Library of peptides: Analysis of a complex reactive mixture with the use of statistical physics and inference
arXiv:2611.17130
Laura Messio, Taleen Peker, Roba Moumné, Hélène Berthoumieux
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A self-assembled peptide mixture can hold dozens of species whose spectra sit on top of each other. A Potts-style energy model plus the law of mass action reads an 81-peptide library from 13 overlapping peaks.
Read free explainer →Self-assembled mixtures enable the exploration of vast chemical diversity, yet characterizing the hundreds of coexisting species they contain remains a major challenge. Standard analytical techniques such as chromatography and spectroscopy produce overlapping signals that cannot be resolved by conventional deconvolution methods, which neglect the concentration correlations imposed by the reactive network. Here, we introduce a framework that bridges statistical physics and analytical chemistry to deconvolute complex self-assembled mixtures from partial spectral data. Inspired by Potts models of protein folding, we express the chemical potential of each compound as a function of a reduced set of parameters-intrinsic building-block energies and pairwise coupling terms between neighboring building blocks in the sequence. Coupling this model with the law of mass action yields equilibrium concentrations across the entire reactive network. The model parameters can be infered by fitting predicted spectroscopic signals to experimentally measured partial data. We demonstrate this approach using dynamic combinatorial libraries (DCLs) of peptides as a model system. Applied to a DCL of 81 peptides, our method resolves the full mixture composition from just 13 overlapping peaks, revealing that lateral couplings dominate the assembly energetics. Beyond DCLs, this strategy offers a general route for analyzing self-assembled systems from partial data and extends naturally to target-driven identification of high-affinity binders.