Comput. Biol. Med. - New layers in understanding and predicting a-linolenic acid content in plants using amino acid characteristics of omega-3 fatty acid desaturase.

Tópicos

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Resumo

a-linolenic acid (ALA) is the most frequent omega-3 in plants. The content of ALA is highly variable, ranging from 0 to 1% in rice and corn to >50% in perilla and flax. ALA production is strongly correlated with the enzymatic activity of omega-3 fatty acid desaturase. To unravel the underlying mechanisms of omega-3 diversity, 895 protein features of omega-3 fatty acid desaturase were compared between plants with high and low omega-3. Attribute weighting showed that this enzyme in plants with high omega-3 content has higher amounts of Lys, Lys-Phe, and Pro-Asn but lower Aliphatic index, Gly-His, and Pro-Leu. The Random Forest model with Accuracy criterion when run on the dataset pre-filtered with Info Gain algorithm was the best model in distinguishing high omega-3 content based on the frequency of Lys-Lys in the structure of fatty acid desaturase. Interestingly, the discriminant function algorithm could predict the level of omega-3 only based on the six important selected attributes (out of 895 protein attributes) of fatty acid desaturase with 75% accuracy. We developed "Plant omega3 predictor" to predict the content of a-linolenic acid based on structural features of omega-3 fatty acid desaturase. The software calculates the 6 key structural protein features from imported Fasta sequence of omega-3 fatty acid desaturase or utilizes the imported features and predicts the ALA content using discriminant function formula. This work unravels an underpinning mechanism of omega-3 diversity via discovery of the key protein attributes in the structure of omega-3 desaturase offering a new approach to obtain higher omega-3 content.

Resumo Limpo

alinolen acid ala frequent omega plant content ala high variabl rang rice corn perilla flax ala product strong correl enzymat activ omega fatti acid desaturas unravel under mechan omega divers protein featur omega fatti acid desaturas compar plant high low omega attribut weight show enzym plant high omega content higher amount lys lysph proasn lower aliphat index glyhi proleu random forest model accuraci criterion run dataset prefilt info gain algorithm best model distinguish high omega content base frequenc lysli structur fatti acid desaturas interest discrimin function algorithm predict level omega base six import select attribut protein attribut fatti acid desaturas accuraci develop plant omega predictor predict content alinolen acid base structur featur omega fatti acid desaturas softwar calcul key structur protein featur import fasta sequenc omega fatti acid desaturas util import featur predict ala content use discrimin function formula work unravel underpin mechan omega divers via discoveri key protein attribut structur omega desaturas offer new approach obtain higher omega content

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