Searching, Protein Patterns

Zhang, Z., et al., Protein sequence similarity searches using patterns as seeds. Nucleic Acids Res, 1998. 26(17) p. 3986-90. 

Public annotations Homology searching Protein structure homology Functional prediction Pattern matching Text mining... 

The basic premise of this book is that we can create a molecular theory of mental illness, analogous to the germ theory of disease. Molecular imaging makes it possible to search for patterns in the chemical processes in the brain that are related to violence and other forms of mental illness, involving hormones, neurotransmitters, neuroreceptors, reuptake sites, ions, peptides, and proteins. 

Far-uv circular dichroism is probably the most appropriate way to determine this structure. A helical protein exhibits double minima at wavelengths of 208 and 222 nm. However, this would only show that on average the protein contains some helical structure. To determine which residues contained helical structure, it would be necessary to use NMR spectroscopy to assign spectral peaks to residues and search for patterns of chemical shifts of peaks in the spectra, that are typical of a-helical structure. The first approach is rapid, the second takes longer but provides more information overall. 

Ho YP, Hsu PH. Investigating the effects of protein patterns on microorganism identification by high-performance liquid chromatography-mass spectrometry and protein database searches. J ChromatogrA. 2002 976(1-2) 103-11. 

A more computationally demanding search of patterns of atoms in a protein occurs when a protein must be inspected for the presence or absence of an arbitrary pattern of atoms in three-dimensional space, i.e., the atoms can be in any possible topological relationship to each other. 

The importance of secondary structures in determining protein function has led to interest in the design of retrieval systems that would allow users to search for patterns of secondary structures, or motifsThese studies have made use of one of the major features of the common helix and strand secondary struaure elements (SSEs), viz., that they are approximately linear repeating structures and that they can hence be described by a vector drawn along their linear axis, using one of the several programs that are available for the identification of such Once these have been identified,... 

The basic structural unit of these two-sheet p helix structures contains 18 amino acids, three in each p strand and six in each loop. A specific amino acid sequence pattern identifies this unit namely a double repeat of a nine-residue consensus sequence Gly-Gly-X-Gly-X-Asp-X-U-X where X is any amino acid and U is large, hydrophobic and frequently leucine. The first six residues form the loop and the last three form a p strand with the side chain of U involved in the hydrophobic packing of the two p sheets. The loops are stabilized by calcium ions which bind to the Asp residue (Figure S.28). This sequence pattern can be used to search for possible two-sheet p structures in databases of amino acid sequences of proteins of unknown structure. 

The aim of the fust dimension breadth is to reveal sequence-function relationships by comparing protein sequences by sequence similarity. Simple bioinformatic algorithms can be used to compare a pair of related proteins or for sequence similarity searches e.g., BLAST (Basic Local Alignment Search Tool). Improved algorithms allow multiple alignments of larger number of proteins and extraction of consensus sequence pattern and sequence profiles or structural templates, which can be related to some functions, see e.g., under http //www. expasy.ch/tools/ similarity. 

The PepSeq program of Micromass s ProteinLynx software package was used for de novo analysis of the sequence (MS/MS) data. MS-Pattern of ProteinProspectror55 used the sequence tag determined from PepSeq to search the nonredundant database of the National Center for Biotechnology Information (NCBI) for protein identification. 

ExPASy Proteomics tools (http //expasy.org/tools/), tools and online programs for protein identification and characterization, similarity searches, pattern and profile searches, posttranslational modification prediction, topology prediction, primary structure analysis, or secondary and tertiary structure prediction. 

Another computational approach for detecting /1-solenoid sequences is implemented in a program called BETAWRAP (Bradley et al., 2001). This approach aims to identify /1-solenoid sequences by using hydrophobic-residue sequence patterns of strand-turn-strand regions that were learned from non-/l-solenoid structures. This method also takes into consideration the repetitive character of these patterns in /1-solenoids. Unlike the sequence profile approaches, BETAWRAP can make ab initio predictions of /1-solenoid domains. However, it is less sensitive than the profile search and, sometimes, cannot distinguish /1-solenoids from other solenoids (A. V. K, unpublished observation) such as, for example, LRR proteins (Kobe and Deisenhofer, 1994 Kobe and Kajava, 2001). The latest modification of BETAWRAP algorithm, which is called BETAWRAPPRO (McDonnell et al., 2006), employs additional data provided by sequence profiles and this improves the results of /1-solenoid predictions. 

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