Abstract EANA2025-100

Organic molecules such as amino acids and peptides are produced both by life as we know it (LAWKI) and via abiotic processes ranging from hydrothermal reactions in deep-sea vents to extraterrestrial synthesis in interstellar clouds, meteorites, and comets. A key challenge in molecular biosignature detection is distinguishing between these origins to confidently interpret organic detections in space missions targeting ancient or extant life beyond Earth.

To overcome this limitation, we developed LUMOS (Life Unveiled via Molecular Orbital Signatures), a statistical framework that distinguishes between biotic and abiotic origins by analyzing the distribution of the energy gaps between the highest occupied molecular orbitals (HOMO) and the lowest unoccupied molecular orbitals (LUMO). This framework employs unweighted and weighted approaches: the first assesses the distribution of HOMO-LUMO gap (HLG) values of the amino acids present within a sample, while the latter weigths each amino acid’s HLG value by its abundance. For weighting, we employed three statistical metrics capturing central tendency (weighted mean), distribution spread (weighted variance), and inequality (gini coefficient) among abundance-weighted HLG values. Abiotic and biotic class separation was further assessed using relative entropy, receiver operating characteristic (ROC) curve analysis, and machine learning models.

Analysis of 189 amino acid profiles revealed that abiotic samples (n = 102) exhibit a highly uniform HLG distribution, while biotic samples (n = 87) showed more variable and lower HLG values, likely reflecting LAWKI’s need to control molecules interactions and chemical reactions. Consequently, LUMOS achieved >95% accuracy in separating biotic from abiotc samples across diverse environmental and extraterrestrial contexts. To test practical implementation in space missions, we performed Monte Carlo simulations, using the amino acids profiles from our database, to establish guidelines for applying LUMOS based on the number of amino acids detected and their resulting statistical assessment.

Furthermore, we extended the framework to peptides, analyzing 106 compounds reported in nature (n = 56) and detected in experimental abiotic simulations (n = 50). This preliminary analysis revealed variable size-dependent HLG distributions for abiotic and biotic peptides (2-5 residues), with biotic peptides occupying specific non-overlapping regions toward lower HLG values. Collectively, these results support the hypothesis that life overcomes thermodynamic barriers to maintain functional systems.

Based on our findings, we suggest that varied chemical reactivity of cellular machinery may represent a universal feature of life, constituting an agnostic biosignature independent of specific amino acid compositions. Importantly, LUMOS is compatible with existing analytical instrumentation for both returned samples and in situ analyses. Moving forward, broader sampling of abiotic and biotic environments will refine the boundaries distinguishing these chemical systems, thereby enhancing biosignature detection capabilities for astrobiology missions.