Electrostatic features are fundamental to biomolecular functions and protein-protein, protein-DNA/RNA interactions. Studying highly charged biomolecules is extremely challenging nowadays. An important reason is the complexity of the heterogeneous ionic cloud around the highly charged biomolecules. Here we report a new computational method, Hybridizing Ions Treatment (HIT), to hybrid bound ions with the implicit solvation model to calculate the electrostatic features. We applied an efficient algorithm to calculate the position of bound ions from the molecular dynamics simulations. The parameters were optimized by machine learning methods from thousands of cases. The optimized parameters produced the results of bound ions with errors lower than 0.2 Å. The testing results on bound Ca<sup>2+</sup> and Zn<sup>2+</sup> in NAMD simulations also proved that HIT can successfully identify bound ions' types, numbers, and positions. Multiple tests by HIT can even handle the biomolecules with conformational changes. The hybridizing ions treatment significantly improves electrostatic calculations in computational biophysics.

HIT: Implement machine learning algorithms to locate bound ions in biomolecules from all-atom molecular dynamics simulations

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