Computational studies of TGF-βRI (ALK-5) inhibitors: analysis of the binding interactions between ligand-receptor using 2D and 3D techniques

Searching for Novel Anaplastic Lymphoma Kinase Inhibitors: Structure-Guided Screening of Natural Compounds for a Tyrosine Kinase Therapeutic Target in Cancers

Anaplastic lymphoma kinase (ALK) is a receptor tyrosine kinase molecular target with broad importance for drug discovery, especially in the field of cancer therapeutics. ALK belongs to the insulin receptor superfamily that is involved in various malignancies, including non-small cell lung cancer, anaplastic large cell lymphoma, and neuroblastoma. ALK has been shown to play a role in cancer progression and metastasis, making it one of the prime targets to develop novel anticancer therapeutics. In this context, natural compounds can be an important resource to unravel novel ALK inhibitors. In this study, we report a structure-based virtual screening of natural compounds from the ZINC database, with an eye to potential inhibitors of ALK. Molecular docking was performed on a natural compound library, and top hits holding good binding affinity, docking score, and specificity toward ALK were selected. The hits were further evaluated based on the PAINS (pan-assay interference compounds) filter, ADMET (absorption, distribution, metabolism, excretion, toxicity) properties, PASS (prediction of activity spectra for substances) analysis, and two-dimensional interaction of protein-ligand complexes. Importantly, two natural compounds (ZINC03845566 and ZINC03999625) were identified as potential candidates for ALK, having appreciable affinity and specificity toward the ALK binding pocket and depicting drug-like properties as predicted from ADMET analysis and their physicochemical parameters. An all-atom molecular dynamics simulation for 100 ns on ALK promised stable ALK-ligand complexes. Hence, we conclude that ZINC03845566 and ZINC03999625 can act as potential ALK inhibitors against cancers where ALK plays a role, for example, in lung cancer, among others. All in all, these findings inform future discovery and translational research for ALK inhibitors as anticancer drugs.

Discovery of New Hits as Antitrypanosomal Agents by In Silico and In Vitro Assays Using Neolignan-Inspired Natural Products from Nectandra leucantha

In the present study, the phytochemical study of the n-hexane extract from flowers of Nectandra leucantha (Lauraceae) afforded six known neolignans (1–6) as well as one new metabolite (7), which were characterized by analysis of NMR, IR, UV, and ESI-HRMS data. The new compound 7 exhibited potent activity against the clinically relevant intracellular forms of T. cruzi (amastigotes), with an IC₅₀ value of 4.3 μM and no observed mammalian cytotoxicity in fibroblasts (CC₅₀ > 200 μM). Based on the results obtained and our previous antitrypanosomal data of 50 natural and semi-synthetic related neolignans, 2D and 3D molecular modeling techniques were employed to help the design of new neolignan-based compounds with higher activity. The results obtained from the models were important to understand the main structural features related to the biological response of the neolignans and to aid in the design of new neolignan-based compounds with better biological activity. Therefore, the results acquired from phytochemical, biological, and in silico studies showed that the integration of experimental and computational techniques consists of a powerful tool for the discovery of new prototypes for development of new drugs to treat CD.

QSAR Modeling, Molecular Docking and Molecular Dynamics Simulations Studies of Lysine-Specific Demethylase 1 (LSD1) Inhibitors as Anticancer Agents

BACKGROUND

Histone Lysine Demetylases1 (LSD1) is a promising medication to treat cancer, which plays a crucial role in epigenetic modulation of gene expression. Inhibition of LSD1with small molecules has emerged as a vital mechanism to treat cancer.

OBJECTIVE

In the present research, molecular modeling investigations, such as CoMFA, CoMFA-RF, CoMSIA and HQSAR, molecular docking and Molecular Dynamics (MD) simulations were carried out on some tranylcypromine derivatives as LSD1 inhibitors.

METHODS

The QSAR models were carried out on a series of Tranylcypromine derivatives as data set via the SYBYL-X2.1.1 program. Molecular docking and MD simulations were carried out by the MOE software and the SYBYL program, respectively. The internal and external predictability performances related to the generated models for these LSD1 inhibitors were justified by evaluating cross-validated correlation coefficient (q²), noncross- validated correlation coefficient (r²_ncv) and predicted correlation coefficient (r²_pred) of the training and test set molecules, respectively.

RESULTS

The CoMFA (q², 0.670; r²_ncv, 0.930; r²_pred, 0.968), CoMFA-RF (q², 0.694; r²_ncr, 0.926; r²_pred, 0.927), CoMSIA (q², 0.834; r²_ncv, 0.956; r²_pred, 0.958) and HQSAR models (q², 0.854; r²_ncv, 0.900; r²_pred, 0.728) for training as well as the test set of LSD1 inhibition resulted in significant findings.

CONCLUSION

These QSAR models were found to be perfect and strong with better predictability. Contour maps of all models were generated and it was proven by molecular docking studies and molecular dynamics simulation that the hydrophobic, electrostatic and hydrogen bonding fields are crucial in these models for improving the binding affinity and determining the structure-activity relationship. These theoretical results are possibly beneficial to design new strong LSD1 inhibitors with enhanced activity to treat cancer.

Structure-Based Virtual Screening, Molecular Dynamics and Binding Free Energy Calculations of Hit Candidates as ALK-5 Inhibitors

Activin-like kinase 5 (ALK-5) is involved in the physiopathology of several conditions, such as pancreatic carcinoma, cervical cancer and liver hepatoma. Cellular events that are landmarks of tumorigenesis, such as loss of cell polarity and acquisition of motile properties and mesenchymal phenotype, are associated to deregulated ALK-5 signaling. ALK-5 inhibitors, such as SB505154, GW6604, SD208, and LY2157299, have recently been reported to inhibit ALK-5 autophosphorylation and induce the transcription of matrix genes. Due to their ability to impair cell migration, invasion and metastasis, ALK-5 inhibitors have been explored as worthwhile hits as anticancer agents. This work reports the development of a structure-based virtual screening (SBVS) protocol aimed to prospect promising hits for further studies as novel ALK-5 inhibitors. From a lead-like subset of purchasable compounds, five molecules were identified as putative ALK-5 inhibitors. In addition, molecular dynamics and binding free energy calculations combined with pharmacokinetics and toxicity profiling demonstrated the suitability of these compounds to be further investigated as novel ALK-5 inhibitors.

Integrated Protocol to Design Potential Inhibitors of Dipeptidyl Peptidase- 4 (DPP-4)

BACKGROUND

A strategy for the treatment of type II diabetes mellitus is the inhibition of the enzyme known as dipeptidyl peptidase-4 (DPP-4).

AIMS

This study aims to investigate the main interactions between DPP-4 and a set of inhibitors, as well as proposing potential candidates to inhibit this enzyme.

METHODS

We performed molecular docking studies followed by the construction and validation of CoMFA and CoMSIA models. The information provided from these models was used to aid in the search for new candidates to inhibit DPP-4 and the design of new bioactive ligands from structural modifications in the most active molecule of the studied series.

RESULTS

We were able to propose a set of analogues with biological activity predicted by the CoMFA and CoMSIA models, suggesting that our protocol can be used to guide the design of new DPP-4 inhibitors as drug candidates to treat diabetes.

CONCLUSION

Once the integration of the techniques mentioned in this article was effective, our strategy can be applied to design possible new DPP-4 inhibitors as candidates to treat diabetes.

Two- and three-dimensional QSAR studies on hURAT1 inhibitors with flexible linkers: topomer CoMFA and HQSAR

hURAT1 (human urate transporter 1) is a successful target for hyperuricemia. Recently, the modification work on hURAT1 inhibitors showed that the flexible linkers would benefit biological activity. The study aimed to investigate the contribution of the linkers and give modification strategies on this kind of structures based on QSAR models (HQSAR and topomer CoMFA). The most effective HQSAR and topomer CoMFA models were generated by applying the training set containing 63 compounds, with the cross-validated q² values of 0.869/0.818 and the non-cross-validated correlation coefficients r² of 0.951/0.978, respectively. The Y-randomization test was applied to ensure the robustness of the models. The external predictive correlation coefficient (r_pred²) grounded on the external test set (21 compounds) of two models was 0.910 and 0.907, respectively. In addition, the models were validated by Golbraikh-Tropsha and Roy methods, as well as other statistical metrics. The results showed that both models were reliable. Topomer CoMFA steric/electrostatic contours and HQSAR atomic contribution maps illustrated the structural features which governed their inhibitory potency. The dependable results could provide important insights to guide the designing of more potential hURAT1 inhibitors.

Studies on the Dual Activity of EGFR and HER-2 Inhibitors Using Structure-Based Drug Design Techniques

HER-2 and EGFR are biological targets related to the development of cancer and the discovery and/or development of a dual inhibitor could be a good strategy to design an effective drug candidate. In this study, analyses of the chemical properties of a group of substances having affinity for both HER-2 and EGFR were carried out with the aim of understanding the main factors involved in the interaction between these inhibitors and the biological targets. Comparative analysis of molecular interaction fields (CoMFA) and comparative molecular similarity index analysis (CoMSIA) techniques were applied on 63 compounds. From CoMFA analyses, we found for both HER-2 (r² calibration = 0.98 and q²_cv = 0.83) and EGFR (r² calibration = 0.98 and q²_cv = 0.73) good predictive models. Good models for CoMSIA technique have also been found for HER-2 (r² calibration = 0.92 and q²_cv = 0.74) and EGFR (r² calibration = 0.97 and q²_cv = 0.72). The constructed models could indicate some important characteristics for the inhibition of the biological targets. New compounds were proposed as candidates to inhibit both proteins. Therefore, this study may guide future projects for the development of new drug candidates for the treatment of breast cancer.

Molecular description of α-keto-based inhibitors of cruzain with activity against Chagas disease combining 3D-QSAR studies and molecular dynamics

In this work, a group of α-keto-based inhibitors of the cruzain enzyme with anti-chagas activity was selected for a three-dimensional quantitative structure-activity relationship study (3D-QSAR) combined with molecular dynamics (MD). Firstly, statistical models based on Partial Least Square (PLS) regression were developed employing comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) descriptors. Validation parameters (q² and r² )for the models were, respectively, 0.910 and 0.997 (CoMFA) and 0.913 and 0.992 (CoMSIA). In addition, external validation for the models using a test group revealed r²_pred  = 0.728 (CoMFA) and 0.971 (CoMSIA). The most relevant aspect in this study was the generation of molecular fields in both favorable and unfavorable regions based on the models developed. These fields are important to interpret modifications necessary to enhance the biological activities of the inhibitors. This analysis was restricted considering the inhibitors in a fixed conformation, not interacting with their target, the cruzain enzyme. Then, MD was employed taking into account important variables such as time and temperature. MD helped describe the behavior of the inhibitors and their properties showed similar results as those generated by QSAR-3D study.

Computational analyses of interactions between ALK-5 and bioactive ligands: insights for the design of potential anticancer agents

Activin Receptor-Like Kinase 5 (ALK-5) is related to some types of cancer, such as breast, lung, and pancreas. In this study, we have used molecular docking, molecular dynamics simulations, and free energy calculations in order to explore key interactions between ALK-5 and six bioactive ligands with different ranges of biological activity. The motivation of this work is the lack of crystal structure for inhibitor-protein complexes for this set of ligands. The understanding of the molecular structure and the protein-ligand interaction could give support for the development of new drugs against cancer. The results show that the calculated binding free energy using MM-GBSA, MM-PBSA, and SIE is correlated with experimental data with r² = 0.88, 0.80, and 0.94, respectively, which indicates that the calculated binding free energy is in excellent agreement with experimental data. In addition, the results demonstrate that H bonds with Lys232, Glu245, Tyr249, His283, Asp351, and one structural water molecule play an important role for the inhibition of ALK-5. Overall, we discussed the main interactions between ALK-5 and six inhibitors that may be used as starting points for designing new molecules to the treatment of cancer.

Combined HQSAR, topomer CoMFA, homology modeling and docking studies on triazole derivatives as SGLT2 inhibitors

Aim: Sodium–glucose cotransporter 2 (SGLT2) is a promising target for diabetes therapy. We aimed to develop computational approaches to identify structural features for more potential SGLT2 inhibitors. Materials & methods: In this work, 46 triazole derivatives as SGLT2 inhibitors were studied using a combination of several approaches, including hologram quantitative structure–activity relationships (HQSAR), topomer comparative molecular field analysis (CoMFA), homology modeling, and molecular docking. HQSAR and topomer CoMFA were used to construct models. Molecular docking was conducted to investigate the interaction of triazole derivatives and homology modeling of SGLT2, as well as to validate the results of the HQSAR and topomer CoMFA models. Results: The most effective HQSAR and topomer CoMFA models exhibited noncross-validated correlation coefficients of 0.928 and 0.891 for the training set, respectively. External predictions were made successfully on a test set and then compared with previously reported models. The graphical results of HQSAR and topomer CoMFA were proven to be consistent with the binding mode of the inhibitors and SGLT2 from molecular docking. Conclusion: The models and docking provided important insights into the design of potent inhibitors for SGLT2.

Understanding PPAR-δ affinity and selectivity using hologram quantitative structure-activity modeling, molecular docking and GRID calculations

AIM

Type 2 diabetes mellitus and metabolic syndrome are two diseases related to disorders of lipid and carbohydrate metabolism and insulin resistance. Peroxisome proliferator-activated receptors (PPARs) are a class of nuclear receptors that control the metabolism of lipids/carbohydrates and are considered targets for both diseases. PPAR affinity and selectivity are critical points to design drug candidates with appropriated pharmacodynamic/kinetic profiles.

MATERIALS & METHODS

Hologram quantitative structure-activity relationships studies were conducted, as well molecular docking and molecular interaction field calculations, in order to explain affinity and selectivity of selected compounds.

RESULTS

The constructed hologram quantitative structure-activity relationship models are robust and predictive (values of q² and r²_test above 0.70).

CONCLUSION

The quantitative structure-activity relationship models and docking/GRID analyses indicated that carboxyl group of indole-sulfonamide derivatives could interact at helix-3 region, being considered important point of PPAR-δ selectivity.

Molecular docking studies and 2D analyses of DPP-4 inhibitors as candidates in the treatment of diabetes

Dipeptidyl peptidase-4 (DPP-4) is an important biological target related to the treatment of diabetes as DPP-4 inhibitors can lead to an increase in the insulin levels and a prolonged activity of glucagon-like peptide-1 (GLP-1) and gastric inhibitory polypeptide (GIP), being effective in glycemic control. Thus, this study analyses the main molecular interactions between DPP-4 and a series of bioactive ligands. The methodology used here employed molecular modeling methods, such as HQSAR (Hologram Quantitative Structure-Activity) analyses and molecular docking, with the aim of understanding the main structural features of the compound series that are essential for the biological activity. Analyses of the main interactions in the active site of DPP-4, in particular, the contribution of the hydroxyl coordination between Tyr547 and Ser630 by the water molecule, which is described in the literature as important for the coordinated interactions in the active site, were performed. Significant correlation coefficients of the best 2D model (r(2) = 0.942 and q(2) = 0.836) were obtained, indicating the predictive power of this model for untested compounds. Therefore, the final model constructed in this study, along with the information from the contribution maps, could be useful in the design of novel DPP-4 ligands with improved activity.

Hologram quantitative structure–activity relationship and comparative molecular interaction field analysis of aminothiazole and thiazolesulfonamide as reversible LSD1 inhibitors

Background: LSD-1 is an enzyme that removes methyl groups from lysine residues of histone proteins. LSD-1 inhibition decreases cellular proliferation and therefore represents a therapeutic target for cancer treatment. MAO and LSD-1 are both flavin adenine dinucleotide-dependent MAOs, and the MAO inhibitor, tranylcypromine, is currently undergoing clinical trials for cancer treatment because it acts as an irreversible LSD-1 inhibitor. Materials & methods: The present study investigated new reversible LSD-1 inhibitors, in order to develop novel selective anticancer agents. We constructed 2 and 3D quantitative structure–activity relationship models by using a series of 54 aminothiazole and thiazolesulfonamide derivatives. Results: The models were validated internally and externally (q2 , 0.691 and 0.701; r2 , 0.894 and 0.937; r2 test , 0.785 and 0.644, for 2 and 3D models, respectively). Fragment contribution maps, as well as steric and electrostatic contour maps were generated in order to obtain chemical information related to LSD-1 inhibition. Conclusion: The thiazolesulfonamide group was fundamental to the inhibition of LSD-1 by these compounds and that bulky and aromatic substituents at the thiazole ring were important for their steric and electrostatic interactions with the active site of LSD-1.

Convergent QSAR studies on a series of NK₃ receptor antagonists for schizophrenia treatment

The dopamine hypothesis states that decreased dopaminergic neurotransmission reduces schizophrenia symptoms. Neurokinin-3 receptor (NK3) antagonists reduce dopamine release and have shown positive effects in pre-clinical and clinical trials. We employed 2D and 3D-QSAR analysis on a series of 40 non-peptide NK3 antagonists. Multivariate statistical analysis, PCA and HCA, were performed to rational training/test set splitting and PLS regression was employed to construct all QSAR models. We constructed one highly predictive CoMFA model (q(2)= 0.810 and r(2)= 0.929) and acceptable HQSAR and CoMSIA models (HQSAR q(2)= 0.644 and r(2)= 0.910; CoMSIA q(2)= 0.691, r(2)= 0.911). The three different techniques provided convergent physicochemical results. All models indicate cyclopropane, piperidine and di-chloro-phenyl ring attached to cyclopropane ring and also the amide group attached to the piperidine ring could play an important role in ligand-receptor interactions. These findings may contribute to develop potential NK3 receptor antagonists for schizophrenia.

Hologram QSAR studies of antiprotozoal activities of sesquiterpene lactones

Infectious diseases such as trypanosomiasis and leishmaniasis are considered neglected tropical diseases due the lack for many years of research and development into new drug treatments besides the high incidence of mortality and the lack of current safe and effective drug therapies. Natural products such as sesquiterpene lactones have shown activity against T. brucei and L. donovani, the parasites responsible for these neglected diseases. To evaluate structure activity relationships, HQSAR models were constructed to relate a series of 40 sesquiterpene lactones (STLs) with activity against T. brucei, T. cruzi, L. donovani and P. falciparum and also with their cytotoxicity. All constructed models showed good internal (leave-one-out q² values ranging from 0.637 to 0.775) and external validation coefficients (r²_test values ranging from 0.653 to 0.944). From HQSAR contribution maps, several differences between the most and least potent compounds were found. The fragment contribution of PLS-generated models confirmed the results of previous QSAR studies that the presence of α,β-unsatured carbonyl groups is fundamental to biological activity. QSAR models for the activity of these compounds against T. cruzi, L. donovani and P. falciparum are reported here for the first time. The constructed HQSAR models are suitable to predict the activity of untested STLs.