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NCBI: db=pubmed; Term=(Im, Wonpil[Author] AND Kansas) OR (Vakser, Ilya[Author]) OR (Karanicolas, John[Author] AND Kansas) OR (Deeds, Eric[Author]) OR (Ray, Christian[Author]) OR (Slusky, Joanna[Author]) OR (Ray JC[Author] AND Kansas)
Updated: 6 hours 32 min ago

"Solvent hydrogen-bond occlusion": A new model of polar desolvation for biomolecular energetics.

Tue, 03/21/2017 - 06:10

"Solvent hydrogen-bond occlusion": A new model of polar desolvation for biomolecular energetics.

J Comput Chem. 2017 Mar 20;:

Authors: Bazzoli A, Karanicolas J

Abstract
Water engages in two important types of interactions near biomolecules: it forms ordered "cages" around exposed hydrophobic regions, and it participates in hydrogen bonds with surface polar groups. Both types of interaction are critical to biomolecular structure and function, but explicitly including an appropriate number of solvent molecules makes many applications computationally intractable. A number of implicit solvent models have been developed to address this problem, many of which treat these two solvation effects separately. Here, we describe a new model to capture polar solvation effects, called SHO ("solvent hydrogen-bond occlusion"); our model aims to directly evaluate the energetic penalty associated with displacing discrete first-shell water molecules near each solute polar group. We have incorporated SHO into the Rosetta energy function, and find that scoring protein structures with SHO provides superior performance in loop modeling, virtual screening, and protein structure prediction benchmarks. These improvements stem from the fact that SHO accurately identifies and penalizes polar groups that do not participate in hydrogen bonds, either with solvent or with other solute atoms ("unsatisfied" polar groups). We expect that in future, SHO will enable higher-resolution predictions for a variety of molecular modeling applications. © 2017 Wiley Periodicals, Inc.

PMID: 28318014 [PubMed - as supplied by publisher]

Survival of Phenotypic Information during Cellular Growth Transitions.

Sat, 03/04/2017 - 09:04
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Survival of Phenotypic Information during Cellular Growth Transitions.

ACS Synth Biol. 2016 08 19;5(8):810-6

Authors: Ray JC

Abstract
Phenotypic memory can predispose cells to physiological outcomes, contribute to heterogeneity in cellular populations, and allow computation of environmental features, such as nutrient gradients. In bacteria and synthetic circuits in general, memory can often be set by protein concentrations: because of the relative stability of proteins, the degradation rate is often dominated by the growth rate, and inheritance is a significant factor. Cells can then be primed to respond to events that recur with frequencies faster than the time to eliminate proteins. Protein memory can be extended if cells reach extremely low growth rates or no growth. Here we characterize the necessary time scales for different quantities of protein memory, measured as relative entropy (Kullback-Leibler divergence), for a variety of cellular growth arrest transition dynamics. We identify a critical manifold in relative protein degradation/growth arrest time scales where information is, in principle, preserved indefinitely because proteins are trapped at a concentration determined by the competing time scales as long as nongrowth-mediated protein degradation is negligible. We next asked what characteristics of growth arrest dynamics and initial protein distributions best preserve or eliminate information about previous environments. We identified that sharp growth arrest transitions with skewed initial protein distributions optimize flexibility, with information preservation and minimal cost of residual protein. As a result, a nearly memoryless regime, corresponding to a form of bet-hedging, may be an optimal strategy for storage of information by protein concentrations in growth-arrested cells.

PMID: 26910476 [PubMed - indexed for MEDLINE]

Computing the Dynamic Supramolecular Structural Proteome.

Fri, 01/20/2017 - 11:03
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Computing the Dynamic Supramolecular Structural Proteome.

PLoS Comput Biol. 2017 Jan;13(1):e1005290

Authors: Nussinov R, Papin JA, Vakser I

PMID: 28103234 [PubMed - in process]


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