Phase behavior of 1-ribbon and 2-ribbon fibril self-assembly in a simple network Hamiltonian model of protein fibrillization

Diessner, Elizabeth M.; Thomas, Loring J.; Butts, Carter T.

doi:10.1016/j.bpj.2022.11.1240

Cited by 2 publications

(11 citation statements)

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“…Because the goal here is to discover network Hamiltonian models that can maximally and reliably simulate the self-assembly of amyloid fibril network structures, we define a metric for amyloid fibril production called fibril fraction, which is defined simply as the number of nodes in the system that make up part of a region of perfect amyloid fibril divided by the total number of nodes in the simulation (e.g., in Figure ). Calculating fibril fraction is carried out using custom scripts in the R statistical computing environment, along with a variety of R packages, − using approaches standard to the network Hamiltonian methodology. ,,,,, The R script used to calculate fibril fraction for the present work ( fibril_assay.R , ) was constructed with the goal of rewarding parameters that produce longer fibrils; thus, fibrillar nodes that belong to the interior of an amyloid fibril structure were prioritized in fibril fraction calculations for the present work. Although this stringent metric can lead to a slight undercounting of fibrillar nodes (e.g., the 0.25 fibril fraction in Figure ), the strategy was deemed fit-for-purpose given the success of the genetic algorithm in optimizing for models of high fibril fraction using this metric (demonstrated in the Results and Discussion section).…”

Section: Methodsmentioning

confidence: 99%

“…Network Hamiltonian models are a type of coarse-grained molecular simulation that can be used to model amyloid fibril self-assembly, or other types of aggregation events, ,,,, and are built within the framework of exponential-family random graph models (ERGMs) . The principal objects in network Hamiltonian simulations are graphs (networks), where each node represents a molecule in the aggregating system and a pair of nodes share an edge if a pair of molecules in the system share a noncovalent bond.…”

Section: Methodsmentioning

confidence: 99%

“…This model has been used to propose a mechanism of amyloid fibril formation whereby amyloid fibrils are preceded by the formation of prefibrillar oligomers. 39 Another coarse-grained modeling approach of a similar level of coarseness to the aforementioned work by S ̌aricé t al., 39 i.e., whereby the fundamental interacting bodies are entire protein molecules, is the network Hamiltonian model (NHM), 13,29,36,40,41 which is the focus of the present work.…”

Section: ■ Introductionmentioning

confidence: 99%

“…42−44 Though not an exhaustive list, some of the leading coarse-grained modeling approaches that have been applied toward studying amyloid fibril formation include: single bead per amino acid models, 11,45,46 midresolution models (multiple beads per residue), 10,15,47−50 the UNRES model, 37,51 the Martini model, 52,53 lattice models, 34,54,55 and network Hamiltonian models. 13,29,36,40,41 Network Hamiltonian Simulations of Amyloid Fibril Formation. Network Hamiltonian models are highly computationally efficient coarse-grained (CG) molecular simulations, which are capable of reproducing multiple known experimental observables (e.g., topological structures measured via NMR and fibril formation growth curves from dye-binding fluorescence kinetics assays 13,28 ), and can provide insights into the complex interactions between proteins that lead to the formation of amyloid fibrils.…”

Section: ■ Introductionmentioning

confidence: 99%

“…One particularly interesting mechanistic insight into amyloid fibril formation, which was brought to light using network Hamiltonian models by Yu et al, is that fibrillar fraction growth curves for the simple 1-ribbon fibril topology exhibit steady gentle growth compared to the higher ordered structures that display a sharp increase in fibril formation after the initial lag phase. Such developments offer interesting targets for experimental probes involving comparisons between computed fibril fraction growth curves and fibril growth kinetics data obtained via dye-binding fluorescence assays. − Though not an exhaustive list, some of the leading coarse-grained modeling approaches that have been applied toward studying amyloid fibril formation include: single bead per amino acid models, ,, midresolution models (multiple beads per residue), ,,− the UNRES model, , the Martini model, , lattice models, ,, and network Hamiltonian models. ,,,, …”

Section: Introductionmentioning

confidence: 99%

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Genetic Algorithm for Automated Parameterization of Network Hamiltonian Models of Amyloid Fibril Formation

Grazioli,

Tao,

Bhatia

et al. 2024

J. Phys. Chem. B

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The time scales of long-time atomistic molecular dynamics simulations are typically reported in microseconds, while the time scales for experiments studying the kinetics of amyloid fibril formation are typically reported in minutes or hours. This time scale deficit of roughly 9 orders of magnitude presents a major challenge in the design of computer simulation methods for studying protein aggregation events. Coarse-grained molecular simulations offer a computationally tractable path forward for exploring the molecular mechanism driving the formation of these structures, which are implicated in diseases such as Alzheimer's, Parkinson's, and type-II diabetes. Network Hamiltonian models of aggregation are centered around a Hamiltonian function that returns the total energy of a system of aggregating proteins, given the graph structure of the system as an input. In the graph, or network, representation of the system, each protein molecule is represented as a node, and noncovalent bonds between proteins are represented as edges. The parameter, i.e., a set of coefficients that determine the degree to which each topological degree of freedom is favored or disfavored, must be determined for each network Hamiltonian model, and is a well-known technical challenge. The methodology is first demonstrated by beginning with an initial set of randomly parametrized models of low fibril fraction (<5% fibrillar), and evolving to subsequent generations of models, ultimately leading to high fibril fraction models (>70% fibrillar). The methodology is also demonstrated by applying it to optimizing previously published network Hamiltonian models for the 5 key amyloid fibril topologies that have been reported in the Protein Data Bank (PDB). The models generated by the AI produced fibril fractions that surpass previously published fibril fractions in 3 of 5 cases, including the most naturally abundant amyloid fibril topology, the 1,2 2-ribbon, which features a steric zipper. The authors also aim to encourage more widespread use of the network Hamiltonian methodology for fitting a wide variety of self-assembling systems by releasing a free open-source implementation of the genetic algorithm introduced here.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Genetic Algorithm for Automated Parameterization of Network Hamiltonian Models of Amyloid Fibril Formation

Grazioli,

Tao,

Bhatia

et al. 2024

J. Phys. Chem. B

View full text Add to dashboard Cite

show abstract

A Genetic Algorithm for Automated Parameterization of Network Hamiltonian Models of Amyloid Fibril Formation

Grazioli,

Tao,

Bhatia

et al. 2023

Preprint

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The timescales of long-time atomistic molecular dynamics simulations are typically reported in microseconds, while the timescales for experiments studying the kinetics of amyloid fibril formation are typically reported in minutes or hours. This timescale deficit of roughly 9 orders of magnitude presents a major challenge in the design of computer simulation methods for studying protein aggregation events. Thus, coarse-grained molecular simulations of amyloid fibril formation are crucial for understanding the molecular mechanism behind the formation of these structures, which are implicated in diseases such as Alzheimer’s, Parkinson’s, and Type II diabetes. Network Hamiltonian simulations of aggregation are centered around a Hamiltonian function that returns the total energy of a system of aggregating proteins, given the graph structure of the system as input. In the graph, or network, representation of the system, each protein molecule is represented as a node, and non-covalent bonds between proteins are represented as edges. The parameter, i.e. a set of coefficients that determine the degree to which each topological degree of freedom is favored or disfavored, must be determined for each network Hamiltonian model, and is a well-known technical challenge. Here, a type of artificial intelligence (AI) called a genetic algorithm is introduced for autonomously parameterizing network Hamiltonian models, whereby an initial set of randomly parameterized models, typically of low fibril yield (e.g. < 5 %), is used to initiate the evolution of subsequent model generations, ultimately leading to high fibril yield models (e.g. > 70 %). The methodology is also demonstrated by applying it to optimizing previously published network Hamiltonian models for the 5 key amyloid fibril topologies that have been reported in the Protein Data Bank (PDB), and showing that the models generated by the AI produce fibril yields that surpass or match previously published fibril yields in all cases. The authors also aim to encourage more widespread use of the network Hamiltonian methodology by introducing a free open-source implementation of the genetic algorithm for fitting network Hamiltonian models to other self-assembling systems.

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Phase behavior of 1-ribbon and 2-ribbon fibril self-assembly in a simple network Hamiltonian model of protein fibrillization

Cited by 2 publications

References 0 publications

Genetic Algorithm for Automated Parameterization of Network Hamiltonian Models of Amyloid Fibril Formation

Genetic Algorithm for Automated Parameterization of Network Hamiltonian Models of Amyloid Fibril Formation

A Genetic Algorithm for Automated Parameterization of Network Hamiltonian Models of Amyloid Fibril Formation

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