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Optimizing nucleotide sequence ensembles for combinatorial protein libraries using a genetic algorithm.

ABSTRACT: 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.

SUBMITTER: Craig RA

PROVIDER: S-EPMC2811015 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature

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Optimizing nucleotide sequence ensembles for combinatorial protein libraries using a genetic algorithm.

Craig Roger A RA Lu Jin J Luo Jinquan J Shi Lei L Liao Li L

Nucleic acids research 20091104 2

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 ...[more]

PMID: 19889723

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Project description:BackgroundAntibody fragments can be expressed in Escherichia coli, where they are commonly directed to the periplasm via Sec pathway to enable disulphide bridge formations and correct folding. In order to transport antibody fragments to the periplasmic space via Sec pathway, they are equipped with N-terminal signal sequence. Periplasmic expression has many benefits but it's also subjected to many hurdles like inefficient translocation across the inner membrane and insufficient capacity of the translocation system. One solution to overcome these hurdles is a modulation of codon usage of signal sequence which has proved to be an efficient way of tuning the translocation process. Modulation of codon usage of signal sequences has been successfully employed also in improving the expression levels of antibody fragments, but unfortunately the effect of codon usage on the expression has not been thoroughly analyzed.ResultsIn the present study we established three synonymous PelB signal sequence libraries by modulating codon usage of light chain and heavy chain PelB signal sequences of a Fab fragment. Each region (n-region, hydrophobic region and c-region) of the PelB signal sequence in the both chains of the Fab fragment in a bicistronic expression vector was mutated separately. We then screened for clones with improved expression profile. The best source for improved clones was the n-region library but in general, improved clones were obtained from all of the three libraries. After screening, we analyzed the effects of codon usage and mRNA secondary structures of chosen clones on the expression levels of the Fab fragment. When it comes to codon usage based factors, it was discovered that especially codon usage of fifth leucine position of the light chain PelB affects the expression levels of Fab fragment. In addition, we observed that mRNA secondary structures in the translation initiation regions of the light and heavy chain have an effect on expression levels as well.ConclusionsIn conclusion, the established synonymous signal sequence libraries are good sources for discovering Fab fragments with improved expression profile and obtaining new codon usage related information.

Project description:BackgroundGenomic sequence data cannot be fully appreciated in isolation. Comparative genomics--the practice of comparing genomic sequences from different species--plays an increasingly important role in understanding the genotypic differences between species that result in phenotypic differences as well as in revealing patterns of evolutionary relationships. One of the major challenges in comparative genomics is producing a high-quality alignment between two or more related genomic sequences. In recent years, a number of tools have been developed for aligning large genomic sequences. Most utilize heuristic strategies to identify a series of strong sequence similarities, which are then used as anchors to align the regions between the anchor points. The resulting alignment is globally correct, but in many cases is suboptimal locally. We describe a new program, GenAlignRefine, which improves the overall quality of global multiple alignments by using a genetic algorithm to improve local regions of alignment. Regions of low quality are identified, realigned using the program T-Coffee, and then refined using a genetic algorithm. Because a better COFFEE (Consistency based Objective Function For alignmEnt Evaluation) score generally reflects greater alignment quality, the algorithm searches for an alignment that yields a better COFFEE score. To improve the intrinsic slowness of the genetic algorithm, GenAlignRefine was implemented as a parallel, cluster-based program.ResultsWe tested the GenAlignRefine algorithm by running it on a Linux cluster to refine sequences from a simulation, as well as refine a multiple alignment of 15 Orthopoxvirus genomic sequences approximately 260,000 nucleotides in length that initially had been aligned by Multi-LAGAN. It took approximately 150 minutes for a 40-processor Linux cluster to optimize some 200 fuzzy (poorly aligned) regions of the orthopoxvirus alignment. Overall sequence identity increased only slightly; but significantly, this occurred at the same time that the overall alignment length decreased--through the removal of gaps--by approximately 200 gapped regions representing roughly 1,300 gaps.ConclusionWe have implemented a genetic algorithm in parallel mode to optimize multiple genomic sequence alignments initially generated by various alignment tools. Benchmarking experiments showed that the refinement algorithm improved genomic sequence alignments within a reasonable period of time.

Dataset Information

Optimizing nucleotide sequence ensembles for combinatorial protein libraries using a genetic algorithm.

Publications

Optimizing nucleotide sequence ensembles for combinatorial protein libraries using a genetic algorithm.

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