Combined 3D-QSAR and Docking Modelling Study on Indolocarbazole Series Compounds as Tie-2 Inhibitors

Tie-2, a kind of endothelial cell tyrosine kinase receptor, is required for embryonic blood vessel development and tumor angiogenesis. Several compounds that showed potent activity toward this attractive anticancer drug target in the assay have been reported. In order to investigate the structure-activity correlation of indolocarbazole series compounds and modify them to improve their selectivity and activity, 3D-QSAR models were built using CoMFA and CoMSIA methods and molecular docking was used to check the results. Based on the common sketch align, two good QSAR models with high predictabilities (CoMFA model: q(2) = 0.823, r(2) = 0.979; CoMSIA model: q(2) = 0.804, r(2) = 0.967) were obtained and the contour maps obtained from both models were applied to identify the influence on the biological activity. Molecular docking was then used to confirm the results. Combined with the molecular docking results, the detail binding mode between the ligands and Tie-2 was elucidated, which enabled us to interpret the structure-activity relationship. These satisf actory results not only offered help to comprehend the action mechanism of indolocarbazole series compounds, but also provide new information for the design of new potent inhibitors.     

Generation of Non-Nucleotide CD73 Inhibitors Using a Molecular Docking and 3D-QSAR Approach

Radiotherapy and chemotherapy are conventional cancer treatments. Around 60% of all patients who are diagnosed with cancer receive radio- or chemotherapy in combination with surgery during their disease. Only a few patients respond to the blockage of immune checkpoints alone, or in combination therapy, because their tumours might not be immunogenic. Under these circumstances, an increasing level of extracellular adenosine via the activation of ecto-5’-nucleotidase (CD73) and consequent adenosine receptor signalling is a typical mechanism that tumours use to evade immune surveillance. CD73 is responsible for the conversion of adenosine monophosphate to adenosine. CD73 is overexpressed in various tumour types. Hence, targetting CD73’s signalling is important for the reversal of adenosine-facilitated immune suppression. In this study, we selected a potent series of the non-nucleotide small molecule inhibitors of CD73. Molecular docking studies were performed in order to examine the binding mode of the inhibitors inside the active site of CD73 and 3D-QSAR was used to study the structure–activity relationship. The obtained CoMFA (q² = 0.844, ONC = 5, r² = 0.947) and CoMSIA (q² = 0.804, ONC = 4, r² = 0.954) models showed reasonable statistical values. The 3D-QSAR contour map analysis revealed useful structural characteristics that were needed to modify non-nucleotide small molecule inhibitors. We used the structural information from the overall docking and 3D-QSAR results to design new, potent CD73 non-nucleotide inhibitors. The newly designed CD73 inhibitors exhibited higher activity (predicted pIC₅₀) than the most active compound of all of the derivatives that were selected for this study. Further experimental studies are needed in order to validate the new CD73 inhibitors.     

Pharmacophore and Molecular Docking Guided 3D-QSAR Study of Bacterial Enoyl-ACP Reductase (FabI) Inhibitors

Enoyl acyl carrier protein (ACP) reductase (FabI) is a potential target for the development of antibacterial agents. Three-dimensional quantitative structure-activity relationships (3D-QSAR) for substituted formamides series of FabI inhibitors were investigated using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. Pharmacophore and molecular docking methods were used for construction of the molecular alignments. A training set of 36 compounds was performed to create the 3D-QSAR models and their external predictivity was proven using a test set of 11 compounds. Graphical interpretation of the results revealed important structural features of the formamides related to the active site of FabI. The results may be exploited for further optimization of the design of new potent FabI inhibitors.     

Molecular Modeling Studies of 4,5-Dihydro-1H-pyrazolo[4,3-h] quinazoline Derivatives as Potent CDK2/Cyclin A Inhibitors Using 3D-QSAR and Docking

CDK2/cyclin A has appeared as an attractive drug targets over the years with diverse therapeutic potentials. A computational strategy based on comparative molecular fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) followed by molecular docking studies were performed on a series of 4,5-dihydro-1H-pyrazolo[4,3-h]quinazoline derivatives as potent CDK2/cyclin A inhibitors. The CoMFA and CoMSIA models, using 38 molecules in the training set, gave r(2) (cv) values of 0.747 and 0.518 and r(2) values of 0.970 and 0.934, respectively. 3D contour maps generated by the CoMFA and CoMSIA models were used to identify the key structural requirements responsible for the biological activity. Molecular docking was applied to explore the binding mode between the ligands and the receptor. The information obtained from molecular modeling studies may be helpful to design novel inhibitors of CDK2/cyclin A with desired activity.     

Studies of New Fused Benzazepine as Selective Dopamine D3 Receptor Antagonists Using 3D-QSAR, Molecular Docking and Molecular Dynamics

In recent years, great interest has been paid to the development of compounds with high selectivity for central dopamine (DA) D3 receptors, an interesting therapeutic target in the treatment of different neurological disorders. In the present work, based on a dataset of 110 collected benzazepine (BAZ) DA D3 antagonists with diverse kinds of structures, a variety of in silico modeling approaches, including comparative molecular field analysis (CoMFA), comparative similarity indices analysis (CoMSIA), homology modeling, molecular docking and molecular dynamics (MD) were carried out to reveal the requisite 3D structural features for activity. Our results show that both the receptor-based (Q(2) = 0.603, R(2) (ncv) = 0.829, R(2) (pre) = 0.690, SEE = 0.316, SEP = 0.406) and ligand-based 3D-QSAR models (Q(2) = 0.506, R(2) (ncv) =0.838, R(2) (pre) = 0.794, SEE = 0.316, SEP = 0.296) are reliable with proper predictive capacity. In addition, a combined analysis between the CoMFA, CoMSIA contour maps and MD results with a homology DA receptor model shows that: (1) ring-A, position-2 and R(3) substituent in ring-D are crucial in the design of antagonists with higher activity; (2) more bulky R(1) substituents (at position-2 of ring-A) of antagonists may well fit in the binding pocket; (3) hydrophobicity represented by MlogP is important for building satisfactory QSAR models; (4) key amino acids of the binding pocket are CYS101, ILE105, LEU106, VAL151, PHE175, PHE184, PRO254 and ALA251. To our best knowledge, this work is the first report on 3D-QSAR modeling of the new fused BAZs as DA D3 antagonists. These results might provide information for a better understanding of the mechanism of antagonism and thus be helpful in designing new potent DA D3 antagonists.     

A Combination of 3D-QSAR, Molecular Docking and Molecular Dynamics Simulation Studies of Benzimidazole-Quinolinone Derivatives as iNOS Inhibitors

Inducible Nitric Oxide Synthase (iNOS) has been involved in a variety of diseases, and thus it is interesting to discover and optimize new iNOS inhibitors. In previous studies, a series of benzimidazole-quinolinone derivatives with high inhibitory activity against human iNOS were discovered. In this work, three-dimensional quantitative structure-activity relationships (3D-QSAR), molecular docking and molecular dynamics (MD) simulation approaches were applied to investigate the functionalities of active molecular interaction between these active ligands and iNOS. A QSAR model with R² of 0.9356, Q² of 0.8373 and Pearson-R value of 0.9406 was constructed, which presents a good predictive ability in both internal and external validation. Furthermore, a combined analysis incorporating the obtained model and the MD results indicates: (1) compounds with the proper-size hydrophobic substituents at position 3 in ring-C (R₃ substituent), hydrophilic substituents near the X₆ of ring-D and hydrophilic or H-bond acceptor groups at position 2 in ring-B show enhanced biological activities; (2) Met368, Trp366, Gly365, Tyr367, Phe363, Pro344, Gln257, Val346, Asn364, Met349, Thr370, Glu371 and Tyr485 are key amino acids in the active pocket, and activities of iNOS inhibitors are consistent with their capability to alter the position of these important residues, especially Glu371 and Thr370. The results provide a set of useful guidelines for the rational design of novel iNOS inhibitors.     

3D-QSAR and Molecular Docking Studies on Derivatives of MK-0457, GSK1070916 and SNS-314 as Inhibitors against Aurora B Kinase

Development of anticancer drugs targeting Aurora B, an important member of the serine/threonine kinases family, has been extensively focused on in recent years. In this work, by applying an integrated computational method, including comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), homology modeling and molecular docking, we investigated the structural determinants of Aurora B inhibitors based on three different series of derivatives of 108 molecules. The resultant optimum 3D-QSAR models exhibited (q(2) = 0.605, r(2) (pred) = 0.826), (q(2) = 0.52, r(2) (pred) = 0.798) and (q(2) = 0.582, r(2) (pred) = 0.971) for MK-0457, GSK1070916 and SNS-314 classes, respectively, and the 3D contour maps generated from these models were analyzed individually. The contour map analysis for the MK-0457 model revealed the relative importance of steric and electrostatic effects for Aurora B inhibition, whereas, the electronegative groups with hydrogen bond donating capacity showed a great impact on the inhibitory activity for the derivatives of GSK1070916. Additionally, the predictive model of the SNS-314 class revealed the great importance of hydrophobic favorable contour, since hydrophobic favorable substituents added to this region bind to a deep and narrow hydrophobic pocket composed of residues that are hydrophobic in nature and thus enhanced the inhibitory activity. Moreover, based on the docking study, a further comparison of the binding modes was accomplished to identify a set of critical residues that play a key role in stabilizing the drug-target interactions. Overall, the high level of consistency between the 3D contour maps and the topographical features of binding sites led to our identification of several key structural requirements for more potency inhibitors. Taken together, the results will serve as a basis for future drug development of inhibitors against Aurora B kinase for various tumors.     

Combined 3D-QSAR Modeling and Molecular Docking Studies on Pyrrole-Indolin-2-ones as Aurora A Kinase Inhibitors

Aurora kinases have emerged as attractive targets for the design of anticancer drugs. 3D-QSAR (comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA)) and Surflex-docking studies were performed on a series of pyrrole-indoline-2-ones as Aurora A inhibitors. The CoMFA and CoMSIA models using 25 inhibitors in the training set gave r(2) (cv) values of 0.726 and 0.566, and r(2) values of 0.972 and 0.984, respectively. The adapted alignment method with the suitable parameters resulted in reliable models. The contour maps produced by the CoMFA and CoMSIA models were employed to rationalize the key structural requirements responsible for the activity. Surflex-docking studies revealed that the sulfo group, secondary amine group on indolin-2-one, and carbonyl of 6,7-dihydro-1H-indol-4(5H)-one groups were significant for binding to the receptor, and some essential features were also identified. Based on the 3D-QSAR and docking results, a set of new molecules with high predicted activities were designed.     

3D-QSAR Studies on Barbituric Acid Derivatives as Urease Inhibitors and the Effect of Charges on the Quality of a Model

Urease enzyme (EC 3.5.1.5) has been determined as a virulence factor in pathogenic microorganisms that are accountable for the development of different diseases in humans and animals. In continuance of our earlier study on the helicobacter pylori urease inhibition by barbituric acid derivatives, 3D-QSAR (three dimensional quantitative structural activity relationship) advance studies were performed by Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods. Different partial charges were calculated to examine their consequences on the predictive ability of the developed models. The finest developed model for CoMFA and CoMSIA were achieved by using MMFF94 charges. The developed CoMFA model gives significant results with cross-validation (q²) value of 0.597 and correlation coefficients (r²) of 0.897. Moreover, five different fields i.e., steric, electrostatic, and hydrophobic, H-bond acceptor and H-bond donors were used to produce a CoMSIA model, with q² and r² of 0.602 and 0.98, respectively. The generated models were further validated by using an external test set. Both models display good predictive power with r²_pred ≥ 0.8. The analysis of obtained CoMFA and CoMSIA contour maps provided detailed insight for the promising modification of the barbituric acid derivatives with an enhanced biological activity.     

A Computational Study on Thiourea Analogs as Potent MK-2 Inhibitors

Mitogen-activated protein kinase-activated protein kinase 2 (MK-2) has been identified as a drug target for the treatment of inflammatory diseases. Currently, a series of thiourea analogs as potent MK-2 inhibitors were studied using comprehensive computational methods by 3D-QSAR, molecular docking and molecular dynamics simulations for a further improvement in activities. The optimal 3D models exhibit high statistical significance of the results, especially for the CoMFA results with r(2) (ncv), q(2) values of 0.974, 0.536 for the internal validation, and r(2) (pred), r(2) (m) values of 0.910, 0.723 for the external validation and Roy's index, respectively. In addition, more rigorous validation criteria suggested by Tropsha were also employed to check the built models. Graphic representation of the results, as contoured 3D coefficient plots, also provides a clue to the reasonable modification of molecules: (i) The substituent with a bulky size and electron-rich group at the C5 position of the pyrazine ring is required to enhance the potency; (ii) The H-bond acceptor group in the C3 position of the pyrazine ring is likely to be helpful to increase MK-2 inhibition; (iii) The small and electropositive substituent as a hydrogen bond donor of the C2 position in the oxazolone ring is favored; In addition, several important amino acid residues were also identified as playing an important role in MK-2 inhibition. The agreement between 3D-QSAR, molecular docking and molecular dynamics simulations also proves the rationality of the developed models. These results, we hope, may be helpful in designing novel and potential MK-2 inhibitors.     

The Three Dimensional Quantitative Structure Activity Relationships (3D-QSAR) and Docking Studies of Curcumin Derivatives as Androgen Receptor Antagonists

Androgen receptor antagonists have been proved to be effective anti-prostate cancer agents. 3D-QSAR and Molecular docking methods were performed on curcumin derivatives as androgen receptor antagonists. The bioactive conformation was explored by docking the potent compound 29 into the binding site of AR. The constructed Comparative Molecular Field Analysis (CoMFA) and Comparative Similarity Indices Analysis (CoMSIA) models produced statistically significant results with the cross-validated correlation coefficients q(2) of 0.658 and 0.567, non-cross-validated correlation coefficients r(2) of 0.988 and 0.978, and predicted correction coefficients r(2) (pred) of 0.715 and 0.793, respectively. These results ensure the CoMFA and CoMSIA models as a tool to guide the design of novel potent AR antagonists. A set of 30 new analogs were proposed by utilizing the results revealed in the present study, and were predicted with potential activities in the developed models.     

An Integrated Pharmacophore/Docking/3D-QSAR Approach to Screening a Large Library of Products in Search of Future Botulinum Neurotoxin A Inhibitors

Botulinum toxins are neurotoxins produced by Clostridium botulinum. This toxin can be lethal for humans as a cause of botulism; however, in small doses, the same toxin is used to treat different conditions. Even if the therapeutic doses are effective and safe, the adverse reactions could be local and could unmask a subclinical impairment of neuromuscular transmissions. There are not many cases of adverse events in the literature; however, it is possible that sometimes they do not occur as they are transient and, if they do occur, there is no possibility of a cure other than to wait for the pharmacological effect to end. Inhibition of botulinum neurotoxin type A (BoNT/A) effects is a strategy for treating botulism as it can provide an effective post-exposure remedy. In this paper, 13,592,287 compounds were screened through a pharmacophore filter, a 3D-QSAR model, and a virtual screening; then, the compounds with the best affinity were selected. Molecular dynamics simulation studies on the first four compounds predicted to be the most active were conducted to verify that the poses foreseen by the docking were stable. This approach allowed us to identify compounds with a calculated inhibitory activity in the range of 316–500 nM.     

Combined Structure-Based Pharmacophore and 3D-QSAR Studies on Phenylalanine Series Compounds as TPH1 Inhibitors

Tryptophan hydroxylase-1 (TPH1) is a key enzyme in the synthesis of serotonin. As a neurotransmitter, serotonin plays important physiological roles both peripherally and centrally. In this study, a combination of ligand-based and structure-based methods is used to clarify the essential quantitative structure-activity relationship (QSAR) of known TPH1 inhibitors. A multicomplex-based pharmacophore (MCBP) guided method has been suggested to generate a comprehensive pharmacophore of TPH1 kinase based on three crystal structures of TPH1-inhibitor complex. This model has been successfully used to identify the bioactive conformation and align 32 structurally diverse substituted phenylalanine derivatives. The QSAR analyses have been performed on these TPH1 inhibitors based on the MCBP guided alignment. These results may provide important information for further design and virtual screening of novel TPH1 inhibitors.     

Investigation of Antigen-Antibody Interactions of Sulfonamides with a Monoclonal Antibody in a Fluorescence Polarization Immunoassay Using 3D-QSAR Models

A three-dimensional quantitative structure-activity relationship (3D-QSAR) model of sulfonamide analogs binding a monoclonal antibody (MAb(SMR)) produced against sulfamerazine was carried out by Distance Comparison (DISCOtech), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA). The affinities of the MAb(SMR), expressed as Log(10)IC(50), for 17 sulfonamide analogs were determined by competitive fluorescence polarization immunoassay (FPIA). The results demonstrated that the proposed pharmacophore model containing two hydrogen-bond acceptors, two hydrogen-bond donors and two hydrophobic centers characterized the structural features of the sulfonamides necessary for MAb(SMR) binding. Removal of two outliers from the initial set of 17 sulfonamide analogs improved the predictability of the models. The 3D-QSAR models of 15 sulfonamides based on CoMFA and CoMSIA resulted in q(2) (cv) values of 0.600 and 0.523, and r(2) values of 0.995 and 0.994, respectively, which indicates that both methods have significant predictive capability. Connolly surface analysis, which mainly focused on steric force fields, was performed to complement the results from CoMFA and CoMSIA. This novel study combining FPIA with pharmacophore modeling demonstrates that multidisciplinary research is useful for investigating antigen-antibody interactions and also may provide information required for the design of new haptens.     

In silico identification of structure requirement for novel thiazole and oxazole derivatives as potent fructose 1,6-bisphosphatase inhibitors

Fructose 1,6-bisphosphatase (FBPase) has been identified as a drug discovery target for lowering glucose in type 2 diabetes mellitus. In this study, a large series of 105 FBPase inhibitors were studied using a combinational method by 3D-QSAR, molecular docking and molecular dynamics simulations for a further improvement in potency. The optimal 3D models exhibit high statistical significance of the results, especially for the CoMFA results with r(ncv) (2), q(2) values of 0.986, 0.514 for internal validation, and r(pred) (2), r(m) (2) statistics of 0.902, 0.828 statistics for external validation. Graphic representation of the results, as contoured 3D coefficient plots, also provides a clue to the reasonable modification of molecules. (1) Substituents with a proper length and size at the C5 position of the thiazole core are required to enhance the potency; (2) A small and electron-withdrawing group at the C2 position linked to the thiazole core is likely to help increase the FBPase inhibition; (3) Substituent groups as hydrogen bond acceptors at the C2 position of the furan ring are favored. In addition, the agreement between 3D-QSAR, molecular docking and molecular dynamics simulation proves the rationality of the developed models. These results, we hope, may be helpful in designing novel and potential FBPase inhibitors.     

Synthesis of Dihydrobenzofuro[3,2-b]chromenes as Potential 3CLpro Inhibitors of SARS-CoV-2: A Molecular Docking and Molecular Dynamics Study

The recent emergence of pandemic of coronavirus (COVID-19) caused by SARS-CoV-2 has raised significant global health concerns. More importantly, there is no specific therapeutics currently available to combat against this deadly infection. The enzyme 3-chymotrypsin-like cysteine protease (3CLpro) is known to be essential for viral life cycle as it controls the coronavirus replication. 3CLpro could be a potential drug target as established before in the case of severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). In the current study, we wanted to explore the potential of fused flavonoids as 3CLpro inhibitors. Fused flavonoids (5a,10a-dihydro-11H-benzofuro[3,2-b]chromene) are unexplored for their potential bioactivities due to their low natural occurrences. Their synthetic congeners are also rare due to unavailability of general synthetic methodology. Here we designed a simple strategy to synthesize 5a,10a-dihydro-11H-benzofuro[3,2-b]chromene skeleton and it's four novel derivatives. Our structural bioinformatics study clearly shows excellent potential of the synthesized compounds in comparison to experimentally validated inhibitor N3. Moreover, in-silico ADMET study displays excellent druggability and extremely low level of toxicity of the synthesized molecules. Further, for better understanding, the molecular dynamic approach was implemented to study the change in dynamicity after the compounds bind to the protein. A detailed investigation through clustering analysis and distance calculation gave us sound comprehensive data about their molecular interaction. In summary, we anticipate that the currently synthesized molecules could not only be a potential set of inhibitors against 3CLpro but also the insights acquired from the current study would be instrumental in further developing novel natural flavonoid based anti-COVID therapeutic spectrums.     

Structural Determinants of CX-4945 Derivatives as Protein Kinase CK2 Inhibitors: A Computational Study

Protein kinase CK2, also known as casein kinase-2, is involved in a broad range of physiological events including cell growth, proliferation and suppression of apoptosis which are related to human cancers. A series of compounds were identified as CK2 inhibitors and their inhibitory activities varied depending on their structures. In order to explore the structure-activity correlation of CX-4945 derivatives as inhibitors of CK2, in the present study, a set of ligand- and receptor-based 3D-QSAR models were developed employing Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Index Analysis (CoMSIA). The optimum CoMFA (R(cv) (2) = 0.618, R(pred) (2) = 0.892) and CoMSIA (R(cv) (2) = 0.681, R(pred) (2) = 0.843) models exhibited reasonable statistical characteristics for CX-4945 derivatives. The results indicated that electrostatic effects contributed the most to both CoMFA and CoMSIA models. The combination of docking analysis and molecular dynamics (MD) simulation showed that Leu45, Lys68, Glu81, Val116, Asp175 and Trp176 of CK2 which formed several direct or water-bridged H-bonds with CX-4945 are crucial for CX-4945 derivatives recognition to CK2. These results can offer useful theoretical references for designing more potent CK2 inhibitors.     

靛玉红类衍生物的三维定量构效关系研究

采用比较分子场方法(CoMFA)对16个靛玉红类衍生物进行三维定量构效关系(3D-QSAR)研究,探讨靛玉红类衍生物的抗癌活性与其结构的关系。建立了3D-QSAR的CoMFA模型,得到了非交叉验证相关系数r2=0.987,标准偏差SD=2.141,统计方差比F=117.136,交叉验证相关系数q2=0.496。该模型合理、可信,具有一定的预测能力,表明改变取代基R1基团的体积才是提高靛玉红类衍生物抗癌活性的主要途径,为开发活性更好的抗癌药物提供一定的理论参考。     

Studies on [5,6]-Fused Bicyclic Scaffolds Derivatives as Potent Dual B-RafV600E/KDR Inhibitors Using Docking and 3D-QSAR Approaches

Research and development of multi-target inhibitors has attracted increasing attention as anticancer therapeutics. B-Raf^V600E synergistically works with vascular endothelial growth factor receptor 2 (KDR) to promote the occurrence and progression of cancers, and the development of dual-target drugs simultaneously against these two kinds of kinase may offer a better treatment advantage. In this paper, docking and three-dimensional quantitative structure activity relationship (3D-QSAR) studies were performed on a series of dual B-Raf/KDR inhibitors with a novel hinge-binding group, [5,6]-fused bicyclic scaffold. Docking studies revealed optimal binding conformations of these compounds interacting with both B-Raf and KDR. Based on these conformations, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) 3D-QSAR models were constructed, and the best CoMFA (q² = 0.542, r² = 0.989 for B-Raf; q² = 0.768, r² = 0.991 for KDR) and CoMSIA models (q² = 0.519, r² = 0.992 for B-Raf; q² = 0.849, r² = 0.993 for KDR) were generated. Further external validations confirmed their predictability, yielding satisfactory correlation coefficients (r²_pred = 0.764 (CoMFA), r²_pred = 0.841 (CoMSIA) for B-Raf, r²_pred = 0.912 (CoMFA), r²_pred = 0.846 (CoMSIA) for KDR, respectively). Through graphical analysis and comparison on docking results and 3D-QSAR contour maps, key amino acids that affect the ligand-receptor interactions were identified and structural features influencing the activities were discussed. New potent derivatives were designed, and subjected to preliminary pharmacological evaluation. The study may offer useful references for the modification and development of novel dual B-Raf/KDR inhibitors.     

Exploration of N-Arylsulfonyl-indole-2-carboxamide Derivatives as Novel Fructose-1,6-bisphosphatase Inhibitors by Molecular Simulation

A series of N-arylsulfonyl-indole-2-carboxamide derivatives have been identified as potent fructose-1,6-bisphosphatase (FBPase) inhibitors (FBPIs) with excellent selectivity for the potential therapy of type II diabetes mellitus. To explore the structure–activity relationships (SARs) and the mechanisms of action of these FBPIs, a systematic computational study was performed in the present study, including three-dimensional quantitative structure–activity relationship (3D-QSAR) modeling, pharmacophore modeling, molecular dynamics (MD), and virtual screening. The constructed 3D-QSAR models exhibited good predictive ability with reasonable parameters using comparative molecular field analysis (q² = 0.709, R² = 0.979, r_pre² = 0.932) and comparative molecular similarity indices analysis (q² = 0.716, R² = 0.978, r_pre² = 0.890). Twelve hit compounds were obtained by virtual screening using the best pharmacophore model in combination with molecular dockings. Three compounds with relatively higher docking scores and better ADME properties were then selected for further studies by docking and MD analyses. The docking results revealed that the amino acid residues Met18, Gly21, Gly26, Leu30, and Thr31 at the binding site were of great importance for the effective bindings of these FBPIs. The MD results indicated that the screened compounds VS01 and VS02 could bind with FBPase stably as its cognate ligand in dynamic conditions. This work identified several potential FBPIs by modeling studies and might provide important insights into developing novel FBPIs.