In silico study to recognize novel angiotensin-converting-enzyme-I inhibitors by 2D-QSAR and constraint-based molecular simulations

Abstract

Cardiovascular diseases (CVD) such as heart failure, stroke, and hypertension affect 64.3 million people worldwide and are responsible for 30% of all deaths. Primary inhibition of the angiotensin-converting enzyme (ACE) is significant in the management of CVD. In the present study, the genetic algorithm-multiple linear regressions (GA-MLR) method is used to generate highly predictive and statistically significant (R² = 0.70–0.75, Q²_LOO=0.67–0.73, Q²_LMO=0.66–0.72, CCC_ex=0.70–0.78) quantitative structure-activity relationships (QSAR) models conferring to OECD requirements using a dataset of 255 structurally diverse and experimentally validated ACE inhibitors. The models contain simply illustratable Padel, Estate, and PyDescriptors that correlate structural scaffold requisite for ACE inhibition. Also, constraint-based molecular docking reveals an interaction profile between ligands and enzymes which is then correlated with the essential structural features associated with the QSAR models. The QSAR-based virtual screening was utilized to find novel lead molecules from a designed database of 102 thiadiazole derivatives. The Applicability domain (AD), Molecular Docking, Molecular dynamics, and ADMET analysis suggest two compound D24 and D40 are inflexibly linked to the protein binding site and follows drug-likeness properties.

Communicated by Ramaswamy H. Sarma

Keywords:

• Cardiovascular diseases
• angiotensin-converting enzyme inhibitors
• QSAR
• validation
• molecular docking
• structural features

Acknowledgment

The authors are thankful to Mr. M. I. Bashir, University Sains, Malaysia for providing facilities to conduct molecular docking studies.

Authors’ Contributions

In the present research all authors have contributed and have given cooperation throughout to approve the manuscript. S. K. Shah and D. R. Chaple: Conceptualization, Writing - Original Draft, Formal analysis, Final Drafting, Implementation of methodology and result analysis. V. H. Masand: Review & Editing, Descriptor Calculation, and Data Curation. Magdi E.A. Zaki and Sami A Al-Hussain: Writing, Figures and Tables and Molecular docking. Rahul Jawarkar: Molecular dynamics simulation and Interpretation of results. Ashish Shah, Sumit Arora, and Mohammad Tauqeer: Original Draft, Review of style and content.

Disclosure Statement

The authors have no relevant financial or non-financial interests to disclose.

Ethics Approval

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Consent for Publication

All authors agree to publish the article.

Additional information

Funding

The authors acknowledge the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia, for its support of this research work.