Prediction of Ca²⁺binding site in proteins with a fast and accurate method based on statistical mechanics and analysis of crystal structures

Abstract

Predicting the precise locations of metal binding sites within metalloproteins is a crucial challenge in biophysics. It is challenging for both experimental and computational approaches. In the current work, we have predicted the location of Ca2+ ions in calcium-binding proteins using a physics-based method with an all-atom description of the proteins, which is substantially faster than the molecular dynamics simulation-based methods with accuracy as good as data-driven approaches. Our methodology uses the three-dimensional reference interaction site model (3D-RISM), a statistical mechanical theory, to calculate Ca2+ ion density around protein structures. We have taken previously used datasets to assess the efficacy of our method as compared to previous works. Our accuracy is found to be 88%, comparable with the FEATURE program, one of the well-known data-driven methods. Moreover, our method being physical, the reasons for failures can be ascertained in most cases. We have thoroughly examined the failed cases using different structural and crystallographic measures, such as B-factor, R-factor, electron density map, and geometry at the binding site. It has been found that X-ray structures have issues in many of the failed cases. Our algorithm, along with the checks for structural accuracy, is a major step in predicting calcium ion positions in metalloproteins.