A reinforced merging methodology for mapping unique peptide motifs in members of protein families View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2006-12

AUTHORS

Hao-Teng Chang, Tun-Wen Pai, Tan-chi Fan, Bo-Han Su, Pei-Chih Wu, Chuan-Yi Tang, Chun-Tien Chang, Shi-Hwei Liu, Margaret Dah-Tsyr Chang

ABSTRACT

BACKGROUND: Members of a protein family often have highly conserved sequences; most of these sequences carry identical biological functions and possess similar three-dimensional (3-D) structures. However, enzymes with high sequence identity may acquire differential functions other than the common catalytic ability. It is probable that each of their variable regions consists of a unique peptide motif (UPM), which selectively interacts with other cellular proteins, rendering additional biological activities. The ability to identify and localize such UPMs is paramount in recognizing the characteristic role of each member of a protein family. RESULTS: We have developed a reinforced merging algorithm (RMA) with which non-gapped UPMs were identified in a variety of query protein sequences including members of human ribonuclease A (RNaseA), epidermal growth factor receptor (EGFR), matrix metalloproteinase (MMP), and Sma-and-Mad related protein families (Smad). The UPMs generally occupy specific positions in the resolved 3-D structures, especially the loop regions on the structural surfaces. These motifs coincide with the recognition sites for antibodies, as the epitopes of four monoclonal antibodies and two polyclonal antibodies were shown to overlap with the UPMs. Most of the UPMs were found to correlate well with the potential antigenic regions predicted by PROTEAN. Furthermore, an accuracy of 70% can be achieved in terms of mapping a UPM to an epitope. CONCLUSION: Our study provides a bioinformatic approach for searching and predicting potential epitopes and interacting motifs that distinguish different members of a protein family. More... »

PAGES

38

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-7-38

DOI

http://dx.doi.org/10.1186/1471-2105-7-38

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1045636772

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/16433931


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