Jul 2026· Journal of Chemical Information and Modeling· 0 citations· 43 references
Medicine
TL;DR
TPS-Flow is presented, a physics-guided flow-based generative framework for end point-conditioned conformational path sampling between predefined protein states (not equilibrium ensembles), thereby bridging atomistic simulation and deep generative modeling of protein transition paths.
Abstract
Transition paths between metastable protein states encode both equilibrium structure statistics and dynamical connectivity yet are costly to obtain with molecular dynamics (MD) and remain challenging to emulate with machine learning. Here, we present TPS-Flow, a physics-guided flow-based generative framework for end point-conditioned conformational path sampling between predefined protein states (not equilibrium ensembles). TPS-Flow represents structures as residue-level SE(3) transforms, uses a spatiotemporal gated attention encoder to learn a flow-matching interpolation velocity field from MD trajectories, and incorporates optional energy and structure-aware constraints together with a short physics-based relaxation step. Across a mycobacterial membrane transporter, a monomeric protein, a protein-protein complex, and a soluble enzyme, TPS-Flow preserves residue-wise fluctuation patterns with damped amplitudes and occupies TICA-projected conformational corridors consistent with reference MD, provides conformational coverage complementary to finite reference MD sampling and generates intermediates with reference-comparable docking scores while reducing model size and computational cost compared to a state-of-the-art trajectory generator (MDGen). In an out-of-distribution structural generalization test using PN-subdomain mutants, TPS-Flow preserved fold continuity and wild-type-like global RMSF patterns when conditioned on AF3-derived mutant end point structures, thereby bridging atomistic simulation and deep generative modeling of protein transition paths.
Transition path sampling (TPS) aims to efficiently generate rare molecular transition trajectories between metastable states and is essential for understanding biomolecular mechanisms. Beyond traditional molecular dynamics (MD)-based sampling, machine learning has become central to state-of-the-art TPS. One major class of methods learns control forces during explicit MD rollouts. By preserving the underlying molecular dynamics, these methods tend to produce more physically plausible trajectories than endpoint-conditioned generators that construct paths directly. However, rollout-based control methods have been reported to exhibit unstable and strongly seed-dependent performance. We recast rollout-based control as learning a path-space proposal distribution and investigate stochasticity placement as a design choice for improving exploration and optimization robustness. We develop two stochastic policies: FS-TPS, which directly parameterizes a state-dependent Gaussian distribution over the control policy output, and LaS-TPS, which samples a compact latent control variable and decodes it into structured, cross-atom-correlated force variation. We conduct extensive multi-seed experiments on three biomolecular systems of increasing size: alanine dipeptide, chignolin, and BBL, a fast-folding protein. Stochastic policies consistently improve transition success and path quality over deterministic-policy baselines while substantially reducing sensitivity to random initialization.
Jing-Qian Liu, Yu-Hsiang Wang, Yanru Qu et al.· 0 citations
Pi-Ensemble (Predicting Interpolated Ensemble), a sequence-guided framework for generating protein conformational ensembles interpolating between two structural anchor states, provides an extensible framework for studying protein flexibility, guiding adaptive sampling, and accelerating mechanistic investigations of protein function.
Hassan Nadeem, D. Kleiman, Yu-Ming Zhou et al.· bioRxiv· 0 citations
UniFlow is introduced, the first scalable generative model that unifies protein ensemble generation and machine-learned coarse-grained force fields for molecular dynamics simulation within a single framework, and paves the way for a unified class of models that bridges generative ensemble modeling with physics-based molecular simulation.
Yikai Liu, Ming Chen, Guang Lin· bioRxiv· 0 citations
P pHaseMD4AI is presented, a molecular dynamics dataset that combines a globally equilibrated peptide branch with a protein-scale constant-pH molecular dynamics (CpHMD) branch spanning hundreds of soluble proteins and provides a resource for developing and benchmarking molecular machine learning methods.
Tiefeng Song, Yi-Xin Guo, Jiahao He et al.· bioRxiv· 0 citations
PHASE (Protein Hamiltonians for Sampling of Ensembles), a system-specific framework that converts atomistic conformational ensembles into an explicit and interpretable statistical model, is introduced.
Daniele Angioletti, Marco S. Nobile, Matteo Carli et al.· 0 citations
Proteins are dynamic molecules existing in diverse conformational states underlying their biological functions. Although recent approaches have enabled diverse conformational sampling by emulating molecular dynamics simulations, perturbing evolutionary information, or steering internal mechanisms of structure prediction models, predicting conformations resulting from major domain motions or motions that occur over long timescales still remains a challenge. To this end, we introduce GNMCADS, a conformational sampling strategy that enhances the diversity of protein diffusion models by selectively annealing the conditioning signal guided by the intrinsic dynamical organization of the sampled protein. Further, we implement GNMCADS in the diffusion module of AlphaFold3, enabling the generation of diverse protein conformations. When benchmarked across 92 proteins that include 54 class A GPCRs, 15 transporters, and 23 proteins with major domain movements, GNMCADS exhibits improved sampling diversity compared to other current conformational sampling methods.
Ahmed Selim Uzum, T. Haliloglu· bioRxiv· 0 citations
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