arXiv subjects

Christophe Chipot

Publications and source records attributed to Christophe Chipot.

10 recordsLinked to original sources

arXiv2026

Learning multistate kinetics with a variational multistate committor network

The long-time dynamics of complex molecular systems often involves rare transitions across networks of metastable states. Building on transition-path theory, which provides a rigorous framework for describing rare transitions between two metastable states, we introduce the variational multistate committor network (VMCN), a neural framework that learns the probabilities of reaching each metastable state directly from molecular simulation data. From this representation, VMCN identifies state-specific commitment, candidate transition regions and committor-consistent pathways between state pairs, and an effective kinetic network characterized by transition rates. The model is trained using finite time-lag trajectory data together with boundary conditions defined on conservative state cores. Applications to a triple-well potential, trialanine isomerization, and the $c$--ring rotation in the V$_{\rm o}$ domain of a vacuolar ATPase show that VMCN recovers metastable organization, provides committor-consistent descriptions of transition mechanisms, and estimates state-to-state kinetics. VMCN further provides diagnostics for incomplete state decompositions and enables adaptive exploration of candidate metastable states and their connecting regions. By integrating VMCN with generative committor-guided path sampling (Gen-COMPAS) for chignolin, we start from two end point structures, identify a misfolded state and a candidate folding intermediate, and we direct subsequent sampling toward the resulting multistate transition network.

physics.chem-ph↗

arXiv2026

A self-adaptive tube restraint for free-energy calculations along path collective variables

Path collective variables (PCVs) reduce a high-dimensional transition to a progress coordinate, s, and an orthogonal distance, z. Computing the free energy along s often requires restraining z, so that sampling embraces a tube centered on the reference path. However, a conventional fixed half-harmonic wall demands an a priori tube width that is both system dependent and s-dependent. Too tight a tube biases the path-projected free energy. Conversely, too loose a tube induces numerical instability and inter-channel leakage. We present an adaptive tube restraint the half-width of which evolves on the fly to track a fixed contour {\Delta}F* of the orthogonal free energy F(z|s), widening automatically in flat basins and narrowing at pinched saddles, with minimalist user intervention. We prove that, for a locally harmonic perpendicular well, a contour-following tube captures an s-independent fraction of the orthogonal partition function, so that in the hard-wall limit, its bias cancels out. In stark contrast, the bias of a fixed-width tube varies with s and distorts the free-energy landscape. We probe the algorithm on several model potentials, on N-Acetyl-N'-methylalanylamide isomerization, on the folding of the mini-protein chignolin, and on the folding-upon-binding of the ribose-binding protein. The method is implemented in the open-source Colvars library, and is, therefore, usable within popular MD engines, such as NAMD, LAMMPS, GROMACS, and Tinker-HP.

physics.chem-ph↗

arXiv2026

A Force-Kernel Reformulation of the Extended-System Adaptive Biasing Force for Free-Energy Calculations

We introduce force-kernel extended-system adaptive biasing force (FK-eABF), a force-based kernel reformulation of eABF that replaces the histogram-based mean-force accumulator of conventional eABF with a sparse population of Gaussian kernels storing local running-mean forces. Biasing forces are recovered by Nadaraya-Watson regression, yielding smooth estimates from the earliest stages of a simulation without a minimum-count threshold, while the same kernel population also defines an auxiliary, self-attenuating exploration force that requires no prior knowledge of barrier heights. On N-acetyl-N'-methylalanylamide in explicit water, FK-eABF achieves full free-energy landscape coverage faster than well-tempered metadynamics (WT-MetaD), on-the-fly probability enhanced sampling (OPES), and WTM-eABF, while all four methods converge to comparable accuracy given sufficient time. FK-eABF also retains long-time accuracy: on the DFG-in/out transition of Abl1 kinase, multi-microsecond simulations recover the established near-isoenergetic balance between states. At the opposite extreme, applied to the electrocyclic ring closure of 1,3-butadiene at the ab initio molecular dynamics level, FK-eABF recovers the free-energy landscape within 30 ps. Together, these benchmarks, spanning more than four orders of magnitude in simulation time, establish FK-eABF as more than a kernelized implementation of eABF: A force-based kernel reformulation that delivers faster early-time convergence without sacrificing long-time quantitative accuracy.

physics.chem-ph↗

arXiv2025

From Static Pathways to Dynamic Mechanisms: A Committor-Based Data-Driven Approach to Chemical Reactions

As computational chemistry methods evolve, dynamic effects have been increasingly recognized to govern chemical reaction pathways in both organic and inorganic systems. Here, we introduce a committor-based workflow that integrates a path-committor-consistent artificial neural network (PCCANN) with an iteratively trained hybrid-DFT-level message passing atomic convolutional encoder (MACE) potential. Beginning with a static nudged elastic band path, PCCANN extracts a committor-consistent string to represent the reactive ensemble. We illustrate the power of this methodology through two representative applications. First, we investigate an SNAr reaction using MACE trained at hybrid DFT level with implicit solvent. The mechanism is found to be concerted, and the dynamic approach reveals a lower barrier than static treatments. Second, we apply the same protocol to the isomerization of protonated isobutanol to protonated 2-butanol, yielding a quantitatively accurate free-energy landscape. We uncover three competing channels: the established concerted mechanism and two asynchronous stepwise routes mediated by water and methyl transfer, all with comparable activation barriers. Notably, the stepwise pathways traverse metastable intermediates that, to the best of our knowledge, have not been described in prior mechanistic studies. Calculated barrier heights and intermediate stabilities are in close agreement with high-level DFT benchmarks, demonstrating the framework's accuracy. Together, these studies highlight mechanistic diversity across distinct systems and establish the synergistic PCCANN-MACE protocol as a proof-of-concept approach for committor-based discovery of complex reaction dynamics.

cond-mat.stat-mech↗

arXiv2025

Breaking the Timescale Barrier: Generative Discovery of Conformational Free-Energy Landscapes and Transition Pathways

Molecular transitions -- such as protein folding, allostery, and membrane transport -- are central to biology yet remain notoriously difficult to simulate. Their intrinsic rarity pushes them beyond reach of standard molecular dynamics, while enhanced-sampling methods are costly and often depend on arbitrary variables that bias outcomes. We introduce Gen-COMPAS, a generative committor-guided path sampling framework that reconstructs transition pathways without predefined variables and at a fraction of the cost. Gen-COMPAS couples a generative diffusion model, which produces physically realistic intermediates, with committor-based filtering to pinpoint transition states. Short unbiased simulations from these intermediates rapidly yield full transition-path ensembles that converge within nanoseconds, where conventional methods require orders of magnitude more sampling. Applied to systems from a miniprotein to a ribose-binding protein to a mitochondrial carrier, Gen-COMPAS retrieves committors, transition states, and free-energy landscapes efficiently, uniting machine learning and molecular dynamics for broad mechanistic and practical insight.

physics.comp-ph↗

arXiv2025

Following the Committor Flow: A Data-Driven Discovery of Transition Pathways

The discovery of transition pathways to unravel distinct reaction mechanisms and, in general, rare events that occur in molecular systems is still a challenge. Recent advances have focused on analyzing the transition path ensemble using the committor probability, widely regarded as the most informative one-dimensional reaction coordinate. Consistency between transition pathways and the committor function is essential for accurate mechanistic insight. In this work, we propose an iterative framework to infer the committor and, subsequently, to identify the most relevant transition pathways. Starting from an initial guess for the transition path, we generate biased sampling from which we train a neural network to approximate the committor probability. From this learned committor, we extract dominant transition channels as discretized strings lying on isocommittor surfaces. These pathways are then used to enhance sampling and iteratively refine both the committor and the transition paths until convergence. The resulting committor enables accurate estimation of the reaction rate constant. We demonstrate the effectiveness of our approach on benchmark systems, including a two-dimensional model potential, peptide conformational transitions, and a Diels--Alder reaction.

physics.comp-ph↗

arXiv2025

From Atoms to Dynamics: Learning the Committor Without Collective Variables

This Brief Communication introduces a graph-neural-network architecture built on geometric vector perceptrons to predict the committor function directly from atomic coordinates, bypassing the need for hand-crafted collective variables (CVs). The method offers atom-level interpretability, pinpointing the key atomic players in complex transitions without relying on prior assumptions. Applied across diverse molecular systems, the method accurately infers the committor function and highlights the importance of each heavy atom in the transition mechanism. It also yields precise estimates of the rate constants for the underlying processes. The proposed approach opens new avenues for understanding and modeling complex dynamics, by enabling CV-free learning and automated identification of physically meaningful reaction coordinates of complex molecular processes.

physics.comp-ph↗

arXiv2024

Iterative variational learning of committor-consistent transition pathways using artificial neural networks

This contribution introduces a neural-network-based approach to discover meaningful transition pathways underlying complex biomolecular transformations in coherence with the committor function. The proposed path-committor-consistent artificial neural network (PCCANN) iteratively refines the transition pathway by aligning it to the gradient of the committor. This method addresses the challenges of sampling in molecular dynamics simulations rare events in high-dimensional spaces, which is often limited computationally. Applied to various benchmark potentials and biological processes such as peptide isomerization and protein-model folding, PCCANN successfully reproduces established dynamics and rate constants, while revealing bifurcations and alternate pathways. By enabling precise estimation of transition states and free-energy barriers, this approach provides a robust framework for enhanced-sampling simulations of rare events in complex biomolecular systems.

physics.comp-ph↗

arXiv2024

Isoleucine gate blocks K+ conduction in C-type inactivation

Many voltage-gated potassium (Kv) channels display a time-dependent phenomenon called C-type inactivation, whereby prolonged activation by voltage leads to the inhibition of ionic conduction, a process that involves a conformational change at the selectivity filter toward a non-conductive state. Recently, a high-resolution structure of a strongly inactivating triple-mutant channel kv1.2-kv2.1-3m revealed a novel conformation of the selectivity filter that is dilated at its outer end, distinct from the well-characterized conductive state. While the experimental structure was interpreted as the elusive non-conductive state, molecular dynamics simulations and electrophysiology measurements demonstrate that the dilated filter of kv1.2-kv2.1-3m, however, is conductive and, as such, cannot completely account for the inactivation of the channel observed in functional experiments. An additional conformational change implicating isoleucine residues at position 398 along the pore lining segment S6 is required to effectively block ion conduction. It is shown that the I398 residues from the four subunits act as a state-dependent hydrophobic gate located immediately beneath the selectivity filter. As a critical piece of the C-type inactivation machinery, this structural feature is the potential target of a broad class of QA blockers and negatively charged activators thus opening new research directions towards the development of drugs that specifically modulate gating-states of Kv channels.

physics.bio-ph↗

arXiv2010

Reconciling alternate methods for the determination of charge distributions: A probabilistic approach to high-dimensional least-squares approximations

We propose extensions and improvements of the statistical analysis of distributed multipoles (SADM) algorithm put forth by Chipot et al. in [6] for the derivation of distributed atomic multipoles from the quantum-mechanical electrostatic potential. The method is mathematically extended to general least-squares problems and provides an alternative approximation method in cases where the original least-squares problem is computationally not tractable, either because of its ill-posedness or its high-dimensionality. The solution is approximated employing a Monte Carlo method that takes the average of a random variable defined as the solutions of random small least-squares problems drawn as subsystems of the original problem. The conditions that ensure convergence and consistency of the method are discussed, along with an analysis of the computational cost in specific instances.

math.NA↗