Refine
Year of publication
- 2009 (12) (remove)
Document Type
- Article (12)
Language
- English (12)
Has Fulltext
- yes (12)
Is part of the Bibliography
- no (12) (remove)
Keywords
- Virtual Screening (2)
- Compound Database (1)
- Gaussian Process (1)
- Identical Topology (1)
- Lead Structure (1)
- Multiple Kernel (1)
- Pairwise Sequence Alignment (1)
- Support Vector Regression (1)
- Support Vector Regression Model (1)
- bacterial autotransporter (1)
- pattern (1)
- protein targeting (1)
- protein trafficking (1)
- sequence analysis (1)
- signal peptide (1)
Institute
- Biochemie und Chemie (9)
- Biowissenschaften (4)
- Pharmazie (1)
We developed the Pharmacophore Alignment Search Tool (PhAST), a text-based technique for rapid hit and lead structure searching in large compound databases. For each molecule, a two-dimensional graph of potential pharmacophoric points (PPPs) is created, which has an identical topology as the original molecule with implicit hydrogen atoms. Each vertex is coloured by a symbol representing the corresponding PPP. The vertices of the graph are canonically labelled. The symbols associated with the vertices are combined to a so-called PhAST-Sequence beginning with the vertex with the lowest canonical label. Due to the canonical labelling the created PhAST-Sequence is characteristic for each molecule. For similarity assessment, PhAST-Sequences are compared using the sequence identity in their global pairwise alignment. The alignment score lies between 0 (no similarity) and 1 (identical PhAST-Sequences). In order to use global pairwise sequence alignment, a score matrix for pharmacophoric symbols was developed and gap penalties were optimized. PhAST performed comparably and sometimes superior to other similarity search tools (CATS2D, MOE pharmacophore quadruples) in retrospective virtual screenings using the COBRA collection of drugs and lead structures. Most importantly, the PhAST alignment technique allows for the computation of significance estimates that help prioritize a virtual hit list.
Bacterial autotransporters represent a diverse family of proteins that autonomously translocate across the inner membrane of Gram-negative bacteria via the Sec complex and across the outer bacterial membrane. They often possess exceptionally long N-terminal signal sequences. We analyzed 90 long signal sequences of bacterial autotransporters and members of the two-partner secretion pathway in silico and describe common domain organization found in 79 of these sequences. The domains are in agreement with previously published experimental data. Our algorithmic approach allows for the systematic identification of functionally different domains in long signal sequences. Keywords: bacterial autotransporter, sequence analysis, pattern, protein targeting, signal peptide, protein trafficking