A novel committor learning framework grounded in the AlphaFold 3 paradigm is proposed that elucidates how ligand substituents regulate the ratio between distinct binding pathways, offering new perspectives for structure-based drug design.
Abstract
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.
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
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
This study presents a method to derive optimized CV from transition state region (TS) via an interpretable machine learning (ML) model, Elastic Net, which greatly accelerate ligand binding-unbinding transitions and achieves rapid free energy surface (FES) convergence across diverse systems.
An improved force field is developed, derived from its parent, Amber ff24EXP-GA, and its evaluation against Amber ff14SB and other contemporary force fields, such as CHARMM36m, in capturing the empirically determined conformational properties of unfolded systems: short peptides that serve as model systems for IDPs, and longer unfolded proteins.
Modelling sequences both “dry” and in the presence of explicit potassium cations are suggested as a simple, practical way to sample alternative conformations and to expose disordered regions that current predictors tend to over-fold.
Conformational dynamics in toxin inhibitors are an important contributor to ion channel affinity, yet toxin multidimensional energy landscapes remain largely unexplored. In the current work, we combine parallel-bias metadynamics-metainference (PBMetaD) simulations with relaxation dispersion NMR to define, at atomistic resolution, the thermodynamics and kinetics of Hui1, a de novo three disulfide toxin derived from the SAK-I family that targets K+-channels. Using the three χ3 disulfide dihedrals as collective variables, an extensive 48-replica well-tempered PBmetaD simulation (16.2 μs cumulative sampling) resulted in a fully converged three-dimensional (3D) free-energy surface comprising eight Hui1 conformers. These basins account for ∼96% of the bias-weighted ensemble and partition into four low- and four high-energy states separated by 7.5 kJ/mol associated with the (-) and (+)Cys12-Cys28 χ3 states, respectively. Transition-state theory identifies rota-isomerization of Cys3-Cys35 as the slowest, and therefore rate-determining, coordinate, while the analogous motions around Cys12-Cys28 and Cys17-Cys32 are ∼5-fold faster. 15N R1ρ relaxation dispersion NMR measurements confirmed these kinetics, identifying two structurally distinct residue clusters exhibiting intermediate and faster exchange processes. The Key Interaction Finder (KIF) approach reveals that Cys17-Cys32 conformation modifies connectivities within a dense interaction network between Cys17 and residues Gln14, Tyr23, Arg24, and Lys29, and correlates with the accessibility of residues Tyr23 and Arg24 of the helix-kink-helix region for interaction with the channel vestibule. Our work establishes PBMetaD as a powerful framework for mapping coupled disulfide and backbone dynamics in toxins and reveals specific conformers and interactions likely to control K+ channel recognition.
Chen Timsit Shmueli, Miriam Gulman, D. T. Major et al.· Journal of the American Chem...· 0 citations
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