Molecular dynamics simulation, free energy calculation and structure-based 3D-QSAR studies of B-RAF kinase inhibitors

Computational Simulation Study of Potential Inhibition of c-Met Kinase Receptor by Phenoxy pyridine Derivatives: Based on QSAR, Molecular Docking, Molecular Dynamics

The mesenchymal-epithelial transition factor (c-Met) is a tyrosine kinase receptor protein, and excessive cell transformation can lead to cancer. Therefore, there is an urgent need to develop novel receptor tyrosine kinase inhibitors by inhibiting the activity of c-Met protein. In this study, 41 compounds are selected from the reported literature, and the interactions between phenoxy pyridine derivatives and tumor-associated proteins are systematically investigated using a series of computer-assisted drug design (CADD) methods, aiming to predict potential c-Met inhibitors with high activity. The Topomer CoMFA (q2=0.620, R2=0.837) and HQSAR (q2=0.684, R2=0.877) models demonstrate a high level of robustness. Further internal and external validation assessments show high applicability and accuracy. Based on the results of the Topomer CoMFA model, structural fragments with higher contribution values are identified and randomly combined using a fragment splice technique, result in a total of 20 compounds with predicted activities higher than the template molecules. Molecular docking results show that these compounds have good interactions and van der Waals forces with the target proteins. The results of molecular dynamics and ADMET predictions indicate that compounds Y4, Y5, and Y14 have potential as c-Met inhibitors. Among them, compound Y14 exhibits superior stability with a binding free energy of -165.18 KJ/mol. These studies provide a reference for the future design and development of novel compounds with c-Met inhibitory activity.

Searching for Protein Off-Targets of Prostate-Specific Membrane Antigen-Targeting Radioligands in the Salivary Glands

Background: Prostate specific membrane antigen (PSMA)-targeted radioligand therapies represent a highly effective treatment for metastatic prostate cancer. However, high and sustain uptake of PSMA-ligands in the salivary glands led to dose limiting dry mouth (xerostomia), especially with α-emitters. The expression of PSMA and histologic analysis couldn't directly explain the toxicity, suggesting a potential off-target mediator for uptake. In this study, we set out to search for possible off-target non-PSMA protein(s) in the salivary glands. Methods: A machine-learning based quantitative structure activity relationship (QSAR) model was built for seeking the possible off-target(s). The resulting target candidates from the model prediction were subjected to further analysis for salivary protein expression and structural homology at key regions required for PSMA-ligand binding. Furthermore, cellular binding assays were performed utilizing multiple cell lines with high expression of the candidate proteins and low expression of PSMA. Finally, PSMA knockout (PSMA-/-) mice were scanned by small animal PET/MR using [68Ga]Ga-PSMA-11 for in-vivo validation. Results: The screening of the trained QSAR model did not yield a solid off-target protein, which was corroborated in part by cellular binding assays. Imaging using PSMA-/- mice further demonstrated markedly reduced PSMA-radioligand uptake in the salivary glands. Conclusion: Uptake of the PSMA-targeted radioligands in the salivary glands remains primarily PSMA-mediated. Further investigations are needed to illustrate a seemingly different process of uptake and retention in the salivary glands than that in prostate cancer.

From pixels to druggable leads: A CADD strategy for the design and synthesis of potent DDR1 inhibitors

BACKGROUND AND OBJECTIVE

While numerous in silico tools exist for target-based drug discovery, the inconsistent integration of in vitro data with predictive models hinders research and development productivity. This is particularly apparent during the Hit-to-Lead stage, where unreliable in-silico tools often lead to suboptimal lead selection. Herein, we address this challenge by presenting a CADD-guided pipeline that successfully integrates rational drug design with in-silico hits to identify a promising DDR1 lead.

METHODS

2 × 1000 ns MD simulations along with their respective FEL and MMPBSA analyses were employed to guide the rational design and synthesis of 12 novel compounds which were evaluated for their DDR inhibition.

RESULTS

The molecular dynamics investigation of the initial hit led to the identification of key structural features within the DDR1 binding pocket. The identified key features were used to guide the rational design and synthesis of twelve novel derivatives. SAR analysis, biological evaluation, molecular dynamics, and free energy calculations were carried out for the synthesized derivatives to understand their mechanism of action. Compound 4c exhibited the strongest inhibition and selectivity for DDR1, with an IC50 of 0.11 µM.

CONCLUSIONS

The MD simulations led to the identification of a key hydrophobic groove in the DDR1 binding pocket. The integrated approach of SAR analysis with molecular dynamics led to the identification of compound 4c as a promising lead for further development of potent and selective DDR1 inhibitors. Moreover, this work establishes a protocol for translating in silico hits to real world bioactive druggable leads.

Exploration of 2D and 3D-QSAR analysis and docking studies for novel dihydropteridone derivatives as promising therapeutic agents targeting glioblastoma

Background: Dihydropteridone derivatives represent a novel class of PLK1 inhibitors, exhibiting promising anticancer activity and potential as chemotherapeutic drugs for glioblastoma. Objective: The aim of this study is to develop 2D and 3D-QSAR models to validate the anticancer activity of dihydropteridone derivatives and identify optimal structural characteristics for the design of new therapeutic agents. Methods: The Heuristic method (HM) was employed to construct a 2D-linear QSAR model, while the gene expression programming (GEP) algorithm was utilized to develop a 2D-nonlinear QSAR model. Additionally, the CoMSIA approach was introduced to investigate the impact of drug structure on activity. A total of 200 novel anti-glioma dihydropteridone compounds were designed, and their activity levels were predicted using chemical descriptors and molecular field maps. The compounds with the highest activity were subjected to molecular docking to confirm their binding affinity. Results: Within the analytical purview, the coefficient of determination (R2) for the HM linear model is elucidated at 0.6682, accompanied by an R2 cv of 0.5669 and a residual sum of squares (S2) of 0.0199. The GEP nonlinear model delineates coefficients of determination for the training and validation sets at 0.79 and 0.76, respectively. Empirical modeling outcomes underscore the preeminence of the 3D-QSAR model, succeeded by the GEP nonlinear model, whilst the HM linear model manifested suboptimal efficacy. The 3D paradigm evinced an exemplary fit, characterized by formidable Q2 (0.628) and R2 (0.928) values, complemented by an impressive F-value (12.194) and a minimized standard error of estimate (SEE) at 0.160. The most significant molecular descriptor in the 2D model, which included six descriptors, was identified as "Min exchange energy for a C-N bond" (MECN). By combining the MECN descriptor with the hydrophobic field, suggestions for the creation of novel medications were generated. This led to the identification of compound 21E.153, a novel dihydropteridone derivative, which exhibited outstanding antitumor properties and docking capabilities. Conclusion: The development of 2D and 3D-QSAR models, along with the innovative integration of contour maps and molecular descriptors, offer novel concepts and techniques for the design of glioblastoma chemotherapeutic agents.

3D- and 2D-QSAR models’ study and molecular docking of novel nitrogen-mustard compounds for osteosarcoma

Background: The dipeptide-alkylated nitrogen-mustard compound is a new kind of nitrogen-mustard derivative with a strong anti-tumor activity, which can be used as a potential anti-osteosarcoma chemotherapy drug.

Objective: 2D- and 3D-QSAR (structure–activity relationship quantification) models were established to predict the anti-tumor activity of dipeptide-alkylated nitrogen-mustard compounds.

Method: In this study, a linear model was established using a heuristic method (HM) and a non-linear model was established using the gene expression programming (GEP) algorithm, but there were more limitations in the 2D model, so a 3D-QSAR model was introduced and established through the CoMSIA method. Finally, a series of new dipeptide-alkylated nitrogen-mustard compounds were redesigned using the 3D-QSAR model; docking experiments were carried out on several compounds with the highest activity against tumors.

Result: The 2D- and 3D-QSAR models obtained in this experiment were satisfactory. A linear model with six descriptors was obtained in this experiment using the HM through CODESSA software, where the descriptor “Min electroph react index for a C atom” has the greatest effect on the compound activity; a reliable non-linear model was obtained using the GEP algorithm model (the best model was generated in the 89th generation cycle, with a correlation coefficient of 0.95 and 0.87 for the training and test set, respectively, and a mean error of 0.02 and 0.06, respectively). Finally, 200 new compounds were designed by combining the contour plots of the CoMSIA model with each other, together with the descriptors in the 2D-QSAR, among which compound I1.10 had a high anti-tumor and docking ability.

Conclusion: Through the model established in this study, the factors influencing the anti-tumor activity of dipeptide-alkylated nitrogen-thaliana compounds were revealed, providing direction and guidance for the further design of efficient chemotherapy drugs against osteosarcoma.

2D, 3D-QSAR study and docking of vascular endothelial growth factor receptor 3 (VEGFR3) inhibitors for potential treatment of retinoblastoma

Background: Retinoblastoma is currently the most common malignant tumor seen in newborns and children's eyes worldwide, posing a life-threatening hazard. Chemotherapy is an integral part of retinoblastoma treatment. However, the chemotherapeutic agents used in clinics often lead to drug resistance. Thus there is a need to investigate new chemotherapy-targeted agents. VEGFR3 inhibitors are anti-tumour-growth and could be used to develop novel retinoblastoma-targeted agents. Objective: To predict drug activity, discover influencing factors and design new drugs by building 2D, 3D-QSAR models. Method: First, linear and non-linear QSAR models were built using heuristic methods and gene expression programming (GEP). The comparative molecular similarity indices analysis (COMISA) was then used to construct 3D-QSAR models through the SYBYL software. New drugs were designed by changing drug activity factors in both models, and molecular docking experiments were performed. Result: The best linear model created using HM had an R², S², and R²cv of 0.82, 0.02, and 0.77, respectively. For the training and test sets, the best non-linear model created using GEP had correlation coefficients of 0.83 and 0.72 with mean errors of 0.02 and 0.04. The 3D model designed using SYBYL passed external validation due to its high Q² (0.503), R² (0.805), and F-value (76.52), as well as its low standard error of SEE value (0.172). This demonstrates the model's reliability and excellent predictive ability. Based on the molecular descriptors of the 2D model and the contour plots of the 3D model, we designed 100 new compounds using the best active compound 14 as a template. We performed activity prediction and molecular docking experiments on them, in which compound 14.d performed best regarding combined drug activity and docking ability. Conclusion: The non-linear model created using GEP was more stable and had a more substantial predictive power than the linear model built using the heuristic technique (HM). The compound 14.d designed in this experiment has the potential for anti-retinoblastoma treatment, which provides new design ideas and directions for retinoblastoma-targeted drugs.

3D,2D-QSAR study and docking of novel quinazolines as potential target drugs for osteosarcoma

Background: Quinazolines are an important class of benzopyrimidine heterocyclic compounds with a promising antitumor activity that can be used for the design and development of osteosarcoma target compounds. Objective: To predict the compound activity of quinazoline compounds by constructing 2D- and 3D-QSAR models, and to design new compounds according to the main influencing factors of compound activity in the two models. Methods: First, heuristic method and GEP (gene expression programming) algorithm were used to construct linear and non-linear 2D-QSAR models. Then a 3D-QSAR model was constructed using CoMSIA method in SYBYL software package. Finally, new compounds were designed according to molecular descriptors of 2D-QSAR model and contour maps of 3D-QSAR model. Several compounds with optimal activity were used for docking experiments with osteosarcoma related targets (FGFR4). Results: The non-linear model constructed by GEP algorithm was more stable and predictive than the linear model constructed by heuristic method. A 3D-QSAR model with high Q² (0.63) and R ² (0.987) values and low error values (0.05) was obtained in this study. The success of the model fully passed the external validation formula, proving that the model is very stable and has strong predictive power. 200 quinazoline derivatives were designed according to molecular descriptors and contour maps, and docking experiments were carried out for the most active compounds. Compound 19g.10 has the best compound activity with good target binding capability. Conclusion: To sum up, the two novel QSAR models constructed were very reliable. The combination of descriptors in 2D-QSAR with COMSIA contour maps provides new design ideas for future compound design in osteosarcoma.

Malvidin-3-O-arabinoside ameliorates ethyl carbamate-induced oxidative damage by stimulating AMPK-mediated autophagy

Ethyl carbamate (EC) is an environmental toxin, commonly present in various fermented foods and beverages, as well as tobacco and polluted ambient air. However, studies on the effects of EC-induced toxicity on the intestines and potential protection methods are limited. In this study, we show that EC could cause severe toxicity in intestinal epithelial cells (IECs) triggering the induction of decreased cell viability, ROS accumulation and glutathione (GSH) depletion in a dose-dependent manner. Based on these results, we established an EC-treated IEC model to screen the potential protective effects of 12 kinds of anthocyanins extracted from blueberry. Interestingly, we found that malvidin-3-O-arabinoside (M3A) significantly reversed the oxidative damage caused by EC exposure by stimulating autophagy flux, which was determined by the LC3-II level and GFP-RFP-LC3 transfection experiment. Enhancement of autophagy was mainly ascribed to the regulation of lysosomes. M3A pretreatment remarkably upregulated LAMP-1 expression, which indicated elevated lysosomal mass. Besides, M3A also successfully restored lysosomal acidity and subsequently strengthened lysosomal functions. Furthermore, M3A stimulated phosphorylation of AMP-activated protein kinase (AMPK), a master regulator of autophagy. Furthermore, our study indicated the possibility of EC-caused oxidative damage to the intestines and unveiled the remarkably protective benefits of M3A-induced AMPK-mediated autophagy against this toxicity.

Theoretical Studies on the Selectivity Mechanisms of Glycogen Synthase Kinase 3β (GSK3β) with Pyrazine ATP-competitive Inhibitors by 3DQSAR, Molecular Docking, Molecular Dynamics Simulation and Free Energy Calculations

Background

Glycogen synthase kinase-3 (GSK3) is associated with various key biological processes and has been considered as an important therapeutic target for the treatment of many diseases. Great efforts have been made on the development of GSK3 inhibitors, especially ATP-competitive GSK3β inhibitor, but it is still a great challenge to develop selective GSK3β inhibitors because of the high sequence homology with other kinases.

Objective

In order to reveal the selectivity mechanisms of GSK3β inhibition at the molecular level, a series of ATP-competitive GSK3β inhibitor was analyzed by a systematic computational method, combining 3D-QSAR, molecular docking, molecular dynamic simulations and free energy calculations.

Methods

Firstly, 3D-QSAR with CoMFA was built to explore the general structure activity relationships. Secondly, CDOCKER and Flexible docking were employed to predicted the reasonable docking poses of all studied inhibitors. And then, both GSK3β and CDK2 complexes were selected to conduct molecular dynamics simulations. Finally, the free energy calculations were employed to find the key selective-residues.

Results

CoMFA model suggested the steric, hydrophobic fields play key roles in the bioactivities of inhibitors, and the binding mechanisms were well analyzed through molecular docking. The binding free energies predicted are in good agreement with the experimental bioactivities and the free energy calculations showed that the binding of GSK3β/inhibitors was mainly contributed from hydrogen bonding and hydrophobic interaction.

Conclusion

Some key residues for selective binding were highlighted, which may afford important guidance for the rational design of novel ATP-competitive GSK3β inhibitors.

Insights into the EGFR SAR of N-phenylquinazolin-4-amine-derivatives using quantum mechanical pairwise-interaction energies

Protein kinases are an important class of enzymes that play an essential role in virtually all major disease areas. In addition, they account for approximately 50% of the current targets pursued in drug discovery research. In this work, we explore the generation of structure-based quantum mechanical (QM) quantitative structure-activity relationship models (QSAR) as a means to facilitate structure-guided optimization of protein kinase inhibitors. We explore whether more accurate, interpretable QSAR models can be generated for a series of 76 N-phenylquinazolin-4-amine inhibitors of epidermal growth factor receptor (EGFR) kinase by comparing and contrasting them to other standard QSAR methodologies. The QM-based method involved molecular docking of inhibitors followed by their QM optimization within a ~ 300 atom cluster model of the EGFR active site at the M062X/6-31G(d,p) level. Pairwise computations of the interaction energies with each active site residue were performed. QSAR models were generated by splitting the datasets 75:25 into a training and test set followed by modelling using partial least squares (PLS). Additional QSAR models were generated using alignment dependent CoMFA and CoMSIA methods as well as alignment independent physicochemical, e-state indices and fingerprint descriptors. The structure-based QM-QSAR model displayed good performance on the training and test sets (r² ~ 0.7) and was demonstrably more predictive than the QSAR models built using other methods. The descriptor coefficients from the QM-QSAR models allowed for a detailed rationalization of the active site SAR, which has implications for subsequent design iterations.

Combined 3D-QSAR, molecular docking and molecular dynamics study on the benzimidazole inhibitors targeting HCV NS5B polymerase

The hepatitis C virus (HCV)-infected population has continued to grow during recent years, and novel HCV antiviral agents are urgently needed. In this work, a combined theoretical study was performed on the HCV non-structural 5B (NS5B) polymerase and 53 benzimidazole inhibitors. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were carried out with ligand-based and receptor-based alignments. Ligand-based QSAR models (cross-validated q² of 0.918 for CoMFA and 0.825 for CoMSIA) were found to be superior to receptor-based approaches (cross-validated q² of 0.765 for CoMFA and 0.740 for CoMSIA). Based on the most predictive CoMFA and CoMSIA models, the structural features that were essential for the inhibitory activity of benzimidazoles were characterized. A molecular dynamics study revealed that the induced fit effect between NS5B and its substrate may be responsible for the inferiority of the receptor-based CoMFA and CoMSIA models. The binding-free energy calculated using the MM/PBSA method correlated well with the experimental results and revealed that the van der Waals and electrostatic interactions most contributed to the binding. In addition, energetically favorable NS5B residues were identified by the per-residue decomposition of binding-free energy. The results presented in this work provide meaningful information for the design of novel benzimidazole inhibitors targeting the NS5B polymerase.Communicated by Ramaswamy H. Sarma.

Hybrid 2D/3D-quantitative structure-activity relationship and modeling studies perspectives of pepstatin A analogs as cathepsin D inhibitors

AIM

Cathepsin D, one of the attractive targets in the treatment of breast cancer, has been implicated in HIV neuropathogenesis with potential proteolytic effects on chemokines. Methodology/result: Diverse modeling tools were used to reveal the key structural features affecting the inhibitory activities of 78 pepstatin A analogs. Analyses were performed to investigate the stability, rationality and fluctuation of the analogs. Results showed a clear correlation between the experimental and predicted activities of the analogs as well as the variation in their activities relative to structural modifications.

CONCLUSION

The insight gained from this study offers theoretical references for understanding the mechanism of action of cathepsin D and will aid in the design of more potent and clinically-relevant drugs. Graphical abstract [Formula: see text].

Investigations of FAK inhibitors: a combination of 3D-QSAR, docking, and molecular dynamics simulations studies

Focal adhesion kinase (FAK) is one kind of tyrosine kinases that modulates integrin and growth factor signaling pathways, which is a promising therapeutic target because of involving in cancer cell migration, proliferation, and survival. To investigate the mechanism between FAK and triazinic inhibitors and design high activity inhibitors, a molecular modeling integrated with 3D-QSAR, molecular docking, molecular dynamics simulations, and binding free energy calculations was performed. The optimum CoMFA and CoMSIA models showed good reliability and satisfactory predictability (with Q² = 0.663, R² = 0.987, [Formula: see text] = 0.921 and Q² = 0.670, R² = 0.981, [Formula: see text] = 0.953). Its contour maps could provide structural features to improve inhibitory activity. Furthermore, a good consistency between contour maps, docking, and molecular dynamics simulations strongly demonstrates that the molecular modeling is reliable. Based on it, we designed several new compounds and their inhibitory activities were validated by the molecular models. We expect our studies could bring new ideas to promote the development of novel inhibitors with higher inhibitory activity for FAK.

Recent Developments in 3D QSAR and Molecular Docking Studies of Organic and Nanostructures

The development of quantitative structure–activity relationship (QSAR) methods is going very fast for the last decades. OSAR approach already plays an important role in lead structure optimization, and nowadays, with development of big data approaches and computer power, it can even handle a huge amount of data associated with combinatorial chemistry. One of the recent developments is a three-dimensional QSAR, i.e., 3D QSAR. For the last two decades, 3D-OSAR has already been successfully applied to many datasets, especially of enzyme and receptor ligands. Moreover, quite often 3D QSAR investigations are going together with protein–ligand docking studies and this combination works synergistically. In this review, we outline recent advances in development and applications of 3D QSAR and protein–ligand docking approaches, as well as combined approaches for conventional organic compounds and for nanostructured materials, such as fullerenes and carbon nanotubes.

Effects of the central potassium ions on the G-quadruplex and stabilizer binding

Human telomeres undertake the structure of intra-molecular parallel G-quadruplex in the presence of K⁺ in eukaryotic cell. Stabilization of the telomere G-quadruplex represents a potential strategy to prevent telomere lengthening by telomerase in cancer therapy. Current work demonstrates that the binding of central K⁺ with the parallel G-quadruplex is a coordinated water directed step-wise process. The K⁺ above the top G-tetrad is prone to leak into environment and the 5'-adenine quickly flips over the top G-tetrad, leading to the bottom gate of G-tetrads as the only viable pathway of K⁺ binding. Present molecular dynamics studies on the two most potent stabilizers RHPS4 and BRACO-19 reveal that the central K⁺ has little influence on the binding conformations of the bound stabilizers. But without the central K⁺, either RHPS4 or BRACO-19 cannot stabilize the structure of G-quadruplex. The binding strength of stabilizers evaluated by the MM-PBSA method follows the order of BRACO-19> RHPS4, which agrees with the experimental results. The difference in binding affinities between RHPS4 and BRACO-19 is probably related to the ability to form intramolecular hydrogen bonds and favorable van del Waals interactions with G-quadruplex. In the models that have one central K⁺ located at the upper/lower binding site, the corresponding top/bottom stacked stabilizers show more favorable binding affinities, indicating the apparent promoting effect of central K⁺ on the stabilizer binding. Our findings provide further insights into the regulatory effect of K⁺ on the G-quadruplex targeted binding, which is meaningful to the development of G-quadruplex stabilizers.

Theoretical studies on the selective mechanisms of GSK3β and CDK2 by molecular dynamics simulations and free energy calculations

Glycogen synthase kinase 3 (GSK3) is a serine/threonine protein kinase which is widely involved in cell signaling and controls a broad number of cellular functions. GSK3 contains α and β isoforms, and GSK3β has received more attention and becomes an attractive drug target for the treatment of several diseases. The binding pocket of cyclin-dependent kinase 2 (CDK2) shares high sequence identity to that of GSK3β, and therefore, the design of highly selective inhibitors toward GSK3β remains a big challenge. In this study, a computational strategy, which combines molecular docking, molecular dynamics simulations, free energy calculations, and umbrella sampling simulations, was employed to explore the binding mechanisms of two selective inhibitors to GSK3β and CDK2. The simulation results highlighted the key residues critical for GSK3β selectivity. It was observed that although GSK3β and CDK2 share the conserved ATP-binding pockets, some different residues have significant contributions to protein selectivity. This study provides valuable information for understanding the GSK3β-selective binding mechanisms and the rational design of selective GSK3β inhibitors.

Identification of Thyroid Hormone Disruptors among HO-PBDEs: In Vitro Investigations and Coregulator Involved Simulations

Some hydroxylated polybrominated diphenyl ethers (HO-PBDEs), that have been widely detected in the environment and tissues of humans and wildlife, bind to thyroid hormone (TH) receptor (TR) and can disrupt functioning of systems modulated by the TR. However, mechanisms of TH disrupting effects are still equivocal. Here, disruption of functions of TH modulated pathways by HO-PBDEs was evaluated by assays of competitive binding, coactivator recruitment, and proliferation of GH3 cells. In silico simulations considering effects of coregulators were carried out to investigate molecular mechanisms and to predict potencies for disrupting functions of the TH. Some HO-PBDEs were able to bind to TR with moderate affinities but were not agonists. In GH3 proliferation assays, 13 out of 16 HO-PBDEs were antagonists for the TH. In silico simulations of molecular dynamics revealed that coregulators were essential for identification of TH disruptors. Among HO-PBDEs, binding of passive antagonists induced repositioning of H12, blocking AF-2 (transactivation function 2) and preventing recruitment of the coactivator. Binding of active antagonists exposed the coregulator binding site, which tended to bind to the corepressor rather than the coactivator. By considering both passive and active antagonisms, anti-TH potencies of HO-PBDEs could be predicted from free energy of binding.

Insights into mechanism of pyrido[2,3-d]pyrimidines as DYRK1A inhibitors based on molecular dynamic simulations

DYRK1A is characterized by the early development and regulation of neuronal proliferation, and its over expression gives rise to neurological abnormalities. As the promising DYRK1A inhibitors, the binding mechanism between DYRK1A and pyrido[2,3-d]pyrimidines derivatives at molecular level are still veiled. In this article, it was achieved to get the structural insights into pyrido[2,3-d]pyrimidines derivatives as DYRK1A inhibitors by means of comprehensive computational approaches involving molecular docking, molecular dynamics simulation, free energy calculation, and energy decomposition analysis. The calculated energy values were highly consistent with the experimental activities. Based on the individual energy terms analysis, the van der Waals interaction was the major leading force in the DYRK1A-ligand interaction. Lys188 was the important residue that formed the hydrogen bond, which improved the inhibitory activity. Furthermore, four novel inhibitors with higher predicted activity were designed based on the obtained findings and confirmed by molecular simulations. Our study is expected to provide significant drug design strategy for the development of more promising DYRK1A inhibitors. Proteins 2016; 84:1108-1123. © 2016 Wiley Periodicals, Inc.

Molecular docking and 3D-QSAR studies on the glucocorticoid receptor antagonistic activity of hydroxylated polychlorinated biphenyls

The glucocorticoid receptor (GR) antagonistic activities of hydroxylated polychlorinated biphenyls (HO-PCBs) were recently characterised. To further explore the interactions between HO-PCBs and the GR, and to elucidate structural characteristics that influence the GR antagonistic activity of HO-PCBs, molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were performed. Comparative molecular similarity indices analysis (CoMSIA) was performed using both ligand- and receptor-based alignment schemes. Results generated from the receptor-based model were found to be more satisfactory, with q(2) of 0.632 and r(2) of 0.931 compared with those from the ligand-based model. Some internal validation strategies (e.g. cross-validation analysis, bootstrapping analysis and Y-randomisation) and an external validation method were used respectively to further assess the stability and predictive ability of the derived model. Graphical interpretation of the model provided some insights into the structural features that affected the GR antagonistic activity of HO-PCBs. Molecular docking studies revealed that some key residues were critical for ligand-receptor interactions by forming hydrogen bonds (Glu540) and hydrophobic interactions with ligands (Ile539, Val543 and Trp577). Although CoMSIA sometimes depends on the alignment of the molecules, the information provided is beneficial for predicting the GR antagonistic activities of HO-PCB homologues and is helpful for understanding the binding mechanisms of HO-PCBs to GR.

Molecular modeling studies to characterize N-phenylpyrimidin-2-amine selectivity for CDK2 and CDK4 through 3D-QSAR and molecular dynamics simulations

Molecular modeling simulations were carried out to understand the structure–activity and selectivity correlation of N-phenylpyrimidin-2-amines binding to CDK2 and CDK4.

Discovery of vascular endothelial growth factor receptor tyrosine kinase inhibitors by quantitative structure–activity relationships, molecular dynamics simulation and free energy calculation

Employing the combined strategy to understand the features of KDR–ligands complexes, and provide a basis for rational design of inhibitors.

Molecular principle of the cyclin-dependent kinase selectivity of 4-(thiazol-5-yl)-2-(phenylamino) pyrimidine-5-carbonitrile derivatives revealed by molecular modeling studies

Due to the high sequence identity of the binding pockets of cyclin-dependent kinases (CDKs), designing highly selective inhibitors towards a specific CDK member remains a big challenge.

Identification of potential CCR5 inhibitors through pharmacophore-based virtual screening, molecular dynamics simulation and binding free energy analysis

Employing the combined strategy to identify novel CCR5 inhibitors and provide a basis for rational drug design.

Computational analysis of binding between benzamide-based derivatives and Abl wt and T315I mutant kinases

An integrated computational study was performed to identify the binding mechanisms of benzamide-based derivatives with Abl_wt/Abl_T315I kinases for designing Abl inhibitors.

Molecular Dynamics: New Frontier in Personalized Medicine

The field of drug discovery has witnessed infinite development over the last decade with the demand for discovery of novel efficient lead compounds. Although the development of novel compounds in this field has seen large failure, a breakthrough in this area might be the establishment of personalized medicine. The trend of personalized medicine has shown stupendous growth being a hot topic after the successful completion of Human Genome Project and 1000 genomes pilot project. Genomic variant such as SNPs play a vital role with respect to inter individual's disease susceptibility and drug response. Hence, identification of such genetic variants has to be performed before administration of a drug. This process requires high-end techniques to understand the complexity of the molecules which might bring an insight to understand the compounds at their molecular level. To sustenance this, field of bioinformatics plays a crucial role in revealing the molecular mechanism of the mutation and thereby designing a drug for an individual in fast and affordable manner. High-end computational methods, such as molecular dynamics (MD) simulation has proved to be a constitutive approach to detecting the minor changes associated with an SNP for better understanding of the structural and functional relationship. The parameters used in molecular dynamic simulation elucidate different properties of a macromolecule, such as protein stability and flexibility. MD along with docking analysis can reveal the synergetic effect of an SNP in protein-ligand interaction and provides a foundation for designing a particular drug molecule for an individual. This compelling application of computational power and the advent of other technologies have paved a promising way toward personalized medicine. In this in-depth review, we tried to highlight the different wings of MD toward personalized medicine.

Measured and predicted affinities of binding and relative potencies to activate the AhR of PAHs and their alkylated analogues

Polycyclic aromatic hydrocarbons (PAHs) and their alkylated forms are important components of crude oil. Both groups of PAHs have been reported to cause dioxin-like responses, mediated by aryl hydrocarbon receptor (AhR). Thus, characterization of binding affinity to the AhR of unsubstituted or alkylated PAHs is important to understand the toxicological consequences of oil contamination on ecosystems. We investigated the potencies of major PAHs of crude oil, e.g., chrysene, phenanthrene and dibenzothiophene, and their alkylated forms (n=17) to upregulate expression of AhR-mediated processes by use of the H4IIE-luc transactivation bioassay. In addition, molecular descriptors of different AhR activation potencies among PAHs were investigated by use of computational molecular docking models. Based on responses of the H4IIE-luc in vitro assay, it was shown that potencies of PAHs were determined by alkylation in addition to the number and conformation of rings. Potencies of AhR-mediated processes were generally greater when a chrysene group was substituted, especially in 1-methyl-chrysene. Significant negative correlations were observed between the in vitro dioxin-like potency measured in H4IIE-luc cells and the binding distance estimated from the in silico modeling. The difference in relative potency for AhR activation observed among PAHs and their alkylated forms could be explained by differences among binding distances in the ligand binding domain of the AhR caused by alkylation. The docking model developed in the present study may have utility in predicting risks of environmental contaminants of which toxicities are mediated by AhR binding.

Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set

By using different evaluation strategies, we systemically evaluated the performance of Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) and Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) methodologies based on more than 1800 protein-ligand crystal structures in the PDBbind database. The results can be summarized as follows: (1) for the one-protein-family/one-binding-ligand case which represents the unbiased protein-ligand complex sampling, both MM/GBSA and MM/PBSA methodologies achieve approximately equal accuracies at the interior dielectric constant of 4 (with rp = 0.408 ± 0.006 of MM/GBSA and rp = 0.388 ± 0.006 of MM/PBSA based on the minimized structures); while for the total dataset (1864 crystal structures), the overall best Pearson correlation coefficient (rp = 0.579 ± 0.002) based on MM/GBSA is better than that of MM/PBSA (rp = 0.491 ± 0.003), indicating that biased sampling may significantly affect the accuracy of the predicted result (some protein families contain too many instances and can bias the overall predicted accuracy). Therefore, family based classification is needed to evaluate the two methodologies; (2) the prediction accuracies of MM/GBSA and MM/PBSA for different protein families are quite different with rp ranging from 0 to 0.9, whereas the correlation and ranking scores (an averaged rp/rs over a list of protein folds and also representing the unbiased sampling) given by MM/PBSA (rp-score = 0.506 ± 0.050 and rs-score = 0.481 ± 0.052) are comparable to those given by MM/GBSA (rp-score = 0.516 ± 0.047 and rs-score = 0.463 ± 0.047) at the fold family level; (3) for the overall prediction accuracies, molecular dynamics (MD) simulation may not be quite necessary for MM/GBSA (rp-minimized = 0.579 ± 0.002 and rp-1ns = 0.564 ± 0.002), but is needed for MM/PBSA (rp-minimized = 0.412 ± 0.003 and rp-1ns = 0.491 ± 0.003). However, for the individual systems, whether to use MD simulation is depended. (4) both MM/GBSA and MM/PBSA may be unable to give successful predictions for the ligands with high formal charges, with the Pearson correlation coefficient ranging from 0.621 ± 0.003 (neutral ligands) to 0.125 ± 0.142 (ligands with a formal charge of 5). Therefore, it can be summarized that, although MM/GBSA and MM/PBSA perform similarly in the unbiased dataset, for the currently available crystal structures in the PDBbind database, compared with MM/GBSA, which may be used in multi-target comparisons, MM/PBSA is more sensitive to the investigated systems, and may be more suitable for individual-target-level binding free energy ranking. This study may provide useful guidance for the post-processing of docking based studies.

Profiling the interaction mechanism of indole-based derivatives targeting the HIV-1 gp120 receptor

A glycoprotein exposed on a viral surface, human immunodeficiency virus type 1 (HIV-1) gp120 is essential for virus entry into cells as it plays a vital role in seeking out specific cell surface receptors for entry.

Identifying MurI uncompetitive inhibitors by correlating decomposed binding energies with bioactivity

MurI uncompetitive inhibitors can be virtually identified by a new method that correlates decomposed binding free energies with the bioactivity.

Importance of protein flexibility in ranking inhibitor affinities: modeling the binding mechanisms of piperidine carboxamides as Type I1/2 ALK inhibitors

Anaplastic lymphoma kinase (ALK) has gained increased attention as an attractive therapeutic target for the treatment of various cancers, especially non-small-cell lung cancer (NSCLC).

Hydrogen bonding plays a significant role in the binding of coomassie brilliant blue-R to hemoglobin: FT-IR, fluorescence and molecular dynamics studies

Coomassie brilliant blue-R (CBB-R) specifically binds to bovine hemoglobin with a stoichiometric ratio of 1 : 1.

Exploring the prominent performance of CX-4945 derivatives as protein kinase CK2 inhibitors by a combined computational study

Protein kinase CK2, also known as casein kinase II, is related to various cellular events and is a potential target for numerous cancers. In this study, we attempted to gain more insight into the inhibition process of CK2 by a series of CX-4945 derivatives through an integrated computational study that combines molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations. Based on the binding poses predicted by molecular docking, the MD simulations were performed to explore the dynamic binding processes for ten selected inhibitors. Then, both Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) techniques were employed to predict the binding affinities of the studied systems. The predicted binding energies of the selected inhibitors correlate well with their experimental activities (r(2) = 0.78). The van der Waals term is the most favorable component for the total energies. The free energy decomposition on a per residue basis reveals that the residue K68 is essential for the electrostatic interactions between CK2 and the studied inhibitors and numerous residues, including L45, V53, V66, F113, M163 and I174, play critical roles in forming van der Waals interactions with the inhibitors. Finally, a number of new derivatives were designed and the binding affinity and the predicted binding free energies of each designed molecule were obtained on the basis of molecular docking and MM/PBSA. It is expected that our research will benefit the future rational design of novel and potent inhibitors of CK2.

Studies of N(9)-arenthenyl purines as novel DFG-in and DFG-out dual Src/Abl inhibitors using 3D-QSAR, docking and molecular dynamics simulations

Recently, the development of Src/Abl (c-Src/Bcr-Abl tyrosine kinases) dual inhibitors has attracted considerable attention from the research community for treatment of malignancies. In order to explore the different structural features impacting the Src and Abl inhibitory activities of N(9)-arenethenyl purines and to investigate the molecular mechanisms of ligand-receptor interactions, a molecular modeling study combining the three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics (MD) simulations was performed. The obtained CoMFA (comparative molecular field analysis) models exhibited satisfactory internal and external predictability. The plots of the CoMFA fields could be used to investigate the structural differences between DFG-in (targeting the active enzyme conformation) and DFG-out (targeting the inactive enzyme conformation) inhibitors. The key amino acid residues were identified by docking studies, and the detailed binding modes of the compounds with different activities were determined by MD simulations. The binding free energies gave a good correlation with the experimental determined activities. In an energetic analysis, the MM-PBSA (molecular mechanics Poisson-Boltzmann surface) energy decomposition revealed that the van der Waals interactions were the major driving force for the binding of the DFG-in and DFG-out compounds to Src and Abl, especially the hydrophobic interactions between ligands and residues Ala403/380, Asp404/381, and Phe405/382 in DFG-out Src and Abl complexes. They also help to stabilize the DFG-out conformations. These results can offer useful references for designing novel potential DFG-in and DFG-out dual Src/Abl inhibitors.

Computational studies on the binding mechanism between triazolone inhibitors and Chk1 by molecular docking and molecular dynamics

Chk1, a serine/threonine protein kinase that participates in transducing DNA damage signals, is an attractive target due to its involvement in tumor initiation and progression. As a novel Chk1 inhibitor, the triazolone's bioactivity mechanism is not clear. In this study, we carried out an integrated computational study that combines molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations to identify the key factors necessary for the bioactivities. With the aim of discerning the structural features that affect the inhibitory activity of triazolones, MK-8776, a Chk1 inhibitor that reached the clinical stage, was also used as a reference for simulations. A comparative analysis of the triazolone inhibitors at the molecular level offers valuable insight into the structural and energetic properties. A general feature is that all the studied inhibitors bind in the pocket characterized by residues Leu14, Val22, Ala35, Glu84, Tyr85, Cys86, and Leu136 of Chk1. Moreover, introducing hydrophobic groups into triazolone inhibitors is favorable for binding to Chk1, which is corroborated by residue Leu136 with a relatively large difference in the contribution between MK-8776 and five triazolones to the total binding free energies. A hydrogen bond between the polar hydrogen atoms at R1 and Cys86 can facilitate proper placement of the inhibitor in the binding pocket of Chk1 that favors binding. However, the introduction of hydrophilic groups into the R2 position diminishes binding affinity. The information provided by this research is of benefit for further rational design of novel promising inhibitors of Chk1.

MD and QM/MM studies on long-chain L-α-hydroxy acid oxidase: substrate binding features and oxidation mechanism

Long-chain L-α-hydroxy acid oxidase (LCHAO) is a flavin mononucleotide (FMN)-dependent oxidase that dehydrogenates l-α-hydroxy acids to keto acids. There were two different mechanisms, named as hydride transfer (HT) mechanism and carbanion (CA) mechanism, respectively, proposed about the catalytic process for the FMN-dependent L-α-hydroxy acid oxidases on the basis of biochemical data. However, crystallographic and kinetic studies could not provide enough evidence to prove one of the mechanisms or eliminate the alternative. In the present studies, theoretical computations were carried out to study the molecular mechanism for LCHAO-catalyzed dehydrogenation of L-lactate. Our molecular dynamics (MD) simulations indicated that L-lactate prefers to bind with LCHAO in a hydride transfer mode rather than a carbanion mode. Quantum mechanics/molecular mechanics (QM/MM) calculations were further carried out to obtain the optimized structures of reactants, transition states, and products at the level of ONIOM-EE (B3LYP/6-311++G(d,p)//B3LYP/6-31G(d,p):AMBER). Quantum chemical studies indicated that LCHAO-catalyzed dehydrogenation of L-lactate would be a stepwise catalytic reaction in a hydride transfer mechanism but not a carbanion mechanism. MD simulations, binding free energy calculations, and QM/MM computations were also implemented on the complex between L-lactate and Y129F mutant LCHAO. By comparing the Y129F mutant system with the wild-type system, it was further confirmed that the key residue Tyr129 in the active site of LCHAO would not affect L-lactate's binding to LCHAO but play an important role on the catalytic reaction process through an H-bond interaction.

Insight into the structural features of pyrazolopyrimidine- and pyrazolopyridine-based B-Raf(V600E) kinase inhibitors by computational explorations

Presently, both ligand-based and receptor-based 3D-QSAR modelings were performed on 107 pyrazolopyrimidine- and pyrazolopyridine-based inhibitors of B-Raf(V600E) kinase. The optimal model is successful to predict the inhibitors' activity with Q(2) of 0.504, R(2) ncv of 0.960, and R(2) pred of 0.872. Besides, the 3D contour maps explain well the structural requirements of the interaction between the ligand and the receptor. Furthermore, molecular docking and MD were also carried out to study the binding mode. Our findings are the following: (i) Bulky substituents at position 3, 10 and ring D improve the inhibitory activity, but impair the activity at position 5, 11, and 19. (ii) Electropositive groups at position 10, 13 and 20 and electronegative groups at position 2 increase the biological activity. (iii) Hydrophobic substituents at ring C are beneficial to improve the biological activity, while hydrophilic substituents at position 11 and ring D are good for the activity. (4) This scaffold of inhibitors may bind to the B-Raf kinase with an 'L' conformation and belong to type III binding mode, which is fixed by hydrophobic interaction and hydrogen bonds with residues from hinge region and DFG motif. These results may be a guidance to develop new B-Raf(V600E) kinase inhibitors.

Molecular docking and molecular dynamics studies on the interactions of hydroxylated polybrominated diphenyl ethers to estrogen receptor alpha

Environmental estrogens have attracted great concerns. Recent studies have indicated that some hydroxylated polybrominated diphenyl ethers (HO-PBDEs) can interact with estrogen receptor (ER), and exhibit estrogenic activity. However, interactions between HO-PBDEs and ER are not well understood. In this work, molecular docking and molecular dynamics (MD) simulations were performed to characterize interactions of two HO-PBDEs (4'-HO-BDE30 and 4'-HO-BDE121) with ERα. Surflex-Dock was employed to reveal the probable binding conformations of the compounds at the active site of ERα; MD simulation was used to determine the detailed binding process. The driving forces of the binding between HO-PBDEs and ERα were van der Waals and electrostatic interactions. The decomposition of the binding free energy indicated that the hydrogen bonds between the residues Glu353, Gly521 and ligands were crucial for anchoring the ligands into the active site of ERα and stabilizing their conformations. The results showed that different interaction modes and different specific interactions with some residues were responsible for the different estrogenic activities of the two HO-PBDEs.

Predicting the mixture effects of three pesticides by integrating molecular simulation with concentration addition modeling

Molecular simulation techniques are used to identify the mode of inhibition of chemicals at the ligand–receptor level.

Theoretical studies on beta and delta isoform-specific binding mechanisms of phosphoinositide 3-kinase inhibitors

Phosphoinositide 3-kinase (PI3K) is known to be closely related to tumorigenesis and cell proliferation, and controls a variety of cellular processes, including proliferation, growth, apoptosis, migration, metabolism, etc. The PI3K family comprises eight catalytic isoforms, which are subdivided into three classes. Recently, the discovery of inhibitors that block a single isoform of PI3K has continued to attract special attention because they may have higher selectivity for certain tumors and less toxicity for healthy cells. The PI3Kβ and PI3Kδ share fewer studies than α/γ, and therefore, in this work, the combination of molecular dynamics simulations and free energy calculations was employed to explore the binding of three isoform-specific PI3K inhibitors (COM8, IC87114, and GDC-0941) to PI3Kβ or PI3Kδ. The isoform specificities of the studied inhibitors derived from the predicted binding free energies are in good agreement with the experimental data. In addition, the key residues critical for PI3Kβ or PI3Kδ selectivity were highlighted by decomposing the binding free energies into the contributions from individual residues. It was observed that although PI3Kβ and PI3Kδ share the conserved ATP-binding pockets, individual residues do behave differently, particularly the residues critical for PI3Kβ or PI3Kδ selectivity. It can be concluded that the inhibitor specificity between PI3Kβ and PI3Kδ is determined by the additive contributions from multiple residues, not just a single one. This study provides valuable information for understanding the isoform-specific binding mechanisms of PI3K inhibitors, and should be useful for the rational design of novel and selective PI3K inhibitors.

Molecular modeling, quantum polarized ligand docking and structure-based 3D-QSAR analysis of the imidazole series as dual AT(1) and ET(A) receptor antagonists

AIM

Both endothelin ETA receptor antagonists and angiotensin AT1 receptor antagonists lower blood pressure in hypertensive patients. A dual AT1 and ETA receptor antagonist may be more efficacious antihypertensive drug. In this study we identified the mode and mechanism of binding of imidazole series of compounds as dual AT1 and ETA receptor antagonists.

METHODS

Molecular modeling approach combining quantum-polarized ligand docking (QPLD), MM/GBSA free-energy calculation and 3D-QSAR analysis was used to evaluate 24 compounds as dual AT1 and ETA receptor antagonists and to reveal their binding modes and structural basis of the inhibitory activity. Pharmacophore-based virtual screening and docking studies were performed to identify more potent dual antagonists.

RESULTS

3D-QSAR models of the imidazole compounds were developed from the conformer generated by QPLD, and the resulting models showed a good correlation between the predicted and experimental activity. The visualization of the 3D-QSAR model in the context of the compounds under study revealed the details of the structure-activity relationship: substitution of methoxymethyl and cyclooctanone might increase the activity against AT1 receptor, while substitution of cyclohexone and trimethylpyrrolidinone was important for the activity against ETA receptor; addition of a trimethylpyrrolidinone to compound 9 significantly reduced its activity against AT1 receptor but significantly increased its activity against ETA receptor, which was likely due to the larger size and higher intensities of the H-bond donor and acceptor regions in the active site of ETA receptor. Pharmacophore-based virtual screening followed by subsequent Glide SP, XP, QPLD and MM/GBSA calculation identified 5 potential lead compounds that might act as dual AT1 and ETA receptor antagonists.

CONCLUSION

This study may provide some insights into the development of novel potent dual ETA and AT1 receptor antagonists. As a result, five compounds are found to be the best dual antagonists against AT1R and ETA receptors.