Discrete Backbone Elasticity and Curvature-Dependent Torsion Statistics in Proteins

Abstract

Coarse-grained models of protein conformational dynamics use $\mathrm{C}_α$ geometry to describe the probabilities of regular helices, ordered turns and disordered conformations. To test how much of this order arises from local chain elasticity, we connect discrete Hasimoto frame transport to neighboring bending mismatch, deriving a curvature-product torsion interaction and a six-parameter generalized von Mises density. Fitted on 194 chains from the ATLAS molecular dynamics database and tested on 1744 sequence-nonredundant proteins, this baseline reduces negative log-density loss from 1.490 to 1.160 nats, torsion-sign error from 12.26 to 2.15 percentage points, and helix-fraction error from 21.84 to 10.41 points. Ablations and circular-moment projections against reference generative models motivate extensions to anisotropic stiffness and two partition-function-weighted elastic states, further lowering test loss to 0.790 nats and helix error to 1.65 points. The fixed hierarchy also improves predictions across 72 trajectories of peptides $\mathrm{Fs}$ and $(\mathrm{AAQAA})_3$ and miniprotein $\mathrm{HP35}$ spanning varied starting conformations and force fields. Remaining angular errors in these trajectories are associated with atomic context at shared bends, as bend-matched peptide frames adopt opposite torsion signs with distinct hydrogen-bond patterns and a proline-containing $\mathrm{HP35}$ turn remains at negative torsion. Introducing a bilinearly coupled $\mathrm{C}_α$--$\mathrm{N}$ orientation angle largely reparameterizes the existing bend-dependent harmonics, with an empirical $L^2$ Gram projection placing 96.7\% to 99.6\% of its added coefficient-function squared norm within the original six-function span. These results link discrete Hasimoto elasticity to statistical backbone potentials and clarify the scope and limits of local $\mathrm{C}_α$ geometry.