Modelling Water: A Lifetime Enigma

Ouyang, John F.; Bettens, Ryan P. A.

doi:10.2533/chimia.2015.104

Cited by 65 publications

(45 citation statements)

References 105 publications

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“…A large number of sophisticated empirical water models are available, see for example, Ref for a recent review. Many of these models represent the potential energy of the system as a many‐body expansion.…”

Section: Explicit Water Modelsmentioning

confidence: 99%

Water models for biomolecular simulations

Onufriev

Izadi²

2017

WIREs Comput Mol Sci

170

225

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Modern simulation and modeling approaches to investigation of biomolecular structure and function rely heavily on a variety of methods—water models—to approximate the influence of solvent. We give a brief overview of several distinct classes of available water models, with the emphasis on the conceptual basis at each level of approximation. The main focus is on classes of models most widely used in atomistic simulations, including popular implicit and explicit solvent models. Among the latter, nonpolarizable N‐point models are covered in most detail, including some recent methodological advances and nuances. Notes on practical availability and usage in biomolecular simulations are included. Atomistic simulations that were hardly possible only a short while ago have revealed significant problems that can be traced to deficiencies of most commonly used N‐point water models. Recently developed models of this class approximate experimental properties of liquid water much closer than before, and show promise in practical biomolecular simulations. Obstacles to wider adoption of these more accurate water models, both technical and conceptual, are discussed. It is argued that verifying robustness of simulation results to the choice of water model can be of immediate benefit even in the absence of a clear replacement for older models; a specific strategy is proposed. The review is concluded with a discussion on how force‐field development efforts can benefit from better solvent models, and vice versa. WIREs Comput Mol Sci 2018, 8:e1347. doi: 10.1002/wcms.1347 This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods

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Section: Explicit Water Modelsmentioning

confidence: 99%

Water models for biomolecular simulations

Onufriev

Izadi²

2017

WIREs Comput Mol Sci

170

225

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“…Several popular choices include Amber (Cornell et al, 1995), CHARMM (Brooks et al, 1983), GROMOS (Scott et al, 1999), and OPLS-AA (Jorgensen, Maxwell, and Tirado-Rives, 1996), and they differ mostly in the functional form of the potential and the level of details included. Popular models of water include the three-site TIP3P and SPC/E or the four-site water models such as TIP4P, TIP4P-Ew, and TIP4P/2005 (Ouyang & Bettens, 2015). However, some intrinsic biases of the physical models include an abnormal propensity for helical structure formation compared with NMR experiments (R. B.…”

Section: Physical Models For Simulating Idps: Where Are We Now?mentioning

confidence: 99%

Revisiting the earliest signatures of amyloidogenesis: Roadmaps emerging from computational modeling and experiment

Bhattacharya

Thompson

2018

WIREs Comput Mol Sci

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Neurodegenerative amyloidogenesis begins with the aggregation of intrinsically disordered proteins (IDPs), which is the first step in a cascade of assembly events that can lead to insoluble fibrous deposits in brain tissue. IDP conformations that promote formation of toxic oligomers remain poorly understood, and are the most fundamental target of putative treatments for neurodegenerative disease. Rapid advances in theory, simulation and experimental methods, hold the promise of reversing protein aggregation by identifying and developing inhibitors of the transient amyloidogenic IDP conformations. To make meaningful progress it is important to appreciate the benefits and limitations of the latest developments in computational methods of conformational and ensemble modeling, and their integration and validation with experiments. Integrated studies are beginning to provide significant conceptual and mechanistic insights, including identification of the properties of amyloidogenic IDPs in their free, unbound form. At the same time, contradicting viewpoints have emerged concerning convergence of IDP ensemble signatures and properties from parallel studies, and there also remains a pressing need to develop physical models that can deliver reliable predictions across different IDP families. Focussing on the four most common amyloidogenic IDPs of Amyloid β, Tau, α‐synuclein and Prions, improvements are proposed for next‐generation models and experiments that can potentially identify drug treatments for neurodegenerative disease via incorporation of the extended cellular environment. This article is categorized under: Molecular and Statistical Mechanics > Molecular Mechanics Structure and Mechanism > Computational Biochemistry and Biophysics Structure and Mechanism > Molecular Structures

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“…The possibility offered by MD simulation to explore the conformational energy landscape of proteins in very realistic settings, including explicit solvent molecules and specific solution conditions (e.g. temperature, pH, concentration, pressure), alongside the continuous progress made in related areas, such as the development of force fields [139], water models [140] and next-generation graphics processing units (GPUs) (high-performance computing), makes this tool a logical choice to address the analysis of SAV. In this context, two different situations can arise when MD is used to simulate protein mutation effects.…”

Section: Protein Dynamics In Interpretation Of Mutationsmentioning

confidence: 99%

Molecular dynamics simulations for genetic interpretation in protein coding regions: where we are, where to go and when

Galano‐Frutos

García-Cebollada

Sancho

2019

Briefings in Bioinformatics

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The increasing ease with which massive genetic information can be obtained from patients or healthy individuals has stimulated the development of interpretive bioinformatics tools as aids in clinical practice. Most such tools analyze evolutionary information and simple physical–chemical properties to predict whether replacement of one amino acid residue with another will be tolerated or cause disease. Those approaches achieve up to 80–85% accuracy as binary classifiers (neutral/pathogenic). As such accuracy is insufficient for medical decision to be based on, and it does not appear to be increasing, more precise methods, such as full-atom molecular dynamics (MD) simulations in explicit solvent, are also discussed. Then, to describe the goal of interpreting human genetic variations at large scale through MD simulations, we restrictively refer to all possible protein variants carrying single-amino-acid substitutions arising from single-nucleotide variations as the human variome. We calculate its size and develop a simple model that allows calculating the simulation time needed to have a 0.99 probability of observing unfolding events of any unstable variant. The knowledge of that time enables performing a binary classification of the variants (stable-potentially neutral/unstable-pathogenic). Our model indicates that the human variome cannot be simulated with present computing capabilities. However, if they continue to increase as per Moore’s law, it could be simulated (at 65°C) spending only 3 years in the task if we started in 2031. The simulation of individual protein variomes is achievable in short times starting at present. International coordination seems appropriate to embark upon massive MD simulations of protein variants.

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Modelling Water: A Lifetime Enigma

Cited by 65 publications

References 105 publications

Water models for biomolecular simulations

Water models for biomolecular simulations

Revisiting the earliest signatures of amyloidogenesis: Roadmaps emerging from computational modeling and experiment

Molecular dynamics simulations for genetic interpretation in protein coding regions: where we are, where to go and when

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