Bioinformatic analysis of metalloproteins

Metal ions in protein are critical to the function, structure and stability of protein. For this reason, accurate prediction of metal binding sites in protein is very important. Here, we present our study which is performed for predicting metal binding sites for histidines (HIS) and cysteines from protein sequence. Four different methods are applied for this task: Support Vector Machine (SVM), Naive Bayes, Variable-length Markov chain and Smith Waterman Algorithm. All these methods use only sequence information to classify a residue as metal binding or not. Several feature sets are employed to evaluate impact on prediction results. We predict metal binding sites for mentioned amino acids at 35% precision and 75% recall with Naive Bayes, at 25% precision and 23% recall with Support Vector Machine and at 0.05% precision and 60% recall with Variable-length Markov chain, at very low performance with Smith Waterman Algorithm. We observe significant differences in performance depending on the selected feature set. The results show that Naive Bayes is competitive for metal binding site detection.