Constant pH Coarse-Grained Molecular Dynamics with Stochastic Charge Neutralization

Teijlingen, Alexander van; Swanson, Hamish W A; Lau, King Hang Aaron; Tuttle, Tell

doi:10.1021/acs.jpclett.2c00544

Cited by 13 publications

(17 citation statements)

References 33 publications

(77 reference statements)

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“… 34 Our model was also able to reproduce the two shifted apparent pK a values of the C-termini of FmocFF reported by Tang et al. 4 , 35 …”

Section: Self-assembly Within the Martini Force Fieldsupporting

confidence: 66%

“…This method was used to reproduce the experimental results of Adams et al, whereby 9-fluorenylmethoxycarbonyl-Phe-Phe (FmocFF) nanotubes are stable at neutral and basic pH but undergo syneresis under acidic conditions. 34 Our model was also able to reproduce the two shifted apparent pK a values of the C-termini of FmocFF reported by Tang et al 4,35 The use of CGMD to search for properties related to selfassembly has also been demonstrated via screening for tetrapeptide emulsifiers that do not contain aromatic amino acids. Using Martini 2.1, zwitterionic peptides were equilibrated in water/octanol for 100 ns.…”

Section: Accounts Ofmentioning

confidence: 52%

“…This method was used to reproduce the experimental results of Adams et al., whereby 9-fluorenylmethoxycarbonyl-Phe-Phe (FmocFF) nanotubes are stable at neutral and basic pH but undergo syneresis under acidic conditions . Our model was also able to reproduce the two shifted apparent pK a values of the C-termini of FmocFF reported by Tang et al. , …”

Section: Self-assembly Within the Martini Force Fieldmentioning

confidence: 83%

“… 2022 13 4046 4051 . 4 Developing a constant-charge pH algorithm and accurately reproducing the experimental pH-dependent behavior of two peptide systems.…”

Section: Key Referencesmentioning

confidence: 99%

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Short Peptide Self-Assembly in the Martini Coarse-Grain Force Field Family

2023

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Conspectus Pivotal to the success of any computational experiment is the ability to make reliable predictions about the system under study and the time required to yield these results. Biomolecular interactions is one area of research that sits in every camp of resolution vs the time required, from the quantum mechanical level to in vivo studies. At an approximate midpoint, there is coarse-grained molecular dynamics, for which the Martini force fields have become the most widely used, fast enough to simulate the entire membrane of a mitochondrion though lacking atom-specific precision. While many force fields have been parametrized to account for a specific system under study, the Martini force field has aimed at casting a wider net with more generalized bead types that have demonstrated suitability for broad use and reuse in applications from protein–graphene oxide coassembly to polysaccharides interactions. In this Account, the progressive (Martini versions 1 through 3) and peripheral (Sour Martini, constant pH, Martini Straight, Dry Martini, etc.) developmental trajectory of the Martini force field will be analyzed in terms of self-assembling systems with a focus on short (two to three amino acids) peptide self-assembly in aqueous environments. In particular, this will focus on the effects of the Martini solvent model and compare how changes in bead definitions and mapping have effects on different systems. Considerable effort in the development of Martini has been expended to reduce the “stickiness” of amino acids to better simulate proteins in bilayers. We have included in this Account a short study of dipeptide self-assembly in water, using all mainstream Martini force fields, to examine their ability to reproduce this behavior. The three most recently released versions of Martini and variations in their solvents are used to simulate in triplicate all 400 dipeptides of the 20 gene-encoded amino acids. The ability of the force fields to model the self-assembly of the dipeptides in aqueoues environments is determined by the measurement of the aggregation propensity, and additional descriptors are used to gain further insight into the dipeptide aggregates.

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“… 34 Our model was also able to reproduce the two shifted apparent pK a values of the C-termini of FmocFF reported by Tang et al. 4 , 35 …”

Section: Self-assembly Within the Martini Force Fieldsupporting

confidence: 66%

Section: Accounts Ofmentioning

confidence: 52%

Section: Self-assembly Within the Martini Force Fieldmentioning

confidence: 83%

“… 2022 13 4046 4051 . 4 Developing a constant-charge pH algorithm and accurately reproducing the experimental pH-dependent behavior of two peptide systems.…”

Section: Key Referencesmentioning

confidence: 99%

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Short Peptide Self-Assembly in the Martini Coarse-Grain Force Field Family

2023

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“…[27] In order to appropriately accommodate these pK a shifts, a variant of MD was developed that can handle this chemical change, namely constant pH [coarse-grained] molecular dynamic CpHMD. [43,44] This approach enables in situ changing of protonation states of titratable groups depending on the system pH and local environment. This dynamic method is preferential to fixing charge states based on pH as the theoretical pK a of a residue is only relevant in dilute systems where the local environment is only water, i.e., the ability to switch charge states could facilitate selfassembly.…”

Section: Computationally Driven Materials Discoverymentioning

confidence: 99%

Integrating Computation, Experiment, and Machine Learning in the Design of Peptide‐Based Supramolecular Materials and Systems

et al. 2023

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Interest in peptide-based supramolecular materials has grown extensively since the 1980s and the application of computational methods has paralleled this. These methods contribute to the understanding of experimental observations based on interactions and inform the design of new supramolecular systems. They are also used to virtually screen and navigate these very large design spaces. Increasingly, the use of artificial intelligence is employed to screen far more candidates than traditional methods. Based on a brief history of computational and experimentally integrated investigations of peptide structures, we explore recent impactful examples of computationally driven investigation into peptide self-assembly, focusing on recent advances in methodology development. It is clear that the integration between experiment and computation to understand and design new systems is becoming near seamless in this growing field.

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