Nucleic Acids Research Advance Access published online on November 4, 2009
Nucleic Acids Research, doi:10.1093/nar/gkp906
Methods Online |
Optimizing nucleotide sequence ensembles for combinatorial protein libraries using a genetic algorithm
1Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716 and 2Centocor Research and Development, Inc, Radnor, PA 19087, USA
*To whom correspondence should be addressed. Tel: +1 302 831 3500; Fax: +1 302 831 8458; Email: lliao{at}cis.udel.edu
Received July 14, 2009. Revised September 17, 2009. Accepted October 7, 2009.
Protein libraries are essential to the field of protein engineering. Increasingly, probabilistic protein design is being used to synthesize combinatorial protein libraries, which allow the protein engineer to explore a vast space of amino acid sequences, while at the same time placing restrictions on the amino acid distributions. To this end, if site-specific amino acid probabilities are input as the target, then the codon nucleotide distributions that match this target distribution can be used to generate a partially randomized gene library. However, it turns out to be a highly nontrivial computational task to find the codon nucleotide distributions that exactly matches a given target distribution of amino acids. We first showed that for any given target distribution an exact solution may not exist at all. Formulated as a constrained optimization problem, we then developed a genetic algorithm-based approach to find codon nucleotide distributions that match as closely as possible to the target amino acid distribution. As compared with the previous gradient descent method on various objective functions, the new method consistently gave more optimized distributions as measured by the relative entropy between the calculated and the target distributions. To simulate the actual lab solutions, new objective functions were designed to allow for two separate sets of codons in seeking a better match to the target amino acid distribution.