Abstract
Antimicrobial peptides (AMPs) are peptides commonly found in organism immune systems, fighting off bacterial, fungal, and other infections. Their potential for application in the medical field includes combating antibacterial resistance and developing novel oral ingestible options to decrease toxicity in infection treatment. A major difficulty in working with AMPs is their high instability and expensive storage procedures, which create challenges in lab environments and experimental procedures. Additionally, high AMP instability can reduce effectiveness and create safety risks in medicinal applications. Previous models created do not specifically target factors that affect AMP stability, such as chemical structure, size, shape, hydrophobicity, temperature, and more. To holistically scale the results of the model runs, a new "stability score" system ranging from 0 (least stable) to 1 (most stable) was developed to easily determine the st ability of the inputed sequence. Two key tools used in this study included the DeepSTABp model for assessing thermal stability and the ThermoFisher Peptide Synthesis and Proteotypic Peptide Analyzing Tool for evaluating chemical composition. Data samples of amino acid sequences were selected from various thermal stability model datasets with a uniform size distribution. The DeepSTABp results were further complemented by the Peptide Analyzing Tool to analyze chemical composition and compatibility. Results were then analyzed and scaled based on their hydrophobicity and thermal stability, resulting in a stability score ranging from 0 to 1. Through this study, stability scores for amino acid prediction were standardized and evaluated based on multiple factors.
How to Cite:
, J. & , I.,
(2023) “Sequence-based Models of Antimicrobial Peptide Stability ”,
Journal of Student-Scientists' Research 5.
doi: https://doi.org/10.13021/jssr2023.3981
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