Discovery of a novel SHP2 allosteric inhibitor using virtual screening, FMO calculation and molecular dynamic simulation

Discovery of a novel SHP2 allosteric inhibitor using virtual screening, FMO calculation and molecular dynamic simulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Discovery of a novel SHP2 allosteric inhibitor using virtual screening, FMO calculation and molecular dynamic simulation Zhen Yuan, Manzhan Zhang, Longfeng Chang, Xingyu Chen, Shanshan Ruan, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3565398/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Apr, 2024 Read the published version in Journal of Molecular Modeling → Version 1 posted 4 You are reading this latest preprint version Abstract Investigating the role of protein tyrosine phosphatase SHP2 is a continuing concern in the context of various human diseases, including Noonan syndrome, LEOPARD syndrome, and cancers. SHP2 is an essential bridge to connect numerous oncogenic cell-signaling cascades including RAS-ERK, PI3K-AKT, JAK-STAT and PD-1/PD-L1 pathways. This study aims to discover novel and potent SHP2 inhibitors using a hierarchical structure-based virtual screening strategy that combines molecular docking and the fragment molecular orbital method (FMO) for calculating binding affinity (referred to as the Dock-FMO protocol). We employed Dock-FMO virtual screening of ChemDiv database of ∼2,990,000 compounds to identify a novel SHP2 allosteric inhibitor bearing hydroxyimino acetamide scaffold. Experimental validation demonstrated that the new compound (E)-2-(hydroxyimino)-2-phenyl-N-(piperidin-4-ylmethyl)acetamide ( 7188-0011) effectively inhibited SHP2 in a dose-dependent manner. Molecular dynamics (MD) simulation analysis revealed the binding stability of compound 7188-0011 and the SHP2 protein, along with the key interacting residues in the allosteric binding site. Overall, our work has identified a novel and promising allosteric inhibitor that targets SHP2, providing a new starting point for further optimization to develop more potent inhibitors. SHP2 allosteric inhibitor fragment molecular orbital method virtual screening molecular dynamics simulation Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Src homology-2-containing protein tyrosine phosphatase 2 (SHP2), the first oncogenic non-receptor protein tyrosine phosphatase to be identified, is encoded by the proto-oncogene PTPN11 . It plays a critical role in various cellular processes including growth, proliferation, differentiation, migration, and apoptosis 1 . SHP2 is comprised of four distinct domains, namely two tandem Src homology 2 domains (N-SH2 and C-SH2), a conserved PTP catalytic domain, and a C-terminal tail housing two tyrosine phosphorylation sites (Tyr542 and Tyr580) 2 . Accumulated evidence has demonstrated that SHP2 actively participates in numerous oncogenic cell-signaling pathways, which encompass RAS-ERK, PI3K-AKT, and JAK-STAT, situated downstream of several receptor tyrosine kinases (RTKs) 1, 3–6 . Moreover, it has been reported that SHP2 is involved in the downstream signal transduction of the immunosuppressive receptor PD-1 within the PD-L1/PD-1 pathway, resulting in the suppression of T cell activation 7–9 . SHP2 has been shown to serve dual roles, functioning either as an oncogenic factor or as a tumor suppressor. Dysregulation of SHP2 contributes to a variety of sporadic hematological malignancies, developmental disorders, and solid tumors 10–12 . This extensive range of diseases highlights the potential of SHP2 as a promising target for cancer treatment, leading to substantial interest in SHP2 inhibitors as prospective agents for inhibiting tumor growth. Over the last two decades, an effort has been devoted to discovering active site inhibitors in the highly conserved catalytic PTP domain 13–16 . Nevertheless, these inhibitors have not advanced to clinical applications owing to challenges associated with poor oral bioavailability, low selectivity, and poor membrane permeability. Until recently, the first allosteric inhibitor ( SHP099 ) of SHP2 was reported to stabilize SHP2 in an auto-inhibited conformation that blocks the access of substrates, resulting in suppressed PTP activity 17 . At present, eight allosteric inhibitors of SHP2 exhibiting high selectivity and oral bioavailability (TNO155, JAB-3068, BBP‐398, ERAS‐601, JAB‐3312, SH3809, RMC‐4630, and HBI‐2376) have progressed to clinical trials. 10 SHP2 allosteric inhibitors were designed to overcome the poor druggability drawbacks of catalytic site inhibitors and exhibit high therapeutic potential for cancer treatment. In this study, we utilized Dock-FMO virtual screening to identify novel compounds targeting the SHP2 allosteric site. The top-ranked compounds were subsequently selected for biological testing. Among them, compound 7188-0011 , possessing a unique structure, exhibited moderate enzymatic potency. To evaluate the dynamic behavior and binding stability of the 7188-0011 -SHP2 complex, molecular dynamics (MD) simulations further performed. 2. Methods and Materials 2.1 Virtual screening The crystal structure of SHP2 was obtained from the Protein Data Bank with the accession code 5EHR, of which the resolution is 1.7 Å. The coactivators, as well as all water molecules, were removed from the structure. Hydrogen atoms and charges were added using the Protein Preparation Wizard module in Maestro (Schrödinger Inc, version 10.1). The crystal structure, after optimization of the hydrogen bond network, was subjected to energy minimization using the OPLS3 force field. The grid-enclosing box was placed on the center of the crystallographic ligand and defined to encompass residues within a 15 Å radius around the ligand, constituting the binding pocket. The remaining settings were kept at their default values. Under conditions of pH ranging from 5.0 to 9.0, the addition of hydrogen atoms and ionization facilitated the generation of stereoisomers and 3D conformers for compounds housed within the ChemDiv database (∼2,990,000 compounds), employing the Ligprep v3.3 module. Utilizing both the Glide standard precision (SP) and extra precision (XP) methods 18, 19 , molecular docking was performed with default parameters, retaining only the highest-ranked pose for each molecule. After Glide SP scorings, the top 15,215 ranked molecules were retained for subsequent calculations using the more precise Glide XP scoring function. Finally, the top-ranked 1,520 molecules were retained for further visual observation. Based on the Tanimoto similarity metric, a hierarchical clustering method was employed to cluster the 1,520 MOLPRINT2D fingerprint features into 25 groups. Taking into consideration ligand binding poses, structural diversity, and novelty, 25 candidates were selected from the pool of 1,520 compounds and subsequently subjected to activity testing. 2.2 Fragment molecular orbital (FMO) calculation Structure preparation . Prior to the FMO calculation, the complex structures of the ligands and SHP2 were prepared by the Molecular Operating Environment (MOE, version 2014.09). All binding modes of SHP2-ligand complexes in this report were derived from crystallography and molecular docking simulations. The ligand and residues around 4.5 Å of the ligand were cut off into the entire system researched. Hydrogen atoms were added to the complex structure and ionization states of the acidic and basic amino acid residues were assigned with the Protonate 3D tool implemented in MOE applied by default settings (pH = 7.0, a solvent dielectric constant of 80). Then, the structures were performed energy minimization using the semiempirical Amber10: EHT forced field. During this process, a constraint was applied, restricting each atom from deviating no more than 0.5 Å from its original position, a parameter enforced by the MOE program. We fragmented the entire optimized system using Facio (version 19.1.5) to prepare the FMO input files. FMO method and inter-fragment interaction energy (IFIE) . The FMO method was employed to partition a complex biological system, such as a protein, into smaller parts referred to as fragments, typically corresponding to individual residues. This approach saves a large amount of computation time compared to the traditional quantum mechanical (QM) methods 20, 21 . Generally, the FMO calculation contains the following steps: ( 1 ) partition a large system into multiple smaller fragments, ( 2 ) iteratively perform Ab initio calculations on each individual fragment (referred to as monomers) within the context of an embedding polarizable potential generated by the surrounding fragments until self-consistency is achieved in the electron densities, ( 3 ) calculate self-consistent field (SCF) of fragment pair, introducing interfragment charge transfer along with other quantum effects into the calculations, ( 4 ) calculate the total energy of the system. In our calculation, all FMO calculations were performed using General Atomic and Molecular Electronic Structure System (GAMESS, version Sep, 2020) 22, 23 and were performed by second-order Møller-Plesset perturbation (MP2) methods with the 6-31G* basis set (FMO-MP2/6-31G*). We treated the amino group as neutralized residue to avoid overestimating charge-charge interactions due to calculations in vacuum conditions. The FMO-based IFIE between fragment i and j, \(\Delta E_{{ij}}^{{\operatorname{int} }}\) , is given by \(\Delta E_{{ij}}^{{\operatorname{int} }}=\Delta E_{{ij}}^{{{\text{ES}}}}+\Delta E_{{ij}}^{{{\text{EX}}}}+\Delta E_{{ij}}^{{{\text{CT+MIX}}}}+\Delta E_{{ij}}^{{{\text{DI}}}}\) Where \(\Delta E_{{ij}}^{{{\text{ES}}}}\) , \(\Delta E_{{ij}}^{{{\text{EX}}}}\) , \(\Delta E_{{ij}}^{{{\text{CT+MIX}}}}\) and \(\Delta E_{{ij}}^{{{\text{DI}}}}\) represent the electrostatic, exchange repulsion, charge transfer with higher-order mixed and dispersion terms, obtained by the pair interaction energies decomposition analysis (PIEDA) 24 . 2.3 Molecular dynamics simulation In order to investigate the stability and behavior of the 7188-0011 -protein complex, molecular dynamics (MD) was performed using AmberTools18 simulation package (University of California, San Francisco, CA, USA ) for 100 ns. The parameters file was generated for protein using the FF14SB forcefield, while the corresponding ligand parameters file was generated using antechamber and parmchk modules in AmberTools18. Subsequently, the complex was neutralized by adding appropriate counter ions (Cl − ) and was solvated in a cubic box using TIP3PBOX water molecule with the box’s edge at least 1.0 nm away from the complex. Energy minimization was achieved by employing the steepest-descent algorithm for 5000 steps, which was subsequently followed by the conjugate-gradient algorithm for an additional 5000 steps. Subsequently, pre-equilibration took place in two sequential steps, comprising a constant NVT ensemble (maintaining a constant particle number, volume, and temperature) and a constant NPT ensemble (maintaining a constant particle number, pressure, and temperature). The former indicates that the simulation system was heated up progressively from 0 to 300 K for 200 ps using Langevin thermostat, and the latter was used to perform pressure equilibration for 100 ps at 300K using Berendsen barostat. Finally, the position restraint was removed, and the production run was carried out for 100 ns with a 2 fs step. The RMSD, RMSF, H-bonds, and cluster analysis were calculated with cpptraj utility in AmberTools18. Furthermore, the binding energy of the complex was estimated for 250 snapshots using MM/GB(PB)SA methods implemented in the mmpbsa.py tool of AmberTools18. 2.4 Biochemical assay The gene encoding human SHP2 WT (1-593) and SHP2 PTP (237–529) were inserted into the pET30 vector respectively. Two proteins were purified and expressed as described previously in the literature 25 . The allosteric activation of SHP2 was observed upon the binding of a phosphorylated peptide containing bis-tyrosyl to its Src homology 2 (SH2) domain. This activation process leads to the liberation of the inhibitory interface of SHP2, thereby enabling the accessibility of its PTP domain for substrate identification and catalysis. The rapid fluorescence assay was employed to detect the catalytic activity of SHP2, utilizing DiFMUP as a surrogate substrate. The experimental reagents prepared were as follows: 12.5 µL of buffer (75 mM NaCl, 75 mM KCl, 60 mM HEPES, pH 7.2, 1 mM EDTA, 0.05% P-20, 5 mM DTT), 2.5 µL of peptide IRS1_pY1172 (dPEG8) pY1222 (Shanghai Shengzhao Biotech Co., Ltd. cat. no. 79319-2), 2.5 µL of test compound, 2.5 µL of SHP2 WT or SHP2 PTP . And 20 µL reaction buffer was added to the 384-well plate (Perkin Elmer, cat. no. 6008260) and incubated for one hour at ambient temperature. Afterwards, the reaction was initiated by adding 200 µM surrogate substrate DiFMUP (Invitrogen, cat. no. D6567), followed by incubation at 25°C for an additional 30 minutes. Subsequently, a 5 µL solution containing bpV(Phen) with a concentration of 160 µM (Enzo Life Sciences cat# ALX-270-204) was added to halt the reaction. The fluorescence signal was then monitored by exciting the sample at 340 nm and measuring the emitted light at 450 nm. The inhibition rates were calculated by measuring the percentage of dephosphorylation of DiFMUP catalyzed by the enzyme. For each inhibitor, seven different concentrations were used to determine the IC 50 . SHP099 (Guangzhou Qiyun Biotechnology Co., Ltd., cat. no.1801747-42-1) was used as the positive control. Each experiment was performed at least three parallels. 3. Results and Discussion 3.1 Construction of Dock-FMO virtual screening protocol According to the previous report 26–30 , the fragment molecular orbital (FMO) method, developed by Kitaura and co-workers 20, 21, 31 , can achieve high precision to predict a complete list of the pair interaction energy (PIE) to study ligand − protein interactions by performing ab initio quantum-chemical calculations for biomolecular systems. Therefore, the FMO calculation for predicting intermolecular interactions is recognized as a reliable method for calculating binding energies with satisfactory accuracy, but this method was limited to computational cost and is not suitable for large-scale virtual screening purposes. In order to improve hit rates, this study incorporated the FMO method as a high-precision binding affinity prediction step following the traditional virtual screening workflow, resulting in a approach known as the Dock-FMO virtual screening method. Prior to the virtual screening, the Dock-FMO protocol was validated by redocking and correlation analysis between FMO-based interaction energies and the experimental binding free energies. Initially, pose validation was carried out by redocking the co-crystallized ligand into the allosteric site of SHP2 (PDB: 5EHR). The RMSD between the co-crystallized conformation and the best pose docked by Glide SP is 0.574 Å, which indicates that the Glide SP can predict active conformation of the ligand. Next, to investigate the prediction accuracy of the FMO method for calculating protein-ligand interactions of the SHP2 target, we manually curated a dataset including 51 ligand structures and affinities (IC 50 ) from papers 17, 25, 32–35 ( Table S1 ). The sums of PIEs between 51 ligands with diverse chemical scaffolds and the SHP2 protein correlated well with their biological potencies ( R 2 = 0.55, Fig. 1 A and Supplemental Data ). Comparing FMO results on this dataset with results for MM/PBSA and MM/GBSA ( R 2 = 0.02 and 0.15, respectively, Fig. 1 A and Supplemental Data ), we found that FMO method showed a higher correlation to experimental data. Therefore, the FMO analysis was employed as a critical strategy to determine the binding affinities between ligands and proteins. 3.2 Virtual screening and compounds selection To identify the key residues in the SHP2 allosteric site, we performed FMO calculations and PIEDA analysis. We calculated the interaction energies between SHP099 , a lead compound, and the surrounding residues of SHP2 protein (PDB:5EHR) using the FMO method to understand the ligand binding mechanism of SHP2. The interaction energies of R111 and E250 have high ES and CT + MIX terms calculated from the PIEDA analysis ( Figure S1 and Table S2 ), indicating they could form hydrogen bonds with the ligand. In addition, E249 and H114 could form a salt bridge with SHP099, as indicated by their high contributions in ES and CT + MIX terms. Furthermore, L254 and P491 could form hydrophobic and CH/π interactions with DI term contribution (-3.0 ~ -7.2 kcal/mol). As indicated by the PIEDA analysis, E250 (PTP), E249 (PTP), R111 (N-SH2), and H114 (N-SH2) are identified as key residues involved in ligand binding at the SHP2 allosteric site. The result provides a reliable reference for subsequent virtual screening research. In order to identify potent SHP2 allosteric inhibitors, we first carried out the Dock-FMO virtual screening protocol against the ChemDiv database (∼2,990,000 compounds), followed by in vitro activity assays, as shown in Fig. 1 B. All compounds were filtered by Lipinski's rule of five, PAINS (pan assay interference filters), and ADME/T calculated by the Qikprop module. After successively docking and scoring by high throughput virtual screening (HTVS), Glide SP, and Glide XP, the remaining compounds were then grouped into 25 clusters based on the Tanimoto similarity metric. After individually checking these compounds for the formation of critical interactions, we selected 39 compounds for further evaluation by FMO analysis. The top 60% of compounds ranked by binding affinities (25 compounds, whose sum of PIE was lower than − 85 kcal/mol) were further selected and purchased for biochemical assays ( Table S3 ). 3.3 In vitro biological activity After a hierarchical structure-based virtual screening of the SHP2 protein, we obtained 25 candidate compounds to test for the SHP2 enzyme activity. In this study, we built a fluorescence-based phosphatase biochemical assay was built to assess the dephosphorylation of 6,8-difluoro-4-methylumbelliferyl phosphate (0.5 µM 2P-IRS-1, DIFMUP assay 35 ) as a means of validating SHP2 allosteric inhibitors. SHP099 reported by Novartis 17 was used as the positive control (SHP2 WT IC 50 = 0.078 µM, SHP2 PTP IC 50 > 100 µM), and the data was broadly consistent with the experimental value reported previously 17 . Therefore, we established the SHP2 allosteric inhibitor screening system. As shown in Fig. 2 A, the 25 tested compounds were initially screened against SHP2 full length (SHP2 WT ) at 50 µM, and 1 hit ( 7188-0011 , Fig. 2 A) with 52% of inhibitory rate was further validated by the SHP2 PTP -based dephosphorylation assay (SHP2 PTP IC 50 > 100 µM), which indicated that the inhibitor could not bind to the PTP active site. The positive control (0.1 µM SHP099 ) showed 65% inhibition. The hit compound effectively inhibited the activity of SHP2 WT with the IC 50 value of 54.31 ± 0.67 µM (Fig. 2 B). 3.4 Binding mode analysis The predicted binding mode of 7188-0011 showed a similar pattern to the observed SHP099 binding mode in the crystal structure (Fig. 3 A and Figure S2 ). FMO analysis of 7188-0011 revealed 5 key interactions with SHP2 (Fig. 3 A). The inhibitor is positioned in a narrow polar pocket between PTP, C-SH2, and N-SH2 domains of SHP2. PIEDA results indicated that the inhibitor could form hydrogen bonds with E250 and R111 and could form a salt bridge with E249, which have high ES and CT + MIX terms contribution (Fig. 3 C). These residues played important roles in molecular recognition. In addition, T253, L524, and P491 could form CH/π interactions with the inhibitor, which have slightly high DI term contribution. The PIEDA results indicated that the binding mode of 7188-0011 and SHP099 are consistent, suggesting that 7188-0011 exerts its inhibitory function by interacting with the same key residues of the protein. Therefore, the binding stability of 7188-0011 to the protein was next explored using molecular dynamics (MD) simulations. 3.5 MD simulations MD simulations have been regarded as a vital computational tool to investigate the time-dependent stability of ligands in the active site of proteins. The binding stability of the hit compound ( 7188 − 1100 ) was further evaluated by performing MD simulations for 100 ns. The RMSD of backbone atoms of the complex and the apo structure (without ligand) were plotted in Fig. 4 A. The backbone of the complex and the apo structure reached equilibration at about 30 ns and then stabilized, oscillating around the 2 Å value. Meanwhile, we computed the root-mean-square fluctuation (RMSF) of all residues after the 30 ns simulation to assess both ligand deviation from the initial conformation and the extent of residue movement in SHP2 in both the unbound state and upon ligand binding. (Fig. 4 B). The RMSF value for the free protein and the protein involved in the complex displayed a constant trend, but these overall fluctuations were minimized in the binding state in general. These results further indicated that the ligand could enhance the stability of protein during the simulation. The detailed analysis revealed that strong hydrogen bond interactions were found between the ligand and residues E250 and F113, kept for 93.64% and 62.22%, respectively. The π-cation interaction and hydrogen bond interactions were found between the 7188-0011 and R111 (Fig. 4 C- 4 D). However, the high flexibility of the 7188-0011 compound itself, as well as its inadequate hydrophobicity at the head region (phenyl), was considered to be the reasons for its poor activity. This could potentially be a promising direction for future hit optimization to improve affinity with the target. 4. Conclusion SHP2, a protein tyrosine phosphatase, has emerged as a therapeutic target for various human diseases, particularly cancer. In this study, we employed a rigorous virtual screening method called Dock-FMO, including molecular docking for primary screening and FMO methods for binding affinity calculation. Therefore, to identify potent SHP2 allosteric inhibitors, we carried out the Dock-FMO virtual screening protocol against the ChemDiv database. Subsequently, the 25 compounds were selected for in vitro activity assays, and experimental validation revealed that compound 7188-0011 inhibited SHP2 in a dose-dependent manner (IC 50 = 54.31 ± 0.67 μM). MD simulations further confirmed the high binding stability between 7188-0011 and SHP2 protein. This study provided a novel and promising allosteric inhibitor, laying the foundation for the development of anti-tumor drugs targeting the SHP2 protein. It also serves as the basis for further lead compound optimization to develop more active inhibitors. Declarations CRediT authorship contribution statement Zhen Yuan: Methodology, Investigation, Data curation, Visualization, Writing – original draft, Writing – review and editing. Manzhan Zhang: Methodology, Investigation, Writing – original draft. Longfeng Chang: Resources, Validation. Xingyu Chen : Data curation. Shanshan Ruan: Data curation, Shanshan Shi: Data curation, Yiqing Zhang: Data curation. Lili Zhu : Writing – review and editing, Supervision, Funding acquisition. Honglin Li: Conceptualization – review and editing, Supervision, Funding acquisition. Shiliang Li: Conceptualization, Writing – review and editing, Supervision, Funding acquisition. All authors have read and agreed to the published version of the manuscript. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This work was supported in part by the National Key R&D Program of China (2022YFC3400501, 2022YFC3400504); the National Natural Science Foundation of China (grants 82173690 to S.L.L., 81825020 and 82150208 to H.L.,); the Lingang Laboratory (grant LG-QS-202206-02 to S.L.L.); the Fundamental Research Funds for the Central Universities; S.L.L. is also sponsored by the Shanghai Rising-Star Program (23QA1402800). Honglin Li was also sponsored by the National Program for Special Supports of Eminent Professionals and National Program for Support of Top-Notch Young Professionals. Appendix A. Supplementary material The Supporting Information to this article can be found in the online version. 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P., et al., Allosteric Inhibition of SHP2: Identification of a Potent, Selective, and Orally Efficacious Phosphatase Inhibitor. J. Med. Chem. 2016, 59 (17), 7773-7782. Additional Declarations No competing interests reported. Supplementary Files Supplementarydata.xlsx SupportingInformation.docx Cite Share Download PDF Status: Published Journal Publication published 13 Apr, 2024 Read the published version in Journal of Molecular Modeling → Version 1 posted Editorial decision: Revision requested 07 Nov, 2023 Submission checks completed at journal 06 Nov, 2023 Editor assigned by journal 06 Nov, 2023 First submitted to journal 05 Nov, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3565398","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":246663698,"identity":"5f212617-f9b9-4278-8018-d02eb725b0fb","order_by":0,"name":"Zhen Yuan","email":"","orcid":"","institution":"East China University of Science \u0026 Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Yuan","suffix":""},{"id":246663699,"identity":"6b3e965d-5e7d-4e8f-8b84-9291f0a0db3b","order_by":1,"name":"Manzhan Zhang","email":"","orcid":"","institution":"East China University of Science \u0026 Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Manzhan","middleName":"","lastName":"Zhang","suffix":""},{"id":246663700,"identity":"6bf2e71e-03d0-49da-8898-c3266c8a1f2c","order_by":2,"name":"Longfeng Chang","email":"","orcid":"","institution":"East China University of Science \u0026 Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Longfeng","middleName":"","lastName":"Chang","suffix":""},{"id":246663701,"identity":"68e8d7a4-8de8-40b6-b192-5e62f94ac128","order_by":3,"name":"Xingyu Chen","email":"","orcid":"","institution":"East China University of Science \u0026 Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xingyu","middleName":"","lastName":"Chen","suffix":""},{"id":246663702,"identity":"d609e43b-381f-45fa-8ede-5293093f2d0d","order_by":4,"name":"Shanshan Ruan","email":"","orcid":"","institution":"East China University of Science \u0026 Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shanshan","middleName":"","lastName":"Ruan","suffix":""},{"id":246663703,"identity":"2daf3d97-b6df-4919-9033-6ca1ee015464","order_by":5,"name":"Shanshan Shi","email":"","orcid":"","institution":"East China University of Science \u0026 Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shanshan","middleName":"","lastName":"Shi","suffix":""},{"id":246663704,"identity":"70eabffd-52d9-4986-8c57-f1c62116de2c","order_by":6,"name":"Yiqing Zhang","email":"","orcid":"","institution":"East China University of Science \u0026 Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yiqing","middleName":"","lastName":"Zhang","suffix":""},{"id":246663705,"identity":"d8561926-4cad-4696-8b4d-ce1abb2eb8c5","order_by":7,"name":"Lili Zhu","email":"","orcid":"","institution":"East China University of Science \u0026 Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Zhu","suffix":""},{"id":246663706,"identity":"24311ae3-fbd6-4395-815b-4f9d08247a6a","order_by":8,"name":"Honglin Li","email":"","orcid":"","institution":"East China University of Science \u0026 Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Honglin","middleName":"","lastName":"Li","suffix":""},{"id":246663707,"identity":"ccb71cea-7ba7-4a34-b456-6a107b73efb0","order_by":9,"name":"Shiliang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIie3RIQ7CMBTG8TZNOkPQJSz0CiUYJEcpamq+AtGF5NVwAQzHQL+lyVQvQDAcATlDQoPCEB4O0Z/uP/naMlYU/6gSHpXZLWQVkJgI7nHthtV0kiw9YS6J7UltDK3Qe97hBWQDilk2uvP3xETu+yPULcw75Id0JSR5WJyBbKFGKzgQkjzMxweIRipraAnLw1AlYemJeSVuWEJ+5J50Fx328Z6/UusQ+tvoKMPe4Y/ni6Ioik+eBz48xlNbLW4AAAAASUVORK5CYII=","orcid":"","institution":"East China University of Science \u0026 Technology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Shiliang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2023-11-06 03:14:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3565398/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3565398/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00894-024-05935-y","type":"published","date":"2024-04-13T15:00:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":46104542,"identity":"885d84de-98a4-4a70-b647-3ef4da0c5a58","added_by":"auto","created_at":"2023-11-08 16:23:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57991,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiscovery of a novel SHP2 allosteric inhibitor using the Dock-FMO protocol.\u003c/strong\u003e (A) Correlations between experimental affinities (Δ\u003cem\u003eG\u003c/em\u003e\u003csub\u003eexp\u003c/sub\u003e ≈ \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eB\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eT\u003c/em\u003e log IC\u003csub\u003e50\u003c/sub\u003e) and ΔG values for the reported SHP2 inhibitors predicted by FMO, MM/PBSA, and MM/GBSA, respectively. (B) Workflow for the Dock-FMO virtual screening protocol.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3565398/v1/396cb041f90df346cce5acc3.jpg"},{"id":46104544,"identity":"fbe0b045-1743-41b1-b054-fe9b06b15b14","added_by":"auto","created_at":"2023-11-08 16:23:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24019,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnzymatic assay of SHP2 allosteric inhibitors \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein vitro\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e. \u003c/strong\u003e(A) Screening results of candidate compounds on the enzyme activity of SHP2 full length (SHP2\u003csup\u003eWT\u003c/sup\u003e). Red dotted line represents the 50% inhibition threshold for hit identification. Blue dot indicates compound \u003cstrong\u003e7188-0011\u003c/strong\u003e. Red dot indicates compound \u003cstrong\u003eSHP099\u003c/strong\u003e, which served as a positive control. The initial screening concentration of the candidate compounds was 50 μM. (B) The dose–response curve of \u003cstrong\u003e7188-0011\u003c/strong\u003e on SHP2 full length (SHP2\u003csup\u003eWT\u003c/sup\u003e).\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3565398/v1/3327bb0325434d7a92a9b836.jpg"},{"id":46104541,"identity":"8eca4b85-e205-423e-bbb6-2372329c3c86","added_by":"auto","created_at":"2023-11-08 16:23:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":70209,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBinding mode prediction and interaction analysis. \u003c/strong\u003e(A) Predicted binding mode of compound \u003cstrong\u003e7188-0011\u003c/strong\u003e in the allosteric site of SHP2 (PDB: 5EHR). Hydrogen bonds and salt bridge were depicted as dotted yellow lines. The key residues and the ligand were shown in sticks. The ligand's carbon atoms are shown in green, and carbon atoms of the key residues are colored according to the pair interaction energy (PIE) values calculated using the FMO method. (B) Chemical structure of compound \u003cstrong\u003e7188-0011\u003c/strong\u003e. (C) The pair interaction energies (PIE) of the compound \u003cstrong\u003e7188-0011\u003c/strong\u003e and the surrounding residues were drawn on the left graph, and the pair interaction energies decomposition analysis (PIEDA) for these residues was drawn on the right graph. The electrostatic (ES), exchange-repulsion (EX), charge transfer (CT+MIX), and dispersion terms (DI) were represented in green, blue, orange, and yellow, respectively.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3565398/v1/b9d39729e214a48b002d036f.jpg"},{"id":46105708,"identity":"54725d38-ecbd-44b9-9ae9-ed41640b802b","added_by":"auto","created_at":"2023-11-08 16:31:45","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":70750,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of molecular dynamics simulations.\u003c/strong\u003e (A) The RMSD plots and (B) RMSF plots of the complex and apo protein backbone for 100 ns MD simulations (in blue and black, respectively). (C) The binding mode of the ligand and the surrounding residues in the biggest conformational cluster of 100 ns MD trajectories. Hydrogen bonds and salt bridges were depicted as dotted yellow lines. The key residues and ligand were shown in sticks. (D) The occupancies of hydrogen bonds were represented as bar charts.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3565398/v1/19ac56dad1cbabe36462fd44.jpg"},{"id":54712889,"identity":"c8482d86-3e3a-4445-9d98-c7c7894c23c8","added_by":"auto","created_at":"2024-04-15 15:13:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":717207,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3565398/v1/256e3016-6c5c-4003-ac2a-49725893c27e.pdf"},{"id":46104545,"identity":"6351950e-eda2-420f-bbe1-964944c94d03","added_by":"auto","created_at":"2023-11-08 16:23:45","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":31568,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3565398/v1/a62e9aff12b33ffa5f6b98f3.xlsx"},{"id":46104546,"identity":"a27cfa5d-5057-4ac8-b508-845f57e642d8","added_by":"auto","created_at":"2023-11-08 16:23:46","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":4178631,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-3565398/v1/f8b94232ceda4f7eee1b9f67.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Discovery of a novel SHP2 allosteric inhibitor using virtual screening, FMO calculation and molecular dynamic simulation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSrc homology-2-containing protein tyrosine phosphatase 2 (SHP2), the first oncogenic non-receptor protein tyrosine phosphatase to be identified, is encoded by the proto-oncogene \u003cem\u003ePTPN11\u003c/em\u003e. It plays a critical role in various cellular processes including growth, proliferation, differentiation, migration, and apoptosis\u003csup\u003e1\u003c/sup\u003e. SHP2 is comprised of four distinct domains, namely two tandem Src homology 2 domains (N-SH2 and C-SH2), a conserved PTP catalytic domain, and a C-terminal tail housing two tyrosine phosphorylation sites (Tyr542 and Tyr580)\u003csup\u003e2\u003c/sup\u003e. Accumulated evidence has demonstrated that SHP2 actively participates in numerous oncogenic cell-signaling pathways, which encompass RAS-ERK, PI3K-AKT, and JAK-STAT, situated downstream of several receptor tyrosine kinases (RTKs)\u003csup\u003e1, 3\u0026ndash;6\u003c/sup\u003e. Moreover, it has been reported that SHP2 is involved in the downstream signal transduction of the immunosuppressive receptor PD-1 within the PD-L1/PD-1 pathway, resulting in the suppression of T cell activation\u003csup\u003e7\u0026ndash;9\u003c/sup\u003e. SHP2 has been shown to serve dual roles, functioning either as an oncogenic factor or as a tumor suppressor. Dysregulation of SHP2 contributes to a variety of sporadic hematological malignancies, developmental disorders, and solid tumors\u003csup\u003e10\u0026ndash;12\u003c/sup\u003e. This extensive range of diseases highlights the potential of SHP2 as a promising target for cancer treatment, leading to substantial interest in SHP2 inhibitors as prospective agents for inhibiting tumor growth.\u003c/p\u003e \u003cp\u003eOver the last two decades, an effort has been devoted to discovering active site inhibitors in the highly conserved catalytic PTP domain\u003csup\u003e13\u0026ndash;16\u003c/sup\u003e. Nevertheless, these inhibitors have not advanced to clinical applications owing to challenges associated with poor oral bioavailability, low selectivity, and poor membrane permeability. Until recently, the first allosteric inhibitor (\u003cb\u003eSHP099\u003c/b\u003e) of SHP2 was reported to stabilize SHP2 in an auto-inhibited conformation that blocks the access of substrates, resulting in suppressed PTP activity\u003csup\u003e17\u003c/sup\u003e. At present, eight allosteric inhibitors of SHP2 exhibiting high selectivity and oral bioavailability (TNO155, JAB-3068, BBP‐398, ERAS‐601, JAB‐3312, SH3809, RMC‐4630, and HBI‐2376) have progressed to clinical trials.\u003csup\u003e10\u003c/sup\u003e SHP2 allosteric inhibitors were designed to overcome the poor druggability drawbacks of catalytic site inhibitors and exhibit high therapeutic potential for cancer treatment. In this study, we utilized Dock-FMO virtual screening to identify novel compounds targeting the SHP2 allosteric site. The top-ranked compounds were subsequently selected for biological testing. Among them, compound \u003cb\u003e7188-0011\u003c/b\u003e, possessing a unique structure, exhibited moderate enzymatic potency. To evaluate the dynamic behavior and binding stability of the \u003cb\u003e7188-0011\u003c/b\u003e-SHP2 complex, molecular dynamics (MD) simulations further performed.\u003c/p\u003e"},{"header":"2. Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Virtual screening\u003c/h2\u003e \u003cp\u003eThe crystal structure of SHP2 was obtained from the Protein Data Bank with the accession code 5EHR, of which the resolution is 1.7 \u0026Aring;. The coactivators, as well as all water molecules, were removed from the structure. Hydrogen atoms and charges were added using the Protein Preparation Wizard module in Maestro (Schr\u0026ouml;dinger Inc, version 10.1). The crystal structure, after optimization of the hydrogen bond network, was subjected to energy minimization using the OPLS3 force field. The grid-enclosing box was placed on the center of the crystallographic ligand and defined to encompass residues within a 15 \u0026Aring; radius around the ligand, constituting the binding pocket. The remaining settings were kept at their default values. Under conditions of pH ranging from 5.0 to 9.0, the addition of hydrogen atoms and ionization facilitated the generation of stereoisomers and 3D conformers for compounds housed within the ChemDiv database (\u0026sim;2,990,000 compounds), employing the Ligprep v3.3 module. Utilizing both the Glide standard precision (SP) and extra precision (XP) methods\u003csup\u003e18, 19\u003c/sup\u003e, molecular docking was performed with default parameters, retaining only the highest-ranked pose for each molecule. After Glide SP scorings, the top 15,215 ranked molecules were retained for subsequent calculations using the more precise Glide XP scoring function. Finally, the top-ranked 1,520 molecules were retained for further visual observation. Based on the Tanimoto similarity metric, a hierarchical clustering method was employed to cluster the 1,520 MOLPRINT2D fingerprint features into 25 groups. Taking into consideration ligand binding poses, structural diversity, and novelty, 25 candidates were selected from the pool of 1,520 compounds and subsequently subjected to activity testing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Fragment molecular orbital (FMO) calculation\u003c/h2\u003e \u003cp\u003e \u003cb\u003eStructure preparation\u003c/b\u003e. Prior to the FMO calculation, the complex structures of the ligands and SHP2 were prepared by the Molecular Operating Environment (MOE, version 2014.09). All binding modes of SHP2-ligand complexes in this report were derived from crystallography and molecular docking simulations. The ligand and residues around 4.5 \u0026Aring; of the ligand were cut off into the entire system researched. Hydrogen atoms were added to the complex structure and ionization states of the acidic and basic amino acid residues were assigned with the Protonate 3D tool implemented in MOE applied by default settings (pH\u0026thinsp;=\u0026thinsp;7.0, a solvent dielectric constant of 80). Then, the structures were performed energy minimization using the semiempirical Amber10: EHT forced field. During this process, a constraint was applied, restricting each atom from deviating no more than 0.5 \u0026Aring; from its original position, a parameter enforced by the MOE program. We fragmented the entire optimized system using Facio (version 19.1.5) to prepare the FMO input files.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFMO method and inter-fragment interaction energy (IFIE)\u003c/b\u003e. The FMO method was employed to partition a complex biological system, such as a protein, into smaller parts referred to as fragments, typically corresponding to individual residues. This approach saves a large amount of computation time compared to the traditional quantum mechanical (QM) methods\u003csup\u003e20, 21\u003c/sup\u003e. Generally, the FMO calculation contains the following steps: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) partition a large system into multiple smaller fragments, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) iteratively perform Ab initio calculations on each individual fragment (referred to as monomers) within the context of an embedding polarizable potential generated by the surrounding fragments until self-consistency is achieved in the electron densities, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) calculate self-consistent field (SCF) of fragment pair, introducing interfragment charge transfer along with other quantum effects into the calculations, (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) calculate the total energy of the system.\u003c/p\u003e \u003cp\u003eIn our calculation, all FMO calculations were performed using General Atomic and Molecular Electronic Structure System (GAMESS, version Sep, 2020) \u003csup\u003e22, 23\u003c/sup\u003e and were performed by second-order M\u0026oslash;ller-Plesset perturbation (MP2) methods with the 6-31G* basis set (FMO-MP2/6-31G*). We treated the amino group as neutralized residue to avoid overestimating charge-charge interactions due to calculations in vacuum conditions. The FMO-based IFIE between fragment i and j, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\Delta E_{{ij}}^{{\\operatorname{int} }}\\)\u003c/span\u003e\u003c/span\u003e, is given by\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\Delta E_{{ij}}^{{\\operatorname{int} }}=\\Delta E_{{ij}}^{{{\\text{ES}}}}+\\Delta E_{{ij}}^{{{\\text{EX}}}}+\\Delta E_{{ij}}^{{{\\text{CT+MIX}}}}+\\Delta E_{{ij}}^{{{\\text{DI}}}}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\Delta E_{{ij}}^{{{\\text{ES}}}}\\)\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\Delta E_{{ij}}^{{{\\text{EX}}}}\\)\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\Delta E_{{ij}}^{{{\\text{CT+MIX}}}}\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\Delta E_{{ij}}^{{{\\text{DI}}}}\\)\u003c/span\u003e\u003c/span\u003erepresent the electrostatic, exchange repulsion, charge transfer with higher-order mixed and dispersion terms, obtained by the pair interaction energies decomposition analysis (PIEDA)\u003csup\u003e24\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Molecular dynamics simulation\u003c/h2\u003e \u003cp\u003eIn order to investigate the stability and behavior of the \u003cb\u003e7188-0011\u003c/b\u003e-protein complex, molecular dynamics (MD) was performed using AmberTools18 simulation package (University of California, San Francisco, CA, USA ) for 100 ns. The parameters file was generated for protein using the FF14SB forcefield, while the corresponding ligand parameters file was generated using antechamber and parmchk modules in AmberTools18. Subsequently, the complex was neutralized by adding appropriate counter ions (Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e) and was solvated in a cubic box using TIP3PBOX water molecule with the box\u0026rsquo;s edge at least 1.0 nm away from the complex. Energy minimization was achieved by employing the steepest-descent algorithm for 5000 steps, which was subsequently followed by the conjugate-gradient algorithm for an additional 5000 steps. Subsequently, pre-equilibration took place in two sequential steps, comprising a constant NVT ensemble (maintaining a constant particle number, volume, and temperature) and a constant NPT ensemble (maintaining a constant particle number, pressure, and temperature). The former indicates that the simulation system was heated up progressively from 0 to 300 K for 200 ps using \u003cem\u003eLangevin\u003c/em\u003e thermostat, and the latter was used to perform pressure equilibration for 100 ps at 300K using \u003cem\u003eBerendsen\u003c/em\u003e barostat. Finally, the position restraint was removed, and the production run was carried out for 100 ns with a 2 fs step. The RMSD, RMSF, H-bonds, and cluster analysis were calculated with \u003cem\u003ecpptraj\u003c/em\u003e utility in AmberTools18. Furthermore, the binding energy of the complex was estimated for 250 snapshots using MM/GB(PB)SA methods implemented in the mmpbsa.py tool of AmberTools18.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Biochemical assay\u003c/h2\u003e \u003cp\u003eThe gene encoding human SHP2\u003csup\u003eWT\u003c/sup\u003e (1-593) and SHP2\u003csup\u003ePTP\u003c/sup\u003e (237\u0026ndash;529) were inserted into the pET30 vector respectively. Two proteins were purified and expressed as described previously in the literature\u003csup\u003e25\u003c/sup\u003e. The allosteric activation of SHP2 was observed upon the binding of a phosphorylated peptide containing bis-tyrosyl to its Src homology 2 (SH2) domain. This activation process leads to the liberation of the inhibitory interface of SHP2, thereby enabling the accessibility of its PTP domain for substrate identification and catalysis. The rapid fluorescence assay was employed to detect the catalytic activity of SHP2, utilizing DiFMUP as a surrogate substrate. The experimental reagents prepared were as follows: 12.5 \u0026micro;L of buffer (75 mM NaCl, 75 mM KCl, 60 mM HEPES, pH 7.2, 1 mM EDTA, 0.05% P-20, 5 mM DTT), 2.5 \u0026micro;L of peptide IRS1_pY1172 (dPEG8) pY1222 (Shanghai Shengzhao Biotech Co., Ltd. cat. no. 79319-2), 2.5 \u0026micro;L of test compound, 2.5 \u0026micro;L of SHP2\u003csup\u003eWT\u003c/sup\u003e or SHP2\u003csup\u003ePTP\u003c/sup\u003e. And 20 \u0026micro;L reaction buffer was added to the 384-well plate (Perkin Elmer, cat. no. 6008260) and incubated for one hour at ambient temperature. Afterwards, the reaction was initiated by adding 200 \u0026micro;M surrogate substrate DiFMUP (Invitrogen, cat. no. D6567), followed by incubation at 25\u0026deg;C for an additional 30 minutes. Subsequently, a 5 \u0026micro;L solution containing bpV(Phen) with a concentration of 160 \u0026micro;M (Enzo Life Sciences cat# ALX-270-204) was added to halt the reaction. The fluorescence signal was then monitored by exciting the sample at 340 nm and measuring the emitted light at 450 nm. The inhibition rates were calculated by measuring the percentage of dephosphorylation of DiFMUP catalyzed by the enzyme. For each inhibitor, seven different concentrations were used to determine the IC\u003csub\u003e50\u003c/sub\u003e. \u003cb\u003eSHP099\u003c/b\u003e (Guangzhou Qiyun Biotechnology Co., Ltd., cat. no.1801747-42-1) was used as the positive control. Each experiment was performed at least three parallels.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Construction of Dock-FMO virtual screening protocol\u003c/h2\u003e \u003cp\u003eAccording to the previous report\u003csup\u003e26\u0026ndash;30\u003c/sup\u003e, the fragment molecular orbital (FMO) method, developed by Kitaura and co-workers\u003csup\u003e20, 21, 31\u003c/sup\u003e, can achieve high precision to predict a complete list of the pair interaction energy (PIE) to study ligand\u0026thinsp;\u0026minus;\u0026thinsp;protein interactions by performing ab initio quantum-chemical calculations for biomolecular systems. Therefore, the FMO calculation for predicting intermolecular interactions is recognized as a reliable method for calculating binding energies with satisfactory accuracy, but this method was limited to computational cost and is not suitable for large-scale virtual screening purposes. In order to improve hit rates, this study incorporated the FMO method as a high-precision binding affinity prediction step following the traditional virtual screening workflow, resulting in a approach known as the Dock-FMO virtual screening method.\u003c/p\u003e \u003cp\u003ePrior to the virtual screening, the Dock-FMO protocol was validated by redocking and correlation analysis between FMO-based interaction energies and the experimental binding free energies. Initially, pose validation was carried out by redocking the co-crystallized ligand into the allosteric site of SHP2 (PDB: 5EHR). The RMSD between the co-crystallized conformation and the best pose docked by Glide SP is 0.574 \u0026Aring;, which indicates that the Glide SP can predict active conformation of the ligand. Next, to investigate the prediction accuracy of the FMO method for calculating protein-ligand interactions of the SHP2 target, we manually curated a dataset including 51 ligand structures and affinities (IC\u003csub\u003e50\u003c/sub\u003e) from papers\u003csup\u003e17, 25, 32\u0026ndash;35\u003c/sup\u003e (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). The sums of PIEs between 51 ligands with diverse chemical scaffolds and the SHP2 protein correlated well with their biological potencies (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.55, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA \u003cb\u003eand Supplemental Data\u003c/b\u003e). Comparing FMO results on this dataset with results for MM/PBSA and MM/GBSA (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.02 and 0.15, respectively, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA \u003cb\u003eand Supplemental Data\u003c/b\u003e), we found that FMO method showed a higher correlation to experimental data. Therefore, the FMO analysis was employed as a critical strategy to determine the binding affinities between ligands and proteins.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Virtual screening and compounds selection\u003c/h2\u003e \u003cp\u003eTo identify the key residues in the SHP2 allosteric site, we performed FMO calculations and PIEDA analysis. We calculated the interaction energies between \u003cb\u003eSHP099\u003c/b\u003e, a lead compound, and the surrounding residues of SHP2 protein (PDB:5EHR) using the FMO method to understand the ligand binding mechanism of SHP2. The interaction energies of R111 and E250 have high ES and CT\u0026thinsp;+\u0026thinsp;MIX terms calculated from the PIEDA analysis (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e), indicating they could form hydrogen bonds with the ligand. In addition, E249 and H114 could form a salt bridge with SHP099, as indicated by their high contributions in ES and CT\u0026thinsp;+\u0026thinsp;MIX terms. Furthermore, L254 and P491 could form hydrophobic and CH/π interactions with DI term contribution (-3.0 ~ -7.2 kcal/mol). As indicated by the PIEDA analysis, E250 (PTP), E249 (PTP), R111 (N-SH2), and H114 (N-SH2) are identified as key residues involved in ligand binding at the SHP2 allosteric site. The result provides a reliable reference for subsequent virtual screening research.\u003c/p\u003e \u003cp\u003eIn order to identify potent SHP2 allosteric inhibitors, we first carried out the Dock-FMO virtual screening protocol against the ChemDiv database (\u0026sim;2,990,000 compounds), followed by in vitro activity assays, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB. All compounds were filtered by Lipinski's rule of five, PAINS (pan assay interference filters), and ADME/T calculated by the Qikprop module. After successively docking and scoring by high throughput virtual screening (HTVS), Glide SP, and Glide XP, the remaining compounds were then grouped into 25 clusters based on the Tanimoto similarity metric. After individually checking these compounds for the formation of critical interactions, we selected 39 compounds for further evaluation by FMO analysis. The top 60% of compounds ranked by binding affinities (25 compounds, whose sum of PIE was lower than \u0026minus;\u0026thinsp;85 kcal/mol) were further selected and purchased for biochemical assays (\u003cb\u003eTable \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 In vitro biological activity\u003c/h2\u003e \u003cp\u003eAfter a hierarchical structure-based virtual screening of the SHP2 protein, we obtained 25 candidate compounds to test for the SHP2 enzyme activity. In this study, we built a fluorescence-based phosphatase biochemical assay was built to assess the dephosphorylation of 6,8-difluoro-4-methylumbelliferyl phosphate (0.5 \u0026micro;M 2P-IRS-1, DIFMUP assay\u003csup\u003e35\u003c/sup\u003e) as a means of validating SHP2 allosteric inhibitors. SHP099 reported by Novartis\u003csup\u003e17\u003c/sup\u003e was used as the positive control (SHP2\u003csup\u003eWT\u003c/sup\u003e IC\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.078 \u0026micro;M, SHP2\u003csup\u003ePTP\u003c/sup\u003e IC\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;100 \u0026micro;M), and the data was broadly consistent with the experimental value reported previously\u003csup\u003e17\u003c/sup\u003e. Therefore, we established the SHP2 allosteric inhibitor screening system. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, the 25 tested compounds were initially screened against SHP2 full length (SHP2\u003csup\u003eWT\u003c/sup\u003e) at 50 \u0026micro;M, and 1 hit (\u003cb\u003e7188-0011\u003c/b\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) with 52% of inhibitory rate was further validated by the SHP2\u003csup\u003ePTP\u003c/sup\u003e -based dephosphorylation assay (SHP2\u003csup\u003ePTP\u003c/sup\u003e IC\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;100 \u0026micro;M), which indicated that the inhibitor could not bind to the PTP active site. The positive control (0.1 \u0026micro;M \u003cb\u003eSHP099\u003c/b\u003e) showed 65% inhibition. The hit compound effectively inhibited the activity of SHP2\u003csup\u003eWT\u003c/sup\u003e with the IC\u003csub\u003e50\u003c/sub\u003e value of 54.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67 \u0026micro;M (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Binding mode analysis\u003c/h2\u003e \u003cp\u003eThe predicted binding mode of \u003cb\u003e7188-0011\u003c/b\u003e showed a similar pattern to the observed \u003cb\u003eSHP099\u003c/b\u003e binding mode in the crystal structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA \u003cb\u003eand Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). FMO analysis of \u003cb\u003e7188-0011\u003c/b\u003e revealed 5 key interactions with SHP2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The inhibitor is positioned in a narrow polar pocket between PTP, C-SH2, and N-SH2 domains of SHP2. PIEDA results indicated that the inhibitor could form hydrogen bonds with E250 and R111 and could form a salt bridge with E249, which have high ES and CT\u0026thinsp;+\u0026thinsp;MIX terms contribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). These residues played important roles in molecular recognition. In addition, T253, L524, and P491 could form CH/π interactions with the inhibitor, which have slightly high DI term contribution. The PIEDA results indicated that the binding mode of \u003cb\u003e7188-0011\u003c/b\u003e and \u003cb\u003eSHP099\u003c/b\u003e are consistent, suggesting that \u003cb\u003e7188-0011\u003c/b\u003e exerts its inhibitory function by interacting with the same key residues of the protein. Therefore, the binding stability of \u003cb\u003e7188-0011\u003c/b\u003e to the protein was next explored using molecular dynamics (MD) simulations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 MD simulations\u003c/h2\u003e \u003cp\u003eMD simulations have been regarded as a vital computational tool to investigate the time-dependent stability of ligands in the active site of proteins. The binding stability of the hit compound (\u003cb\u003e7188\u0026thinsp;\u0026minus;\u0026thinsp;1100\u003c/b\u003e) was further evaluated by performing MD simulations for 100 ns. The RMSD of backbone atoms of the complex and the apo structure (without ligand) were plotted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. The backbone of the complex and the apo structure reached equilibration at about 30 ns and then stabilized, oscillating around the 2 \u0026Aring; value. Meanwhile, we computed the root-mean-square fluctuation (RMSF) of all residues after the 30 ns simulation to assess both ligand deviation from the initial conformation and the extent of residue movement in SHP2 in both the unbound state and upon ligand binding. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The RMSF value for the free protein and the protein involved in the complex displayed a constant trend, but these overall fluctuations were minimized in the binding state in general. These results further indicated that the ligand could enhance the stability of protein during the simulation. The detailed analysis revealed that strong hydrogen bond interactions were found between the ligand and residues E250 and F113, kept for 93.64% and 62.22%, respectively. The π-cation interaction and hydrogen bond interactions were found between the \u003cb\u003e7188-0011\u003c/b\u003e and R111 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). However, the high flexibility of the \u003cb\u003e7188-0011\u003c/b\u003e compound itself, as well as its inadequate hydrophobicity at the head region (phenyl), was considered to be the reasons for its poor activity. This could potentially be a promising direction for future hit optimization to improve affinity with the target.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eSHP2, a protein tyrosine phosphatase, has emerged as a therapeutic target for various human diseases, particularly cancer. In this study, we employed a rigorous virtual screening method called Dock-FMO, including molecular docking for primary screening and FMO methods for binding affinity calculation. Therefore, to identify potent SHP2 allosteric inhibitors, we carried out the Dock-FMO virtual screening protocol against the ChemDiv database. Subsequently, the 25 compounds were selected for in vitro activity assays, and experimental validation revealed that compound \u003cstrong\u003e7188-0011\u003c/strong\u003e inhibited SHP2 in a dose-dependent manner (IC\u003csub\u003e50\u003c/sub\u003e = 54.31 \u0026plusmn; 0.67\u0026nbsp;\u0026mu;M).\u0026nbsp;MD simulations further confirmed the high binding stability between \u003cstrong\u003e7188-0011\u003c/strong\u003e and SHP2 protein. This study provided a novel and promising allosteric inhibitor, laying the foundation for the development of anti-tumor drugs targeting the SHP2 protein. It also serves as the basis for further lead compound optimization to develop more active inhibitors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZhen Yuan:\u003c/strong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eMethodology, Investigation, Data curation, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review and editing. \u003cstrong\u003eManzhan Zhang:\u003c/strong\u003e Methodology, Investigation, Writing \u0026ndash; original draft. \u003cstrong\u003eLongfeng Chang:\u0026nbsp;\u003c/strong\u003eResources, Validation. \u003cstrong\u003eXingyu Chen\u003c/strong\u003e: Data curation. \u003cstrong\u003eShanshan Ruan:\u003c/strong\u003e Data curation, \u003cstrong\u003eShanshan Shi:\u003c/strong\u003e Data curation, \u003cstrong\u003eYiqing Zhang:\u003c/strong\u003e Data curation. \u003cstrong\u003eLili Zhu\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; review and editing, Supervision, Funding acquisition. \u003cstrong\u003eHonglin Li:\u003c/strong\u003e Conceptualization \u0026ndash; review and editing, Supervision, Funding acquisition. \u003cstrong\u003eShiliang Li:\u0026nbsp;\u003c/strong\u003eConceptualization, Writing \u0026ndash; review and editing, Supervision, Funding acquisition. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported in part by the National Key R\u0026amp;D Program of China (2022YFC3400501, 2022YFC3400504); the National Natural Science Foundation of China (grants 82173690 to S.L.L., 81825020 and 82150208 to H.L.,); the Lingang Laboratory (grant LG-QS-202206-02 to S.L.L.); the Fundamental Research Funds for the Central Universities; S.L.L. is also sponsored by the Shanghai Rising-Star Program (23QA1402800). Honglin Li was also sponsored by the National Program for Special Supports of Eminent Professionals and National Program for Support of Top-Notch Young Professionals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAppendix A. Supplementary material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Supporting Information to this article can be found in the online version. Results of Dock-FMO virtual screening and biochemical assay; information for the binding mode analysis of \u003cstrong\u003eSHP099\u003c/strong\u003e; PIEDA analysis for ligands (\u003cstrong\u003eSHP099\u003c/strong\u003e and \u003cstrong\u003e7188-0011\u003c/strong\u003e) and the surrounding residues with FMO method. Supplementary data of a dataset for testing the performance of FMO calculation, MM/PBSA and MM/GBSA as csv files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChan, R. J.; Feng, G. 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Chem. \u003c/em\u003e\u003cstrong\u003e2016,\u003c/strong\u003e \u003cem\u003e59\u003c/em\u003e (17), 7773-7782.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-molecular-modeling","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmmo","sideBox":"Learn more about [Journal of Molecular Modeling](https://www.springer.com/journal/894)","snPcode":"894","submissionUrl":"https://submission.nature.com/new-submission/894/3","title":"Journal of Molecular Modeling","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"SHP2, allosteric inhibitor, fragment molecular orbital method, virtual screening, molecular dynamics simulation","lastPublishedDoi":"10.21203/rs.3.rs-3565398/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3565398/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInvestigating the role of protein tyrosine phosphatase SHP2 is a continuing concern in the context of various human diseases, including Noonan syndrome, LEOPARD syndrome, and cancers. SHP2 is an essential bridge to connect numerous oncogenic cell-signaling cascades including RAS-ERK, PI3K-AKT, JAK-STAT and PD-1/PD-L1 pathways. This study aims to discover novel and potent SHP2 inhibitors using a hierarchical structure-based virtual screening strategy that combines molecular docking and the fragment molecular orbital method (FMO) for calculating binding affinity (referred to as the Dock-FMO protocol). We employed Dock-FMO virtual screening of ChemDiv database of ∼2,990,000 compounds to identify a novel SHP2 allosteric inhibitor bearing hydroxyimino acetamide scaffold. Experimental validation demonstrated that the new compound (E)-2-(hydroxyimino)-2-phenyl-N-(piperidin-4-ylmethyl)acetamide (\u003cstrong\u003e7188-0011)\u003c/strong\u003eeffectively inhibited SHP2 in a dose-dependent manner. Molecular dynamics (MD) simulation analysis revealed the binding stability of compound \u003cstrong\u003e7188-0011\u003c/strong\u003eand the SHP2 protein, along with the key interacting residues in the allosteric binding site. Overall, our work has identified a novel and promising allosteric inhibitor that targets SHP2, providing a new starting point for further optimization to develop more potent inhibitors.\u003c/p\u003e","manuscriptTitle":"Discovery of a novel SHP2 allosteric inhibitor using virtual screening, FMO calculation and molecular dynamic simulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-08 16:23:40","doi":"10.21203/rs.3.rs-3565398/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2023-11-07T08:26:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-11-06T23:24:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-11-06T23:24:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Molecular Modeling","date":"2023-11-06T03:04:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-molecular-modeling","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmmo","sideBox":"Learn more about [Journal of Molecular Modeling](https://www.springer.com/journal/894)","snPcode":"894","submissionUrl":"https://submission.nature.com/new-submission/894/3","title":"Journal of Molecular Modeling","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e2e93334-943b-4cb1-a527-6a8772828193","owner":[],"postedDate":"November 8th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-04-15T15:08:15+00:00","versionOfRecord":{"articleIdentity":"rs-3565398","link":"https://doi.org/10.1007/s00894-024-05935-y","journal":{"identity":"journal-of-molecular-modeling","isVorOnly":false,"title":"Journal of Molecular Modeling"},"publishedOn":"2024-04-13 15:00:52","publishedOnDateReadable":"April 13th, 2024"},"versionCreatedAt":"2023-11-08 16:23:40","video":"","vorDoi":"10.1007/s00894-024-05935-y","vorDoiUrl":"https://doi.org/10.1007/s00894-024-05935-y","workflowStages":[]},"version":"v1","identity":"rs-3565398","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3565398","identity":"rs-3565398","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}