MIonSite: Ligand-specific prediction of metal ion-binding sites via enhanced AdaBoost algorithm with protein sequence information.

Accurately targeting metal ion-binding sites solely from protein sequences is valuable for both basic experimental biology and drug discovery studies. Although considerable progress has been made, metal ion-binding site prediction is still a challenging problem due to the small size and high versatility of the metal ions. In this paper, we develop a ligand-specific predictor called MIonSite for predicting metal ion-binding sites from protein sequences. MIonSite first employs protein evolutionary information, predicted secondary structure, predicted solvent accessibility, and conservation information calculated by Jensen-Shannon Divergence score to extract the discriminative feature of each residue. An enhanced AdaBoost algorithm is then designed to cope with the serious imbalance problem buried in the metal ion-binding site prediction, where the number of non-binding sites is far more than that of metal ion-binding sites. A new gold-standard benchmark dataset, consisting of training and independent validation subsets of Zn, Ca, Mg, Mn, Fe, Cu, Fe, Co, Na, K, Cd, and Ni, is constructed to evaluate the proposed MIonSite with other existing predictors. Experimental results demonstrate that the proposed MIonSite achieves high prediction performance and outperforms other state-of-the-art sequence-based predictors. The standalone program of MIonSite and corresponding datasets can be freely downloaded at https://github.com/LiangQiaoGu/MIonSite.git for academic use.