# Osmolyte effects: revisiting solubility measurements, accessible surface area categorization, and language for communicating with a broad audience

## TL;DR

This paper revisits osmolyte effects, corrects solubility measurements, compares surface area models, and suggests a new way to describe osmolyte interactions for better communication.

## Contribution

The paper corrects a solubility measurement error and proposes reframing the 'osmophobic effect' to include side chain interactions using atom-based models.

## Key findings

- New solubility measurements show unfavorable interactions between glutamate and glycine betaine, contradicting earlier findings.
- Atom-based surface area models are computationally simpler and more generalizable than group-based models for osmolyte effects.
- The term 'osmophobic effect' should be expanded to include interactions with side chains and proteins, improving communication with a broader audience.

## Abstract

The osmophobic effect has been an accessible phrase to introduce biochemists to ideas of how osmolytes affect biochemical processes. It was built by analogy with the hydrophobic effect, central to protein folding, inspired by the initial observation that urea primarily interacts with the backbone of proteins, and less significantly with side chains. First, the author and his students revisited an experiment underpinning the original formulation of the osmophobic effect. New solubility measurements for glutamate in the presence of glycine betaine indicate that these molecules interact extremely unfavorably, in contrast to the small, favorable interaction previously reported from a solubility study, and in agreement with the interaction reported from vapor pressure osmometry measurements. This error in solubility measurements, in combination with numerous experimental findings published by other researchers, confirms a significant role for side chain-glycine betaine interactions, particularly with anionic oxygen. Second, the author compares two accessible surface area categorizations: dividing protein surface into backbone and side chains (used in the group transfer free energy model), and dividing molecules into atomic surface types (used in the solute partitioning model). Accessible surface areas of the Trp-cage peptide are used to illustrate the greater computational simplicity and generalizability of an atom based partitioning model over a chemical group based model. Third, the author offers a perspective on communicating osmolyte effects to a broader audience. Combining the widespread observation that osmolyte side-chain interactions are very significant, with the illustrated advantages of an atom-based surface area model, the author suggests reframing and reclaiming the term “osmophobic effect”. By separating the phrase “osmophobic effect” from its roots emphasizing protein backbone interactions influencing protein folding, and expanding it to include osmophobic and osmophilic interactions with proteins and other biological molecules, researchers and educators will foster easier understanding of osmolyte effects through analogy with the hydrophobic effect. Further, this analogy could be strengthened through application of increasingly accurate osmophobic/osmophilic models that rely on atomic surface types—like the hydrophobic effect relies on hydrophobic surfaces rather than specific side chains, and is valuable for understanding lipid aggregation and nucleic acid structure formation in addition to protein folding.