The problem of identifying the minimal gene set required to sustain life is of crucial importance in understanding cellular mechanisms and designing therapeutic drugs. This work describes several kernel-based solutions for predicting essential genes that outperform existing models while using less training data. Our first solution is based on a semi-manually designed kernel derived from the Pfam database, which includes several Pfam domains. We then present novel and general domain-based sequence kernels that capture sequence similarity with respect to several domains made of large sets of protein sequences. We show how to deal with the large size of the problem - several thousands of domains with individual domains sometimes containing thousands of sequences - by representing and efficiently computing these kernels using automata. We report results of extensive experiments demonstrating that they compare favorably with the Pfam kernel in predicting protein essentiality, while requiring no manual tuning.