Building polarization into protein-inhibitor binding dynamics in rational drug design for rheumatoid arthritis

Abstract

Standard force field-based simulations to accomplish structure-based evaluations of lead molecules is a powerful tool. Combining protein fragmentation into tractable sub-systems with continuum solvation method is envisaged to enable quantum mechanics-based electronic structure calculations of macromolecules in their realistic environment. This along with incorporation of many-body polarization effect in molecular dynamics simulations may augment an accurate description of electrostatics of protein-inhibitor systems for effective drug design. Rheumatoid arthritis (RA) is a complex autoimmune disorder plagued by the ceiling effect of current targeted therapies, encouraging identification of new druggable targets and corresponding drug design to tackle the refractory form of disease. In this study, polarization-inclusive force field approach has been used to model protein solvation and ligand binding for ‘Mitogen-activated protein kinase’ (MAP3K8), a regulatory node of notable pharmacological relevance in RA synovial biology. For MAP3K8 inhibitors belonging to different scaffold series, the calculations illustrated differential electrostatic contribution to their relative binding affinities and successfully explained examples from available structure-activity relationship studies. Results from this study exemplified i) the advantage of this approach in reliably ranking inhibitors having close nanomolar range activities for the same target; and ii) its prospective application in lead molecule identification aiding drug discovery efforts in RA.

Communicated by Ramaswamy H. Sarma

Keywords:

• electrostatics
• protein polarized charges
• MD simulation
• binding free energy
• drug discovery
• rheumatoid arthritis

Acknowledgements

Authors are grateful to Dr Sanjay Kumar Dey, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, for a critical reading of the manuscript and his valuable inputs. The computational facility at Biotech Centre, University of Delhi, South Campus is gratefully acknowledged.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data underlying this article are available in the article and in its online supplementary material. Additional data, if required, will be available on request to the authors.

Additional information

Funding

This work was supported by Department of Biotechnology, Government of India, New Delhi through the “Centre of Excellence in Genome Sciences and Predictive Medicine” grant (#BT/COE/34/SP15246/2015-Phase-II) to BKT.