Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: EC:3.4.24.27 (thermolysin)
 1,894 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

A key problem in QSAR is the selection of appropriate descriptors to form accurate regression equations for the compounds under study. Inductive logic programming (ILP) algorithms are a class of machine-learning algorithms that have been successfully applied to a number of SAR problems. Unlike other QSAR methods, which use attributes to describe chemical structure, ILP uses relations. This gives ILP the advantages of not requiring explicit superimposition of individual compounds in a dataset, of dealing naturally with multiple conformations, and of using a language much closer to that used normally by chemists. We unify ILP and standard regression techniques to give a QSAR method that has the strength of ILP at describing steric structure with the familiarity and power of regression methods. Complex pharmacophores, correlating with activity, were identified and used as new indicator variables, along with the comparative molecular field analysis (CoMFA) prediction, to form predictive regression equations. We compared the formation of 3D-QSARs using standard CoMFA with the use of ILP on the well-studied thermolysin zinc protease inhibitor dataset and a glycogen phosphorylase inhibitor dataset. In each case the addition of ILP variables produced statistically better results (P < 0.01 for thermolysin and P < 0.05 for GP datasets) than the CoMFA analysis. Moreover, the new ILP variables were not found to increase the complexity of the final QSAR equations and gave possible insight into the binding mechanism of the ligand-protein complex under study.
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PMID:New approach to pharmacophore mapping and QSAR analysis using inductive logic programming. Application to thermolysin inhibitors and glycogen phosphorylase B inhibitors. 1178 44

Botulinum neurotoxin serotype A is the most lethal of all known toxins. Here, we report the crystal structure, along with SAR data, of the zinc metalloprotease domain of BoNT/A bound to a potent peptidomimetic inhibitor (K(i)=41 nM) that resembles the local sequence of the SNAP-25 substrate. Surprisingly, the inhibitor adopts a helical conformation around the cleavage site, in contrast to the extended conformation of the native substrate. The backbone of the inhibitor's P1 residue displaces the putative catalytic water molecule and concomitantly interacts with the "proton shuttle" E224. This mechanism of inhibition is aided by residue contacts in the conserved S1' pocket of the substrate binding cleft and by the induction of new hydrophobic pockets, which are not present in the apo form, especially for the P2' residue of the inhibitor. Our inhibitor is specific for BoNT/A as it does not inhibit other BoNT serotypes or thermolysin.
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PMID:A potent peptidomimetic inhibitor of botulinum neurotoxin serotype A has a very different conformation than SNAP-25 substrate. 1894 Jun 13

Novel N-tuple topological/geometric cutoffs to consider specific inter-atomic relations in the QuBiLS-MIDAS framework are introduced in this manuscript. These molecular cutoffs permit the taking into account of relations between more than two atoms by using (dis-)similarity multi-metrics and the concepts related with topological and Euclidean-geometric distances. To this end, the kth two-, three- and four-tuple topological and geometric neighbourhood quotient (NQ) total (or local-fragment) spatial-(dis)similarity matrices are defined, to represent 3D information corresponding to the relations between two, three and four atoms of the molecular structures that satisfy certain cutoff criteria. First, an analysis of a diverse chemical space for the most common values of topological/Euclidean-geometric distances, bond/dihedral angles, triangle/quadrilateral perimeters, triangle area and volume was performed in order to determine the intervals to take into account in the cutoff procedures. A variability analysis based on Shannon's entropy reveals that better distribution patterns are attained with the descriptors based on the cutoffs proposed (QuBiLS-MIDAS NQ-MDs) with regard to the results obtained when all inter-atomic relations are considered (QuBiLS-MIDAS KA-MDs - 'Keep All'). A principal component analysis shows that the novel molecular cutoffs codify chemical information captured by the respective QuBiLS-MIDAS KA-MDs, as well as information not captured by the latter. Lastly, a QSAR study to obtain deeper knowledge of the contribution of the proposed methods was carried out, using four molecular datasets (steroids (STER), angiotensin converting enzyme (ACE), thermolysin inhibitors (THER) and thrombin inhibitors (THR)) widely used as benchmarks in the evaluation of several methodologies. One to four variable QSAR models based on multiple linear regression were developed for each compound dataset following the original division into training and test sets. The results obtained reveal that the novel cutoff procedures yield superior performances relative to those of the QuBiLS-MIDAS KA-MDs in the prediction of the biological activities considered. From the results achieved, it can be suggested that the proposed N-tuple topological/geometric cutoffs constitute a relevant criteria for generating MDs codifying particular atomic relations, ultimately useful in enhancing the modelling capacity of the QuBiLS-MIDAS 3D-MDs.
SAR QSAR Environ Res 2016 Dec
PMID:N-tuple topological/geometric cutoffs for 3D N-linear algebraic molecular codifications: variability, linear independence and QSAR analysis. 2770 4