Search PubMed⌕ Search
Home/Archive/PubMed

PubMed · 11297934

Polarizable force fields.

T A Halgren·W Damm
View original source ↗Export metadata ↓

Abstract

Standard force fields used in biomolecular computing describe electrostatic interactions in terms of fixed, usually atom-centered, charges. Real physical systems, however, polarize substantially when placed in a high-dielectric medium such as water--or even when a strongly charged system approaches a neutral body in the gas phase. Such polarization strongly affects the geometry and energetics of molecular recognition. First introduced more than 20 years ago, polarizable force fields seek to account for appropriate variations in charge distribution with dielectric environment. Over the past five years, an accelerated pace of development of such force fields has taken place on systems ranging from liquid water to metalloenzymes. Noteworthy progress has been made in better understanding the capabilities and limitations of polarizable models for water and in the formulation and utilization of complete specifically parameterized polarizable force fields for peptides and proteins.

Explore related subjects

EnzymesMetalsModels, ChemicalModels, MolecularProteinsSolutionsWaterJournal ArticleReview

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

T A Halgren, W Damm. 2001. Polarizable force fields.. https://doi.org/10.1016/s0959-440x(00)00196-2

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

How not to be seen: predicting unseen enzyme functions using contrastive learning.

MOTIVATION: Predicting enzyme function from its sequence is still an unsolved problem in the life sciences. Moreover, with the explosion of annotated genome data, we are inundated with potential enzymatic sequences that have not yet been biochemically characterized. While it is not possible to assign a not-yet-existing label to such a sequence, there is high value in placing the sequence as accurately as possible in known function space. Doing so can help provide more accurate falsifiable hypotheses for experimentalists wishing to characterize enzymes from specific functional families. RESULTS: Here we present a contrastive learning algorithm for predicting enzyme function from sequence. Our method, EnzPlacer, predicts the third, second, and first EC numbers for a protein whose fourth EC number is not in the training corpus. This novel prediction mechanism accurately places a protein sequence within a narrowed-down functional context, even if the precise function remains unknown. AVAILABILITY AND IMPLEMENTATION: EnzPlacer and data is available at https://github.com/drxiangma/EnzPlacer under a GPL3 license.

Enzymes↗
PubMed2002

Product dependence and bifunctionality compromise the ultrasensitivity of signal transduction cascades.

Covalent modification cycles are ubiquitous. Theoretical studies have suggested that they serve to increase sensitivity. However, this suggestion has not been corroborated experimentally in vivo. Here, we demonstrate that the assumptions of the theoretical studies, i.e., irreversibility and absence of product inhibition, were not trivial: when the conversion reactions are close to equilibrium or saturated by their product, "zero-order" ultrasensitivity disappears. For high sensitivities to arise, not only substrate saturation (zero-order) but also high equilibrium constants and low product saturation are required. Many covalent modification cycles are catalyzed by one bifunctional 'ambiguous' enzyme rather than by two independent proteins. This makes high substrate concentration and low product concentration for both reactions of the cycle inconsistent; such modification cycles cannot have high responses. Defining signal strength as ratios of modified (e.g., phosphorylated) over unmodified protein, signal-to-signal response sensitivity equals 1: signal strength should remain constant along a cascade of ambiguous modification cycles. We also show that the total concentration of a signalling effector protein cannot affect the signal emanating from a modification cycle catalyzed by an ambiguous enzyme if the ratio of the two forms of the effector protein is not altered. This finding may explain the experimental result that the pivotal signal transduction protein PII plus its paralogue GlnK do not control steady-state N-signal transduction in Escherichia coli. It also rationalizes the absence of strong phenotypes for many signal-transduction proteins. Emphasis on extent of modification of these proteins is perhaps more urgent than transcriptome analysis.

Enzymes↗