GC-MS Analysis and Prediction of Anti-Inflammatory Effects of Lima Bean (Phaseolus lunatus L.) Chemical Constituents Using Computational Modeling
Abstract
Malnutrition is associated with immune dysfunction and chronic inflammation, which may contribute to adverse health outcomes. This study aimed to explore bioactive compounds derived from lima beans (Phaseolus lunatus L.) with potential relevance to tumor necrosis factor-alpha (TNF-α)-mediated inflammation related to malnutrition using an integrated in silico approach. Bioactive compounds were identified by gas chromatography–mass spectrometry (GC–MS), followed by bioactivity prediction using PASS analysis. Selected compounds were further evaluated through molecular docking against TNF-α, along with in silico absorption, distribution, metabolism, excretion, and toxicity (ADME/toxicity) predictions. GC–MS analysis identified nineteen compounds in the lima bean extract. Based on PASS screening and docking analysis, five compounds were prioritized, including fatty acid ester derivatives and α-linolenic acid-related compounds. Among these, 9,12,15-octadecatrienoic acid, 2-phenyl-1,3-dioxan-5-yl ester exhibited the most favorable predicted binding profile toward TNF-α (ΔG = −6.8 kcal/mol), indicating a weak to moderate interaction typical of preliminary virtual screening. In silico ADME and toxicity predictions suggested generally favorable pharmacokinetic properties with low-to-moderate toxicity, although these results remain predictive in nature. This study provides a hypothesis-generating computational framework linking lima bean-derived compounds to TNF-α-associated inflammatory pathways, supporting future experimental validation.
Keywords: Lima bean; Inflammation; Malnutrition; TNF-α; In silico analysis.
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