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Table of Contents
ORIGINAL ARTICLE
Year : 2022  |  Volume : 1  |  Issue : 2  |  Page : 127-133

In silico Study of shape complementarity, binding affinity, and protein–ligand interactions of systematic evolution of ligands by exponential enrichment-aptamer to programmed death ligand-1 using patchdock


1 Institute for Research in Molecular Medicine (INFORMM), Higher Institution Centre of Excellence, University Sains Malaysia, Health Campus, Kelantan; Faculty of Arts and Science, International University of Malaya-Wales, Kuala Lumpur, Malaysia
2 Institute for Research in Molecular Medicine (INFORMM), Higher Institution Centre of Excellence, University Sains Malaysia, Health Campus, Kelantan, Malaysia
3 Faculty of Arts and Science, International University of Malaya-Wales, Kuala Lumpur, Malaysia

Date of Submission15-Mar-2022
Date of Decision20-May-2022
Date of Acceptance28-May-2022
Date of Web Publication15-Jun-2022

Correspondence Address:
Noor Fatmawati Mokhtar
PhD, Institute for Research in Molecular Medicine (INFORMM), Higher Institution Centre of Excellence, University Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan
Malaysia
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jpdtsm.jpdtsm_17_22

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  Abstract 


BACKGROUND: Nucleic acid aptamers hold great promise in diagnostic and therapeutic applications for a wide range of diseases due to their analog feature to antibodies. Despite the utility of systematic evolution of ligands by exponential enrichment (SELEX) method for aptamer determination, complementarity in silico aptamer design is highly sought after to facilitate virtual screening and increased understanding of important aptamer–protein interactions.
MATERIALS AND METHODS: We previously obtained aptamers against programmed death ligand-1 (PD-L1) through SELEX: P12, P32, and P33. In the present work, structure prediction and binding mode of these aptamers to PD-L1 were evaluated using mFold and DNA sequence to structure (IIT Delhi) for two-dimensional and three-dimensional structure prediction, respectively, and PatchDock for docking. PD-L1 model protein 5N2F was used as the target protein. Docking was performed and analyzed based on three aspects: shape complementarity score, binding affinity, and interactions with aptamer.
RESULTS: All three aptamers combine steadily with 5N2F protein through strong hydrogen (polar bonds), hydrophobic interactions (nonpolar bonds), and π-cation interactions, which can be accessed through a fully automated protein–ligand interaction profiler.
CONCLUSIONS: Molecular docking experiments indicated the feasibility of using in silico technique to select aptamers that can function as antibodies analog.

Keywords: Aptamers, binding affinity, PatchDock, programmed cell death-ligand 1, shape complementarity score


How to cite this article:
Ching KW, Nazri MN, Rachman AR, Mustafa KM, Mokhtar NF. In silico Study of shape complementarity, binding affinity, and protein–ligand interactions of systematic evolution of ligands by exponential enrichment-aptamer to programmed death ligand-1 using patchdock. J Prev Diagn Treat Strategies Med 2022;1:127-33

How to cite this URL:
Ching KW, Nazri MN, Rachman AR, Mustafa KM, Mokhtar NF. In silico Study of shape complementarity, binding affinity, and protein–ligand interactions of systematic evolution of ligands by exponential enrichment-aptamer to programmed death ligand-1 using patchdock. J Prev Diagn Treat Strategies Med [serial online] 2022 [cited 2022 Jun 26];1:127-33. Available from: http://www.jpdtsm.com/text.asp?2022/1/2/127/347541




  Introduction Top


Aptamers are defined as synthetic molecules made of short, single-stranded nucleic acids of DNA or RNA that can selectively bind to a specific target such as proteins and small molecules.[1] Aptamers exert analog features of antibodies; however, unlike antibodies, aptamers are known to be stable at room temperature, smaller in size for better targets, can bind to very small, nonimmunogenic toxic targets, have cheaper and shorter time of production, superior affinity, and specificity with scalable production.[2]

The conventional method for the selection of aptamers is known as systematic evolution of ligands by exponential enrichment (SELEX) also referred as in vitro selection from a large oligonucleotide library, containing five basic steps for completion of a single round.[3] After completion of SELEX, the selected aptamers are expanded.[4] The latest approach for aptamers selection is via in silico also referred as in silico aptamer–antigen conformational prediction.[5] Compared to the conventional SELEX, in silico selection is found to exhibit few advantages such as lower cost, shorter development period, high target specificity, and wider target range.[6],[7],[8],[9] However, in the current trend of aptamers selection, in silico method is seen as complementing SELEX to facilitate virtual screening and increased understanding of important aptamer–protein interactions.[10] Accordingly, the SELEX-aptamers sequence is converted into three-dimensional (3D) structure, followed by a process that targets protein with specific ligands or molecules with the use of numerous docking software or web servers[11] for the prediction of the conformation and orientation of a ligand when it is bound to target protein.[12]

Programmed death ligand-1 (PD-L1) is a 33-kDa type 1 transmembrane glycoprotein containing 290 amino acids with immunoglobulin (Ig) and IgC domains in its extracellular region.[13] The molecule is commonly expressed on the surface of antigen-presenting cells, i.e., immune-related lymphocytes, such as T-cells, B-cells, and myeloid cells,[14] where it specifically binds to the receptor, programmed cell death protein 1 (PD-1), of which the complex is responsible for inhibition of T-cells activity[15] in the regulation of immune tolerance and autoimmunity.[16] These immunosuppressive molecules are responsible as brakes in regulating the adaptive immune response or maintaining the balance of the immune system.[17] The subsequent discovery of PD-1/PD-L1 overexpression on the surface of cancer cells has led to their role in deactivating T-cells function, thus preventing cancer cells to be detected, destroyed, and removed ffrom the system which lead to immune evasion.[18] Hence, designing molecules to target the PD-1/PD-L1 emerged as a solution for cancer immune evasion.[19] Antibody-based inhibitors and antibody–antigen-based companion/complementarity diagnostic assay are currently the major approach in clinical diagnostic and therapeutic of PD-L1.[20],[21]

In our previous work, SELEX cycles were performed against recombinant PD-L1 protein, a tumor biomarker currently utilized as a target in cancer immunotherapies. Three selected SELEX-aptamers against PD-L1, namely, P12, P32, and P33, were proceeded for bioinformatics or in silico research where the 3D structure of these aptamers was predicted, and molecular/computational docking was conducted for the evaluation of shape complementarity, binding affinity, and PD-L1-aptamer interactions. The findings of this study will likely lead to the better future development of an alternative molecular “tool,” aptamers for molecular diagnostics and therapeutic applications of PD-L1 for cancer and related diseases.


  Materials and Methods Top


Ethics

  • Not applicable (no human study involved, just bioinformatics data).


Study design (type of sampling and reasons for selection)

5N2F preparation as programmed death ligand-1 model protein

Protein Data Bank (PDB) Research Collaboratory for Structural Bioinformatics was searched for the most ideal PD-L1 protein molecule to be used as model protein, where 5N2F (Accession number: Q9NZQ7) with a low resolution of 1.70 Å (high resolution of protein quality) was selected and downloaded. 5N2F initially consists of a small-molecule inhibitor complex that has subsequently been prepared for dockings by Biogenes, Malaysia, via utilization of PyMOL to remove undesired protein chains and ligands. The pure structure or naked protein of 5N2F was later used for this study (to ensure more accurate results).

Two-dimensional and three-dimensional structure generation of the systematic evolution of ligands by exponential enrichment -produced DNA aptamers

Aptamers P12, P32, and P33 were selected from our previous work of 6 SELEX cycles against recombinant human PD-L1 (R and D Systems). mFold was utilized for the initial analysis of secondary structures of the selected aptamers. UNAFold web server of http://www.unafold.org/was accessed before choosing mFold option of DNA folding form. P12, P32, and P33 were pasted into the provided column, sequences been formatted and “fold DNA” under parameters with a folding temperature of 24°C and ionic condition of 137 Na+ (mM), 1.5 Mg++ (mM). Two-dimensional (2D) structures of aptamers of P12, P32, and P33 generated by mFold in PNG format were downloaded [Figure 1]a, [Figure 1]b, [Figure 1]c. The subsequent step involved utilization of DNA sequence to structure by Supercomputing Facility for Bioinformatics and Computational Biology, IIT Delhi, was accessed via http://www.scfbio-iitd.res.in/software/drugdesign/bdna.jsp. 2D nucleic acid aptamer sequences generated from mFold cycle were inserted one by one into the column provided and “Submit Sequence” button was clicked to generate 3D structures in the PDB format file of P12.pdb, P32.pdb, and P33.pdb [Figure 2]a, [Figure 1]b, [Figure 1]c.
Figure 1: 2D structure of (a) P12, (b) P32 and (c) P33 produced using mFold. (Folding temperature of 24°C and ionic condition of 137 Na + [mM], 1.5 Mg++ [mM]). 2D: Two-dimensional

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Figure 2: 3D structure of (a) P12, (b) P32, and (c) P33 produced using DNA Sequence to Structure by Supercomputing Facility for Bioinformatics and Computational Biology, IIT Delhi's. 3D: three-dimensional

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Protein–ligand blind docking using PatchDock: Interaction analysis and docking structure visualizer

PatchDock, a molecular docking algorithm based on shape complementarity principles developed back in the year 2002, was accessed via http://bioinfo3d.cs.tau.ac.il/PatchDock/and was chosen as a docking tool. This freely available, highly efficient, fully automated online server only requires users to input specific protein files and ligand of PDB file format in provided columns with few optional fields in the docking request form. The output docking results link representing the top 20 geometry score, desolvation energy, interface area size, and the actual rigid transformation of the solution were received via e-mail and downloaded for further data analysis.[22] Briefly, clustering RMSD was set as a default value of 4.0, while the complex type was set as a protein–small ligand category. The advanced options for uploading files of receptor and ligand binding sites together with distance constraints remained unused as blind docking was conducted. Top 5 “score” (shape complementarity score) and “atomic contact energy (ACE)” (binding affinity) with solution No. 1 of the highest “score” of each PDB file of the docking structures of P12, P32, and P33 were downloaded for further structural analysis. Interaction analysis was analyzed using a fully automated protein–ligand interaction profiler (PLIP), freely available at https://plip-tool.biotec.tu-dresden.de/plip-web/plip/index, while BIOVIA Discovery Studio 2021 Client was used as docking structure visualizer to view all three docking structures of 5N2F-P12, 5N2F-P32, and 5N2F-P33.


  Results Top


Shape complementarity score of systematic evolution of ligands by exponential enrichment -produced DNA aptamers

Top 5 “score” or known as shape complementarity scores in PatchDock were collected. The first docking, 5N2F-P12, generated top 5 scores of 13518, 13264, 13188, 12754, and 12576. The second docking of 5N2F-P32 generated scores of 12898, 12804, 12756, 12752, and 12692, while the third docking of 5N2F-P33 generated top 5 scores of 12698, 12624, 12556, 12550, and 12542 [Table 1].
Table 1: Shape complementarity scores of 5N2F-P12, 5N2F-P32, and 5N2F-P33

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Atomic contact energy or binding affinity of systematic evolution of ligands by exponential enrichment-produced DNA aptamers

Top 5 ACE or popularly known as binding affinity were not ranked accordingly but obey the ranking of shape complementarity score, “Score” based on PatchDock system. The first docking, 5N2F-P12, generated top 5 ACE values of −587.70, −118.11, −151.62, −58.10, and − 346.48. The second docking of 5N2F-P32 generated ACE values of −1317.61, −57.41, −157.98, −175.60, and − 159.37, while the third docking of 5N2F-P33 had generated top 5 ACE values of −158.45, −187.95, −4.91, −75.71, and −187.47 [Table 2].
Table 2: Atomic contact energy of 5N2F-P12, 5N2F-P32, and 5N2F-P33

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Docking structures and protein–ligand interactions

Protein–ligand interactions or docking structure interaction analyses were conducted. Solution No. 1 of the highest shape complementarity score (score) of each PDB file of the docking structure was downloaded and analyzed using PLIP. Data for types of bond interactions were generated and collected from automated PLIP: hydrogen (polar bonds), hydrophobic interactions (nonpolar bonds), water bridges, π-cation interactions, and salt bridges. However, for this study, only hydrogen (polar bonds), hydrophobic interactions (nonpolar bonds), and π-cation interactions were focused. For hydrophobic interactions, 5N2F-P12 formed 4 hydrophobic interactions, involving amino acid residues of 60A Glu (3.97), 107A Gln (3.89), 107B Gln (3.80), and 109A Ala (3.65). 5N2F-P32 formed 6 hydrophobic interactions involving amino acid residues 105B Lys (3.82), 106A Leu (1.17), 130A Val (2.72), 132A Ala (3.91), 137A Ala (3.57), and 138A Leu (3.08). 5N2F-P33 formed 3 hydrophobic interactions at residues 32B Tyr (3.89), 83B Gln (3.92), and 107B Gln (3.09). There were huge number of amino acid residues bonding involved in hydrogen bond interactions of all 3 docking structures. A total of 31 hydrogen bonds involving numerous amino acid residues were identified in 5N2F-P12, 40 hydrogen bond interactions were involved in 5N2F-P32, and 5N2F-P33 has the lowest hydrogen bonding of 20 interactions. For the third type interaction involving π-cation interaction, the bonding of 5N2F-P12 involved with only single amino acid of 140B His (3.59), while for 5N2F-P32, two π-cation interactions had been identified, which involve 32A Tyr (5.91) and 142A His (4.34). Similar to 5N2F-P12, only a single amino acid of 140B His (4.36) of π-cation interactions had been recognized in 5N2F-P33 [Table 3]. BIOVIA Discovery Studio 2021 Client, a leading visualization tool for viewing and analyzing protein and modeling data, was responsible as a docking visualization tool in this study. All three docked structures were labeled accordingly, and each was displayed with its best graphic resolution [Figure 3]a, [Figure 3]b, [Figure 3]c.
Figure 3: Docking Structure (a) 5N2F-P12, (b) 5N2F-P32 and (c) 5N2F-P33 using PatchDock. (Default RMSD value of 4.0, the complex type was set as protein–small ligand category with default advanced options). PD-L1: Programmed death ligand-1

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Table 3: Protein–ligand Interactions of 5N2F-P12, 5N2F-P32, and 5N2F-P33

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  Discussion Top


Molecular docking has become an increasingly popular approach in molecular structural research. As compared to conventional laboratory experiments, molecular docking or in silico-based experiments of computer-based modeling technologies are reported to have a few major advantages of rapid speed production, screening on candidates via analysis of prediction models, reducing the need for animal models, and most importantly lower and more reasonable overall research cost.[23] In silico studies can also be conducted anywhere and anytime using a laptop/desktop without requiring researchers to visit the laboratory.

In this study, computational protein–ligand blind docking was conducted using PatchDock, a docking server based on shape complementarity principles, to conduct docking between candidate ligands, DNA aptamers P12, P32, and P33 with PD-L1 protein of “5N2F“. PatchDock was chosen as a specific docking server due to its unique capability where it allows large file sizes of protein and ligand to be uploaded into the system unlike many other types of protein–ligand docking software such as CB-Dock server that only allows a maximum size of 15 kb ligand to be uploaded.[24] DNA aptamers of P12, P32, and P33 in these studies are known to be large size ligands with total PDB structure size of 427 kb. After numerous trials, PatchDock was found as the ideal docking server that can perform molecular docking with large aptamers within a short duration of time of approximately 1.15 h for each docking between “5N2F” with DNA aptamers of P12, P32, and P33. In the aspect of geometry shape complementarity score in PatchDock, “score” result which is the most basic component of a scoring function plays an essential role in determining whether SELEX-produced aptamers are able to bind successfully to PD-L1 protein of “5N2F.” A higher score will generally indicate more ideal shape complementarity between both protein and ligand.[25] Accordingly, from all 3 dockings, 5N2F-P12 produced the highest shape complementarity score of 13518 followed by 5N2F-P32 of 12898 and 5N2F-P33 of 12698. The second aspect is ACE or binding affinity. The ACE score can be considered as an estimate of the change in desolvation energy of the two proteins from the unbound state to becoming a complex. A lower or more negative ACE value implies a more favorable desolvation-free energy in the docking structure, which may prove the effectiveness of the drug candidate.[26] In the present work via analysis of ACE results generated from PatchDock server, the docking structure of 5N2F-P32 has the lowest negative value of −1317.61 followed by 5N2F-P12 of −587.70 and lastly 5N2F-P33 of −158.45. However, shape complementarity score and ACE are not necessarily the best predictor for the determination of the most suitable or ideal DNA aptamer. Researchers are strongly recommended to interpret other additional docking criteria to assure the selection of an accurate aptamer for future clinical applications. Another aspect important in providing a theoretical basis for the design and discovery of novel drugs is target protein–ligand interactions.[27] Hydrophobic interactions or nonpolar bonds which are known as the main driving force for protein folding are responsible to isolate themselves from water molecules, which provides overall better protein stability. Hydrogen or polar bonds act as the minor determinant that is also responsible in supporting protein stability but to a lower degree than hydrophobic interactions.[28] Meanwhile, π-cation interaction is defined as an interaction of a positive charge with the face of an aromatic ring, where the abundance of aromatic and cationic residues in protein side chains are crucial as secondary force for protein folding with high success rates have been reported on predictions of binding affinity based on electrostatics alone.[29] This research had shown that hydrophobic, hydrogen, and π-cation interactions are mediated by different amino acid residues. Based on hydrophobic interaction results, 5N2F-P32 has the highest hydrophobic interaction of 6 with a similar amino acid residue of 107B Gln was found in both hydrophobic interactions of 5N2F-P12 and 5N2F-P33. 5N2F-P32 has the most hydrogen bonds which a total of 40 polar interactions had been observed in PLIP. A total of 10 similar amino acid residues of 107B Gln, 109A Ala, 134A Tyr, 136A Ala, 137A Ala, 138A Leu, 139A Glu, 140A His, 140B His, and 141A His were identified between 5N2F-P12 and 5N2F-P32. On the other hand, an equal total of 10 similar amino acid residues of 79B Ser, 80B Ser, 107B Gln, 131A Asn, 132A Ala, 133A Pro, 134A Tyr, 136A Ala, 139B Glu, and 140B His were found between 5N2F-P33 and 5N2F-P32. For comparison between 5N2F-P12 and 5N2F-P33, a total of 5 similar amino acid residues of 107B Gln, 134A Tyr, 135A Ala, 136A Ala, and 140B His were observed. Meanwhile, 5N2F-P32 has the highest π-cation interaction among all with 5N2F-P12 and 5N2F-P33 was found to have only a single amino acid residue involved in π-cation interaction where both contain similar amino acid residue of 140B His. Even though all docking structures had shown some similar amino acid residues involved in hydrophobic, hydrogen, and π-cation interactions, all of those amino acids were reported to have different distances among each other. For docking structures visualized using BIOVIA Discovery Studio 2021, “5N2F” of PD-L1 protein was found to bind in a slightly different position to each aptamer. “5N2F” protein in 5N2F-P12 was situated at the far side corner of P12 aptamer [Figure 3]a, while “5N2F” protein in both 5N2F-P32 and 5N2F-P33 shifted slightly toward the middle part of those aptamers [Figure 3]b and [Figure 3]c.

In summary, based on three aspects of docking results, even though 5N2F-P32 is most likely to be the most potential docking structure based on its lowest negative ACE value with the most hydrophobic, hydrogen and π-cation interactions been identified and ranked second in terms of shape complementarity score, researchers must take on extra considerations on few limitations of the study before making an assumption on the most ideal aptamer. The few considerations are the availability of protein–ligand docking software that is capable to conduct docking studies between protein, PD-L1 with large size aptamers, the precision of PatchDock, and finally the eligibility or quality of the selected SELEX-produced aptamers that are produced in the wet laboratory. These are essential to ensure that ideal aptamers can be produced in replacing current antibody-based detection for future cancer applications.


  Conclusions Top


In the present study, in silico research had shown that all 3 DNA SELEX-produced aptamers of P12, P32, and P33 could be the next ideal alternative “tool” in exploring the diagnostics and therapeutics of PD-L1.The findings of this research will provide an insight on the possible discovery of ideal aptamer which targets therapy potentiates immune checkpoint inhibitors for future development in clinical diagnostic and therapeutic of PD-L1.

Limitation of the study

This study is categorized as fully bioinformatics-based research. Thus, there is no any wet laboratory studies or experiments been conducted. Further researches such as in vivo and in vitro experimental studies must be performed in determining the most ideal SELEX-aptamers in ensuring their validation and reliability.

Financial support and sponsorship

This study was funded by the Industry-Academic Collaborative Grant (304/CIPPM/6150161/A146) and supported by the Malaysian Ministry of Education through the Higher Institution Centers of Excellence Program (No. 311/CIPPM/4401005).

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Zhou J, Rossi J. Aptamers as targeted therapeutics: Current potential and challenges. Nat Rev Drug Discov 2017;16:181-202.  Back to cited text no. 1
    
2.
Song KM, Lee S, Ban C. Aptamers and their biological applications. Sensors (Basel) 2012;12:612-31.  Back to cited text no. 2
    
3.
Levine HA, Nilsen-Hamilton M. A mathematical analysis of SELEX. Comput Biol Chem 2007;31:11-35.  Back to cited text no. 3
    
4.
Hoinka J, Berezhnoy A, Sauna ZE, Gilboa E, Przytycka TM. AptaCluster – A method to cluster HT-SELEX aptamer pools and lessons from its application. Res Comput Mol Biol 2014;8394:115-28.  Back to cited text no. 4
    
5.
Liu H, Yu J. Challenges of SELEX and demerits of aptamer-based methods: Affinity acquisition and method design. Hoboken, New Jersey, United States: John Wiley& Sons (Wiley); 2018. p. 345-64. Available from: https://doi.org/10.1002/9783527806799.ch12. [Last accessed on 2022 Feb 18].  Back to cited text no. 5
    
6.
Ahirwar R, Nahar S, Aggarwal S, Ramachandran S, Maiti S, Nahar P. In silico selection of an aptamer to estrogen receptor alpha using computational docking employing estrogen response elements as aptamer-alike molecules. Sci Rep 2016;6:21285.  Back to cited text no. 6
    
7.
Satpathy R, Dash N. In silico screening and characterization of novel natural peptides as spike protein inhibitors of novel coronavirus (severe acute respiratory syndrome coronavirus 2). J Prev Diagn Treat Strategies Med 2022;1:54-62.  Back to cited text no. 7
  [Full text]  
8.
Yadav S, Yadav U, Sharma D. In silico approach for the identification of mirror repeats in selected operon genes of Escherichia coli strain K-12 substrain MG1655. Biomed Biotechnol Res J (BBRJ) 2022;6:93-7.  Back to cited text no. 8
    
9.
Selvaraj G, Wilson J, Kanagaraj N, Subashini E, Thangavel S. Enhanced antifungal activity of Piper betle against candidiasis infection causing Candida albicans and in silico analysis with its virulent protein. Biomed Biotechnol Res J (BBRJ) 2022;6:73-80.  Back to cited text no. 9
    
10.
Sabri Z, Abdul Hamid A, Hitam S, Abdul Rahim MZ. In-silico selection of aptamer: A review on the revolutionary approach to understand the aptamer design and interaction through computational chemistry. Mater Today Proc 2019;19:1572-81.  Back to cited text no. 10
    
11.
Buglak AA, Samokhvalov AV, Zherdev AV, Dzantiev BB. Methods and applications of in silico aptamer design and modeling. Int J Mol Sci 2020;21:8420.  Back to cited text no. 11
    
12.
Vieira TF, Sousa SF. Comparing AutoDock and Vina in ligand/decoy discrimination for virtual screening. Appl Sci 2019;9:4538.  Back to cited text no. 12
    
13.
Han Y, Liu D, Li L. PD-1/PD-L1 pathway: Current researches in cancer. Am J Cancer Res 2020;10:727-42.  Back to cited text no. 13
    
14.
Wu Y, Chen W, Xu ZP, Gu W. PD-L1 distribution and perspective for cancer immunotherapy-blockade, knockdown, or inhibition. Front Immunol 2019;10:2022.  Back to cited text no. 14
    
15.
Davis AA, Patel VG. The role of PD-L1 expression as a predictive biomarker: An analysis of all US Food and Drug Administration (FDA) approvals of immune checkpoint inhibitors. J Immunother Cancer 2019;7:278.  Back to cited text no. 15
    
16.
Okazaki T, Honjo T. PD-1 and PD-1 ligands: From discovery to clinical application. Int Immunol 2007;19:813-24.  Back to cited text no. 16
    
17.
Qin W, Hu L, Zhang X, Jiang S, Li J, Zhang Z, et al. The diverse function of PD-1/PD-L pathway beyond cancer. Front Immunol 2019;10:2298.  Back to cited text no. 17
    
18.
Jiang X, Wang J, Deng X, Xiong F, Ge J, Xiang B, et al. Role of the tumor microenvironment in PD-L1/PD-1-mediated tumor immune escape. Mol Cancer 2019;18:10.  Back to cited text no. 18
    
19.
Messerschmidt JL, Prendergast GC, Messerschmidt GL. How cancers escape immune destruction and mechanisms of action for the new significantly active immune therapies: Helping nonimmunologists decipher recent advances. Oncologist 2016;21:233-43.  Back to cited text no. 19
    
20.
Doddawad V, Bannimath G, Shivakumar S, Bannimath N. Biomarkers of oral cancer: A current views and directions. Biomed Biotechnol Res J (BBRJ) 2022;6:33-9.  Back to cited text no. 20
    
21.
Garg P. Radiobioconjugate targeted therapy in cancer, using radiolabeled mediated biological analogs: Desired qualities and selective targeting approach. Biomed Biotechnol Res J (BBRJ) 2022;6:40-9.  Back to cited text no. 21
    
22.
Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ. PatchDock and SymmDock: Servers for rigid and symmetric docking. Nucleic Acids Res 2005;33:W363-7.  Back to cited text no. 22
    
23.
Zloh M, Kirton SB. The benefits of in silico modeling to identify possible small-molecule drugs and their off-target interactions. Future Med Chem 2018;10:423-32.  Back to cited text no. 23
    
24.
Liu Y, Grimm M, Dai WT, Hou MC, Xiao ZX, Cao Y. CB-Dock: A web server for cavity detection-guided protein-ligand blind docking. Acta Pharmacol Sin 2020;41:138-44.  Back to cited text no. 24
    
25.
Yan Y, Huang SY. Pushing the accuracy limit of shape complementarity for protein-protein docking. BMC Bioinformatics 2019;20:696.  Back to cited text no. 25
    
26.
Guo F, Li SC, Wang L, Zhu D. Protein-protein binding site identification by enumerating the configurations. BMC Bioinformatics 2012;13:158.  Back to cited text no. 26
    
27.
Fu Y, Zhao J, Chen Z. Insights into the molecular mechanisms of protein-ligand interactions by molecular docking and molecular dynamics simulation: A case of oligopeptide binding protein. Comput Math Methods Med 2018;2018:3502514.  Back to cited text no. 27
    
28.
Panja AS, Maiti S, Bandyopadhyay B. Protein stability governed by its structural plasticity is inferred by physicochemical factors and salt bridges. Sci Rep 2020;10:1822.  Back to cited text no. 28
    
29.
Newberry RW, Raines RT. Secondary forces in protein folding. ACS Chem Biol 2019;14:1677-86.  Back to cited text no. 29
    


    Figures

  [Figure 1], [Figure 2], [Figure 3]
 
 
    Tables

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