Computational prediction of essential genes in an unculturable endosymbiotic bacterium, Wolbachia of Brugia malayi

wBm protein sequences were aligned using BLAST to the Database of Essential Genes (DEG) version 5.2, a collection of 5,260 experimentally identified essential genes in 15 bacterial strains. A confidence score, the Multiple Hit Score (MHS), was developed to predict each wBm gene's essentiality based on the top alignments to essential genes in each bacterial strain. This method was validated using a jackknife methodology to test the ability to recover known essential genes in a control genome. A second estimation of essentiality, the Gene Conservation Score (GCS), was calculated on the basis of phyletic conservation of genes across Wolbachia's parent order Rickettsiales. Clusters of orthologous genes were predicted within the 27 currently available complete genomes. Druggability of wBm proteins was predicted by alignment to a database of protein targets of known compounds.Ranking wBm genes by either MHS or GCS predicts and prioritizes potentially essential genes. Comparison of the MHS to GCS produces quadrants representing four types of predictions: those with high confidence of essentiality by both methods (245 genes), those highly conserved across Rickettsiales (299 genes), those similar to distant essential genes (8 genes), and those with low confidence of essentiality (253 genes). These data facilitate selection of wBm genes for entry into drug design pipelines.Exponential growth in the amount of available genomic information has produced unprecedented opportunities to computationally predict functional genomics in biologically intractable organisms. One application of these data is facilitation of the rational drug design process. Most high throughput drug discovery techniques screen compounds for biological activity, only determining target and mechanism post hoc. An alternative approach, rational drug design, seeks to utilize genomic information to specifically identify and inhibit targets. Often these methods utilize in silico sequence analysis to choose a tar