Version 1.0 Updated on 5/25/2007 Contact: George Karypis karypis@cs.umn.edu

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You may want to try our new MONSTER server that besides YASSPP-based secondary structure prediction also provides predictions for other structural and functional properties of a protein's residues.

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Name:
		A name by which you can easily identify this sequence.
Sequence:
		The amino acid sequence whose secondary structure will like to predict. Valid amino acid codes: ARNDCQEGHILKMFPSTWYVBZX. Spaces in the sequence are not significant.
Email:
		A valid email address to email back the prediction results.
Advanced Settings:		Training Set Input Coding Scheme PSI-Blast DB Astral25 Family Astral25 Superfamily PB396 PB513 PSSM+BLOSUM62 PSSM NCBI nr Swissprot
		Selects among the different pre-trained YASSPP models. Reasonable defaults have already being selected, but you can always experiment with the different choices.

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Reference:

Karypis G. "YASSPP: Better kernels and coding schemes lead to improvements in SVM-based secondary structure prediction". Proteins: Structure, Function, and Bioinformatics, Vol 64, Issue 3, pp. 575-586, 2006.

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