Skip to content
Open access

Experimental data-guided parameterization and validation of an AMBER protein force field

TL;DR

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.

Abstract

Folded or globular proteins adopt well-defined three-dimensional structures that correlate directly with function, forming the classical structure-function paradigm. Intrinsically Disordered Proteins (IDPs) and Regions (IDRs), encoded by roughly one-third of the eukaryotic genome, lack this fixed structure; their structural plasticity and major roles in diverse biological phenomena instead challenge this paradigm, making them a critical class of biomolecules studied through both experimental and computational approaches. Molecular dynamics (MD) simulations provide an atomistic understanding of IDP structure and dynamics, but their accuracy is limited by methodological assumptions and by the precision of the force field--the potential-energy function that approximates atomic interactions and governs how faithfully a simulation reflects real conformational behavior. Previous studies show that contemporary force fields fail to adequately capture amino acid residue specificity, limiting their applicability to IDPs. The central focus of this dissertation is therefore the development of an improved force field, Amber ff24EXP-GA, derived from its parent, Amber ff14SB, 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. Chapter 3 addresses this gap by reporting a new force field, Amber ff24EXP-GA, derived from Amber ff14SB by optimizing backbone dihedral potentials for guest glycine and alanine residues in cationic GGG and GAG peptides, respectively, to best match guest-residue-specific spectroscopic data. Amber ff24EXP-GA outperforms Amber ff14SB for conformational ensembles of all 14 guest residues x (G, A, L, V, I, F, Y, D^p, E^p, R, C, N, S, T) in GxG peptides in water--the full set for which spectroscopic data exist--and outperforms CHARMM36m for at least 7 of them (G, A, V, F, C, T, E^p), showing greater amino acid specificity than both Amber ff14SB and CHARMM36m. It also reproduces experimental data on three-folded proteins and three longer IDPs well, while still outperforming Amber ff14SB on short unfolded peptides. Chapter 4 examines the effect of nearest-neighbor (NN) residues on conformational dynamics: extensive experimental evidence shows that the Flory hypothesis--which assumes neighboring residues behave independently--does not hold, as NN residues instead alter the conformational landscape of a given residue. Here, we evaluate CHARMM36m, Amber ff14SB, and Amber ff24EXP-GA for their ability to capture these NN effects on conformational dynamics of amino acid residues in short unfolded peptides in water. Amber ff24EXP-GA, whose reproduction of intrinsic conformational ensembles is significantly better than its parent's, also captures the NN effects better than Amber ff14SB. Despite lacking residue specificity in its intrinsic conformational ensembles, CHARMM36m--calibrated on global IDP properties--reproduces the NN effects on par with Amber ff24EXP-GA. These findings matter for the development of next-generation force fields that capture both residue-specific dynamics and global IDP properties. Chapter 5 examines the current scope, achievements, and limitations of various MD force-field parametrization strategies for modeling proteins, with particular emphasis on intrinsically disordered proteins.

Read PDF

Similar papers

Open access Jul 2026

Carbonara: a SAXS-guided seeding framework for exploring protein solution-state dynamics

Carbonara is presented, a framework that uses experimental small-angle X-ray scattering (SAXS) data to predict alternative physically plausible protein conformations and provides a route from static structural models of flexible multi-domain proteins and multimeric assemblies to solution-state ensembles.

Josh McKeown, Cameron Brown, Arron Bale et al. · 0 citations
#protein folding Open access Sep 2026

Predictive all-atom simulations of disordered proteins and biomolecular condensates through osmometry-guided force-field optimization

All-atom simulations with explicit solvent can provide a detailed and accurate description of dynamics and mechanisms in biomolecular systems, including intrinsically disordered proteins (IDPs) and their condensates. However, interactions involving charged residues and ions remain a persistent source of systematic erro...

Miloš T. Ivanović, Valentin von Roten, Benjamin Schuler et al. · 0 citations
2026

Molecular Modeling and Simulations of Biologics.

This chapter presents a practical, end-to-end protocol for performing MD simulations on large biomolecules, using a full-length antibody as a case study and introduces foundational software tools commonly used in molecular modeling and MD simulations, including PyMOL, visual molecular dynamics (VMD), nanoscale molecula...

D. Baizhigitova, Pin-Kuang Lai · 0 citations
Open access Aug 2026

Development of force-field corrections for the RNA A-bulge motif

GHBfix-18Ab, an 18-component hydrogen-bond correction that distinguishes NH and NH□ donors, is developed, demonstrating that targeted refinement of hydrogen-bond interactions provides a practical strategy for systematic improvement of RNA force fields toward more accurate modeling of noncanonical RNA motifs.

Takafumi Kudo, Toru Ekimoto, Tsutomu Yamane et al. · 0 citations
Open access Aug 2026

Benchmarking AI-generated structural ensembles of membrane proteins against physics-based modelling

It is demonstrated that BioEmu can generate plausible conformational ensembles for relatively large, six-and seven-pass membrane proteins, sampling rare states at a fraction of the computational cost of conventional MD simulations, suggesting that AI-based ensemble generation could provide an accessible approach for ex...

B. Clifton, Adam G. Grieve, Robin A. Corey · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.