A novel knowledge-based approach to design inorganic-binding peptides.
BACKGROUND
The discovery of solid-binding peptide sequences is accelerating along with their practical applications in biotechnology and materials sciences. A better understanding of the relationships between the peptide sequences and their binding affinities or specificities will enable further design of novel peptides with selected properties of interest both in engineering and medicine.


RESULTS
A bioinformatics approach was developed to classify peptides selected by in vivo techniques according to their inorganic solid-binding properties. Our approach performs all-against-all comparisons of experimentally selected peptides with short amino acid sequences that were categorized for their binding affinity and scores the alignments using sequence similarity scoring matrices. We generated novel scoring matrices that optimize the similarities within the strong-binding peptide sequences and the differences between the strong- and weak-binding peptide sequences. Using the scoring matrices thus generated, a given peptide is classified based on the sequence similarity to a set of experimentally selected peptides. We demonstrate the new approach by classifying experimentally characterized quartz-binding peptides and computationally designing new sequences with specific affinities. Experimental verifications of binding of these computationally designed peptides confirm our predictions with high accuracy. We further show that our approach is a general one and can be used to design new sequences that bind to a given inorganic solid with predictable and enhanced affinity.
Oren
Ersin Emre
EE
Materials Science and Engineering Department, University of Washington, Seattle, USA.
Tamerler
Candan
C
Sahin
Deniz
D
Hnilova
Marketa
M
Seker
Urartu Ozgur Safak
UO
Sarikaya
Mehmet
M
Samudrala
Ram
R
D000595:Amino Acid Sequence; D001665:Binding Sites; D003198:Computer Simulation; D007287:Inorganic Chemicals / Q000737:chemistry*; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D010455:Peptides / Q000737:chemistry*; D011485:Protein Binding; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*
D007287:Inorganic Chemicals; D010455:Peptides