23$ \pm 0.08 $	–3.57$ \pm 0.07 $	22.35$ \pm 0.10 $	–4.31$ \pm $0.01	–25.75$ \pm 0.09 $
INCB086550	–50.65$ \pm 0.14 $	–9.87$ \pm 0.17 $	44.44$ \pm 0.27 $	–5.13$ \pm $0.02	–21.22$ \pm $0.15

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map

[1]	Waldman A D, Fritz J M, Lenardo M J 2020 Nat. Rev. Immunol. 20 651 Google Scholar
[2]	Wherry E J, Kurachi M 2015 Nat. Rev. Immunol. 15 486 Google Scholar
[3]	Zou W, Wolchok J D, Chen L 2016 Sci. Transl. Med. 8 328rv4 Google Scholar
[4]	Jiang X, Wang J, Deng X, Xiong F, Ge J, Xiang B, Wu X, Ma J, Zhou M, Li X, Li Y, Li G, Xiong W, Guo C, Zeng Z 2019 Mol. Cancer 18 10 Google Scholar
[5]	Topalian S L, Drake C G, Pardoll D M 2015 Cancer Cell 27 450 Google Scholar
[6]	Postow M A, Callahan M K, Wolchok J D 2015 J. Clin. Oncol. 33 1974 Google Scholar
[7]	Tang Q, Chen Y, Li X, Long S, Shi Y, Yu Y, Wu W, Han L, Wang S 2022 Front. Immunol. 13 964442 Google Scholar
[8]	Ai L, Xu A, Xu J 2020 Adv. Exp. Med. Biol. 1248 33 Google Scholar
[9]	Dermani F K, Samadi P, Rahmani G, Kohlan A K, Najafi R 2019 J. Cell. Physiol. 234 1313 Google Scholar
[10]	Parry R V, Chemnitz J M, Frauwirth K A, Lanfranco A R, Braunstein I, Kobayashi S V, Linsley P S, Thompson C B, Riley J L 2005 Mol. Cell. Biol. 25 9543 Google Scholar
[11]	Patsoukis N, Brown J, Petkova V, Liu F, Li L, Boussiotis V A 2012 Sci. Signal. 5 ra46 Google Scholar
[12]	Hui E, Cheung J, Zhu J, Su X, Taylor M J, Wallweber H A, Sasmal D K, Huang J, Kim J M, Mellman I, Vale R D 2017 Science 355 1428 Google Scholar
[13]	Iwai Y, Ishida M, Tanaka Y, Okazaki T, Honjo T, Minato N 2002 Proc. Natl. Acad. Sci. U.S.A. 99 12293 Google Scholar
[14]	Iwai Y, Terawaki S, Honjo T 2005 Int. Immunol. 17 133 Google Scholar
[15]	Gong J, Chehrazi-Raffle A, Reddi S, Salgia R 2018 J. Immunother. Cancer 6 8 Google Scholar
[16]	Akinleye A, Rasool Z 2019 J. Hematol. Oncol. 12 92 Google Scholar
[17]	Shiravand Y, Khodadadi F, Kashani S M A, Hosseini-Fard S R, Hosseini S, Sadeghirad H, Ladwa R, O'Byrne K, Kulasinghe A 2022 Curr. Oncol. 29 3044 Google Scholar
[18]	Zhang J, Zhang Y, Qu B, Yang H, Hu S, Dong X 2021 Eur. J. Med. Chem. 218 113356 Google Scholar
[19]	Johnson D B, Nebhan C A, Moslehi J J, Balko J M 2022 Nat. Rev. Clin. Oncol. 19 254 Google Scholar
[20]	Mould D R, Meibohm B 2016 BioDrugs 30 275 Google Scholar
[21]	Wu Q, Jiang L, Li S C, He Q J, Yang B, Cao J 2021 Acta Pharmacol. Sin. 42 1 Google Scholar
[22]	Zhan M M, Hu X Q, Liu X X, Ruan B F, Xu J, Liao C 2016 Drug Discov. Today 21 1027 Google Scholar
[23]	Liu C, Seeram N P, Ma H 2021 Cancer Cell Int. 21 239 Google Scholar
[24]	Chupak L S, Zheng X 2015 WO Patent 2015034820 A1
[25]	Zak K M, Grudnik P, Guzik K, Zieba B J, Musielak B, Dömling A, Dubin G, Holak T A 2016 Oncotarget 7 30323 Google Scholar
[26]	Koblish H K, Wu L, Wang L S, Liu P C C, Wynn R, Rios-Doria J, Spitz S, Liu H, Volgina A, Zolotarjova N, Kapilashrami K, Behshad E, Covington M, Yang Y O, Li J, Diamond S, Soloviev M, O'Hayer K, Rubin S, Kanellopoulou C, Yang G, Rupar M, DiMatteo D, Lin L, Stevens C, Zhang Y, Thekkat P, Geschwindt R, Marando C, Yeleswaram S, Jackson J, Scherle P, Huber R, Yao W, Hollis G 2022 Cancer Discov. 12 1482 Google Scholar
[27]	Jiang J, Zou X, Liu Y, Liu X, Dong K, Yao X, Feng Z, Chen X, Sheng L, Li Y 2021 Front. Pharmacol. 12 677120 Google Scholar
[28]	Wang Y, Liu X, Zou X, Wang S, Luo L, Liu Y, Dong K, Yao X, Li Y, Chen X, Sheng L 2021 Pharmaceutics 13 598 Google Scholar
[29]	Sobral P S, Luz V C C, Almeida J, Videira P A, Pereira F 2023 Int. J. Mol. Sci. 24 5908 Google Scholar
[30]	Luo L, Zhong A, Wang Q, Zheng T 2021 Mar. Drugs 20 29 Google Scholar
[31]	Acúrcio R C, Pozzi S, Carreira B, Pojo M, Gómez-Cebrián N, Casimiro S, Fernandes A, Barateiro A, Farricha V, Brito J, Leandro A P, Salvador J A R, Graça L, Puchades-Carrasco L, Costa L, Satchi-Fainaro R, Guedes R C, Florindo H F 2022 J. Immunother. Cancer 10 e004695 Google Scholar
[32]	Lung J, Hung M S, Lin Y C, Hung C H, Chen C C, Lee K D, Tsai Y H 2020 Molecules 25 5293 Google Scholar
[33]	Guo Y, Liang J, Liu B, Jin Y 2021 Int. J. Mol. Sci. 22 10924 Google Scholar
[34]	Sterling T, Irwin J J 2015 J. Chem. Inf. Model. 55 2324 Google Scholar
[35]	Bemis G W, Murcko M A 1996 J. Med. Chem. 39 2887 Google Scholar
[36]	Rogers D, Hahn M 2010 J. Chem. Inf. Model. 50 742 Google Scholar
[37]	Yap C W 2011 J. Comput. Chem. 32 1466 Google Scholar
[38]	Durant J L, Leland B A, Henry D R, Nourse J G 2002 J. Chem. Inf. Comput. Sci. 42 1273 Google Scholar
[39]	Moriwaki H, Tian Y S, Kawashita N, Takagi T 2018 J. Cheminform. 10 4 Google Scholar
[40]	Bickerton G R, Paolini G V, Besnard J, Muresan S, Hopkins A L 2012 Nat. Chem. 4 90 Google Scholar
[41]	Kosugi T, Ohue M 2021 Int. J. Mol. Sci. 22 10925 Google Scholar
[42]	Xiong G, Wu Z, Yi J, Fu L, Yang Z, Hsieh C, Yin M, Zeng X, Wu C, Lu A, Chen X, Hou T, Cao D 2021 Nucleic Acids Res. 49 W5 Google Scholar
[43]	Jing T, Zhang Z, Kang Z, Mo J, Yue X, Lin Z, Fu X, Liu C, Ma H, Zhang X, Hu W 2023 J. Med. Chem. 66 6811 Google Scholar
[44]	Qin M, Meng Y, Yang H, Liu L, Zhang H, Wang S, Liu C, Wu X, Wu D, Tian Y, Hou Y, Zhao Y, Liu Y, Xu C, Wang L 2021 J. Med. Chem. 64 5519 Google Scholar
[45]	Trott O, Olson A J 2010 J. Comput. Chem. 31 455 Google Scholar
[46]	Laskowski R A, Swindells M B 2011 J. Chem. Inf. Model. 51 2778 Google Scholar
[47]	Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J, Darian E, Guvench O, Lopes P, Vorobyov I, Mackerell Jr. A D 2010 J. Comput. Chem. 31 671 Google Scholar
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