Discerning of Antimicrobial Peptides from Whole Genome Sequenced Temperate Fruit Crops
Description
Antimicrobial peptide sequences from 5 temperate fruit crops (apple, blueberry, peach, raspberry and strawberry) were extracted in-silico. Two methods was used to extract the sequences, homology prediction as well as machine learning prediction using software tools. The extracted possible AMP sequences were analyzed for their physico-chemical properties and then categorized according to the same. All the possible AMP sequences are stored in a categorized worksheet. The data can be carried forward for further computational research or can be synthesized for wet lab works.
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Database of AMPs were obtained from the General AMPs of DRAMP database and converted to fasta format. The crop whole genome was retrieved and stored as fasta format. Commandline BLAST was used to annotate the crop protein file to the DRAMP database, the sequences obtained were filtered with e-value less than 1 and identity percentage greater than or equal to 50. TransDecoder was used to obtain the longest ORFs from the whole genome file of each crop, the obtained file was converted to fasta format and taken as input for Macrel. Macrel was used for the machine learning based prediction method. The data obtained from both BLAST as well as Macrel were stored in an excel worksheet and the physico-chemical properties of each sequence was found out from DBAASP database. The sequences were then categorized based upon their physico-chemical parameters.