GeoDSSOP: Geometry-aware prediction of protein backbone order parameters from sequence and structure
Year: 2026
Type: preprint
Abstract: Backbone amide order parameters (S2) describe protein motions on the picosecond-to-nanosecond time scale at residue resolution. However, NMR measurement and calculation from atomistic molecular dynamics (MD) trajectories are time-consuming. We developed GeoDSSOP (Geometry-aware Deep Sequence–Structure Order-Parameter Predictor) to predict backbone N–H S2 from one amino-acid sequence and one corresponding structure. GeoDSSOP combines frozen ESM-2 residue embeddings with a geometric vector perceptron-style encoder of a residue graph. A gated residual module fuses the sequence and structure representations, and a dilated temporal convolutional network gives a bounded prediction for each residue. We trained these models on 3,490 proteins with MD-iRED labels or S-OPPE pseudo-labels. The two label sources were weighted equally, and homologous proteins were kept in the same data partition. The model gave a protein-macro PCC of 0.8250 on a protected and homology-separated test set of 136 MD-labelled proteins, and 0.8424 on a historical experimental NMR benchmark of ten proteins that was not used to train OPPE, S-OPPE, or GeoDSSOP. In a matched three-run ablation, adding the structure pathway increased the mean protein-macro PCC on MD-iRED validation from 0.7220 to 0.8026. This result shows that one protein structure provides useful information beyond the sequence pathway. Label-source ablations also showed domain-specific performance and asymmetric transfer. Balanced dual-source training gave the best overall performance on the mixed-domain development set. GeoDSSOP can rapidly produce residue-level fast-dynamics profiles from a sequence and one available structure. It does not require a new MD simulation or a multi-conformer structure ensemble, but it remains a statistical predictor and does not replace experiment or trajectory-based analysis.
Source: ChemRxiv
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7207593404