SEQUEST is a database-searching engine, which calculates correlation score between observed spectrum and theoretical spectrum deduced from protein sequences stored in a flat text file, despite it is not a relational and object-oriental repository. Nevertheless the SEQUEST score functions fail to discriminate between true and false PSMs accurately. Some approaches, such as PeptideProphet and Percolator have been proposed to address the task of distinguishing true and false PSMs. However, most of these methods employ time-consuming learning algorithms to validate peptide assignments [1]. In this paper, we propose a fast algorithm for validating peptide identification by incorporating heterogeneous information from SEQUEST scores and peptide digested knowledge. To automate the peptide identification process and incorporate additional information, we employ ℓ2 multiple kernel learning (MKL) to implement the current peptide identification task. Results on experimental datasets indicate that compared with state-of-the-art methods, i.e., PeptideProphet and Percolator, our data fusing strategy has comparable performance but reduces the running time significantly.