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Published December 2013 | public
Journal Article

Innovation by homologous recombination

Abstract

Swapping fragments among protein homologs can produce chimeric proteins with a wide range of properties, including properties not exhibited by the parents. Computational methods that use information from structures and sequence alignments have been used to design highly functional chimeras and chimera libraries. Recombination has generated proteins with diverse thermostability and mechanical stability, enzyme substrate specificity, and optogenetic properties. Linear regression, Gaussian processes, and support vector machine learning have been used to model sequence-function relationships and predict useful chimeras. These approaches enable engineering of protein chimeras with desired functions, as well as elucidation of the structural basis for these functions.

Additional Information

© 2013 Elsevier Ltd. Available online 29 October 2013. The authors thank Claire Bedbrook for helpful discussions. This work was supported by the Institute for Collaborative Biotechnologies through grant [W911NF-09-D-0001] from the U.S. Army Research and and the National Science Council of Taiwan, R.O.C., through its grant no. NSC 102-3113-P-008-001. DLT is supported by a Canadian National Science and Engineering Research Council post-graduate fellowship. MAS was supported by a Resnick Sustainability Institute fellowship.

Additional details

Created:
August 19, 2023
Modified:
October 25, 2023