Biology subjects

King, J. T.

Publications and source records attributed to King, J. T..

3 recordsLinked to original sources

bioRxiv2023

Protein unfolding thermodynamics predict multicomponent phase behavior

An increasing number of proteins are known to undergo liquid-liquid phase separation (LLPS), with or without nucleic acids or partner proteins, forming dense liquid-like phases termed biomolecular condensates. This physical phenomenon has been implicated in the existence of cellular membraneless organelles as well as in the formation of pathological protein aggregates in several human diseases. While common structural features of proteins with a propensity to undergo LLPS have been well documented, currently there is no thermodynamic framework capable of predicting the phase behavior of native proteins. Here, we show that two fundamental thermodynamic properties associated with the unfolding of a native protein, change in heat capacity ({Delta}Cunfold) and change in Gibbs free energy ({Delta}Gunfold), are sufficient to predict the formation of multicomponent biomolecular condensates. We find that proteins with small{Delta} Cunfold and{Delta} Gunfold values, which indicate a native state that is thermodynamically similar to the fully unfolded state, promote LLPS. In contrast, proteins with large{Delta} Cunfold and{Delta} Gunfold values promote aggregation. We also demonstrate that the stability of the liquid-like condensate can be predicted from the proximity of a proteins thermodynamic variable values to the phase boundary. This work elucidates a deep connection between single-protein thermodynamics and multicomponent phase behavior, and provides an avenue for predicting pathological droplet-aggregate transition. Significance StatementBiomolecular phase transitions, such as LLPS of proteins and nucleic acids, is emerging as an important concept in understanding the link between dysregulation of membraneless compartmentalization in cells and several human diseases. Currently, analysis of structural features such as intrinsic disorder and charged residue content is the "rule-of-thumb" to predict a proteins propensity to undergo LLPS. There are several instances where this qualitative approach fails, potentially due to not accounting for a proteins native structure. We demonstrate an empirical correlation between the unfolding thermodynamics of native proteins and their phase behavior, which enables a quantitative prediction of multicomponent LLPS. This novel approach, based on single-protein thermodynamics, has the potential to quantitatively predict pathological phase transitions of proteins in degenerative human diseases.

biophysics↗

bioRxiv2021

Stability of a Nonequilibrium Biochemical Cycle Revealed by Single-Molecule Spectroscopy

Biological machinery relies on nonequilibrium dynamics to maintain stable directional fluxes through complex reaction cycles. For such reaction cycles, the presence of microscopically irreversible conformational transitions of the protein, and the accompanying entropy production, is of central interest. In this work, we use multidimensional single-molecule fluorescence lifetime correlation spectroscopy to measure the forward and reverse conformational transitions of bacteriorhodopsin during trans-membrane H+ pumping. We quantify the flux, affinity, enthalpy and entropy production through portions of the reaction cycle as a function of temperature. We find that affinity of irreversible conformational transitions decreases with increasing temperature, resulting in diminishing flux and entropy production. We show that the temperature dependence of the transition affinity is well fit by the Gibbs-Helmholtz relation, allowing the {Delta}Htrans to be experimentally extracted.

biophysics↗

bioRxiv2021

Fast and Robust 2D Inverse Laplace Transformation of Single-Molecule Fluorescence Lifetime Data

Fluorescence spectroscopy at the single-molecule scale has been indispensable for studying conformational dynamics and rare states of biological macromolecules. Single-molecule 2D-fluorescence lifetime correlation spectroscopy (sm-2D-FLCS) is an emerging technique that holds great promise for the study of protein and nucleic acid dynamics as it 1) resolves conformational dynamics using a single chromophore, 2) measures forward and reverse transitions independently, and 3) has a dynamic window ranging from microseconds to seconds. However, the calculation of a 2D fluorescence relaxation spectrum requires an inverse Laplace transition (ILT), which is an ill-conditioned inversion that must be estimated numerically through a regularized minimization. The current methods for performing ILTs of fluorescence relaxation can be computationally inefficient, sensitive to noise corruption, and difficult to implement. Here, we adopt an approach developed for NMR spectroscopy (T1-T2 relaxometry) to perform 1D and 2D-ILTs on single-molecule fluorescence spectroscopy data using singular-valued decomposition and Tikhonov regularization. This approach provides fast, robust, and easy to implement Laplace inversions of single-molecule fluorescence data. Significance StatementInverse Laplace transformations are a powerful approach for analyzing relaxation data. The inversion computes a relaxation rate spectrum from experimentally measured temporal relaxation, circumventing the need to choose appropriate fitting functions. They are routinely performed in NMR spectroscopy and are becoming increasing used in single-molecule fluorescence experiments. However, as Laplace inversions are ill-conditioned transformations, they must be estimated from regularization algorithms that are often computationally costly and difficult to implement. In this work, we adopt an algorithm first developed for NMR relaxometry to provide fast, robust, and easy to implement 1D and 2D inverse Laplace transformations on single-molecule fluorescence data.

biophysics↗