Research on Protein Complex Recognition Using Hidden Markov Model

Abstract

The construction of dynamic protein networks and the recognition of protein complexes are the hot topics in the current research of bioinformatics. In view of the shortcomings of existing algorithms in solving the above problems, a protein complex recognition algorithm (HMM-PC) based on hidden Markov model is proposed. In this paper, the initial protein network is constructed based on the co-expression characteristics of proteins, and then the dynamic protein network is obtained by weighting the initial network with the information of shared function annotation, shared domain and connection strength. On this basis, considering the influence of the previous time protein network topology information on the current protein network topology information, the relationship between protein complex and network individuals is described based on HMM, and then the problem of protein complex recognition in dynamic protein networks is modeled as the problem of optimal state sequence discovery in HMM and the protein complex is identified by the Viterbi algorithm. Finally, theoretical analysis shows that the proposed algorithm has low complexity. The yeast protein network in DIP data set and MIPS data set is used as the test object. A large number of simulation results also show that HMM-PC algorithm has strong robustness, and its performance is better than the existing composite recognition algorithms in terms of recall, precision, F-measure and efficiency.

Keywords

• dynamic protein network
• protein complex
• hidden markov model
• state sequence
• viterbi algorithm