Calcium Ion Binding to the Mutants of Calmodulin: A Structure-Based Computational Predictive Model of Binding Affinity Using a Charge Scaling Approach in Molecular Dynamics Simulation.

The binding of calcium ions (Ca) to the calcium-binding proteins (CBPs) controls a plethora of regulatory processes. Among the roles played by CBPs in several diseases, the onset and progress of some cardiovascular diseases are caused by mutations in calmodulin (CaM), an important member of CBPs. Rationalization and prediction of the binding affinity of Ca ions to the CaM can play important roles in understanding the origin of cardiovascular diseases. However, there is no robust structure-based computational method for predicting the binding affinity of Ca ions to the different forms of CBPs in general and CaM in particular. In the current work, we have devised a fast yet accurate computational technique to accurately calculate the binding affinity of Ca to the different forms of CaM. This method combines the well-known molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method and a charge scaling approach developed by previous authors that takes care of the polarization of CaM and Ca ions. Our detailed analysis of the different components of binding free energy shows that subtle changes in electrostatics and van der Waals contribute to the difference in the binding affinity of mutants from that of the wild type (WT), and the charge scaling approach is superior in calculating these subtle changes in electrostatics as compared to the nonpolarizable force field used in this work. A statistically significant regression model made from our binding free energy calculations gives a correlation coefficient close to 0.8 to the experimental results. This structure-based predictive model can open up a new strategy to understand and predict the binding of Ca to the mutants of CBPs, in general.