Computational Methods for Protein Sequence Comparison and Search
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- Abstract
- Table of Contents
- Figures
- Literature Cited
Abstract
Protein sequence comparison and search has become commonplace not only for bioinformatics researchers but also for experimentalists in many cases. Because of the exponential growth in sequence data, sequence comparison in particular has become an increasingly important tool. Relating a new gene sequence to other known sequences often reveals its function, structure, and evolution. Many sequence comparison and search tools are available through public Web servers, and biologists can use them easily with little knowledge of computers or bioinformatics. This unit provides some theoretical background and describes popular tools for dot plot, sequence search against a database, multiple sequence alignments, protein tree construction, and protein family and motif search. Step?by?step examples are provided to illustrate how to use some of the most well?known tools. Finally, some general advice is given on combining different sequence analysis tools for biological inference. Curr. Protoc. Protein Sci. 56:2.1.1?2.1.27. © 2009 by John Wiley & Sons, Inc.
Keywords: protein sequence comparison; dot plot; multiple sequence alignment; protein tree; protein family; motif search
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PDF or HTML at Wiley Online LibraryTable of Contents
- Introduction
- Theoretical Background for Protein Sequence Analysis
- Matrix Methods for Sequence Comparison: Dot Plots
- Sequence Similarity Searching
- Multiple Alignments
- Protein Trees
- Protein Family and Functional Site Identification
- General Strategy for Sequence Analyses
- Acknowledgement
- Internet Resources
- Literature Cited
- Figures
- Tables
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Figure 2.1.1 Dot plot generated from comparison of peanut allergens Ara h 1 and Ara h 3 using PLALIGN. Regions of similarity between the two sequences appear as lines parallel and offset to the line of identity. The expectation values for the local alignments of these regions are shown in color. The horizontal axis indicates Ara h 1 and the vertical axis indicates Ara h 3. View Image -
Figure 2.1.2 The best local sequence alignment for peanut allergens Ara h 1 and Ara h 3 using PLALIGN. In the alignment, the lower sequence is Ara h 1 and the upper one is Ara h 3. View Image -
Figure 2.1.3 FASTA histogram from a global‐alignment search of the SWISS‐PROT database for a lectin protein. Numbers of windows at each opt score are plotted. Note that there are seven highly significant alignments. View Image -
Figure 2.1.4 FASTA alignment table and the best scoring alignment for the same search illustrated in Figure . The table shows the best alignment scores sorted by the highest opt score. View Image -
Figure 2.1.5 Input sequence file to run TCoffee for multiple sequence alignment. The sequences are from the query protein (“test”) and top seven significant hits in Figure . View Image -
Figure 2.1.6 TCoffee output multiple sequence alignment results in the ClustalW format for the input sequences in Figure . The fully conserved residues are marked with “”, while somewhat conserved residues are indicated with “:” or “.”, the latter of which is less conserved. View Image -
Figure 2.1.7 TreeView display for the phylogenetic produced using TCoffee based on the multiple sequence alignment in Figure . View Image -
Figure 2.1.8 Partial output from MotifScan for protein Sin1, indicating the bipartite localization signals. View Image
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