Research Article
Computational investigation of TGF-β receptor inhibitors for treatment of idiopathic pulmonary fibrosis: Field-based QSAR model and molecular dynamics simulation

https://doi.org/10.1016/j.compbiolchem.2018.07.002Get rights and content

Highlights

  • Molecular dynamics simulation for representative ALK5-ligand complexes was carried out to reveal their stability.

  • The field-based QSAR model (Q2 = 0.548, R2training = 0.840, R2test = 0.750) owns good predictive capabilities.

  • Interaction analysis of ALK5-ligand complexes could provide valuable information about the key residues.

Abstract

The discovery of drugs relevant to transforming growth factor β (TGF-β) receptor inhibitors have been considered as a considerable challenge during therapy idiopathic pulmonary fibrosis diseases. For the first time, herein we illustrate a field-based quantitative structure-activity relationship (QSAR) model and molecular dynamics (MD) simulations for novel 7-substituted-pyrazolo [4, 3-b] pyridine derivatives with biological activity for the TGF-β receptor, with an attempt of elucidating the 3D structural features that are essential for the activity. Results demonstrate that the field-based model (Q2 = 0.548, R2training = 0.840, R2test = 0.750) are acceptable with good predictive capabilities. In addition, MD studies were also carried out on the training set with the aim of exploring their binding modes in the active pocket of TGF-β receptor, resulting in some of the crucial structural fragments which are responsible for inhibitory activity. Therefore, we summarized the following features required for TGF-β receptor inhibition: electronegative in region1, bulky groups in region2 and smaller groups in region3. Based on the model and related information, we hope the above information provides an important insight for understanding the interactions of the inhibitors and TGF-β receptor, which may be useful in discovering novel potent inhibitors.

Introduction

Idiopathic pulmonary fibrosis (IPF) is consider to vary dramatically in an extent of disease that is chronic, progressive and irreversible, which causes severe impairment in patients that need long-term treatment (Duck et al., 2015; Ohkouchi et al., 2015). IPF is the most common, lethal among the idiopathic and interstitial pulmonary (IIP) and estimated to affect patients vary from 14 to 43 of per 100,000 persons (Collum et al., 2017; Xu et al., 2016). IPF is characterized by accumulation of mesenchymal cells in connective tissue within alveolar walls and invade into alveolar air space, which decrease vital capacity (Homma et al., 2015; Xu et al., 2017). As for IPF in human, it is characterized microscopically usual interstitial pneumonia (UIP), which is a member of IIP (Lilja-Maula et al., 2014). Moreover, pathology features of IPF contain fibroproliferative foci and over-deposition of extracellular matrix (ECM), and the histopatholigical alterations of ECM are predominantly discovered in sub-pleural regions (Zhang et al., 2017). A previousage and age-sex-matched showed that IPF was an isolated venture factor for these comorbidities. Although the aetiology of IPF is still indistinct, the lung factors show that it might do harm to the pulmonary parenchyma generating an inflammatory response and subsequent fibrosis which has been implicated to be crucial for the development of the disease (Smoktunowicz et al., 2015).

The polypeptides in TGF-β family were first obtained by de Larco and Todaro in the 1970s making up a group of compounds called the sarcoma growth factor (SGF). These compounds are also able to cause malignant transformation in rat’s kidney fibroblasts (Poniatowski et al., 2015). Cytokines of the TGF-β superfamily are composed of a diverse range of proteins and contain the dimeric proteins with conserved structures, which could regulate numerous aspects of physiological processes, including embryonic development, homeostasis, wound healing, chemotaxis, and cell cycle control (Leask and Abraham, 2004). As an inactive ‘latent’ complex, the TGF-β protein is a TGF-β dimer in a non-covalent complex with two prosegments which are often linked with one of several ‘latent TGF-β binding proteins’. The latent complex which may stabilize and target latent TGF-β to the sequestered extracellular matrix represents a significant safeguard against ‘inadvertent’ activation. Thus, the matrix acts as a reservoir in which TGF-β can be recruited readily without the need of new synthesis (Derynck et al., 2001). Over the last decade, some important papers in TGF-β signaling have been reported from molecular level to cellular level (Shi and Massagué, 2003). In human, over-expression and excitation of transforming growth factor β (TGF-β) as an outcome for reduplicating alveolar epithelial damage and disrupted epithelial repair are regarded as an essential part in the progress of fibrosis (Agassandian et al., 2015; Margadant and Sonnenberg, 2010). Moreover, TGF-β may be best known as its regulatory infection on fibrogenesis in humans which is a family member of dimeric polypeptide growth factors (Shi and Massagué, 2003). The human gene code of TGF-β is located on chromosome 19q13.TGF-β plays a crucial role in growth of tissue fibrosis and inhibition of TGF-β that biological activity has shown potent proinflammatory outcomes (Lahn et al., 2005).

However, the developments of TGF-β receptor inhibitors have been affected through its toxicity (Adamali and Maher, 2012; Porte and Jenkins, 2014). All those substantial evidences show there are not distinct inhibitors of the TGF-β that have revealed as therapies for IPF. This is the first time quantitative structure-activity relationship (QSAR) model is used in statistics and computational chemistry that makes up for our experimental approach which is linked to chemical structures of biological activities (Martinez et al., 2015). Such matrix can be utilized to conduct a prediction on the bioactivities of compounds designed and determination can be made whether these compounds could really be synthesized and tested. Molecular dynamics (MD) simulation explained the interactions of the receptor complexes with its ligand very clearly, and the result is consistent with QSAR model (Ishikawa et al., 2010).

In this context, the application of field-based QSAR model based on molecular alignment studies was carried out to predict the activity of 7-substituted-pyrazolo [4, 3-b] pyridine series and interactive fields might raise their activity (Sabat et al., 2017). Moreover, the predictive capacity of the generated model was verified by test set compounds. In field-based QSAR model, the results that would be came out through this could provide prominent ideology for the binding process and prescribe available information for developing new potent TGF-β receptor inhibitors.

Section snippets

Data set and biological activity

TGF-β receptor inhibitors were obtained from some distinct paper containing inhibitory data in vitro enzyme. The 7-substituted-pyrazolo [4.3-b] pyridine series investigated in this literature were synthesized and their bio-activities were tested by Douglas R. Dougan and co-workers (Sabat et al., 2017). The 7-substituted-pyrazolo [4, 3-b] pyridine derivatives are representative inhibitors with highly potent inhibitory activity towards TGF-β receptor. This series of compounds exhibited a broad

Data set and biological activity

Molecular field-based QSAR study was performed based on biological activity (pIC50) data and field similarity score (alignment) for the molecules. The score of field similarity to the molecules was calculated on the surface and electrostatic properties which include positive, negative electrostatic fields, van der Waals and hydrophobic effects on the molecules. The IPF activity of 7-substituted-pyrazolo [4.3-b] pyridine series was defined with pIC50 value in evaluating the structure–activity

Conclusions

In the present study, we first proposed a field-based model, which was constructed based on the molecular filed of the 38 TGF-β receptor inhibitors a constructed field-based QSAR model has shown good predictability in internal and external validations. Moreover, it could identify the key structural features affecting the activity. The best ligand-based model shows good predictive ability according to their Q2, R2training, and R2test values, and all those outcomes showed insight into the key

Conflict of interest statement

The authors declare that they have no conflicts of interest.

Acknowledgements

The work was financially supported by the Fund for long-term training of young teachers in Shenyang Pharmaceutical University (ZQN2015002), the National Natural Science Foundation of Liaoning province (Grant No. 20170540854) and Training Program Foundation for the Distinguished Young Scholars of University in Liaoning Province (LJQ2015109). Liaoning Provincial Department of Education Research Project (LFD2017004) Liaoning Province Natural Science Foundation (201602588) Shenyang Science and

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