A Probabilistic Model of Disulfide-Bonded Cysteine Pairs in Proteins
Piyumi R Amarasinghe1, Arthur M Lesk2, Lloyd Allison1
1Department of Data Science and Artificial Intelligence, Monash University, Clayton, VIC 3800, Australia.
Abstract:
We present a closed-form joint probability distribution of disulfide-bonded cysteine-cysteine conformations in proteins. The model is inferred under a Bayesian framework and extends the dihedral conformation verification capabilities of PhiSiCal-Checkup [1], previously restricted to individual amino acid conformations, to now include disulfide-bonded cysteine pairs. It further permits rapid, on-demand analytical derivation of marginal and conditional distributions over any subset of dihedral conformational angles, enabling efficient probability evaluation and random sampling of disulfide conformations. These capabilities provide a unified probabilistic framework for downstream analyses, including modelling, refinement, simulation, and design. Software to analyze disulfide bonded cysteine conformations is accessible fromhttps://lcb.infotech.monash.edu/phisical/disulfide/.
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