Predicting structural disruption of proteins caused by crossover

Bauer, D.C., Bodén, M., Thier, R., & Yuan, Z. (2005) Predicting structural disruption of proteins caused by crossover. In Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2005. CIBCB '05. IEEE.

View at publisher


We present a machine learning model that predicts a structural disruption score from a protein s primary structure. SCHEMA was introduced by Frances Arnold and colleagues as a method for determining putative recombination sites of a protein on the basis of the full (PDB) description of its structure. The present method provides an alternative to SCHEMA that is able to determine the same score from sequence data only. Circumventing the need for resolving the full structure enables the exploration of yet unresolved and even hypothetical sequences for protein design efforts. Deriving the SCHEMA score from a primary structure is achieved using a two step approach: first predicting a secondary structure from the sequence and then predicting the SCHEMA score from the predicted secondary structure. The correlation coefficient for the prediction is 0.88 and indicates the feasibility of replacing SCHEMA with little loss of precision.

Impact and interest:

0 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 77439
Item Type: Book Chapter
Keywords: Correlation methods, Learning systems, Mathematical models, Molecular structure, Correlation coefficient, Loss of precision, Machine learning model, Protein design, Proteins
DOI: 10.1109/CIBCB.2005.1594962
ISBN: 9780780393875
Divisions: Current > Schools > School of Clinical Sciences
Current > QUT Faculties and Divisions > Faculty of Health
Copyright Owner: Copyright 2005 IEEE.
Deposited On: 20 Oct 2014 23:43
Last Modified: 28 Oct 2015 16:30

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page