MASIA - Multiple Aligned Sequences Investigation and Analysis

MASIA (Multiple Aligned Sequences Investigation and Analysis) is a program with GUI to search for consistent patterns in multiple aligned sequences. Predictions of secondary structures and inside/outside properties of residues at each position in an aligned sequences are based on generalized rules for globular proteins, which are derived from observations of known 3D-structures of proteins. The secondary and tertiary structure of a protein is related to chemical characteristics of the individual amino acid residues, but a clear picture of the secondary structure may not be apparent for one protein sequence alone. Comparing many aligned, related sequences can reveal patterns of sequence conservation that indicate the location of residues essential for the function, folding or solubility of the protein.

The rules are manipulated as corresponding combinations of commands to predict specific properties. Users can easily extend or create new rules for their specific purposes. Before using MASIA, the best possible alignment of the protein with other proteins in the SWISS-PROT data base should be obtained. MASIA uses as input files generated with PileUP or ClustalW. A recent addition to MASIA is a physical-chemical property (PCP) based motif detection procedure. This procedure helps to detect subtle motifs that are conserved in physical-chemical properties.

This project is supported by the Department of Energy (Grant DE-FG03-96ER62267 & DE-F603-00ER63041) and the Sealy and Smith Foundation.

Mathura, M.S., Schein, C.H. and Braun, W. Identifying property based sequence motifs in protein families and superfamilies: application to DNase-1 related endonucleases. Bioinformatics, 19(11):1381-1390, 2003.
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Schein, C.H., Ozgun, N., Izumi, T. and Braun, W. Total sequence decomposition distinguishes functional modules, "molegos" in apurinic/apyrimidinic endonucleases. BMC Bioinformatics, 3(1):37, 2002.
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Venkatarajan, M.S. and Braun, W. New quantitative descriptors of amino acids based on multidimensional scaling of a large number of physical-chemical properties. J. Mol. Model. 7(12):445-453, 2001.
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Zhu, H., Schein, C.H. and Braun, W. MASIA: recognition of common patterns and properties in multiple aligned protein sequences. Bioinformatics 16(10):950-951, 2000.
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Hanggi, G. & Braun, W. (1994). Pattern recognition and self-correcting distance geometry calculations applied to myohemerythrin. FEBS Letter 34, 147-153.

Mumenthaler, Ch. & Braun, W. (1995). Predicting the helix packing of globular proteins by self-correcting distance geometry. Protein Science 4, 863-871.

Fraczkiewicz, R. & Braun, W. (1998). Exact and efficient analytical calculation of the accessible surface areas and their gradients for macromolecules. J. Comp. Chem. 19, 319-333.

H. Zhu, W. Braun, (1999). Sequence specificity, statistical potentials, and 3D structure prediction with self-correcting distance geometry calculations of ÃŸ-sheet formation in proteins. Protein Science 8, 326-342.