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Newborn screening programs for severe metabolic disorders using tandem mass spectrometry are widely used. Medium-Chain Acyl-CoA dehydrogenase deficiency (MCADD) is the most prevalent mitochondrial fatty acid oxidation defect (1:15,000 newborns) and it has been proven that early detection of this metabolic disease decreases mortality and improves the outcome. In previous studies, data mining methods on derivatized tandem MS datasets have shown high classification accuracies. However, no machine learning methods currently have been applied to datasets based on non-derivatized screening methods. A dataset with 44,159 blood samples was collected using a non-derivatized screening method as part of a systematic newborn screening by the PCMA screening center (Belgium). Twelve MCADD cases were present in this partially MCADD-enriched dataset. We extended three data mining methods, namely C4.5 decision trees, logistic regression and ridge logistic regression, with a parameter and threshold optimization method and evaluated their applicability as a diagnostic support tool. Within a stratified cross-validation setting, a grid search was performed for each model for a wide range of model parameters, included variables and classification thresholds. The best performing model used ridge logistic regression and achieved a sensitivity of 100%, a specificity of 99.987% and a positive predictive value of 32% (recalibrated for a real population), obtained in a stratified cross-validation setting. These results were further validated on an independent test set. Using a method that combines ridge logistic regression with variable selection and threshold optimization, a significantly improved performance was achieved compared to the current state-of-the-art for derivatized data, while retaining more interpretability and requiring less variables. The results indicate the potential value of data mining methods as a diagnostic support tool., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Nuclear receptors, intracellular lipid-binding proteins and metabolic enzymes are responsible for optimal metabolic homeostasis in higher organisms. Recent studies revealed the specific cooperation/competition among the subfamilies of these proteins. In this study, the nuclear receptor-lipid-binding protein-enzyme system, in which the interactions are mostly mediated by ligand molecules, was examined in terms of their ligand-binding structures to detect the similarity of interactions between functionally related subfamilies. The complex structures were dissected into single amino acid motifs for ligand fragment binding, and the presence and evolutionary origin of the motifs were compared among the protein families. As a result, functionally related nuclear receptor and enzyme pairs were found to share more motifs than expected, in agreement with the fact that the two families compete for the same ligand, and thus our study implies the possible co-evolution of the indirectly interacting protein system.

Two large gene and protein superfamilies, SDR and MDR (short- and medium-chain dehydrogenases/reductases), were originally defined from analysis of alcohol and polyol dehydrogenases. The superfamilies contain minimally 82 and 25 genes, respectively, in humans, minimally 324 and 86 enzyme families when known lines in other organisms are also included, and over 47,000 and 15,000 variants in existing sequence data bank entries. SDR enzymes have one-domain subunits without metal and MDR two-domain subunits without or with zinc, and these three lines appear to have emerged in that order from the universal cellular ancestor. This is compatible with their molecular architectures, present multiplicity, and overall distribution in the kingdoms of life, with SDR also of viral occurrence. An MDR-zinc, when present, is often, but not always, catalytic. It appears also to have a structural role in inter-domain interactions, coenzyme binding and substrate pocket formation, as supported by domain variability ratios and ligand positions. Differences among structural and catalytic zinc ions may be relative and involve several states. Combined, the comparisons trace evolutionary properties of huge superfamilies, with partially redundant enzymes in cellular redox functions., (2010 Elsevier Inc. All rights reserved.)

The Medium-Chain Dehydrogenase/Reductase Engineering Database (MDRED, http://www.mdred.uni-stuttgart.de) has been established to serve as an analysis tool for a systematic investigation of sequence-structure-function relationships. It includes sequence and structure information of 2684 and 42 medium-chain dehydrogenases/reductases (MDRs), respectively. Although MDRs are very diverse in sequence, they have a conserved tertiary structure. MDRs are assigned to 199 homologous families and 29 superfamilies. For each family, annotated multiple sequence alignments are provided, and functionally relevant residues are annotated. Twenty-five superfamilies were classified as zinc-containing MDRs, four as non-zinc-containing MDRs. For the zinc-containing MDRs, three subclasses were identified by systematic analysis of a variable loop region, the quaternary structure determining loop (QSDL): the class of short, medium, and long QSDL, which include 11, 3, and 5 superfamilies, respectively. The length of the QSDL is predictive for tetramer (short QSDL) and dimer (long QSDL) formation. The class of medium QSDL includes both tetrameric and dimeric MDRs. The shape of the substrate-binding site is highly conserved in all zinc-containing MDRs with the exception of two variable regions, the substrate recognition sites (SRS): two residues located on the QSDL (SRS1) and, for the class of long QSDL, one residue located in the catalytic domain (SRS2). The MDRED is the first online-accessible resource of MDRs that integrates information on sequence, structure, and function. Annotation of functionally relevant residues assist the understanding of sequence-structure-function relationships. Thus, the MDRED serves as a valuable tool to identify potential hotspots for engineering properties such as substrate specificity.