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.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
Project #
Application #
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Preusch, Peter C
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of California San Francisco
Schools of Pharmacy
San Francisco
United States
Zip Code
Fennell, Christopher J; Ghousifam, Neda; Haseleu, Jennifer M et al. (2018) Computational Signaling Protein Dynamics and Geometric Mass Relations in Biomolecular Diffusion. J Phys Chem B 122:5599-5609
Primorac, Tomislav; Požar, Martina; Sokoli?, Franjo et al. (2018) A Simple Two Dimensional Model of Methanol. J Mol Liq 262:46-57
Kastelic, Miha; Vlachy, Vojko (2018) Theory for the Liquid-Liquid Phase Separation in Aqueous Antibody Solutions. J Phys Chem B 122:5400-5408
Simon?i?, Matjaž; Urbi?, Tomaž (2018) Hydrogen bonding between hydrides of the upper-right part of the periodic table. Chem Phys 507:34-43
Janc, Tadeja; Lukši?, Miha; Vlachy, Vojko et al. (2018) Ion-specificity and surface water dynamics in protein solutions. Phys Chem Chem Phys 20:30340-30350
Urbic, Tomaz (2018) Two dimensional fluid with one site-site associating point. Monte Carlo, integral equation and thermodynamic perturbation theory study. J Mol Liq 270:87-96
Urbic, Tomaz; Najem, Sara; Dias, Cristiano L (2017) Thermodynamic properties of amyloid fibrils in equilibrium. Biophys Chem 231:155-160
Lukši?, Miha; Hribar-Lee, Barbara; Pizio, Orest (2017) Phase behaviour of a continuous shouldered well model fluid. A grand canonical Monte Carlo study. J Mol Liq 228:4-10
Urbic, Tomaz; Dill, Ken A (2017) Analytical theory of the hydrophobic effect of solutes in water. Phys Rev E 96:032101
Brini, Emiliano; Fennell, Christopher J; Fernandez-Serra, Marivi et al. (2017) How Water's Properties Are Encoded in Its Molecular Structure and Energies. Chem Rev 117:12385-12414

Showing the most recent 10 out of 74 publications