Tianshu Yu

Protein function depends on ordered transitions between conformational states, yet molecular dynamics sampling remains costly across large protein panels. TEMPO splits generation into two levels: a slow-scale model advances anchor conformations at the 20-ns training stride, and a fast-scale model generates 1-ns-spaced frames conditioned on those anchors. On held-out mdCATH and ATLAS proteins it reproduced MD ensemble observables; against a single-scale autoregressive model, 400-frame long-horizon RMSD error fell from 8.62 to 1.78 Å, and a 400-frame rollout took under 1 min.

Protein function depends on ordered transitions between conformational states, yet molecular dynamics sampling remains costly across large protein panels. TEMPO splits generation into two levels: a slow-scale model advances anchor conformations at the 20-ns training stride, and a fast-scale model generates 1-ns-spaced frames conditioned on those anchors. On held-out mdCATH and ATLAS proteins it reproduced MD ensemble observables; against a single-scale autoregressive model, 400-frame long-horizon RMSD error fell from 8.62 to 1.78 Å, and a 400-frame rollout took under 1 min.