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

Path collective variables 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 wall position 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 wall position of which evolves on the fly to track a fixed contour ΔF∗ of the orthogonal free energy F(z∣s), widening automatically in flat basins and narrowing at pinched saddles, using a user-selected transverse energy scale. 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.

Authors

Institutions

Publication Details

Journal
The Journal of Chemical Physics
Published
2026-10-14
DOI
https://doi.org/10.1063/5.0351430
Primary Topic
Protein Structure and Dynamics
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Cheng Giuseppe Chen, Radu A. Talmazan, Christophe J. Chipot, Chenyu Tang
The Journal of Chemical Physics
Protein Structure and Dynamics
article

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

Cheng Giuseppe Chen, Radu A. Talmazan, Christophe J. Chipot, Chenyu Tang
article en

Abstract

Path collective variables 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 wall position 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 wall position of which evolves on the fly to track a fixed contour ΔF∗ of the orthogonal free energy F(z∣s), widening automatically in flat basins and narrowing at pinched saddles, using a user-selected transverse energy scale. 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.

The Journal of Chemical PhysicsVol. 165(14)
University of Illinois Urbana-Champaign (US), University of Illinois Chicago (US), University of Chicago (US), Laboratoire de Chimie Théorique (FR)
Agence Nationale de la Recherche, H2020 European Research Council
Openalex Percentile: Top 57%
Protein Structure and Dynamics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.