The automated optimisation of a coarse-grained force field using free energy data

Cáceres-Delpiano, Javier; Wang, Lee‐Ping; Essex, Jonathan W.

doi:10.1039/d0cp05041e

Cited by 4 publications

(2 citation statements)

References 50 publications

(101 reference statements)

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“…The dihedral angles also play a role in the definition of the secondary structure of the system, forcing the existence of both α-helices and β-strands conformations 61 . S2 is a structurally unbiased model optimized to be used with an explicit model of water, the WT4, in which each water is represented by four beads with a partial charge and a tetrahedral geometry 68 . The main purpose is to achieve faster simulations than atomistic calculations while maintaining traits that are lost to other CG FFs, such as the use of implicit solvents that use uniform dielectric constants, the biased imposed secondary structures, or the lack of long-range electrostatic interactions.…”

Section: Force Fieldsmentioning

confidence: 99%

Wild Type α-Synuclein Structure and Aggregation: A Comprehensive Coarse-Grained and All-Atom Molecular Dynamics Study

Martins,

Galamba

2024

Preprint

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α-synuclein (α-syn) is a 140-amino acid intrinsically disordered protein (IDP) and the primary component of cytotoxic oligomers implicated in the etiology of Parkinson's disease (PD). While IDPs lack a stable three-dimensional structure they sample a heterogeneous ensemble of conformations that can, in principle, be assessed through molecular dynamics simulations. However, describing the structure and aggregation of large IDPs is challenging due to force field (FF) accuracy and sampling limitations. To cope with the latter, coarse-grained (CG) FFs emerge as a potential alternative at the expense of atomic detail loss. Whereas CG models can accurately describe the structure of the monomer, less is known about aggregation. The latter is key for assessing aggregation pathways and designing aggregation inhibitor drugs. Herein, we investigate the structure and dynamics of α-syn using different resolution CG (Martini3 and Sirah2) and all-atom (Amber99sb and Charmm36m) FFs, to gain insight into the differences and resemblances between these models. The dependence of the magnitude of protein-water interactions and the putative need of enhanced sampling (replica-exchange) methods, in CG simulations, are analyzed to distinguish between force field accuracy and sampling limitations. The stability of the CG models of an α-syn fibril was also investigated. Additionally, α-syn aggregation was studied through umbrella sampling for the CG models and CG/all-atom models for an 11-mer peptide (NACore) from an amyloidogenic domain of α-syn. Our results show that despite the α-syn structures of Martini3 and Sirah2, with enhanced protein-water interactions, are similar, major differences exist concerning aggregation. The Martini3 fibril is not stable, and the binding free energy of α-syn and NACore is positive, opposite to Sirah2. Sirah2 peptides in a zwitterionic form, in turn, display too strong termini interactions, resulting in end-to-end orientation. Sirah2 with enhanced protein-water interactions and neutral termini provides, however, a peptide aggregation free energy profile similar to that found with all-atom models. Overall, we find that Sirah2 with enhanced protein-water interactions is suitable for studying protein-protein and protein-drug aggregation.

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Section: Force Fieldsmentioning

confidence: 99%

Wild Type α-Synuclein Structure and Aggregation: A Comprehensive Coarse-Grained and All-Atom Molecular Dynamics Study

Martins,

Galamba

2024

Preprint

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“…On the basis of current computing power, conventional MD (cMD) is limited to time scales from hundreds of nanoseconds to tens of microseconds, 2 implying that in the biomolecular simulation the cMD trajectories are commonly not ergodic and the system is likely to be trapped in one of the local minima on the free energy landscape. Coarse-grained MD can reduce computational costs but has difficulty sampling conformations that depart significantly from the initial conformation 3 at the cost of reduced accuracy, 4 resulting in unsuitability for the study of biological functions, such as protein folding or misfolding, protein−ligand binding or dissociation, and other molecular mechanisms related to drug design.…”

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confidence: 99%

Sigmoid Accelerated Molecular Dynamics: An Efficient Enhanced Sampling Method for Biosystems

Zhao

Zhang

et al. 2023

J. Phys. Chem. Lett.

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Gaussian accelerated molecular dynamics (GaMD) is recognized as a popular enhanced sampling method for tackling long-standing challenges in biomolecular simulations. Inspired by GaMD, Sigmoid accelerated molecular dynamics (SaMD) is proposed in this work by adding a Sigmoid boost potential to improve the balance between the highest acceleration and accurate reweighting. Compared with GaMD, SaMD extends the accessible time scale and improves the computational efficiency as tested in three tasks. In the alanine dipeptide task, SaMD can produce the free energy landscape with better accuracy and efficiency. In the chignolin folding task, the estimated Gibbs free energy difference can converge to the experimental value ∼30% faster. In the protein–ligand binding task, the bound conformations are closer to the crystal structure with a minimal ligand root-mean-square deviation of 1.7 Å. The binding of the ligand XK263 to the HIV protease is reproduced by SaMD in ∼60% less simulation time.

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Automatic multi-objective optimization of coarse-grained lipid force fields using SwarmCG

Empereur-mot

Capelli

Perrone

et al. 2022

The Journal of Chemical Physics

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The development of coarse-grained (CG) molecular models typically requires a time-consuming iterative tuning of parameters in order to have the approximated CG models behave correctly and consistently with, e.g., available higher-resolution simulation data and/or experimental observables. Automatic data-driven approaches are increasingly used to develop accurate models for molecular dynamics simulations. However, the parameters obtained via such automatic methods often make use of specifically designed interaction potentials and are typically poorly transferable to molecular systems or conditions other than those used for training them. Using a multi-objective approach in combination with an automatic optimization engine (SwarmCG), here, we show that it is possible to optimize CG models that are also transferable, obtaining optimized CG force fields (FFs). As a proof of concept, here, we use lipids for which we can avail reference experimental data (area per lipid and bilayer thickness) and reliable atomistic simulations to guide the optimization. Once the resolution of the CG models (mapping) is set as an input, SwarmCG optimizes the parameters of the CG lipid models iteratively and simultaneously against higher-resolution simulations (bottom-up) and experimental data (top-down references). Including different types of lipid bilayers in the training set in a parallel optimization guarantees the transferability of the optimized lipid FF parameters. We demonstrate that SwarmCG can reach satisfactory agreement with experimental data for different resolution CG FFs. We also obtain stimulating insights into the precision-resolution balance of the FFs. The approach is general and can be effectively used to develop new FFs and to improve the existing ones.

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The automated optimisation of a coarse-grained force field using free energy data

Abstract: Atomistic models provide a detailed representation of molecular systems, but are sometimes inadequate for simulations of large systems over long timescales. Coarse-grained models enable accelerated simulations by reducing the number...

Cited by 4 publications

References 50 publications

Wild Type α-Synuclein Structure and Aggregation: A Comprehensive Coarse-Grained and All-Atom Molecular Dynamics Study

Wild Type α-Synuclein Structure and Aggregation: A Comprehensive Coarse-Grained and All-Atom Molecular Dynamics Study

Sigmoid Accelerated Molecular Dynamics: An Efficient Enhanced Sampling Method for Biosystems

Automatic multi-objective optimization of coarse-grained lipid force fields using SwarmCG

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