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The phosphoregulation of proteins with multiple phosphorylation sites is governed by biochemical reaction networks that can exhibit multistable behavior. However, the behavior of such networks is typically studied in a single reaction volume, while cells are spatially organized into compartments that can exchange proteins. In this work, we use stochastic simulations to study the impact of compartmentalization on a two-site phosphorylation network. We characterize steady states and fluctuation-driven transitions between them as a function of the rate of protein exchange between two compartments.

With the great progress on determining protein structures over the last decade comes a renewed appreciation that structures must be combined with dynamics and energetics to understand function. Fluorescence spectroscopy, specifically Förster resonance energy transfer (FRET), provides a great window into dynamics and energetics due to its application at physiological temperatures and ability to measure dynamics on the ångström scale. We have recently advanced transition metal FRET (tmFRET) to study allosteric regulation of maltose binding protein and have reported measurements of maltose-dependent distance changes with an accuracy of ∼1.5 Å.

Nucleic acid double helices in their DNA, RNA and DNA-RNA hybrid form play a fundamental role in biology and are main building blocks of artificial nanostructures, but how their properties depend on temperature remains poorly understood. Here we report thermal dependence of dynamic bending persistence length, twist rigidity, stretch modulus and twist-stretch coupling for DNA, RNA and hybrid duplexes between 7 and 47 °C. The results are based on all-atom molecular dynamics (MD) simulations using different force field parameterizations.

Intrinsically disordered regions (IDRs) of proteins contribute to various cellular functions, including signal transduction, gene regulation, and subcellular organization (1). One of the most important aspects of IDRs is their contribution to biomolecular condensate formation within cells. IDRs can undergo multivalent intermolecular interactions that drive phase separation. For the multivalent interactions, IDRs may have preferential interaction sites (‘stickers’) flanked by inert sequences (‘spacers’) (1).

Membrane voltage plays a vital role in the behaviour and functions of the lipid bilayer membrane. For instance, it regulates the exchange of molecules across the membrane through transmembrane proteins such as ion channels. In this paper, we study the membrane voltage sensing mechanism, which entails the reorientation of alpha-helices with a change in the membrane voltage. We consider a helix having a large electrical macrodipole embedded in a lipid bilayer as a model system. We performed extensive molecular dynamics simulations to study the effect of variation of membrane voltage on the tilt angle of peptides and ascertain the optimal parameters for designing such a voltage-sensing peptide.

Proteins are the workhorses of biology, orchestrating a myriad of cellular functions through intricate conformational changes. Protein allostery, the phenomenon where binding of ligands or environmental changes induce conformational rearrangements in the protein, is fundamental to these processes. We have previously shown that transition metal Förster resonance energy transfer (tmFRET) can be used to interrogate the conformational rearrangements associated with protein allostery and have recently introduced novel FRET acceptors utilizing metal-bipyridyl derivatives to measure long (>20 Å) intramolecular distances in proteins.

Candida albicans, a prominent member of the human microbiome, can make an opportunistic switch from commensal coexistence to pathogenicity accompanied by an epigenetic shift between the white and opaque cell states. This transcriptional switch is under precise regulation by a set of transcription factors (TFs), with Enhanced Filamentous Growth Protein 1 (Efg1) playing a central role. Previous research has emphasized the importance of Efg1’s prion-like domain (PrLD) and the protein’s ability to undergo phase separation for the white-to-opaque transition of C.

De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de novo peptide design are largely based on sequence homology that can be restricted based on evolutionarily derived protein sequences and lack the physicochemical context essential in protein folding. Generative machine learning (ML) for de novo peptide design is a promising way to synthesize theoretical data that is based on, but unique from, the observable universe.

RNA molecules play a crucial role in various biological processes, with their functionality closely tied to their structures. The remarkable advancements in machine learning techniques for protein structure prediction have shown promise in the field of RNA structure prediction. In this perspective, we discuss the advances and challenges encountered in constructing machine learning-based models for RNA structure prediction. We explore topics including model building strategies, specific challenges involved in predicting RNA secondary (2D) and tertiary (3D) structures, and approaches to these challenges.

DNA-binding response regulators (DBRRs) are a broad class of proteins that operate in tandem with their partner kinase proteins to form two-component signal transduction systems in bacteria. Typical DBRRs are composed of two domains where the conserved N-terminal domain accepts transduced signals, and the evolutionarily diverse C-terminal domain binds to DNA. These domains are assumed to be functionally independent, and hence recombination of the two domains should yield novel DBRRs of arbitrary input/output response which can be used as biosensors.