ABSTRACT
The original map of mammalian cytochrome P450 (CYP450) residues involved in substrate recognition was prepared for the CYP2 family by Gotoh in 1992 by manual alignment of mammalian CYP450 residues with substrate recognition site (SRS) residues manually delimited from a bacterial cytochrome P450-substrate complex. Using modern structural bioinformatics tools, we have identified CYP450-ligand interactions in mammalian complexes to create a "X-ray structures" SRS map. In a parallel approach, we have built a "docking" SRS map by successful docking of 868 known substrates of 10 mammalian CYP450 isoforms and analysis of contacts made in docking solutions. We subsequently combined these maps to create a unified description of SRSs. The new map largely agrees with the original map by Gotoh with the six original SRS regions appearing in similar locations along the CYP450 sequence as in Gotoh's map. However, important differences also occur: Two new SRS regions appear before SRS1 and we have assigned them as SRS1'a and SRS1'b; SRS1 is much bigger in our map than in Gotoh's (49 aligned positions versus 28); & SRS2 and SRS3 are co-joined in our map to give a single large SRS region (60 aligned positions) we have designated as SRS(2,3), in contrast to the 9 and 10 aligned positions individually covered by SRS2 and SRS3 respectively in Gotoh's original map. These differences result in the SRS zone covering 33 % of the mammalian CYP450 sequence in our map as opposed to 16 % in Gotoh's map.
Subject(s)
Cytochrome P-450 Enzyme System/chemistry , Cytochrome P-450 Enzyme System/metabolism , Restriction Mapping/methods , Amino Acid Sequence , Animals , Binding Sites/physiology , Crystallography, X-Ray/methods , Isoenzymes/chemistry , Isoenzymes/metabolism , Molecular Sequence Data , Protein Binding/physiology , Rabbits , Substrate Specificity/physiologyABSTRACT
Baseline correction is one of the pre-processing steps in the analysis of metabolite signals from chemometric analytical instruments. Fully automated baseline correction techniques, although more convenient to use, tend to be less accurate than semi-automated baseline correction. A fully automated baseline correction algorithm, the automated iterative moving averaging algorithm (AIMA), is presented and compared with three recently introduced semi-automated algorithms, namely the adaptive iteratively reweighted penalized least squares (airPLS), Asymmetric Least Squares baseline correction (ALS) and a parametric method, using NMR, Raman and HPLC chromatograms. AIMA's potential in increasing the accuracy of multivariate analysis via SELTI-TOF and LCMS chromatograms was also assessed. The results show that the AIMA's accuracy is comparable to these semi-automated algorithms and has the advantage of ease of use. An AIMA plug-in for an open source metabolomics analysis tool, MZmine, was also developed. The AIMA plug-in is available at http://padel.nus.edu.sg/software/padelaima.
