Properties and identification of antibiotic drug targets.

BACKGROUND: We analysed 48 non-redundant antibiotic target proteins from all bacteria, 22 antibiotic target proteins from E. coli only and 4243 non-drug targets from E. coli to identify differences in their properties and to predict new potential drug targets. RESULTS: When compared to non-targets, bacterial antibiotic targets tend to be long, have high beta-sheet and low alpha-helix contents, are polar, are found in the cytoplasm rather than in membranes, and are usually enzymes, with ligases particularly favoured. Sequence features were used to build a support vector machine model for E. coli proteins, allowing the assignment of any sequence to the drug target or non-target classes, with an accuracy in the training set of 94%. We identified 319 proteins (7%) in the non-target set that have target-like properties, many of which have unknown function. 63 of these proteins have significant and undesirable similarity to a human protein, leaving 256 target like proteins that are not present in humans. CONCLUSIONS: We suggest that antibiotic discovery programs would be more likely to succeed if new targets are chosen from this set of target like proteins or their homologues. In particular, 64 are essential genes where the cell is not able to recover from a random insertion disruption.

Properties and identification of human protein drug targets.

MOTIVATION: We analysed 148 human drug target proteins and 3573 non-drug targets to identify differences in their properties and to predict new potential drug targets. RESULTS: Drug targets are rare in organelles; they are more likely to be enzymes, particularly oxidoreductases, transferases or lyases and not ligases; they are involved in binding, signalling and communication; they are secreted; and have long lifetimes, shown by lack of PEST signals and the presence of N-glycosylation. This can be summarized into eight key properties that are desirable in a human drug target, namely: high hydrophobicity, high length, SignalP motif present, no PEST motif, more than two N-glycosylated amino acids, not more than one O-glycosylated Ser, low pI and membrane location. The sequence features were used as inputs to a support vector machine (SVM), allowing the assignment of any sequence to the drug target or non-target classes with an accuracy in the training set of 96%. We identified 668 proteins (23%) in the non-target set that have target-like properties. We suggest that drug discovery programmes would be more likely to succeed if new targets are chosen from this set or their homologues.

Quick gene expression profiling of promyelocytic cell line HL-60.

OBJECTIVE: A number of techniques have been developed to perform gene expression profiling. We report preliminary results from our exploratory study, using sequential analysis of gene expression (SAGE) technique, to profile the undifferentiated and differentiated HL-60 cells in line with our interest to characterize the cancer phenotype. The aim of the study is to evaluate the technique and to understand the molecular bases of these 2 states of cells. METHODS: HL-60 cells were differentiated after treatment with dimethyl sulfoxide. Tag libraries were prepared from the messenger RNAs of the undifferentiated and differentiated cells according to the SAGE protocol. The search for genes corresponding to the tags was carried out using SAGE software. The tags and the genes from the 2 libraries were compared for their levels of expression. The study was carried out at the King Faisal Specialist Hospital and Research Centre, Riyadh, Kingdom of Saudi Arabia during the year 2001. RESULTS: A comparison of tags from the 2 libraries revealed that 151 tags corresponding to 57 genes expressed differentially: 60 tags were elevated and 59 were repressed in the undifferentiated cells. Thirty-two tags were equally expressed in both types of cells. Of the corresponding genes, 25 were expressed at higher, 17 at lower, while 15 were expressed at comparable levels in both cell types. In the profile of undifferentiated cells, the genes involved in mitochondrial function and protein synthesis were prominent, while in the differentiated cells, the genes coding for proteins associated with cell membranes, signal transduction and for cell specific functions were prominent. The genes, expressed equally in both the cell types, were concerned with the maintenance of the living state. CONCLUSION: Sequential analysis of gene expression is a useful technique for gene expression profiling. As previously indicated by others, a dedicated team can generate useful data within reasonable time limits.