Solvation and cavity occupation in biomolecules

Lynch, Gillian C.; Perkyns, John S.; Nguyen, Bao Linh; Pettitt, B. Montgomery

doi:10.1016/j.bbagen.2014.09.020

Cited by 9 publications

(9 citation statements)

References 51 publications

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“…While for such a deep buried hydration site, it would require a significant longer simulation time in order for water molecule to diffuse in. 30 In this study, a 30 ns MD simulation might not be long enough.…”

Section: Resultsmentioning

confidence: 97%

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Effects of Acids, Bases, and Heteroatoms on Proximal Radial Distribution Functions for Proteins

Nguyen

Pettitt

2015

J. Chem. Theory Comput.

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The proximal distribution of water around proteins is a convenient method of quantifying solvation. We consider the effect of charged and sulfur-containing amino acid side-chain atoms on the proximal radial distribution function (pRDF) of water molecules around proteins using side-chain analogs. The pRDF represents the relative probability of finding any solvent molecule at a distance from the closest or surface perpendicular protein atom. We consider the near-neighbor distribution. Previously, pRDFs were shown to be universal descriptors of the water molecules around C, N, and O atom types across hundreds of globular proteins. Using averaged pRDFs, a solvent density around any globular protein can be reconstructed with controllable relative error. Solvent reconstruction using the additional information from charged amino acid side-chain atom types from both small models and protein averages reveals the effects of surface charge distribution on solvent density and improves the reconstruction errors relative to simulation. Solvent density reconstructions from the small-molecule models are as effective and less computationally demanding than reconstructions from full macromolecular models in reproducing preferred hydration sites and solvent density fluctuations.

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Section: Resultsmentioning

confidence: 97%

“…30 The reconstructed water density can calculate the probability of finding water near a specific atom type based on interactions and spatial availability.…”

Section: Resultsmentioning

confidence: 99%

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Effects of Acids, Bases, and Heteroatoms on Proximal Radial Distribution Functions for Proteins

Nguyen

Pettitt

2015

J. Chem. Theory Comput.

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“…In this case, it is important to predict which water molecules will form important structural and/or functional interactions. Various prediction methods for water positions have been developed, in particular in the protein–ligand docking field, including thermodynamic prediction methods involving MD simulations, − methods based on integral equation theory, ,− and geometry-based methods considering hydrogen bond geometry. − Other examples are a protein–water docking method, methods refining crystal water positions in protein crystal structures, , and a method utilizing a statistical potential derived from the Protein Structure Databank (PDB) …”

Section: Introductionmentioning

confidence: 99%

GalaxyWater-wKGB: Prediction of Water Positions on Protein Structure Using wKGB Statistical Potential

Heo

Park

Seok

2021

J. Chem. Inf. Model.

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Proteins fold and function in water, and protein–water interactions play important roles in protein structure and function. In computational studies on protein structure and interaction, the effect of water is considered either implicitly or explicitly. Implicit water models are frequently used in protein structure prediction and docking because they are computationally much more efficient than explicit water models, which are often employed in molecular dynamics (MD) simulations. However, implicit water models that treat water as a continuous solvent medium cannot account for specific atomistic protein–water interactions that are critical for structure formation and interactions with other molecules. Various methods for predicting water molecules that form specific atomistic interactions with proteins have been developed. Methods involving MD simulations or the integral equation theory tend to produce more accurate results at a higher computational cost than simple geometry- or energy-based methods. Here, we present a novel method for predicting water positions on a protein surface called GalaxyWater-wKGB, which is based on a statistical potential, a water knowledge-based potential based on the generalized Born model (wKGB). This method is accurate and rapid because it does not require conformational sampling or iterative computation owing to the effective statistical treatment employed to derive the potential. The statistical potential describes specific protein atom–water interactions more accurately than conventional potentials by considering the dependence on the degree of solvent accessibility of protein atoms as well as on protein atom–water distances and orientations. The introduction of solvent accessibility allows effective consideration of competing nonspecific protein–water and intraprotein interactions. When tested on high-resolution protein crystal structures, this method could recover similar or larger fractions of crystallographic water 180 times faster than the sophisticated integral equation theory, 3D-RISM. A web service of this water prediction method is freely available at .

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“…Pettitt and coworkers [7] compare and analyze different methods for describing protein hydration structure, using myoglobin as a test case. Both 3D integral equation theory with long-range Coulomb contributions and proximal radial distribution functions are used together with all-atom MD simulation to determine solvent density inside the protein with encouraging results, showing that the more approximate methods can indeed be useful.…”

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confidence: 99%