Difference between revisions of "Part:BBa K5255003"

Revision as of 10:04, 30 September 2024

MLACS1

Long-chain acyl-coenzyme A (CoA) synthetases (LACSs) activate free fatty acids to acyl-CoA thioesters and play critical roles in fatty acid metabolism. LACS1 uses DHA, CoA and ATP as substrates to synthesize DHA-CoA. Basic parts MLACS1 (BBa_K5255003) is a mutant of wild-type LACS1 (BBa_K5255000). The sequence was obtained by mutating site 277 of LACS1 from histidine to methionine. In practice, MLACS1 was introduced into Saccharomyces cerevisiae INVSC1 , E.coli BL21 (DE3) and E.coli C43 (DE3) to achieve several goals. The activity of the enzyme expressed by MLACS1 was verified by 2 closely related E.coli and the catalyzed product DHA-CoA was verified by Saccharomyces cerevisiae to ensure the synthesis of DHA-PC and the feasibility of subsequent synthesis of DHA-PC.

Usage & biology

LACS consists a whole family of enzymes from different species, and we first selected a few LACS subtypes in species with the closest relation with our target algae. As LACS serves as an vital role in fatty acid metabolic pathway, its existence has been proven in a variety of species, ranging from prokatyotes like bacteria, to eukaryotes like plants and mammals. Since our project identified a type of fungus, schizochytrium limacinum, we want the engineer object enzyme to be a subtype of LACS that has close phylogeny to our target fungus. A set of characterized subtypes of LACS in protein database were screened out, and we first conducted phylogenetic analysis. Finally, Lacs1 was selected through prediction and experimental analysis.

Lacs1 is derived from Arabidopsis thaliana (Mouse-ear cress) and can play a role in yeast (Pulsifer et al.,2012), promoting the uptake of a very long-chain fatty acid. It also can catalyze the following reaction: A long-chain fatty acid + ATP + CoA = a long-chain fatty acyl-CoA + AMP + diphosphate (Shockey et al., 2002). Lacs1 is located in the endoplasmic reticulum (Hua, 2010) and has high synthase activity against VLCFAs (Very Long-Chain Fatty Acids) C(20)-C(30), among which it has the highest activity against C(30) fatty acids (Lu, 2009). In theory, Lacs1 also has a catalytic effect on DHA (22:6), so we used modeling to predict its mutation sites and improve the affinity of Lacs1 to DHA substrates.

Nucleotide sequence optimization was first performed on Lacs1. After obtaining the nucleotide sequence of Lacs1 from protein database, we first parsed the sequence and intercepted the open reading frame to obtain cDNA, and while discarding the rest of the sequence. We then carried out codon optimization by increasing preferred codons, increasing C/G content, and reducing minor secondary structures including hairpins and internal loops. These modifications resulted in a significant increase in mRNA minimum free energy(MFE), and remained consistent with multiple estimation algorithms, proving a solid improvement in mRNA performance. To sum up, the methods of codon optimization we applied aim to improve Lacs1's efficiency of transcription and translation, which will further contribute to a better production of our target product DHA-PC.

Amino acid sequence optimization with protein engineering methods was then conducted. To find promising mutagenesis sites to modify, we first adopted several traditional semi-rational protein engineering methods on LACS1, including random mutagenesis, alanine scanning, conservation analysis and free energy grading. However, semi-rational approaches have its commonly acknowledged limited accuracy. In order to address that limitation and provide a more reasonable protein engineering solution, we further researched on the catalytic mechanism of LACS, and performed rational engineering methods accordingly, including characterization and mutagenesis of motifs that are critical to the reaction process, modification of residues surrounding the substrate-binding pocket with respect to electric charge, hydrophobicity and volume. The semi-rational and rational methods stated above constitute our integrated protein engineering. Integrating our semi-rational and rational approaches, we then selected the most promising mutagenesis among all the candidating sites. We generated sites as candidates with some of the approaches, graded all candidates using other approaches, and finally selected the mutagenesis with the highest-ranking score. The semi-rational and rational approaches that we applied are all based on solid algorithms and principles, and the selected mutagenesis site is expected to yield improvement in the catalytic activity of Lacs1.