Mathematically Complete Nucleotide and Protein Sequence Searching Using Ssearch

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Abstract
Table of Contents
Materials
Figures
Literature Cited

Abstract

In this unit a protocol is described for predicting the structure of simple transmembrane a?helical bundles. The protocol is based on a global molecular dynamics search (GMDS) of the configuration space of the helical bundle, yielding several candidate structures. The correct structure among these candidates is selected using information from silent amino acid substitutions, employing the premise that only the correct structure must (by definition) accept all of the silent amino acid substitutions. Thus, the correct structure is found by repeating the GMDS for several close homologs and selecting the structure that persists in all of the trials.

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Guidelines for Understanding Results
Commentary
Literature Cited
Figures

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Materials

Basic Protocol 1:

Necessary Resources

Hardware
- The Ssearch code is a resource‐intensive code and thus generally requires a substantial computational platform with adequate CPU, memory, and disk space. However, these requirements will vary greatly depending on the usage of the code. For example, searching against compilations of known protein sequences, such as those that have been placed in the PIR database or Swiss‐Prot databases, can be done on an inexpensive PC running Windows, Linux, or Macintosh OS. Performing regular searches of complete nucleic acid libraries with the code is a task best suited to higher‐performance, multiprocessor machines.

Software
- The Ssearch code is part of the FASTA package (unit 3.9 ) from Dr. William Pearson, which is available via anonymous FTP from ftp://ftp.virginia.edu/pub/fasta/. Dr. Pearson can be contacted at the Department of Biochemistry, School of Medicine, University of Virginia, Charlottesville, Va. 22908.

Files
- Sequence files: The Ssearch code requires an input file that contains the query sequence in FASTA format ( appendix 1B ). It also requires that one or more sequence data libraries (e.g., GenBank, NBRF‐PIR, Swiss‐Prot, or EMBL) be installed, or, in lieu of installing a sequence data library, simply having a set of sequences against which one wants to compare the query sequence in FASTA format ( appendix 1C ).
- Scoring matrix file: The Ssearch code enables one to use scoring matrices (unit 3.5 ) that are not internal to the code, such as the BLOSUM35 matrix (Fig. ). The format of the matrix file should be in the same configuration as is acceptable by the BLAST (Altschul et. al., ; units 3.3 & 3.4 ) family of programs.
- A variety of compatible scoring matrices that can be used with the Ssearch program can be found at the NCBI FTP site (ftp://ftp.ncbi.nih.gov/blast/matrices). The Ssearch program does not require the use of an external scoring matrix file if one of the built‐in scoring matrices is selected.

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Figures

Figure 3.10.1 Example of an external matrix file acceptable to the Ssearch program.

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Figure 3.10.2 Histogram of scores: The leftmost numeric column is the score. The center numeric column is the number of times that score occurred within the data search. The rightmost numeric column indicates the number of times that the score was expected to occur in the data library search, after correcting for library sequence length.

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Figure 3.10.3 List of high‐scoring sequences: Information describing the library sequence is presented first, followed by the length of the sequence in parentheses. The next column contains the score of the comparison with the query sequence. The next column presents the score using an information‐content approach. The final column contains the statistical estimate of the likelihood that the match has arisen by chance.

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Literature Cited

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	Altschul, S.F. 1991. Amino acid substitution matricies from an information theoretic perspective. J. Mol. Biol. 219:555‐565
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Key References
	Altschul et al., 1994. See above.
	This review provides detailed information about local alignment statistics, extreme value distributions, scoring matrices, and low‐complexity regions.
	Agarwal and States, 1998. See above.
	This article compares the Smith‐Waterman, FASTA, original BLAST code, WU‐BLAST2, and Probabilistic Smith‐Waterman codes.
	Nicholas et al., 2000. See above.
	This review discusses the advantages and disadvantages of the BLAST, FASTA, and Smith‐Waterman search algorithms, how to select appropriate scoring matrices, scoring insertions and deletions, as well as a few different methods by which statistical significance can be computed.
	Pearson, 1995. See above.
	This article compares FASTA, Smith‐Waterman and original BLAST algorithms in the context of which method did the best job in finding members of 67 different protein superfamilies.
Internet Resources
	ftp://ftp.virginia.edu/pub/fasta/
	The FASTA package (in which the Ssearch code is included) can be obtained here.
	ftp://ftp.ncbi.nih.gov/blast/matrices/
	A variety of protein scoring matrices that can be used with the Ssearch code can be obtained here.