Biology subjects

Pal, T.

Publications and source records attributed to Pal, T..

3 recordsLinked to original sources

bioRxiv2025

The effect of allelic molecular interactions on phenotypic dominance

Mendelian genetics provides us with a framework for studying allelic dominance relationships at a locus at the phenotypic level. These dominance relationships result from different molecular factors, including the mapping of molecular activity onto fitness. For homomeric proteins, physical interactions between alleles provide a mechanism by which one allele can have a dominant effect on the activity of the other. Here, we refer to the effect of these interactions as molecular dominance and examine how they determine total protein activity and contribute to phenotypic dominance. The relative impact of such molecular dominance effects depends on the proportion of subunits that heteromerize relative to those that form homomers. In turn, we show how the effect of physical interactions on phenotypic dominance depends on the function linking protein activity to fitness. Our results show the complex relationships between molecular and phenotypic dominance and highlight the fundamental difference in dominance landscapes for monomeric and homomeric proteins.

genetics↗

bioRxiv2024

Quantifying Unbiased Conformational Ensembles from Biased Simulations Using ShapeGMM

Quantifying the conformational ensembles of biomolecules is fundamental to describing mechanisms of processes such as ligand binding and allosteric regulation. Accurate quantification of these ensembles remains a challenge for all but the simplest molecules. One such challenge is insufficient sampling which enhanced sampling approaches, such as metadynamics, were designed to overcome; however, the non-uniform frame weights that result from many of these approaches present an additional challenge to ensemble quantification techniques such as Markov State Modeling or structural clustering. Here, we present rigorous inclusion of non-uniform frame weights into a structural clustering method entitled shapeGMM. The shapeGMM method fits a Gaussian mixture model to particle positions, and here we advance that approach by incorporating nonuniform frame weights in the estimates of all parameters of the model. The resulting models are high dimensional probability densities for the unbiased systems from which we can compute important thermodynamic properties such as relative free energies and configurational entropy. The accuracy of this approach is demonstrated by the quantitative agreement between GMMs computed by Hamiltonian reweighting and direct simulation of a coarse-grained helix model system. Furthermore, the relative free energy computed from a high dimensional probability density of alanine dipeptide reweighted from a metadynamics simulation quantitatively reproduces the metadynamics free energy in the basins. Finally, the method identifies hidden structures along the actin globular to filamentous-like structural transition from a metadynamics simulation on a linear discriminant analysis coordinate trained on GMM states, demonstrating the broad applicability of combining our prior and new methods, and illustrating how structural clustering of biased data can lead to biophysical insight. Combined, these results demonstrate that frame-weighted shapeGMM is a powerful approach to quantify biomolecular ensembles from biased simulations.

biophysics↗

bioRxiv2022

Biological condensates form percolated networks with molecular motion properties distinctly different from dilute solutions

Formation of membraneless organelles or biological condensates via phase separation hugely expands cellular organelle repertoire. Biological condensates are dense and viscoelastic soft matters instead of canonical dilute solutions. Unlike discoveries of numerous different biological condensates to date, mechanistic understanding of biological condensates remains scarce. In this study, we developed an adaptive single molecule imaging method that allows simultaneous tracking of individual molecules and their motion trajectories in both condensed and dilute phases of various biological condensates. The method enables quantitative measurements of phase boundary, motion behavior and speed of molecules in both condensed and dilute phases as well as the scale and speed of molecular exchanges between the two phases. Surprisingly, molecules in the condensed phase do not undergo uniform Brownian motion, but instead constantly switch between a confined state and a random motion state. The confinement is consistent with formation of large molecular networks (i.e., percolation) specifically in the condensed phase. Thus, molecules in biological condensates behave distinctly different from those in dilute solutions. This finding is of fundamental importance for understanding molecular mechanisms and cellular functions of biological condensates in general.

cell biology↗