In silico studies on the interaction between bioactive ligands and ALK5, a biological target related to the cancer treatment

Computational modeling of transforming growth factor β and activin a receptor complex formation in the context of promiscuous signaling regulation

The Transforming growth factor-beta (TGFβ) superfamily is a group of multipotent growth factors that control proliferation, quiescence and differentiation. Aberrant signal transduction and downstream target activation contribute to tumorigenesis and targeted therapy has therefore been considered a promising avenue. Using various modeling pipelines, we analyzed the structure-function relationship between ligand and receptor molecules of the TGFβ family. We further simulated the molecular docking of Galunisertib, a small molecule inhibitor targeting TGFβ signaling in cancer, which is currently undergoing FDA-approved clinical trials. We found that proprotein dimers of Activin isoforms differ at intrachain disulfide bonds, which support prior evidence of varying pro-domain stability and isoform preference. Further, mature proteins possess flexibility around conserved cystine knots to functionally interact with receptors or regulatory molecules in similar but distinct ways to TGFβ. We show that all Activin isoforms are capable of assuming a closed- or open-dimer state, revealing structural promiscuity of their open forms for receptor binding. We propose the first structural landscape for Activin receptor complexes containing a type I receptor (ACVR1B), which shares a pre-helix extension with TGFβ type I receptor (TGFβR1). Here, we artificially demonstrate that Activin can bind TGFβR1 in a TGFβ-like manner and that TGFβ1 can form signaling complexes with ACVR1B. Interestingly, Galunisertib was found to form stable inhibitory structures within the homologous kinase domains of both TGFβR1 and ACVR1B, thus halting receptor-promiscuous signaling. Overall, these observations highlight the challenges of specific TGFβ cascade targeting in the context of cancer therapies.Communicated by Ramaswamy H. Sarma.

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.

2D-QSAR study, molecular docking, and molecular dynamics simulation studies of interaction mechanism between inhibitors and transforming growth factor-beta receptor I (ALK5)

Transforming growth factor type 1 receptor (ALK5) is kinase associated with a wide variety of pathological processes, and inhibition of ALK5 is a good strategy to treat many kinds of cancer and fibrotic diseases. Recently, a series of compounds have been synthesized as ALK5 inhibitors. However, the study of their selectivity against other potential targets remains elusive. In this research, a data-set of ALK5 inhibitors were collected and studied based on the combination of 2D-QSAR, molecular docking and molecular dynamics simulation. The quality of QSAR models were assessed statistically by F, R², and R²_ADJ, proved to be credible. The cross-validations for the models (q²_LOO = 0.571 and 0.629, respectively) showed their robustness, while the external validations (r²_test = 0.703 and 0.764, respectively) showed their predictive power. Besides, the predicted binding free energy results calculated by MM/GBSA method were in accordance with the experimental data, and the van der Waals energy term was the factor that had the most significant impact on ligand binding. What is more, several important residues were found to significantly affect the binding affinity. Finally, based on our analyses above, a proposed series of molecules were designed.