TL;DR
It is concluded that molecular dynamics has an important place in improving the physicality of existing protein structure prediction paradigms, leading to the development of the Subspace Relaxation Operator (SRO).
This framework provides a clearer understanding of how methodological shifts have shaped the capabilities, limitations, and practical roles of recent models.
The (un)folding rates of natural proteins determine their native stability and functional homeostasis, making them important targets for protein engineering and design. From a prediction standpoint, the rates have been a long‐standing puzzle. We have known for decades that folding rates empirically correlate with properties of the native three dimensional (3D) structures and that both, folding and unfolding rates, scale with protein size. Whereas such rate correlations are too rough for being of practical use, no significant progress in prediction accuracy has occurred since then, despite many efforts even including machine learning approaches. Here, we retake on this challenge by expanding the simple one‐dimensional free energy surface (1D‐FES) model that originally led to demonstrate the size scaling of both rates, and a curated database with rates for 75 single‐domain proteins. We define the weighted sequence order (WSO) as a novel parameter that allows incorporating structural information into the 1D‐FES model explicitly. Via the WSO, we examine the role of global structural properties such as fold topology and core packing in defining the (un)folding rates within the context of a physics‐based model of protein folding. After introducing fold topology and packing at a coarse‐grained level, the model uses three floating parameters to predict the folding and unfolding rates within 6.5‐ and 10‐fold, respectively, resulting in ±6.5 kJ/mol accuracy in native stability, equivalent to the typical perturbation induced by one single‐point mutation. The net improvement over the 2‐parameter size‐only prediction is of 2.5‐fold. These new rate predictions are significantly closer to the threshold of usefulness for engineering and design. More importantly, this WSO‐modified 1D‐FES model can now directly accommodate atomistic, high‐resolution, force‐fields to further optimize the rate predictions, and/or to use rate information as a testbed for force‐field refinement. Finally, the WSO‐1D‐FES model could also serve as foundation for developing more complex models capable of dealing with multi‐domain proteins as well as with the evolutionary information cryptically encoded in natural protein sequences.
Vilya-2 is the structure-prediction oracle that de novo peptide design pipelines require--establishing the all-atom approach as a general foundation for the design and evaluation of de novo peptide therapeutics.
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.
Recent AI advances have enabled protein structure prediction at near-experimental accuracy, largely solving the problem of identifying a dominant conformation from sequence. Many proteins, however, function as dynamic systems populating multiple conformational states with activity emerging from shifts in relative occupancy--an incomplete picture when reduced to one structure. Here, we argue that structure prediction should be reformulated as a state-space inference problem: recovering not one conformation's coordinates but accessible states, their energetic and kinetic relationships, context dependence, and responses to perturbations. We review emerging strategies--deep learning ensemble generators, physics-based simulations, and experimental constraints--and outline a roadmap toward state-space prediction.
Ordered water molecules mediate many protein functions including stability, ligand binding, and catalysis. Predicting their positions with sub-angstrom accuracy would support protein design, binding affinity prediction, and automated model building in X-ray crystallography and cryo-EM. However, water molecule prediction lags behind protein and other molecule structure predictions. We introduce WaterFlow, a flow-matching-based generator model and confidence model for predicting the positions of ordered water molecules in protein structures. WaterFlow outperforms the existing state of the art at every precision level. We demonstrate that this model not only predicts ground truth modeled water molecules, including those around ligands, but also fits the underlying experimental data well, and therefore proposes that it may be used for both prediction and modeling water molecules. We demonstrate that WaterFlow’s novel predictions are often associated with positive electron difference density, meaning the model places water molecules at sites the original structure depositions omitted. We use this improved model to address the data constraint. By mapping the Pareto front of achievable accuracy of water molecule prediction, alongside analysis of different training data schemas, we quantified the tradeoff between data quantity and data quality, demonstrating the diversity of high quality structures is limiting the results possible. Overall, WaterFlow predicts ordered water to serve as a solvent module for structure-based drug design, and predicted structures, as well as for water molecule placement during crystallographic refinement.