Generalizable Protein Folding Pathway Exploration with DA2-GRASP: Extending Beyond Miniproteins.

OrgNet+: towards robust protein stability prediction with convolutional neural networks

OrgNet+, a conformational ensemble-aware and orientation-gnostic framework that explicitly incorporates protein structure flexibility during training, is introduced, which substantially reduces intra-ensemble prediction variance while simultaneously improving predictive accuracy.

AI-Driven Protein Research: From Prediction to Design.

This mini review traces the evolution of AI-driven methods in protein research, from early residue-contact prediction using coevolutionary information to transformative breakthroughs, the rise of protein language models (PLMs), and the emerging era of generative design and functional modeling.

UniFlow: Unifying protein conformational ensemble generation and machine-learned force fields with a scalable normalizing Flow

Molecular dynamics (MD) provides a principled method for modeling equilibrium protein conformational energy landscapes, but its computational cost limits access to long timescales and larger protein systems. Recently, generative protein ensemble models and machine-learned coarse-grained force fields have emerged as complementary approaches for accelerating conformational sampling. However, they are typically developed separately despite modeling the same underlying equilibrium distribution. We introduce UniFlow, 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. UniFlow employs an internal-coordinate normalizing flow that supports efficient i.i.d. sampling, exact likelihood evaluation, and differentiable energy and force computation. Across diverse protein systems, UniFlow generates ensembles that closely match reference MD simulations, generalizes to proteins beyond its training dataset, and samples substantially faster than diffusion-based ensemble-generation baselines. The same learned density further enables stable long-timescale molecular dynamics simulations. Together, UniFlow paves the way for a unified class of models that bridges generative ensemble modeling with physics-based molecular simulation. Code https://github.com/Harrydirk41/UniFlow.git

Neural spectroscopy of AlphaFold2 reveals encoded protein conformational landscapes

AlphaFold2's 93 million parameters, shaped by the evolutionary record of protein structure encoded in the Protein Data Bank and in sequence alignments, are conventionally treated only as machinery for converting sequence to structure. We propose they are also a scientific object that can be analyzed directly: a learned encoding of protein conformational organization that can be probed and characterized. By smoothing the Evoformer's weight tensors with a Gaussian convolution and scaling the result, we show that the trained model produces physically structured conformational landscapes. Under perturbation, ubiquitin's native contacts break in the order established by decades of folding experiments. For KaiB, five independently trained models agree that the alternative fold is not recovered under perturbation. For alpha-synuclein, five models produce five different but coherent landscapes, mapping where the training signal has determined the representation and where it has not. Matched-power noise controls confirm that random corruption of equal magnitude produces debris, not conformations. The model learned to predict static structures; the conformational organization visible under perturbation was not an explicit training target, suggesting it emerged as a byproduct of that objective. AlphaFold2's weights appear to encode structural constraints, shaped by evolutionary and structural training data, that extend beyond what unperturbed inference reveals. We call the approach of reading them neural spectroscopy, and Scaled Gaussian Convolution one such protocol.

Bridging Discrete and Continuous Protein Folding through Hydrophobicity and Molecular Dynamics

Navigating committor landscape of biomolecules with a general pairwise interaction model

Sampling rare conformation transitions between metastable states is a central challenge in atomistic simulations. While the committor function serve as an ideal reaction coordinate for driving enhanced sampling, their high-dimensional inputs and complex functional forms limit the efficacy of standard feedforward neural networks in modeling them. Inspired by recent breakthroughs in biomolecular structure prediction, we propose a novel committor learning framework grounded in the AlphaFold 3 paradigm. By integrating a lightweight, differentiable atom-level embedding with a simplified Pairformer architecture, our method inherently captures intricate dynamical features of diverse biosystems without requiring specialized prior knowledge. We demonstrate the superior expressiveness and accuracy of the proposed framework across multiple atomistic processes. For the folding of the chignolin mini-protein, our model reveals the finer-grained structure of its transition state ensemble (TSE) and a detailed bifurcated reaction mechanism. Furthermore, for calixarene host-guest systems, we develop a unified committor model that elucidates how ligand substituents regulate the ratio between distinct binding pathways, offering new perspectives for structure-based drug design.