  The recognition of essential proteins not only can help to understand the
mechanism of cell operation, but also help to study the mechanism of biological
evolution. At present, many scholars have been discovering essential proteins
according to the topological structure of protein network and complexes. While
some proteins still can not be recognized. In this paper, we proposed two new
methods complex degree centrality (CDC) and complex in-degree and betweenness
definition (CIBD) which integrate the local character of protein complexes and
topological properties to determine the essentiality of proteins. First, we
give the definitions of complex average centrality (CAC) and complex hybrid
centrality (CHC) which both describe the properties of protein complexes. Then
we propose these new methods CDC and CIBD based on CAC and CHC definitions. In
order to access these two methods, different Protein-Protein Interaction (PPI)
networks of Saccharomyces cerevisiae, DIP, MIPS and YMBD are used as
experimental materials. Experimental results in networks show that the methods
of CDC and CIBD can help to improve the precision of predicting essential
proteins.
