To improve computational modeling in biology, we need to deepen our understanding of water and improve our models of solvation. Explicit water models are computationally expensive and implicit water models miss much of the physics, so computer simulations of biomolecules often don't predict experiments as well as they could. We propose here a new approach to solvation that aims to be as accurate as explicit models and as fast as implicit models. We have three aims: (1) To develop 3D analytical and integral-equation approaches to compute structures and energetics of water, (2) To compare explicit with implicit solvation simulations to learn the nature of water structuring in solvation shells, and (3) To develop a Semi-Explicit method for solvation, which is faster than explicit, and more physical than implicit. Our approach is based more on the local statistical mechanics of each water molecule, rather than on continuum approximations (implicit), or brute force stochastic simulations. Our preliminary results give us optimism that this approach is working. Our model gives the density of water vs. temperature as accurately as TIP4P-Ew but 6 orders of magnitude faster. The preliminary phase diagram of water looks good. Our solvation model is capturing the free energies of solvation of neutrals and polar solutes about as accurately as explicit, and is about as fast to compute as GB. Our recent results in the blind SAMPL computational solvation modeling event are highly encouraging.

Public Health Relevance

The foundation of biological processes starts at the molecular level, and one of our key tools for understanding microscopic systems is computational modeling. Computer simulations of biomolecules often don't predict experiments as well as they could, and one of the primary reasons is limitations in the modeling of ever present water. We propose to develop new approaches for treating water that aim to deepen our understanding and lift the limitations of models for solvation.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM063592-12
Application #
8325067
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Preusch, Peter C
Project Start
2001-09-01
Project End
2014-08-31
Budget Start
2012-09-01
Budget End
2013-08-31
Support Year
12
Fiscal Year
2012
Total Cost
$273,415
Indirect Cost
$86,542
Name
State University New York Stony Brook
Department
Chemistry
Type
Schools of Arts and Sciences
DUNS #
804878247
City
Stony Brook
State
NY
Country
United States
Zip Code
11794
Kastelic, Miha; Kalyuzhnyi, Yurij V; Vlachy, Vojko (2016) Modeling phase transitions in mixtures of β-γ lens crystallins. Soft Matter 12:7289-98
Ghosh, Kingshuk; de Graff, Adam M R; Sawle, Lucas et al. (2016) Role of Proteome Physical Chemistry in Cell Behavior. J Phys Chem B 120:9549-63
Urbic, Tomaz (2016) Properties of the two-dimensional heterogeneous Lennard-Jones dimers: An integral equation study. J Chem Phys 145:194503
Kalyuzhnyi, Yuriy V; Vlachy, Vojko (2016) Explicit-water theory for the salt-specific effects and Hofmeister series in protein solutions. J Chem Phys 144:215101
Mohorič, Tomaž; Bren, Urban; Vlachy, Vojko (2015) Effects of Translational and Rotational Degrees of Freedom on the Hydration of Ionic Solutes as Seen by the Popular Water Models. Acta Chim Slov 62:489-97
Kastelic, Miha; Kalyuzhnyi, Yurij V; Hribar-Lee, Barbara et al. (2015) Protein aggregation in salt solutions. Proc Natl Acad Sci U S A 112:6766-70
Aupic, Jana; Urbic, Tomaz (2015) A structural study of a two-dimensional electrolyte by Monte Carlo simulations. J Chem Phys 142:014506
Huš, Matej; Urbic, Tomaz (2014) Existence of a liquid-liquid phase transition in methanol. Phys Rev E Stat Nonlin Soft Matter Phys 90:062306
Huš, Matej; Urbic, Tomaz (2014) The hydrophobic effect in a simple isotropic water-like model: Monte Carlo study. J Chem Phys 140:144904
Gujt, Jure; Bešter-Rogač, Marija; Hribar-Lee, Barbara (2014) An Investigation of Ion-Pairing of Alkali Metal Halides in Aqueous Solutions Using the Electrical Conductivity and the Monte Carlo Computer Simulation Methods. J Mol Liq 190:34-41

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