Essential proteins are indispensable to support cellular life. Identifying essential proteins can help us understand the minimal requirements for cell survival, which plays a significant role in the emerging field of synthetic biology. Moreover, essential proteins also serve as candidates of drug targets for developing novel therapy of diseases, such as cancer or infectious disease caused by emerging pathogens. However, it is expensive and time consuming to experimentally identify essential proteins. With accumulation of sequenced genomes, the gap between genome-wide essential protein data and sequence data become increasingly wide. Thus, computational approaches for detecting essential proteins are useful complements to limited experimental methods. There are many features related to protein essentiality. By taking advantage of these features, many computational approaches have been proposed to identify essential proteins. In this paper, we review the state-of-the-art techniques for computational detection of essential proteins, and discuss some challenges for future research in this field.