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We introduce a computational method to predict and annotate the catalytic residues of a protein using only its sequence information. Knowledge of catalytic residues can play an essential role in elucidating mechanistic details of an enzyme. However, experimental identification of catalytic residues is a tedious and time-consuming task, which can be expedited by computational predictions. While knowing the chemistry of an enzyme's. Because catalytic residues are the key amino acids that drive enzyme 15 function, their accurate prediction facilitates enzyme function prediction. Sequence similarity-based 16 approaches such as BLAST are fast but require previously annotated homologs. Machine learning 18 methods require. In our group, we have developed a consensus based or meta-approach (CSmetaPred) to predict catalytic residues that combines four well-known catalytic residue prediction methods (CRPred, EXIA2, DISCERN, and WCN) to ranks residues in order of their likelihood to be catalytic residues. SCREEN constructs residue representations based on spatial arrangements and incorporates enzyme function priors into such.
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