No abstract

Both Q-SiteFinder and Pocket-Finder have been made available online at http://www.bioinformatics.leeds.ac.uk/qsitefinder and http://www.bioinformatics.leeds.ac.uk/pocketfinder

Aicardi-Goutières syndrome is a mendelian mimic of congenital infection and also shows overlap with systemic lupus erythematosus at both a clinical and biochemical level. The recent identification of mutations in TREX1 and genes encoding the RNASEH2 complex and studies of the function of TREX1 in DNA metabolism have defined a previously unknown mechanism for the initiation of autoimmunity by interferon-stimulatory nucleic acid. Here we describe mutations in SAMHD1 as the cause of AGS at the AGS5 locus and present data to show that SAMHD1 may act as a negative regulator of the cell-intrinsic antiviral response.

Familial exudative vitreoretinopathy (FEVR) is an inherited blinding disorder of the retinal vascular system. Autosomal dominant FEVR is genetically heterogeneous, but its principal locus, EVR1, is on chromosome 11q13-q23. The gene encoding the Wnt receptor frizzled-4 (FZD4) was recently reported to be the EVR1 gene, but our mutation screen revealed fewer patients harboring mutations than expected. Here, we describe mutations in a second gene at the EVR1 locus, low-density-lipoprotein receptor-related protein 5 (LRP5), a Wnt coreceptor. This finding further underlines the significance of Wnt signaling in the vascularization of the eye and highlights the potential dangers of using multiple families to refine genetic intervals in gene-identification studies.

No abstract

Angiotensin-converting enzyme 2 (ACE2), a homologue of ACE, represents a new and potentially important target in cardio-renal disease. A model of the active site of ACE2, based on the crystal structure of testicular ACE, has been developed and indicates that the catalytic mechanism of ACE2 resembles that of ACE. Structural differences exist between the active site of ACE (dipeptidyl carboxypeptidase) and ACE2 (carboxypeptidase) that are responsible for the differences in specificity. The main differences occur in the ligand-binding pockets, particularly at the S2' subsite and in the binding of the peptide carboxy-terminus. The model explains why the classical ACE inhibitor lisinopril is unable to bind to ACE2. On the basis of the ability of ACE2 to cleave a variety of biologically active peptides, a consensus sequence of Pro-X-Pro-hydrophobic/basic for the protease specificity of ACE2 has been defined that is supported by the ACE2 model. The dipeptide, Pro-Phe, completely inhibits ACE2 activity at 180 microM with angiotensin II as the substrate. As with ACE, the chloride dependence of ACE2 is substrate-specific such that the hydrolysis of angiotensin I and the synthetic peptide substrate, Mca-APK(Dnp), are activated in the presence of chloride ions, whereas the cleavage of angiotensin II is inhibited. The ACE2 model is also suggestive of a possible mechanism for chloride activation. The structural insights provided by these analyses for the differences in inhibition pattern and substrate specificity among ACE and its homologue ACE2 and for the chloride dependence of ACE/ACE2 activity are valuable in understanding the function and regulation of ACE2.

No abstract

Docking scoring functions are notoriously weak predictors of binding affinity. They typically assign a common set of weights to the individual energy terms that contribute to the overall energy score, however, these weights should be gene family-dependent. In addition, they incorrectly assume that individual interactions contribute towards the total binding affinity in an additive manner. In reality, noncovalent interactions often depend on one another in a nonlinear manner. In this paper we show how the use of support vector machines (SVMs), trained by associating sets of individual energy terms retrieved from molecular docking with the known binding affinity of each compound from high-throughput screening experiments, can be used to improve the correlation between known binding affinities and those predicted by the docking program eHiTS. We construct two prediction models; a regression model trained using IC 50 values from BindingDB, and a classification model trained using active and decoy compounds from the Directory of Useful Decoys (DUD). Moreover, to address the issue of overrepresentation of negative data in highthroughput screening data sets, we have designed a multiple-planar SVM training procedure for the classification model. The increased performance that both SVMs give when compared with the original eHiTS scoring function highlights the potential for using nonlinear methods when deriving overall energy scores from their individual components. We apply the above methodology to train a new scoring function for direct inhibitors of M.tuberculosis (M.tb) InhA. By combining ligand binding site comparison with the new scoring function, we propose that phosphodiesterase inhibitors can potentially be repurposed to target M.tb InhA. Our methodology may be applied to other gene families for which target structures and activity data are available, as demonstrated in the work presented here.