International Journal of Molecular Sciences

Article Based on the Virtual Screening of Multiple Pharmacophores, Docking and Molecular Dynamics Simulation Approaches toward the Discovery of Novel HPPD Inhibitors

Ying Fu 1 , Tong Ye 1, Yong-Xuan Liu 1, Jian Wang 2,* and Fei Ye 1,*

1 Department of Applied Chemistry, College of Arts and Sciences, Northeast Agricultural University, Harbin 150030, China; [email protected] (Y.F.); [email protected] (T.Y.); [email protected] (Y.-X.L.) 2 Key Laboratory of Structure-Based Drug Design & Discovery, Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, China * Correspondence: [email protected] (J.W.); [email protected] (F.Y.)



Received: 11 July 2020; Accepted: 31 July 2020; Published: 3 August 2020


Abstract: 4-Hydroxyphenylpyruvate dioxygenase (HPPD) is an iron-dependent non-heme oxygenase involved in the catabolic pathway of tyrosine, which is an important enzyme in the transformation of 4-hydroxyphenylpyruvic acid to homogentisic acid, and thus being considered as herbicide target. Within this study, a set of multiple structure-based pharmacophore models for HPPD inhibitors were developed. The ZINC and natural product database were virtually screened, and 29 compounds were obtained. The binding mode of HPPD and its inhibitors obtained through molecular docking study showed that the residues of Phe424, Phe381, His308, His226, Gln307 and Glu394 were crucial for activity. Molecular-mechanics-generalized born surface area (MM/GBSA) results showed that the coulomb force, lipophilic and van der Waals (vdW) interactions made major contributions to the binding affinity. These efforts will greatly contribute to design novel and effective HPPD inhibitory herbicides.

Keywords: HPPD inhibitors; pharmacophore model; molecule docking; molecular dynamics; MM/GBSA

1. Introduction In recent years, crops severely suffer from infestations of various weeds, and the weeds resistance are increasing seriously [1]. The development of novel green herbicides with highly efficient, eco-friendly, high crop selectivity while low toxicity has become urgent [2–4]. 4-Hydroxyphenylpyruvate dioxygenase (HPPD) is one of the most important herbicide target enzymes [5]. It is an iron-dependent non-heme oxygenase involved in the catabolic pathway of tyrosine, which catalyzes the transformation of 4-hydroxyphenylpyruvic acid (HPPA) to homogentisic acid (HGA) [6–8]. HGA is a key precursor of plastoquinone and vitamin E, and they are essential elements for the light-dependent reaction of photosynthesis. The inhibition of HPPD will lead to photodynamic bleaching of the foliage and ultimately results in necrosis and death [9–12]. A total of 14 Arabidopsis thaliana HPPD (AtHPPD) were identified, uploaded and stored in the protein database (RCSB PDB), including 11 protein–ligand complexes and 10 resolutions less than three angstroms (Table1). The 3D structures of HPPD show that all of them share the same 2-His-1-Glu facial triad [13]. In the co-crystallized HPPD inhibitor complexes, Fe2+ coordination is achieved by two His amino acids and one Glu amino acid and the β-keto–enol structure of the pyrazole or triketone

Int. J. Mol. Sci. 2020, 21, 5546; doi:10.3390/ijms21155546 www.mdpi.com/journal/ijms Int. J. Mol. Sci. 2020, 21, 5546 2 of 13 inhibitors [14–16]. The π–π interactions between the phenylalanine and the aromatic moiety of the inhibitors play a key role in the stability of the complex [17–19]. Pharmacophore model is a powerful tool for hit lead compounds in medicinal chemistry [20]. Molecular mechanics generalized born surface area (MM/GBSA) is the most well-known method of binding free energy, which was used to assess docking poses, determine structural stability and predict binding affinities [21]. Increasing studies have made major breakthroughs in HPPD design through the use of computational chemistry. A virtual screening based on a pharmacophore model and molecular docking was successfully constructed for screening potential HPPD inhibitors [22,23]. A detailed experimental and computational study have shown that the slow-binding inhibition kinetics of 5CTO, 5DHW and 5YWG, which enabled the rational design of new effective HPPD-targeted drugs or herbicides with longer target residence time and improved performance [24]. Fu et al. successfully constructed a virtual screening based on a pharmacophore model, and then used molecular docking to discern interactions with key residues at the active site of HPPD and identified potential HPPD inhibitors [25]. He et al. summarized the structure of HPPD, inhibitors binding sites, mode of action and their metabolites were [26]. All of these provided clear and solid insights in HPPD and its inhibitors interaction and contributed to design novel and effective HPPD inhibitory herbicides. In this study, a series of structure-based pharmacophore models for HPPD inhibitors were generated and used to virtual screening ZINC and Natural Product database. Molecular docking, molecular dynamics (MD) simulations and molecular-mechanics-generalized born surface area (MM/GBSA) calculation were applicated to illustrate the detailed binding modes of potential inhibitors with HPPD. The virtual filtered strategy is shown in Figure1.

Table 1. StructuralInt. J. Mol. Sci. 2020 information, 21, x FOR PEER REVIEW of published Arabidopsis thaliana HPPD (AtHPPD)2 of 14 crystals. triketone inhibitors [14–16]. The π–π interactions between the phenylalanine and the aromatic moiety Ref.of the PDB inhibitors ID play aResolution key role in the stability (Å) of the complexLigand [17–19]. Metal Ion Released Pharmacophore model is a powerful tool for hit lead compounds in medicinal chemistry [20]. [27]Molecular 1TFZ mechanics generalized 1.80 born surface area (MM/GBSA) DAS869 is the most well-known Fe method of 2004 [27]binding 1TG5 free energy, which was 1.90 used to assess docking DAS645 poses, determine structural Fe stability and 2004 [27]predict 1SQD binding affinities [21]. 1.80 Increasing studies have made - major breakthroughs in Fe HPPD design 2004 through the use of computational chemistry. A virtual screening based on a pharmacophore model [28]and molecular 1SP9 docking was successfully 3.00 constructed for screening - potential HPPD inhibitors Fe [22,23]. 2004 [29]A detailed 5XGK experimental and 2.60 computational study have HPPA shown that the slow-binding Fe inhibition 2018 [30]kinetics 6ISD of 5CTO, 5DHW and 2.40 5YWG, which enable sulcotrioned the rational design of new Coeffective HPPD- 2018 [30]targeted 5YWG drugs or herbicides with 2.60 longer target residence mesotrione time and improved performance Co [24]. Fu et 2019 al. successfully constructed a virtual screening based on a pharmacophore model, and then used [30]molecular 6J63 docking to discern 2.62interactions with key residues NTBC at the active site of HPPD Fe and identified 2019 [31]potential 5YWH HPPD inhibitors [25]. 2.72 He et al. summarized the Y13508 structure of HPPD, inhibitors Fe binding sites, 2019 [32]mode 5YWI of action and their metabolites 2.58 were [26]. All of these NTBC provided clear and solid insights Co in HPPD 2019 and its inhibitors interaction and contributed to design novel and effective HPPD inhibitory [33]herbicides. 5YWK In this study, a series 2.80 of structure-basedbenquitrione-methyl pharmacophore models for HPPDCo inhibitors were 2019 [30]generated 5YY6 and used to virtual 2.40 screening ZINC and benquitrione Natural Product database. Molecular Co docking, 2019 [34]molecular 6JX9 dynamics (MD) simulations 1.80 and molecular-mechanics-generalized Y17107 born Co surface area 2020 (MM/GBSA) calculation were applicated to illustrate the detailed binding modes of potential inhibitors with HPPD. The virtual filtered strategy is shown in Figure 1.

Figure 1. Figure 1. Study design comprising pharmacophore model development, virtual screening, docking Study designand molecular comprising dynamics pharmacophoresimulation. model development, virtual screening, docking and molecular dynamics simulation. Table 1. Structural information of published Arabidopsis thaliana HPPD (AtHPPD) crystals.

Ref. PDB ID Resolution（Å） Ligand Metal Ion Released [27] 1TFZ 1.80 DAS869 Fe 2004 [27] 1TG5 1.90 DAS645 Fe 2004 [27] 1SQD 1.80 – Fe 2004 [28] 1SP9 3.00 – Fe 2004 [29] 5XGK 2.60 HPPA Fe 2018 [30] 6ISD 2.40 sulcotrione Co 2018 [30] 5YWG 2.60 mesotrione Co 2019 [30] 6J63 2.62 NTBC Fe 2019 [31] 5YWH 2.72 Y13508 Fe 2019

Int. J. Mol. Sci. 2020, 21, x FOR PEER REVIEW 3 of 14

[32] 5YWI 2.58 NTBC Co 2019 benquitrione- [33] 5YWK 2.80 Co 2019 methyl [30] 5YY6 2.40 benquitrione Co 2019 Int. J. Mol.[34] Sci. 2020, 21, 55466JX9 1.80 Y17107 Co 2020 3 of 13

2. Results 2. Results

2.1. Pharmacophores for Virtual Screening The multiple pharmacophore combination screening method has a higher effective hit rate for The multiple pharmacophore combination screening method has a higher effective hit rate for active active compounds than the single pharmacophore screening. The receptor-based multiple compounds than the single pharmacophore screening. The receptor-based multiple pharmacophore pharmacophore combination screening method is more reliable and helpful for the identification combination screening method is more reliable and helpful for the identification research of potential research of potential HPPD inhibitors. The above-mentioned final set pharmacophores were HPPD inhibitors. The above-mentioned final set pharmacophores were constructed as shown in constructed as shown in Figure 2, and the virtual screening process was performed to the 98,133 Figure2, and the virtual screening process was performed to the 98,133 compounds from ZINC and compounds from ZINC and Natural Product database for 6 times to find new HPPD inhibitors. Natural Product database for 6 times to find new HPPD inhibitors. Finally, 29 compounds were found Finally, 29 compounds were found to be highly fitness with the HPPD based pharmacophore model. to be highly fitness with the HPPD based pharmacophore model. In addition, these compounds were In addition, these compounds were optimized for geometry using Schrödinger’s LigPrep module and optimized for geometry using Schrödinger’s LigPrep module and used for further molecular docking. used for further molecular docking.

Figure 2. Two-dimensional (left) and 3D (right) charts of structure-based pharmacophore Figure 2. Two-dimensional (left) and 3D (right) charts of structure-based pharmacophore models (A) models (A) 1TG5, (B) 1TFZ, (C) 5YWH, (D) 5YWK, (E) 5YY6 and (F) 5XGK for potential 1TG5, (B) 1TFZ, (C) 5YWH, (D) 5YWK, (E) 5YY6 and (F) 5XGK for potential 4- 4-hydroxyphenylpyruvate dioxygenase (HPPD) inhibitors in the HPPD binding pocket. The interactions hydroxyphenylpyruvate dioxygenase (HPPD) inhibitors in the HPPD binding pocket. The were visualized with the following color code: hydrogen bond acceptor (red arrow), hydrogen bond interactions were visualized with the following color code: hydrogen bond acceptor (red arrow), donor (green arrow), hydrophobic interaction (yellow sphere), aromatic ring feature interaction (purple hydrogen bond donor (green arrow), hydrophobic interaction (yellow sphere), aromatic ring feature sphere and arrow). interaction (purple sphere and arrow). 2.2. Docking Result 2.2. Docking Result Based on the docking results, the ligand benquitrione was completely embedded in the active capsule.Based benquitrioneon the docking and results, amino the acids ligand His226, benqui His308,trione was Glu394 completely coordinated embedded with in cobalt the active ion, capsule.meanwhile, benquitrione the benzene and ring amino of benquitrione acids His226, formed His308, a face-to-face Glu394 coordinatedπ–π interaction with withcobalt Phe381 ion, meanwhile,and hexahydropyrimidine the benzene ring ring of formedbenquitrione a vertical formedπ–π ainteraction face-to-face with π–π Phe424 interaction (Figure with3A). Phe381 and hexahydropyrimidineCompounds ZINC00004910836, ring formed a vertical ZINC000040310216 π–π interaction and with ZINC000003830381 Phe424 (Figure 3A). were found fully embedCompounds into the active ZINC00004910836, pocket. Compound ZINC000040310216 ZINC000049180836 and ZINC000003830381 not only produced were primary found metalfully embedcoordination into the bonds, active moreover, pocket. theCompound two benzene ZINC000049180836 rings also interacted not withonly Phe424produced and primary Phe381 viametal the coordinationobvious π–π interactions.bonds, moreover, Meanwhile, the two thebenzene hydroxyl rings at also benzene interacted ring generatedwith Phe424 hydrogen and Phe381 bond via with the Gln307 as depicted in Figure3B. The benzene portion of compound ZINC000040310216 formed π–π interactions with Phe424 and Phe381. Compound ZINC000040310216, amino acids His226, His308 and Glu394 all coordinated with cobalt ion. Meanwhile, carbonyl moiety generated hydrogen bond interactions with Gly420 and Asn282 as depicted in Figure3C. Int. J. Mol. Sci. 2020, 21, x FOR PEER REVIEW 4 of 14

obvious π–π interactions. Meanwhile, the hydroxyl at benzene ring generated hydrogen bond with Gln307 as depicted in Figure 3B. The benzene portion of compound ZINC000040310216 formed π–π interactions with Phe424 and Phe381. Compound ZINC000040310216, amino acids His226, His308 and Glu394 all coordinated with cobalt ion. Meanwhile, carbonyl moiety generated hydrogen bond interactions with Gly420 and Asn282 as depicted in Figure 3C. The docking mode of compound ZINC000003830381 was similar as benquitrione. There was a π–π stacking between cyclopentyl, Phe424 and Phe381. ZINC000003830381 also produced metal coordination bonds together with His226, His308 and Glu394. The hydroxyl on the cyclopentane generated hydrogen bond interactions with Asn423 as displayed in Figure 3D. Compounds ZINC000035458722, ZINC000012663485 and ZINC000002032320 (Figure 3E–G) also formed π–π interaction with Phe424 and Phe381. The hydroxyl on the aromatic ring part of Int. J. Mol. Sci. 2020, 21, 5546 4 of 13 ZINC000035458722 generated hydrogen bond interaction with Gln307. The pyrrole ring of ZINC000002032320 also generated important hydrogen bond interactions with Phe419. However, noneThe of these docking three mode compounds of compound produce ZINC000003830381 metal coordination was bonds. similar as benquitrione. There was a π–πHereinstacking we between focused cyclopentyl,on the three Phe424small molecules and Phe381. ZINC000040310216, ZINC000003830381 ZINC000003830381 also produced metal and coordinationZINC000049180836 bonds togetherbecause withthey His226,have His308shown andthe Glu394.function The of hydroxylpotential on HPPD the cyclopentane inhibitors. generatedZINC000002032320 hydrogen with bond the interactions highest docking with Asn423 scores am as displayedong the left in three Figure molecules3D. was also subjected to MD simulations to demonstrate whether the metal coordination was necessary.

Figure 3. Receptor–ligand interaction of (A) benquitrione, (B) ZINC000049180836 (C) ZINC000040310216, Figure 3. Receptor–ligand interaction of (A) benquitrione, (B) ZINC000049180836 (C) (D) ZINC000003830381, (E) ZINC000035458722, (F) ZINC000012663485 and (G) ZINC000002032320 ZINC000040310216, (D) ZINC000003830381, (E) ZINC000035458722, (F) ZINC000012663485 and (G) with the HPPD active site. ZINC000002032320 with the HPPD active site. Compounds ZINC000035458722, ZINC000012663485 and ZINC000002032320 (Figure3E–G) 2.3. MD Simulations also formed π–π interaction with Phe424 and Phe381. The hydroxyl on the aromatic ring part of ZINC000035458722ZINC000049180836, generated ZINC000040310216 hydrogen bond and interactionZINC000003830381 with Gln307. all formed The pyrrole major ring ionic of ZINC000002032320interactions with amino also generatedacids His226, important His308,hydrogen Glu394, resulting bond interactions in metal coordination with Phe419. and However, all the noneabove of three these compounds three compounds inserted produce in the acti metalve pocket coordination between bonds. Phe381and Phe424 via π–π interaction (FigureHerein 4A–C). we ZINC000049180836 focused on the three formed small three molecules new hydrogen ZINC000040310216, bonds with Gln307, ZINC000003830381 Leu427and andLys421 ZINC000049180836 in HPPD binding becausesite. In HPPD they havebindin showng pocket, the ZINC000049180836 function of potential (Figure HPPD 7A) inhibitors.displayed ZINC000002032320 with the highest docking scores among the left three molecules was also subjected to MD simulations to demonstrate whether the metal coordination was necessary.

2.3. MD Simulations ZINC000049180836, ZINC000040310216 and ZINC000003830381 all formed major ionic interactions with amino acids His226, His308, Glu394, resulting in metal coordination and all the above three compounds inserted in the active pocket between Phe381and Phe424 via π–π interaction (Figure4A–C). ZINC000049180836 formed three new hydrogen bonds with Gln307, Leu427and Lys421 in HPPD binding site. In HPPD binding pocket, ZINC000049180836 (Figure 7A) displayed crucial interactions with Phe381(0.9), Phe424(0.8), Leu427(0.9) and His226(1.0), His308(1.0), Glu394(1.0) and Gln307(0.9), Lys421(0.8), respectively. Amino acids His226, His308, Glu394 form coordination bonds with metal ions and ligands form π–π interaction with amino acids Phe381 and Phe424. Int. J. Mol. Sci. 2020, 21, x FOR PEER REVIEW 5 of 14

crucial interactions with Phe381(0.9), Phe424(0.8), Leu427(0.9) and His226(1.0), His308(1.0), Glu394(1.0) and Gln307(0.9), Lys421(0.8), respectively. Amino acids His226, His308, Glu394 form coordination bonds with metal ions and ligands form π–π interaction with amino acids Phe381 and Phe424. ZINC000040310216 formed a new hydrogen bond with Gly420 and Asn282 in HPPD binding site (Figure 4B). ZINC000003830381 (Figure 4C) formed important hydrogen bonds with Asn423, Glu394, Gln293 and Gly420 in HPPD binding site. In 5YY6 binding pocket, ZINC000002032320 displayed crucial interactions with Tyr297(a fraction of 0.75), Pro389(0.95), Phe392(0.8) (Figure 4D). The key interactions included hydrogen bonds with Pro389 and Gln307and π–π interaction with Tyr297 for 5YY6, Hydrophobic interaction with Phe392. ZINC000049180836, ZINC000040310216, ZINC000003830381 and ligand histogram data indicate that these three compounds possess key interactions between ligands and proteins, and both produce important hydrogen bonding properties in drug design. Consequently, the conformations from snapshots were appropriately identical with previous docking modes, which exactly suggested the Int. J. Mol. Sci. 2020 21 binding, stability., 5546 5 of 13

Figure 4. Bar charts of protein–ligand (P–L) contacts. (A) ZINC000049180836 (B) ZINC000040310216, Figure 4. Bar charts of protein–ligand (P–L) contacts. (A) ZINC000049180836 (B) ZINC000040310216, (C) ZINC000003830381, (D) ZINC000002032320. Abscissa represents the amino acid number. Ordinate (C) ZINC000003830381, (D) ZINC000002032320. Abscissa represents the amino acid number. represents interactions fraction. Ordinate represents interactions fraction.

ZINC000040310216The RMSD pattern formed of aprotein new hydrogen(Cα) and ligand bond (fitting with Gly420protein and and ligand Asn282 itself) in HPPDduring the binding 100 site (Figure4B).ns ZINC000003830381simulations is shown in (Figure Figure 5.4 C)In detail, formed ‘Lig important Fit Protein’ hydrogenor ligand aligned bonds on with protein Asn423, meant the Glu394, Gln293 andRMSDs Gly420 measured in HPPD the fluctuations binding of site. the ligand In 5YY6 with binding respect to pocket, the protein ZINC000002032320 [35]. It was obviously displayed that crucial interactionsthere were some with fluctuations Tyr297(a with fraction a 3 Å ofshift 0.75), for the Pro389(0.95), proteins (HPPD) Phe392(0.8) at the beginning. (Figure Hence,4D). Thethe key RMSDs fluctuating around some thermal average by approximately 1–3 Å for the entire simulation interactions included hydrogen bonds with Pro389 and Gln307and π–π interaction with Tyr297 for time exactly illustrated the equilibrated simulation. ZINC000040310216 preserved roughly the same 5YY6, HydrophobicRMSD level interactionwith benquitrione, with Phe392.whereas the average values of 5YY6-ZINC000003830381, 5YY6- ZINC000049180836,ZINC000002032320 ZINC000040310216, and 5YY6-ZINC000049180836 ZINC000003830381 were a little and higher ligand than histogram that of data 5YY6- indicate that theseZINC000040310216 three compounds complex. possess On key the interactions whole, the fluctuat betweenions ligandsof proteins and and proteins, ligands attained and both to producebe importantunchanged hydrogen after bonding 40 ns. properties in drug design. Consequently, the conformations from snapshots were appropriately identical with previous docking modes, which exactly suggested the binding stability. The RMSD pattern of protein (Cα) and ligand (fitting protein and ligand itself) during the 100 ns simulations is shown in Figure5. In detail, ‘Lig Fit Protein’ or ligand aligned on protein meant the

RMSDs measured the fluctuations of the ligand with respect to the protein [35]. It was obviously that there were some fluctuations with a 3 Å shift for the proteins (HPPD) at the beginning. Hence, the RMSDs fluctuating around some thermal average by approximately 1–3 Å for the entire simulation time exactly illustrated the equilibrated simulation. ZINC000040310216 preserved roughly the same RMSD level with benquitrione, whereas the average values of 5YY6-ZINC000003830381, 5YY6-ZINC000002032320 and 5YY6-ZINC000049180836 were a little higher than that of 5YY6-ZINC000040310216 complex. On the whole, theInt. fluctuations J. Mol. Sci. 2020, 21, xof FOR proteins PEER REVIEW and ligands attained to be unchanged6after of 14 40 ns.

Figure 5. Root mean square deviation (RMSD) trajectories of HPPD–ligand complex during Figure 5. Root mean square deviation (RMSD) trajectories of HPPD–ligand complex during 100-ns 100-ns simulations.simulations.

2.4. MM/GBSA Calculation MM/GBSA is a molecular mechanics of binding energy, solvation model, nonpolar solvation term, nonpolar solvent accessible surface area and van der Waals interaction method to define the free energy of binding [35]. MM/GBSA was applied to calculate the binding free energy of the ligand- receptor complexes taking thermodynamic and desolvation parameters into consideration. The ΔGbind, ΔGbind coulomb, ΔGbind Hbond, ΔGbind lipo and ΔGbind vdW parameters involved in the calculation are shown in Table 2. The Glide energy required for compound ZINC00004910836 is the smallest, which was consistent with the previous molecular docking results. Among the calculated parameters of compound ZINC00004910836, the ΔGbind value was 55.129 kcal/mol, the ΔGbind coulomb value was −48.956 kcal/ mol, the ΔGbind Hbond was −0.530 kcal/mol, nonpolar solvation (ΔGbind lipo) value was −26.162 kcal/mol and van der Waals (ΔGbind vdW) value was −41.324 kcal/mol. From the parameters calculated for each compound, it could be easily concluded that ΔG bind coulomb and ΔG bind vdW were the main contributors to the binding free energy of compounds. Noticeably, it was also detected that ΔG covalent, providing unfavorable energy, was negative to the binding of protein.

Table 2. Contributions of various energy components to the binding free energy (kcal mol−1).

ΔGbind ΔGbind ΔGbind ΔGbind ΔGbind Compound ΔGbind Coulomb Covalent Hbond Lipo vdW ZINC000049180836 −55.129 −48.956 6.930 −0.530 −26.162 −41.324 ZINC000040310216 −48.339 0.322 8.513 −0.482 −25.319 −48.749 ZINC000003830381 −30.081 −26.801 9.483 −1.109 −27.654 −41.861 ZINC000002032320 −43.629 −15.992 8.294 −0.565 −23.267 −49.487 ZINC000035458722 −50.129 −13.244 3.182 −0.592 −26.8102 −40.3212 ZINC000012663485 −29.341 −45.45 7.568 −0.449 −24.507 −34.352 Benquitrione −19.652 −27.7484 3.5389 −0.968 −15.086 −32.827

3. Discussion

Six crystallographic structures of HPPD protein were collected from the RCSB PDB database (PDB ID: 1TG5, 1TFZ, 5YWH, 5YWK, 5YY6 and 5XGK). The interaction between the HPPD protein

Int. J. Mol. Sci. 2020, 21, 5546 6 of 13

2.4. MM/GBSA Calculation MM/GBSA is a molecular mechanics of binding energy, solvation model, nonpolar solvation term, nonpolar solvent accessible surface area and van der Waals interaction method to define the free energy of binding [35]. MM/GBSA was applied to calculate the binding free energy of the ligand-receptor complexes taking thermodynamic and desolvation parameters into consideration. The ∆Gbind, ∆Gbind coulomb, ∆Gbind Hbond, ∆Gbind lipo and ∆Gbind vdW parameters involved in the calculation are shown in Table2. The Glide energy required for compound ZINC00004910836 is the smallest, which was consistent with the previous molecular docking results. Among the calculated parameters of compound ZINC00004910836, the ∆Gbind value was 55.129 kcal/mol, the ∆Gbind coulomb value was 48.956 kcal/ mol, the ∆G Hbond was 0.530 kcal/mol, nonpolar solvation (∆G lipo) − bind − bind value was 26.162 kcal/mol and van der Waals (∆G vdW) value was 41.324 kcal/mol. From the − bind − parameters calculated for each compound, it could be easily concluded that ∆G bind coulomb and ∆G bind vdW were the main contributors to the binding free energy of compounds. Noticeably, it was also detected that ∆G covalent, providing unfavorable energy, was negative to the binding of protein.

1 Table 2. Contributions of various energy components to the binding free energy (kcal mol− ).

∆G ∆G ∆G Compound ∆G bind bind bind ∆G Lipo ∆G vdW bind Coulomb Covalent Hbond bind bind ZINC000049180836 55.129 48.956 6.930 0.530 26.162 41.324 − − − − − ZINC000040310216 48.339 0.322 8.513 0.482 25.319 48.749 − − − − ZINC000003830381 30.081 26.801 9.483 1.109 27.654 41.861 − − − − − ZINC000002032320 43.629 15.992 8.294 0.565 23.267 49.487 − − − − − ZINC000035458722 50.129 13.244 3.182 0.592 26.8102 40.3212 − − − − − ZINC000012663485 29.341 45.45 7.568 0.449 24.507 34.352 − − − − − Benquitrione 19.652 27.7484 3.5389 0.968 15.086 32.827 − − − − −

3. Discussion Six crystallographic structures of HPPD protein were collected from the RCSB PDB database (PDB ID: 1TG5, 1TFZ, 5YWH, 5YWK, 5YY6 and 5XGK). The interaction between the HPPD protein and potential ligands was calculated using LS. Although these models have similar chemical characteristics, they all describe slightly different interaction patterns that may occur within the HPPD binding site. For example, both the 1TG5 and 1TFZ models contain a hydrogen bond (green) effect with HIS287 and a hydrophobic (yellow) effect with Phe398 and Phe360, but the 1TFZ model has one more aromatic ring effect (blue) than the 1TG5 model, the same as 5YY6. Among the amino acids in the 5YWK and 5YWH models, Phe424, Phe381, Phe392 and Met335 all produced hydrophobic groups (yellow), but the amino acid His308 in 5YWH produced hydrogen bonds (green) as shown in Figure2. The co-crystallized ligand benquitrione was redocked into the corresponding 5YY6 protein binding pocket (Figure6). The RMSD value was 0.55, which confirmed the accuracy and feasibility of the glide docking method. Flexible docking was performed with the 29 selected candidates against the multiple conformers of the receptor protein 5YY6. Six small molecules with optimal comprehensive conditions were obtained according to the conditions of docking score and the action mode of amino acid. Docking score for the 6 compounds is shown in Table3. The glide score of the six compounds were lower than others, which indicated that all of them bond with the target protein stable. The Glide energy of ZINC000035458722 is similar as benquitrione. Docking score and Glide gscore are the best criteria for docking situation. The scores of the six compounds are better than the co-crystallized complex ligand benquitrione, indicating that the compounds can complement the protein geometrically and energy well. Int.Int. J.J. Mol.Mol. Sci.Sci. 20202020,, 2211,, xx FORFOR PEERPEER REVIEWREVIEW 77 ofof 1414

andand potential potential ligands ligands was was calculated calculated using using LS. LS. Although Although these these models models have have similar similar chemical chemical characteristics,characteristics, they they all all describe describe slightly slightly different different interaction interaction patterns patterns that that may may occur occur within within the the HPPDHPPD bindingbinding site.site. ForFor example,example, bothboth thethe 1TG51TG5 andand 1TFZ1TFZ modelsmodels cocontainntain aa hydrogenhydrogen bondbond (green)(green) effecteffect withwith HIS287HIS287 andand aa hydrophobichydrophobic (yellow)(yellow) effecteffect withwith Phe398Phe398 andand Phe360,Phe360, butbut thethe 1TFZ1TFZ modelmodel hashas oneone moremore aromaticaromatic ringring effecteffect (blue)(blue) thanthan thethe 1TG51TG5 model,model, thethe samesame asas 5YY6.5YY6. AmongAmong thethe aminoamino acidsacidsInt. J. Mol. in in the theSci. 5YWK2020 5YWK, 21 , and andx FOR 5YWH 5YWH PEER REVIEW models, models, Phe424,Phe424, Phe381, Phe381, Phe392 Phe392 and and Met335 Met335 all all produced produced 7 of 14 hydrophobichydrophobic groupsgroups (yellow),(yellow), butbut thethe aminoamino acidacid His308His308 inin 5YWH5YWH producedproduced hydrogenhydrogen bondsbonds (green)(green) asasand shownshown potential inin FigureFigure ligands 2.2. was calculated using LS. Although these models have similar chemical The co-crystallized ligand benquitrione was redocked into the corresponding 5YY6 protein characteristics,The co-crystallized they ligandall describe benquitrione slightly was differ redockedent interaction into the corresponding patterns that 5YY6 may protein occur within the bibindingnding pocketpocket (Fig(Figureure 66).). TheThe RMSDRMSD valuevalue waswas 0.55,0.55, whichwhich confirmedconfirmed thethe accuracyaccuracy andand feasibilityfeasibility HPPD binding site. For example, both the 1TG5 and 1TFZ models contain a hydrogen bond (green) ofof thethe glideglide dockingdocking method.method. effect with HIS287 and a hydrophobic (yellow) effect with Phe398 and Phe360, but the 1TFZ model has one more aromatic ring effect (blue) than the 1TG5 model, the same as 5YY6. Among the amino acids in the 5YWK and 5YWH models, Phe424, Phe381, Phe392 and Met335 all produced hydrophobic groups (yellow), but the amino acid His308 in 5YWH produced hydrogen bonds (green) as shown in Figure 2. The co-crystallized ligand benquitrione was redocked into the corresponding 5YY6 protein Int. J. Mol. Sci. 2020, 21, 5546 7 of 13 binding pocket (Figure 6). The RMSD value was 0.55, which confirmed the accuracy and feasibility of the glide docking method.

FigureFigure 66.. LigandLigand comparedcompared byby thethe glideglide method.method. RedockedRedocked ligandligand waswas greengreen andand thethe nativenative ligandligand inin thethe crystallographiccrystallographic complexcomplex waswas blue.blue.

FlexibleFlexible dockingdocking waswas performedperformed withwith thethe 2929 selectedselected candidatescandidates againstagainst thethe multiplemultiple conformersconformers ofof the the receptor receptor protein protein 5YY6. 5YY6. Six Six small small molecules molecules with with optimal optimal comprehensive comprehensive conditions conditions were were obtainedobtained accordingaccording toto thethe conditionsconditions ofof dockingdocking scorescore andand thethe actionaction modemode ofof aminoamino acid.acid. DockingDocking scorescore forfor thethe 66 compoundscompounds isis shownshown inin TableTable 33.. TheThe glideglide scorescore ofof thethe sixsix compoundscompounds werewere lowerlower thanthan others,others, whichwhich indicatedindicated thatthat allall ofof themthem bondbond withwith thethe targettarget proteinprotein stable.stable. TheThe GlideGlide energyenergy ofof ZINC000035458722ZINC000035458722 isis sisimilarmilar asas benquitrione.benquitrione. DockingDocking scorescore andand GlideGlide gscoregscore areare thethe bestbest criteriacriteria forfor dockingdocking situation.situation. TheThe scoresscores ofof thethe sixsix compoundscompounds areare betterbetter thanthan thethe coco--crystallizedcrystallized complexcomplex ligandligand benquitrione,benquitrione, indicatingindicating thatthat thethe compoundscompounds cancan complementcomplement thethe proteinprotein geogeometricallymetrically andand energyenergy well.well. Figure 6. LigandLigand compared compared by the the glide method. Redocked Redocked ligand ligand was was green green and the native ligand in theTableTable crystallographic 33.. TwoTwo--dimensionaldimensional complex structurestructure was ofof blue.thethe potentialpotential HPPDHPPD InhibitorsInhibitors andand thethe evaluationevaluation value.value.

Table 3. Two-dimensional structure of the potential HPPD Inhibitors and theGlideGlide evaluation value. Flexible docking was performed with the 29Docking Dockingselected candidatesGlideGlide against the multiple conformers InhibitorsInhibitors StructureStructure EnergyEnergy Score Gscore Score Gscore −1 1 of theInhibitors receptor protein 5YY6. Structure Six small molecules Docking with Score optimal Glide comprehensive Gscore ((kcalkcalGlide molmol− Energy 1))conditions (kcal mol were− ) obtained according to the conditions of docking score and the action mode of amino acid. Docking score for the 6 compounds is shown in Table 3. The glide score of the six compounds were lower than ZINC000049180836ZINC000049180836ZINC000049180836 −−12.20612.20612.206 −−12.29412.29412.294 −−52.00252.002 52.002 others, which indicated that all of them bond wi−th the target protein− stable. The Glide− energy of ZINC000035458722 is similar as benquitrione. Docking score and Glide gscore are the best criteria for docking situation. The scores of the six compounds are better than the co-crystallized complex ligand Int.Int.ZINC000040310216 J.ZINC000040310216J. Mol.Mol. Sci.Sci. 20202020,,, 2211,,, xxx FORFORFOR PEERPEERPEER REVIEWREVIEWREVIEW −11.44911.449 −11.44911.449 −47.09688 of of 1414 47.096 ZINC000040310216 −−11.449 −11.449− −47.096 − benquitrione, indicating that the compounds can complement the protein geometrically and energy well.

ZINC000003830381ZINC000003830381ZINC000003830381 −−10.76210.76210.762 −−10.76510.76510.765 −−31.59631.596 31.596 Table 3. Two-dimensional structure of the potential− HPPD Inhibitors− and the evaluation− value.

Glide Docking Glide Inhibitors Structure Energy Score Gscore ZINC000002032320ZINC000002032320 −9.5049.504 −10.85910.859 −30.080 30.080 −1 ZINC000002032320 −−9.504 −10.859− −30.080 − (kcal mol )

ZINC000035458722ZINC000035458722ZINC000049180836ZINC000035458722 −−8.8658.8658.865 −12.206−−10.90310.903 10.903 −12.294−−51.62351.623 51.623−52.002 − − −

ZINC000012663485ZINC000012663485ZINC000012663485 −−8.5498.5498.549 −−8.5498.5498.549 −−32.89532.895 32.895 ZINC000040310216 − −11.449− −11.449 − −47.096

BenquitrioneBenquitrioneBenquitrione −−5.9215.9215.921 −−5.9215.9215.921 −−44.47544.475 44.475 − − −

AnalysisAnalysis ofof thethe cocrystalcocrystal structuresstructures showsshows somesome interactionsinteractions predictedpredicted byby ourour ensembleensemble dockingdocking modelmodel Analysis(Figure(Figure 77A).A). of SimulationSimulation the cocrystal trajectorytrajectory structures analysisanalysis shows showedshowed some thatthat interactions withinwithin thisthis timetime predicted frame,frame, thethe by RMSDRMSD our ensemble ofof docking thethemodel ligandligand (Figure changedchanged7A). byby Simulationaboutabout 22 ÅÅ relativerelative trajectory toto thetheanalysis initialinitial (dockin(dockin showedg)g) conformation,conformation, that within and thisand thethe time postureposture frame, waswas the RMSD of the stable.stable.ligand InteractionsInteractions changed byalongalong about thethe entire 2entire Å relative trajectoriestrajectories to the indicateindicate initial thatthat (docking) thethe interactionsinteractions conformation, withwith His226,His226, and theHis308,His308, posture was stable. Phe381,Phe381,Interactions Phe424Phe424 and alongand Glu394Glu394 the are entireare conservedconserved trajectories andand areare indicate thethe residuesresidues that necessarynecessary the interactions forfor HPPDHPPD inhibitorinhibitor with His226, binding.binding. His308, Phe381, Protein interactions with the ligand were monitored and normalized by a timeline Phe424ProteinProtein and interacti interacti Glu394onsons are with with conserved the the ligand ligand and are were were the monitored monitored residues necessary and and normalized normalized for HPPD by by inhibitora a timeline timeline binding. representationrepresentation overover thethe coursecourse ofof thethe trajectorytrajectory (P(P––LL contacts,contacts, FigureFigure 77B).B). TheThe interacinteractionstions suchsuch asas–– hydrogenhydrogen bondsbonds (≤(≤2.52.5 ÅÅ ), ), hydrophobic,hydrophobic, ionicionic andand waterwater bridgebridge werewere summarizedsummarized andand categorizecategorizedd byby type.type. InIn HPPDHPPD bindingbinding pocket,pocket, ligandligand benquitrionebenquitrione displayeddisplayed crucialcrucial interactionsinteractions withwith Phe381(0.9),Phe381(0.9), Phe424(0.8)Phe424(0.8) andand His226(1.0),His226(1.0), His308(1.0),His308(1.0), Glu394(1.0).Glu394(1.0). TheseThese resultsresults indicatedindicated thethe metalmetal coordinationcoordination bondbond interactioninteraction waswas maintainedmaintained 100%100% betweenbetween benqubenquitrioneitrione andand His226,His226, His308His308 andand Glu394.Glu394. TheThe interactionsinteractions withwith Phe424Phe424 andand Phe381Phe381 hydrophobichydrophobic bondsbonds areare 80%80% andand 90%,90%, respectively.respectively.

Int. J. Mol. Sci. 2020, 21, 5546 8 of 13 Int. J. Mol. Sci. 2020, 21, x FOR PEER REVIEW 9 of 14

Figure 7. Molecular dynamics (MD) simulations results of 5YY6 protein and its ligand benquitrione. Figure(A) 7. Protein–ligand Molecular dynamics contacts; ( B(MD)) bar chartssimulations of protein–ligand results of (P–L) 5YY6 contacts. protein and its ligand benquitrione. (A) Protein–ligandProtein interactions contacts; with ( theB) bar ligand charts were of monitoredprotein–ligand andnormalized (P–L) contacts. by a timeline representation over the course of the trajectory (P–L contacts, Figure7B). The interactions such as–hydrogen bonds (In2.5 conclusion, Å), hydrophobic, a reasonable ionic and water and bridgehierarchical were summarized virtual screening and categorized process by type. was In HPPDsuccessfully ≤ constructedbinding pocket,to identify ligand potential benquitrione HPPD displayed inhibito crucialrs. interactionsMolecular withdocking Phe381(0.9), revealed Phe424(0.8) that compounds and ZINC00004910836,His226(1.0), His308(1.0), ZINC000040310216 Glu394(1.0). These and resultsZINC000003830381 indicated the metal all produced coordination metal bond coordination interaction with aminowas acids maintained His226, 100% His308 between and benquitrioneGlu394 and andformed His226, π–π His308 interactions and Glu394. with The amino interactions acids Phe424 with and Phe381,Phe424 and and all Phe381of them hydrophobic produced bondsmore areimportant 80% and hydrogen 90%, respectively. bond interaction with the protein. MD simulationIn conclusion,proved the a reasonableexistence andof metal hierarchical coordination virtual screening bonds processand the was influence successfully of constructedπ–π interaction, to identify potential HPPD inhibitors. Molecular docking revealed that compounds ZINC00004910836, which was consistent with the previous molecular docking results. The MD simulation also ZINC000040310216 and ZINC000003830381 all produced metal coordination with amino acids His226, confirmed that three of the six compounds had high binding efficiency with HPPD. The binding free His308 and Glu394 and formed π–π interactions with amino acids Phe424 and Phe381, and all of them energyproduced calculated more by important MM/GBSA hydrogen demonstrated bond interaction that the with van the der protein. Waals MD(ΔG simulationbind vdW) energy proved theterm was the majorexistence contributor of metal coordinationto molecular bonds binding. and Overall, the influence the results of π–π interaction,provide a set which of guidelines was consistent for design novelwith and the more previous potent molecular HPPD inhibitors. docking results. The MD simulation also confirmed that three of the six compounds had high binding efficiency with HPPD. The binding free energy calculated by 3. MaterialsMM/GBSA and demonstrated Methods that the van der Waals (∆Gbind vdW) energy term was the major contributor to molecular binding. Overall, the results provide a set of guidelines for design novel and more potent 3.1. PharmacophoreHPPD inhibitors. Model Generation and Theoretical Validation 4.5YWH, Materials 5XGK, and Methods1TFZ, 1TG5, 5YWK and 5YY6 protein–ligand complexes were obtained for a detailed structural analysis after screening the fourteen AtHPPD protein with the rule of an effective 4.1. Pharmacophore Model Generation and Theoretical Validation pharmacophore requires 3 or more structural characteristic elements. Structure-based5YWH, 5XGK, pharmacophore 1TFZ, 1TG5, 5YWK models and 5YY6for HPPD protein–ligand inhibitors complexes were generated were obtained on the for 6 a HPPD crystaldetailed structures structural using analysis LigandScout after screening (LS, theInte: fourteen Ligand,AtHPPD version protein 4.3, withAustria). the rule Then of an optimize effective these pharmacophore requires 3 or more structural characteristic elements. models to optimize their prediction performance. In the first optimization stage, the size of the features were deleted or adjusted to allow as many active ingredients as possible to draw pharmacophores and exclude inactive compounds according to the contribution map (https://www.mrc-lmb.cam.ac.uk/rajini/index.html) (Figure 8) and the action mode of the amino acids in the PDB [36–38]. Features that indicate the interactions with the key residues necessary for inhibition are retained during this process, such as the important interactions with Phe403, Phe398, Phe360, His287 in 1TG5 and 1TFZ. In the second optimization stage, an exclusion volume is added to prevent inactive compounds that are in spatial collision with the protein from being localized by

Int. J. Mol. Sci. 2020, 21, 5546 9 of 13

Structure-based pharmacophore models for HPPD inhibitors were generated on the 6 HPPD crystal structures using LigandScout (LS, Inte: Ligand, version 4.3, Austria). Then optimize these models to optimize their prediction performance. In the first optimization stage, the size of the features were deleted or adjusted to allow as many active ingredients as possible to draw pharmacophores and exclude inactive compounds according to the contribution map (https://www.mrc-lmb.cam.ac.uk/raji Int.ni/ index.htmlJ. Mol. Sci. 2020) (Figure, 21, x FOR8) PEER and REVIEW the action mode of the amino acids in the PDB [ 36–38]. Features 10 thatof 14 indicate the interactions with the key residues necessary for inhibition are retained during this process, thesuch pharmacophore. as the important This interactions optimization with aims Phe403, to reduce Phe398, the Phe360, chances His287 of finding in 1TG5 inactive and 1TFZ.compounds In the whilesecond still optimization correctly identifying stage, an exclusionthe active volume compounds. is added Finally, to prevent six structure-based inactive compounds pharmacophore that are modelsin spatial are collision produced with based the proteinon 6 PDB from proteins being comb localizedined bywith the the pharmacophore. amino acid contribution This optimization map and theaims action to reduce mode the of chancesthe protein offinding ligandsinactive in the PDB compounds library. while still correctly identifying the active compounds.The 6 pharmacophores Finally, six structure-based obtained above pharmacophore are subjec modelsted to aremultiple produced virtual based screening on 6 PDB of proteins 98,133 compoundscombined with from the the amino ZINC acid and contribution Natural Product map database. and the action If the modenumber of theof molecules protein ligands obtained in theby thePDB primary library. screening is too large, the first 200 matching high scores are taken and finally, the selected groups of compounds are summarized to obtain a common intersection compound.

Figure 8. Protein–ligand contacts are represented by asteroid plot. The asteroid plot of AtHPPD Figure 8. Protein–ligand contacts are represented by asteroid plot. The asteroid plot of AtHPPD co- co-crystal structure RCSB PDB database (PDB) code: (A) 1TG5, (B) 1TFZ, (C) 5YWH, (D) 5YWK, crystal structure RCSB PDB database (PDB) code: (A) 1TG5, (B) 1TFZ, (C) 5YWH, (D) 5YWK, (E) 5YY6 (E) 5YY6 and (F) 5XGK). Co-crystal ligand in the center is denoted as blue node. The residues in and (F) 5XGK). Co-crystal ligand in the center is denoted as blue node. The residues in the inner and the inner and outer circle represent directly and indirectly contacting residues, respectively. Size of outer circle represent directly and indirectly contacting residues, respectively. Size of the circle the circle indicates the number of atomic contacts and the residues are colored according to their indicates the number of atomic contacts and the residues are colored according to their secondary secondary structures. structures. The 6 pharmacophores obtained above are subjected to multiple virtual screening of 98,133 3.2. Multiple Receptor Conformers Based Molecular Docking compounds from the ZINC and Natural Product database. If the number of molecules obtained by the primary screening is too large, the first 200 matching high scores are taken and finally, the selected 3.2.1. Ligand Preparation groups of compounds are summarized to obtain a common intersection compound. A total of 29 compounds out of 98,133 were found to be–in high fitness with the pharmacophore model.4.2. Multiple Using Receptor LigPrep, Conformers the ionization Based states, Molecular tautomer Dockingic forms and 3D conformation of 29 intersection compounds were generated (Schrödinger, LLC, New York, 2018–2). The default conditions were 4.2.1. Ligand Preparation maintained, except for the ionization states which were generated at the target pH of 7.0 ± 0.2 using Epik Aincluding total of 29 the compounds original state out of[39]. 98,133 The were resulting found 3D to be–inligand high representations fitness with the were pharmacophore exported as structuremodel. Using data LigPrep,files, and the unique ionization IDs were states, assigned tautomeric based forms on andonly 3D canonical conformation structures of 29 intersectionto facilitate post-dockingcompounds were hierarchical generated data (Schrödinger, fusion. LLC, New York, 2018–2). The default conditions were

3.2.2. Protein Preparation The protein preparation workflow in Maestro (Schrödinger) was used to preprocess the HPPD protein structure to assign bond sequences and optimize the structure, including hydrogen bond optimization and constrained minimization. Use Schrödinger Prime to add missing side chains when necessary. For each structure, a protein chain with the co-crystal ligand was retained, and more than 5 Å of water molecules were removed from the heteroatom group [40]. The internal hydrogen bond network was optimized, and then the energy minimization was limited.

Int. J. Mol. Sci. 2020, 21, 5546 10 of 13 maintained, except for the ionization states which were generated at the target pH of 7.0 0.2 using ± Epik including the original state [39]. The resulting 3D ligand representations were exported as structure data files, and unique IDs were assigned based on only canonical structures to facilitate post-docking hierarchical data fusion.

4.2.2. Protein Preparation The protein preparation workflow in Maestro (Schrödinger) was used to preprocess the HPPD protein structure to assign bond sequences and optimize the structure, including hydrogen bond optimization and constrained minimization. Use Schrödinger Prime to add missing side chains when necessary. For each structure, a protein chain with the co-crystal ligand was retained, and more than 5 Å of water molecules were removed from the heteroatom group [40]. The internal hydrogen bond network was optimized, and then the energy minimization was limited. For the obtained six HPPD protein structures representations, Schrodinger Glide were used to generate docking-grids around the cocrystal binding sites under the default settings. Each cocrystal ligand was re-docked into its corresponding structure validated model; in each case, the cocrystal posture was reproduced with root mean square deviation (RMSD) value <1.5 Å.

4.2.3. Docking Simulation The docking simulation was performed in Glide. The ligand representation of all compounds obtained through LigPrep was docked with the extra precision (XP) of the default settings of all 6 HPPD models prepared using Glide (Schrodinger), except writing out at most 5 poses per ligand representation and including 32 poses per ligand for post-docking minimization [41]. The obtained 29 small molecules were docked with the above-mentioned six PDB proteins in the glide module of the Maestro-scratch project platform for molecular docking, six compounds were obtained after comparing the conditions of glide gscore, glide energy and the mode of interactions of small molecules with amino acids.

4.3. MD Simulation The complex was introduced into the MD simulations in the Desmond (v3.8) module of Schrödinger software. The simulation system was built in an auto calculated trapezoidal box, which was dissolved with simple point charge (SPC) water and neutralized with appropriate number of counter ions. In addition, the OPLS_2005 force field was used to minimize the energy of complex systems, where the maximum interaction was set to 2000 and convergence threshold was set to 1.0 kcal/mol/Å[42]. The steepest descent and limited memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) algorithm minimizes the system energy with a maximum of 5000 steps until it reaches a gradient threshold of 25 kcal/mol/Å. An all-atom explicit water MD simulation of 100 ns was performed on four potential small molecules. Each MD system was created from the docking protein–ligand complex corresponding to the best docking score (i.e., the 5YY6 PDB structure and its corresponding docking ligand result in the highest absolute docking score). Finally, the RMSD of the protein backbone and the root mean square fluctuation (RMSF) of the residues were plotted to analyze the structure convergence to equilibrium. The initial MM/GBSA calculation uses initial and MD to optimize the receptor ligand complex. Use all default parameters, including the VSGB 2.0 solvation model. Calculation of Schrodinger software suite [43,44]. The binding free energy ∆Gbind was calculated with the MM/GBSA methodology was applied based on stable MD trajectory.

∆G = G Greceptor G (1) bind complex − − ligand

Gcomplex is the free energy of the complex; Greceptor is the free energy of the receptor; Gligand is the free energy of the ligand. Int. J. Mol. Sci. 2020, 21, 5546 11 of 13

Author Contributions: J.W. and Y.F. conceived and designed the experiment. T.Y. and Y.-X.L. carried out the methodology, calculation, writing the original draft preparation. T.Y. finished the visualization, investigation work. F.Y. designed and wrote the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: We are grateful to the National Nature Science Foundation of China (31772208) and the Natural Science Foundation of Heilongjiang Province (ZD2017002). Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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