Several therapeutic monoclonal antibodies approved by the FDA are available against the PD-1/PD-L1 (programmed death 1/programmed death ligand 1) immune checkpoint axis, which has been an unprecedented success in cancer treatment. However, existing therapeutics against PD-L1, including small molecule inhibitors, have certain drawbacks such as high cost and drug resistance that challenge the currently available anti-PD-L1 therapy. Therefore, this study presents the screening of 32,552 compounds from the Natural Product Atlas database against PD-L1, including three steps of structure-based virtual screening followed by binding free energy to refine the ideal conformation of potent PD-L1 inhibitors. Subsequently, five natural compounds, i.e., Neoenactin B1, Actinofuranone I, Cosmosporin, Ganocapenoid A, and 3-[3-hydroxy-4-(3-methylbut-2-enyl)phenyl]-5-(4-hydroxybenzyl)-4-methyldihydrofuran-2(3H)-one, were collected based on the ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiling and binding free energy (>−60 kcal/mol) for further computational investigation in comparison to co-crystallized ligand, i.e., JQT inhibitor. Based on interaction mapping, explicit 100 ns molecular dynamics simulation, and end-point binding free energy calculations, the selected natural compounds were marked for substantial stability with PD-L1 via intermolecular interactions (hydrogen and hydrophobic) with essential residues in comparison to the JQT inhibitor. Collectively, the calculated results advocate the selected natural compounds as the putative potent inhibitors of PD-L1 and, therefore, can be considered for further development of PD-L1 immune checkpoint inhibitors in cancer immunotherapy.

Center for Bioinformatics Computational and Systems Biology Pathfinder Research and Training Foundation Greater Noida 201308 Uttar Pradesh India

Department of Applied Sciences Indian Institute of Information Technology Allahabad Allahabad 211015 Uttar Pradesh India

Department of Biology Faculty of Science King Khalid University Abha 62529 Saudi Arabia

Department of Botany and Microbiology Faculty of Science South Valley University Qena 83523 Egypt

Department of Life Science School of Basic Science and Research Sharda University Greater Noida 201310 Uttar Pradesh India

Laboratory of Ligand Engineering Institute of Biotechnology of the Czech Academy of Sciences v v i BIOCEV Research Center 25250 Vestec Czech Republic

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Sung H., Ferlay J., Siegel R.L., Laversanne M., Soerjomataram I., Jemal A., Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021;71:209–249. doi: 10.3322/caac.21660. PubMed DOI

Bukowski K., Kciuk M., Kontek R. Mechanisms of Multidrug Resistance in Cancer Chemotherapy. Int. J. Mol. Sci. 2020;21:3233. doi: 10.3390/ijms21093233. PubMed DOI PMC

Wang Y., Wang M., Wu H.X., Xu R.H. Advancing to the era of cancer immunotherapy. Cancer Commun. 2021;41:803–829. doi: 10.1002/cac2.12178. PubMed DOI PMC

Wyld L., Audisio R.A., Poston G.J. The evolution of cancer surgery and future perspectives. Nat. Rev. Clin. Oncol. 2015;12:115–124. doi: 10.1038/nrclinonc.2014.191. PubMed DOI

Aldeghaither D.S., Zahavi D.J., Murray J.C., Fertig E.J., Graham G.T., Zhang Y.W., O’Connell A., Ma J., Jablonski S.A., Weiner L.M. A Mechanism of Resistance to Antibody-Targeted Immune Attack. Cancer Immunol. Res. 2019;7:230–243. doi: 10.1158/2326-6066.CIR-18-0266. PubMed DOI PMC

Vasan N., Baselga J., Hyman D.M. A view on drug resistance in cancer. Nature. 2019;575:299–309. doi: 10.1038/s41586-019-1730-1. PubMed DOI PMC

Ward R.A., Fawell S., Floc’h N., Flemington V., McKerrecher D., Smith P.D. Challenges and Opportunities in Cancer Drug Resistance. Chem. Rev. 2021;121:3297–3351. doi: 10.1021/acs.chemrev.0c00383. PubMed DOI

Bhattacharya S., Mohanty A., Achuthan S., Kotnala S., Jolly M.K., Kulkarni P., Salgia R. Group Behavior and Emergence of Cancer Drug Resistance. Trends Cancer. 2021;7:323–334. doi: 10.1016/j.trecan.2021.01.009. PubMed DOI PMC

Allen C., Her S., Jaffray D.A. Radiotherapy for Cancer: Present and Future. Adv. Drug Deliv. Rev. 2017;109:1–2. doi: 10.1016/j.addr.2017.01.004. PubMed DOI

Tay R.E., Richardson E.K., Toh H.C. Revisiting the role of CD4(+) T cells in cancer immunotherapy-new insights into old paradigms. Cancer Gene Ther. 2021;28:5–17. doi: 10.1038/s41417-020-0183-x. PubMed DOI PMC

Khalil D.N., Smith E.L., Brentjens R.J., Wolchok J.D. The future of cancer treatment: Immunomodulation, CARs and combination immunotherapy. Nat. Rev. Clin. Oncol. 2016;13:273–290. doi: 10.1038/nrclinonc.2016.25. PubMed DOI PMC

Ribas A., Wolchok J.D. Cancer immunotherapy using checkpoint blockade. Science. 2018;359:1350–1355. doi: 10.1126/science.aar4060. PubMed DOI PMC

Sharma P., Allison J.P. Dissecting the mechanisms of immune checkpoint therapy. Nat. Rev. Immunol. 2020;20:75–76. doi: 10.1038/s41577-020-0275-8. PubMed DOI

Park W., Heo Y.J., Han D.K. New opportunities for nanoparticles in cancer immunotherapy. Biomater. Res. 2018;22:24. doi: 10.1186/s40824-018-0133-y. PubMed DOI PMC

Hu Z. Chapter 11-Using CAR-NK cells to overcome the host resistance to antibody immunotherapy and immune checkpoint blockade therapy. In: Bonavida B., Jewett A., editors. Successes and Challenges of NK Immunotherapy. Academic Press; Cambridge, MA, USA: 2021. pp. 193–212.

Chowdhury P.S., Chamoto K., Honjo T. Combination therapy strategies for improving PD-1 blockade efficacy: A new era in cancer immunotherapy. J. Intern. Med. 2018;283:110–120. doi: 10.1111/joim.12708. PubMed DOI

Qin W., Hu L., Zhang X., Jiang S., Li J., Zhang Z., Wang X. The Diverse Function of PD-1/PD-L Pathway Beyond Cancer. Front. Immunol. 2019;10:2298. doi: 10.3389/fimmu.2019.02298. PubMed DOI PMC

Salmaninejad A., Valilou S.F., Shabgah A.G., Aslani S., Alimardani M., Pasdar A., Sahebkar A. PD-1/PD-L1 pathway: Basic biology and role in cancer immunotherapy. J. Cell. Physiol. 2019;234:16824–16837. doi: 10.1002/jcp.28358. PubMed DOI

Muenst S., Soysal S.D., Tzankov A., Hoeller S. The PD-1/PD-L1 pathway: Biological background and clinical relevance of an emerging treatment target in immunotherapy. Expert Opin. Ther. Targets. 2015;19:201–211. doi: 10.1517/14728222.2014.980235. PubMed DOI

Akinleye A., Rasool Z. Immune checkpoint inhibitors of PD-L1 as cancer therapeutics. J. Hematol. Oncol. 2019;12:92. doi: 10.1186/s13045-019-0779-5. PubMed DOI PMC

Han Y., Liu D., Li L. PD-1/PD-L1 pathway: Current researches in cancer. Am. J. Cancer Res. 2020;10:727–742. PubMed PMC

Herbst R.S., Soria J.C., Kowanetz M., Fine G.D., Hamid O., Gordon M.S., Sosman J.A., McDermott D.F., Powderly J.D., Gettinger S.N., et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature. 2014;515:563–567. doi: 10.1038/nature14011. PubMed DOI PMC

Lee C.M., Tannock I.F. The distribution of the therapeutic monoclonal antibodies cetuximab and trastuzumab within solid tumors. BMC Cancer. 2010;10:255. doi: 10.1186/1471-2407-10-255. PubMed DOI PMC

Maute R.L., Gordon S.R., Mayer A.T., McCracken M.N., Natarajan A., Ring N.G., Kimura R., Tsai J.M., Manglik A., Kruse A.C., et al. Engineering high-affinity PD-1 variants for optimized immunotherapy and immuno-PET imaging. Proc. Natl. Acad. Sci. USA. 2015;112:E6506–E6514. doi: 10.1073/pnas.1519623112. PubMed DOI PMC

Topalian S.L., Hodi F.S., Brahmer J.R., Gettinger S.N., Smith D.C., McDermott D.F., Powderly J.D., Carvajal R.D., Sosman J.A., Atkins M.B., et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N. Engl. J. Med. 2012;366:2443–2454. doi: 10.1056/NEJMoa1200690. PubMed DOI PMC

Hamid O., Robert C., Daud A., Hodi F.S., Hwu W.J., Kefford R., Wolchok J.D., Hersey P., Joseph R.W., Weber J.S., et al. Safety and tumor responses with lambrolizumab (anti-PD-1) in melanoma. N. Engl. J. Med. 2013;369:134–144. doi: 10.1056/NEJMoa1305133. PubMed DOI PMC

Li K., Tian H. Development of small-molecule immune checkpoint inhibitors of PD-1/PD-L1 as a new therapeutic strategy for tumour immunotherapy. J. Drug Target. 2019;27:244–256. doi: 10.1080/1061186X.2018.1440400. PubMed DOI

Zhan M.M., Hu X.Q., Liu X.X., Ruan B.F., Xu J., Liao C. From monoclonal antibodies to small molecules: The development of inhibitors targeting the PD-1/PD-L1 pathway. Drug Discov. Today. 2016;21:1027–1036. doi: 10.1016/j.drudis.2016.04.011. PubMed DOI

Wu Q., Jiang L., Li S.C., He Q.J., Yang B., Cao J. Small molecule inhibitors targeting the PD-1/PD-L1 signaling pathway. Acta Pharmacol. Sin. 2021;42:1–9. doi: 10.1038/s41401-020-0366-x. PubMed DOI PMC

Awadasseid A., Wu Y., Zhang W. Advance investigation on synthetic small-molecule inhibitors targeting PD-1/PD-L1 signaling pathway. Life Sci. 2021;282:119813. doi: 10.1016/j.lfs.2021.119813. PubMed DOI

Ri M.H., Ma J., Jin X. Development of natural products for anti-PD-1/PD-L1 immunotherapy against cancer. J. Ethnopharmacol. 2021;281:114370. doi: 10.1016/j.jep.2021.114370. PubMed DOI

Li X., Yao Z., Jiang X., Sun J., Ran G., Yang X., Zhao Y., Yan Y., Chen Z., Tian L., et al. Bioactive compounds from Cudrania tricuspidata: A natural anticancer source. Crit. Rev. Food Sci. Nutr. 2020;60:494–514. doi: 10.1080/10408398.2018.1541866. PubMed DOI

Newman D.J., Cragg G.M. Natural Products as Sources of New Drugs over the Nearly Four Decades from 01/1981 to 09/2019. J. Nat. Prod. 2020;83:770–803. doi: 10.1021/acs.jnatprod.9b01285. PubMed DOI

Khan F., Pandey P., Mishra R., Arif M., Kumar A., Jafri A., Mazumder R. Elucidation of S-allylcysteine role in inducing apoptosis by inhibiting PD-L1 expression in human lung cancer cells. Anti Cancer Agents Med. Chem. 2021;21:532–541. doi: 10.2174/1871520620666200728121929. PubMed DOI

Rugamba A., Kang D.Y., Sp N., Jo E.S., Lee J.M., Bae S.W., Jang K.J. Silibinin Regulates Tumor Progression and Tumorsphere Formation by Suppressing PD-L1 Expression in Non-Small Cell Lung Cancer (NSCLC) Cells. Cells. 2021;10:1632. doi: 10.3390/cells10071632. PubMed DOI PMC

Zak K.M., Grudnik P., Guzik K., Zieba B.J., Musielak B., Domling A., Dubin G., Holak T.A. Structural basis for small molecule targeting of the programmed death ligand 1 (PD-L1) Oncotarget. 2016;7:30323–30335. doi: 10.18632/oncotarget.8730. PubMed DOI PMC

Zak K.M., Kitel R., Przetocka S., Golik P., Guzik K., Musielak B., Domling A., Dubin G., Holak T.A. Structure of the Complex of Human Programmed Death 1, PD-1, and Its Ligand PD-L1. Structure. 2015;23:2341–2348. doi: 10.1016/j.str.2015.09.010. PubMed DOI PMC

Muszak D., Surmiak E., Plewka J., Magiera-Mularz K., Kocik-Krol J., Musielak B., Sala D., Kitel R., Stec M., Weglarczyk K., et al. Terphenyl-Based Small-Molecule Inhibitors of Programmed Cell Death-1/Programmed Death-Ligand 1 Protein-Protein Interaction. J. Med. Chem. 2021;64:11614–11636. doi: 10.1021/acs.jmedchem.1c00957. PubMed DOI PMC

Van Santen J.A., Poynton E.F., Iskakova D., McMann E., Alsup T.A., Clark T.N., Fergusson C.H., Fewer D.P., Hughes A.H., McCadden C.A., et al. The Natural Products Atlas 2.0: A database of microbially-derived natural products. Nucleic Acids Res. 2021;50:D1317–D1323. doi: 10.1093/nar/gkab941. PubMed DOI PMC

Schrödinger Release 2020-4. Schrödinger, LLC; New York, NY, USA: 2020.

Jacobson M.P., Pincus D.L., Rapp C.S., Day T.J., Honig B., Shaw D.E., Friesner R.A. A hierarchical approach to all-atom protein loop prediction. Proteins. 2004;55:351–367. doi: 10.1002/prot.10613. PubMed DOI

Schrödinger Release 2020-4: Prime. Schrödinger, LLC; New York, NY, USA: 2020.

Sastry G.M., Adzhigirey M., Day T., Annabhimoju R., Sherman W. Protein and ligand preparation: Parameters, protocols, and influence on virtual screening enrichments. J. Comput. Aided Mol. Des. 2013;27:221–234. doi: 10.1007/s10822-013-9644-8. PubMed DOI

Schrödinger Release 2020-4: Glide. Schrödinger, LLC; New York, NY, USA: 2020.

Schrödinger Release 2020-4: LigPrep. Schrödinger, LLC; New York, NY, USA: 2020.

Schrödinger Release 2020-4: QikProp. Schrödinger, LLC; New York, NY, USA: 2020.

Hou T., Wang J., Li Y., Wang W. Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J. Chem. Inf. Model. 2011;51:69–82. doi: 10.1021/ci100275a. PubMed DOI PMC

Culletta G., Gulotta M.R., Perricone U., Zappala M., Almerico A.M., Tutone M. Exploring the SARS-CoV-2 Proteome in the Search of Potential Inhibitors via Structure-Based Pharmacophore Modeling/Docking Approach. Computation. 2020;8:77. doi: 10.3390/computation8030077. DOI

Tutone M., Pibiri I., Lentini L., Pace A., Almerico A.M. Deciphering the Nonsense Readthrough Mechanism of Action of Ataluren: An in Silico Compared Study. ACS Med. Chem. Lett. 2019;10:522–527. doi: 10.1021/acsmedchemlett.8b00558. PubMed DOI PMC

Wang J.M., Morin P., Wang W., Kollman P.A. Use of MM-PBSA in reproducing the binding free energies to HIV-1 RT of TIBO derivatives and predicting the binding mode to HIV-1 RT of efavirenz by docking and MM-PBSA. J. Am. Chem. Soc. 2001;123:5221–5230. doi: 10.1021/ja003834q. PubMed DOI

Lee K.E., Bharadwaj S., Yadava U., Kang S.G. Computational and In Vitro Investigation of (-)-Epicatechin and Proanthocyanidin B2 as Inhibitors of Human Matrix Metalloproteinase 1. Biomolecules. 2020;10:1379. doi: 10.3390/biom10101379. PubMed DOI PMC

Bharadwaj S., Dubey A., Yadava U., Mishra S.K., Kang S.G., Dwivedi V.D. Exploration of natural compounds with anti-SARS-CoV-2 activity via inhibition of SARS-CoV-2 Mpro. Brief Bioinform. 2021;22:1361–1377. doi: 10.1093/bib/bbaa382. PubMed DOI PMC

Mena-Ulecia K., Tiznado W., Caballero J. Study of the Differential Activity of Thrombin Inhibitors Using Docking, QSAR, Molecular Dynamics, and MM-GBSA. PLoS ONE. 2015;10:e0142774. doi: 10.1371/journal.pone.0142774. PubMed DOI PMC

Schrödinger Release 2020-4: Maestro. Schrödinger, LLC; New York, NY, USA: 2020.

Bowers K.J., Chow E., Xu H., Dror R.O., Eastwood M.P., Gregersen B.A., Klepeis J.L., Kolossvary I., Moraes M.A., Sacerdoti F.D. Scalable algorithms for molecular dynamics simulations on commodity clusters; Proceedings of the 2006 ACM/IEEE conference on Supercomputing; Tampa, FL, USA. 11–17 November 2006; p. 84.

Schrödinger Release 2018-4: Maestro. Schrödinger, LLC; New York, NY, USA: 2018.

Guo J.X., Hurley M.M., Wright J.B., Lushington G.H. A docking score function for estimating ligand-protein interactions: Application to acetylcholinesterase inhibition. J. Med. Chem. 2004;47:5492–5500. doi: 10.1021/jm049695v. PubMed DOI

Guedes I.A., Pereira F.S.S., Dardenne L.E. Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges. Front. Pharmacol. 2018;9:1089. doi: 10.3389/fphar.2018.01089. PubMed DOI PMC

Li H.J., Sze K.H., Lu G., Ballester P.J. Machine-learning scoring functions for structure-based drug lead optimization. Wires Comput. Mol. Sci. 2020;10:e1465. doi: 10.1002/wcms.1465. DOI

Rastelli G., Pinzi L. Refinement and Rescoring of Virtual Screening Results. Front. Chem. 2019;7:498. doi: 10.3389/fchem.2019.00498. PubMed DOI PMC

Heinzelmann G., Gilson M.K. Automation of absolute protein-ligand binding free energy calculations for docking refinement and compound evaluation. Sci. Rep. 2021;11:1116. doi: 10.1038/s41598-020-80769-1. PubMed DOI PMC

Hou T., Wang J., Li Y., Wang W. Assessing the performance of the molecular mechanics/Poisson Boltzmann surface area and molecular mechanics/generalized Born surface area methods. II. The accuracy of ranking poses generated from docking. J. Comput. Chem. 2011;32:866–877. doi: 10.1002/jcc.21666. PubMed DOI PMC

Genheden S., Ryde U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin. Drug Discov. 2015;10:449–461. doi: 10.1517/17460441.2015.1032936. PubMed DOI PMC

Pu C., Yan G., Shi J., Li R. Assessing the performance of docking scoring function, FEP, MM-GBSA, and QM/MM-GBSA approaches on a series of PLK1 inhibitors. Medchemcomm. 2017;8:1452–1458. doi: 10.1039/C7MD00184C. PubMed DOI PMC

Rastelli G., Del Rio A., Degliesposti G., Sgobba M. Fast and Accurate Predictions of Binding Free Energies Using MM-PBSA and MM-GBSA. J. Comput. Chem. 2010;31:797–810. doi: 10.1002/jcc.21372. PubMed DOI

Niinivehmas S.P., Virtanen S.I., Lehtonen J.V., Postila P.A., Pentikainen O.T. Comparison of virtual high-throughput screening methods for the identification of phosphodiesterase-5 inhibitors. J. Chem. Inf. Model. 2011;51:1353–1363. doi: 10.1021/ci1004527. PubMed DOI

Roy S.K., Inouye Y., Nakamura S., Furukawa J., Okuda S. Isolation, structural elucidation and biological properties of neoenactins B1, B2, M1 and M2, neoenactin congeners. J. Antibiot. 1987;40:266–274. doi: 10.7164/antibiotics.40.266. PubMed DOI

Ma J., Cao B., Liu C., Guan P., Mu Y., Jiang Y., Han L., Huang X. Actinofuranones DI from a lichen-associated actinomycetes, streptomyces gramineus, and their anti-inflammatory effects. Molecules. 2018;23:2393. doi: 10.3390/molecules23092393. PubMed DOI PMC

Nakamura T., Suzuki T., Ariefta N.R., Koseki T., Aboshi T., Murayama T., Widiyantoro A., Kurniatuhadi R., Malik A., Annas S. Meroterpenoids produced by Pseudocosmospora sp. Bm-1-1 isolated from Acanthus ebracteatus Vahl. Phytochem. Lett. 2019;31:85–91. doi: 10.1016/j.phytol.2019.03.014. DOI

Liao G.-F., Wu Z.-H., Liu Y., Yan Y.-M., Lu R.-M., Cheng Y.-X. Ganocapenoids A–D: Four new aromatic meroterpenoids from Ganoderma capense. Bioorg. Med. Chem. Lett. 2019;29:143–147. doi: 10.1016/j.bmcl.2018.12.011. PubMed DOI

Awaad A.S., Nabilah A.J.A., Zain M.E. New antifungal compounds from Aspergillus terreus isolated from desert soil. Phytother. Res. 2012;26:1872–1877. doi: 10.1002/ptr.4668. PubMed DOI

Bharadwaj S., Lee K.E., Dwivedi V.D., Yadava U., Kang S.G. Computational aided mechanistic understanding of Camellia sinensis bioactive compounds against co-chaperone p23 as potential anticancer agent. J. Cell. Biochem. 2019;120:19064–19075. doi: 10.1002/jcb.29229. PubMed DOI

Filipe H.A.L., Loura L.M.S. Molecular Dynamics Simulations: Advances and Applications. Molecules. 2022;27:2105. doi: 10.3390/molecules27072105. PubMed DOI PMC

Plattner N., Doerr S., De Fabritiis G., Noe F. Complete protein-protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling. Nat. Chem. 2017;9:1005–1011. doi: 10.1038/nchem.2785. PubMed DOI

Wang E., Sun H., Wang J., Wang Z., Liu H., Zhang J.Z.H., Hou T. End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design. Chem. Rev. 2019;119:9478–9508. doi: 10.1021/acs.chemrev.9b00055. PubMed DOI

Swanson J.M., Henchman R.H., McCammon J.A. Revisiting free energy calculations: A theoretical connection to MM/PBSA and direct calculation of the association free energy. Biophys. J. 2004;86:67–74. doi: 10.1016/S0006-3495(04)74084-9. PubMed DOI PMC

Adekoya O.A., Willassen N.-P., Sylte I. Molecular insight into pseudolysin inhibition using the MM-PBSA and LIE methods. J. Struct. Biol. 2006;153:129–144. doi: 10.1016/j.jsb.2005.11.003. PubMed DOI

Genheden S., Ryde U. Comparison of end-point continuum-solvation methods for the calculation of protein-ligand binding free energies. Proteins. 2012;80:1326–1342. doi: 10.1002/prot.24029. PubMed DOI

Shi D.F., An X.L., Bai Q.F., Bing Z.T., Zhou S.Y., Liu H.X., Yao X.J. Computational Insight Into the Small Molecule Intervening PD-L1 Dimerization and the Potential Structure-Activity Relationship. Front. Chem. 2019;7:764. doi: 10.3389/fchem.2019.00764. PubMed DOI PMC

Guo Y., Jin Y., Wang B., Liu B. Molecular Mechanism of Small-Molecule Inhibitors in Blocking the PD-1/PD-L1 Pathway through PD-L1 Dimerization. Int. J. Mol. Sci. 2021;22:4766. doi: 10.3390/ijms22094766. PubMed DOI PMC