In-silico identification of fingerprint of pyrazolyl sulfonamide responsible for inhibition of N-myristoyltransferase using Monte Carlo method with index of ideality of correlation

Abstract

Human African trypanosomiasis (HAT) or sleeping sickness like infections remain a serious health concern around the globe due to unavailability of safe and potential drugs for their treatment. Moreover, developing safe, potential and highly specific target based treatments is still a challenge for present drug discovery programs. A series of pyrazole based sulfonamides are identified as an inhibitor of Trypanosoma brucei N-myristoyltransferase (TbNMT). In the present manuscript, we have developed robust and reliable QSAR models by using the balance of correlation method in CORAL software. The chemical structures are represented by simplified molecular input line entry system (SMILES). The significance of the index of ideality correlation (IIC) with applicability domain (AD) is also studied at depth. The models developed by considering the index of ideality of correlation (IIC) were found to statistically more significant and robust. One QSAR model with best $R_{\mathit{calibration}}^2$ = 0.8638 for split 2 was considered as the leading model. A greater value of cRp² i.e. 0.5 for all models in Y-randomization test showed the robustness of developed models. The outliers and promoters of increase and decrease of endpoint were also extracted independently from the leading models. The mechanistic interpretation of developed models explains the role of different structural attributes in predicting the pIC₅₀ of pyrazole sulfonamides extracted from the crystal structure of Leishmania major N-myristoyltransferase (NMT) along with co-crystallized myristoyl-CoA and ligands NMT106, NMT157, NMT187 and NMT236 (PDB ID: 4A2Z, 4A30, 4A32, 2WSA).

Communicated by Ramaswamy H. Sarma

Keywords:

• Human African trypanosomiasis
• QSAR
• index of ideality of correlation
• SMILES
• applicability domain
• Monte Carlo method

Acknowledgements

The authors are thankful to Dr Andrey A. Toropov and Dr Alla P. Toropova for providing CORAL software. The authors are also thankful to their respective universities for providing the infrastructure.

Disclosure statement

No potential conflict of interest was reported by the authors.

Author contributions

Authors have done equivalent contributions to this work.