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.
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.
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|Item Type:||Book Chapter|
|Keywords:||Correlation methods, Learning systems, Mathematical models, Molecular structure, Correlation coefficient, Loss of precision, Machine learning model, Protein design, Proteins|
|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|
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