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Publication Type : Journal Article
Thematic Areas : Center for Computational Engineering and Networking (CEN)
Source : Journal of Proteome Research 16(8), June 2017
Url : https://pubmed.ncbi.nlm.nih.gov/28633522/
Campus : Coimbatore
School : Computational Engineering and Networking
Center : Computational Engineering and Networking
Department : Center for Computational Engineering and Networking (CEN)
Verified : No
Year : 2017
Abstract : cis-Peptide bonds, whose occurrence in proteins is rare but evolutionarily conserved, are implicated to play an important role in protein function. This has led to their previous use in a homology-independent, fragment-match-based protein function annotation method. However, proteins are not static molecules; dynamics is integral to their activity. This is nicely epitomized by the geometric isomerization of cis-peptide to trans form for molecular activity. Hence we have incorporated both static (cis-peptide) and dynamics information to improve the prediction of protein molecular function. Our results show that cis-peptide information alone cannot detect functional matches in cases where cis-trans isomerization exists but 3D coordinates have been obtained for only the trans isomer or when the cis-peptide bond is incorrectly assigned as trans. On the contrary, use of dynamics information alone includes false-positive matches for cases where fragments with similar secondary structure show similar dynamics, but the proteins do not share a common function. Combining the two methods reduces errors while detecting the true matches, thereby enhancing the utility of our method in function annotation. A combined approach, therefore, opens up new avenues of improving existing automated function annotation methodologies.
Cite this Research Publication : Sreetama Das, Pratiti Bhadra, Suryanarayanarao Ramakumar, Debnath Pal "Molecular Dynamics Information Improves cis-Peptide-Based Function Annotation of Proteins", Journal of Proteome Research 16(8), June 2017