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.
Abstract
Protein function emerges from dynamic conformational ensembles and transitions that are challenging to characterize experimentally and computationally. Recent advances in generative AI have created new opportunities for learning molecular thermodynamics, kinetics, and conformational evolution directly from simulation data, but progress is limited by the availability of large-scale datasets that combine rigorous sampling, complete phase-space information, and diverse physicochemical perturbations. Here, we present pHaseMD4AI, 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. The peptide branch includes a complete set of canonical tripeptide and tetrapeptide systems together with post-translationally modified (PTM) and protonation-state datasets, providing synchronized atomic coordinates (R), velocities (V), forces (F), and Markov state model-based kinetic annotations. An accompanying web portal (https://isb.zju.edu.cn/md4ai/) enables users to browse, visualize, and download trajectories, annotations, and metadata. As an example application, we demonstrate a sequence-based model that can predict residue-level equilibrium dihedral distributions from sequence. pHaseMD4AI provides a resource for developing and benchmarking molecular machine learning methods while supporting broader studies of biomolecular dynamics under sequence, post-translational modification, and protonation-state perturbations.
A new physics-informed representation using Fourier transforms as an inductive bias for the multiscale temporal nature of protein dynamics, DynaMode is presented, achieving strong performance across a set of ensemble-based metrics.
H. Phipps, M. Cagiada, S. Villalba 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, Yuming Zhou et al.· bioRxiv· 0 citations
DynaPPI, a dynamic protein dataset comprising molecular dynamics trajectories of protein complex formation from dissociated chains to the bound state, is presented as a pivotal resource to bridge the gap between static structural biology and the inherently temporal nature of dynamic molecular interactions.
Jiabao Wei, Zilong Geng, Yuze Wang et al.· 0 citations
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.
Kai Xu, Likun Zhao, Yanan Tian et al.· Journal of Chemical Informat...· 0 citations
It is argued that, since physics-based simulations and machine learning provide complementary approximations to the underlying probability distribution associated with biomolecular recognition events, and they excel respectively in consistency with free-energy landscapes and state populations and in predictive accuracy, the central challenge for the coming decade will be integrating them into hybrid frameworks that are scalable and transferable.
R. Khalil, Elena Frasnetti, Han Kurt et al.· Journal of Physical Chemistr...· 0 citations
It is demonstrated that BioEmu can generate plausible conformational ensembles for relatively large, six-and seven-pass membrane proteins, sampling rare states at a fraction of the computational cost of conventional MD simulations, suggesting that AI-based ensemble generation could provide an accessible approach for exploring membrane protein dynamics and complement conventional molecular modelling approaches.
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