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RNAs are major drivers of phase separation in the formation of biomolecular condensates and can undergo protein-free phase separation in the presence of divalent ions or crowding agents. Much remains to be understood regarding how the complex interplay of base stacking, base pairing, electrostatics, ion interactions, and particularly structural propensities governs RNA phase behavior. Here, we develop an intermediate resolution model for condensates of RNAs (iConRNA) that can capture key local and long-range structural features of dynamic RNAs and simulate their spontaneous phase transitions with Mg²⁺. Representing each nucleotide using 6 to 7 beads, iConRNA accurately captures base stacking and pairing and includes explicit Mg²⁺. The model not only reproduces major conformational properties of poly(rA) and poly(rU) but also correctly folds small structured RNAs and predicts their melting temperatures. With an effective model of explicit Mg²⁺, iConRNA successfully recapitulates experimentally observed lower critical solution temperature phase separation of poly(rA) and triplet repeats, and critically, the nontrivial dependence of phase transitions on RNA sequence, length, concentration, and Mg²⁺level. Further mechanistic analysis reveals a key role of RNA folding in modulating phase separation as well as its temperature and ion dependence, besides other driving forces such as Mg²⁺–phosphate interactions, base stacking, and base pairing. These studies also support iConRNA as a powerful tool for direct simulation of RNA-driven phase transitions, enabling molecular studies of how RNA conformational dynamics and its response to complex condensate environments control the phase behavior and condensate material properties. 

Intrinsically disordered proteins (IDPs) rely on inherent conformational dynamics to support vital cellular functions such as signaling and regulation. The heterogeneity of the conformational ensembles of IDPs poses a major challenge for classical biophysical techniques such as nuclear magnetic resonance, small-angle x-ray scattering, and circular dichroism, which measure ensemble-averaged properties and are unable to resolve the full range of substates sampled by IDPs or their interconversions. To address these limitations of ensemble-based methods, single-molecule techniques are essential to resolve the conformational heterogeneity and dynamics by continuously monitoring various properties of individual IDPs. In this review, we focus on the most commonly employed single-molecule techniques, briefly describe their methodologies, and highlight recent developments and applications of each technique toward the study of IDP structure, dynamics and interactions. 

Gram-positive pathogenic bacteria Staphylococcus express and secret staphylococcal peroxidase inhibitor (SPIN) proteins to help evade neutrophil-mediated immunity by inhibiting the activity of the main oxidative-defense player myeloperoxidase (MPO) enzyme. SPIN contains a structured 3-helix bundle C-terminal domain, which can specifically bind to MPO with high affinity, and an intrinsically disordered N-terminal domain (NTD), which folds into a structured β-hairpin and inserts itself into the active site of MPO for inhibition. Mechanistic insights of the coupled folding and binding process are needed in order to better understand how residual structures and/or conformational flexibility of NTD contribute to the different strengths of inhibition of SPIN homologs. In this work, we applied atomistic molecular dynamics simulations on two SPIN homologs, from S. aureus and S. delphini , respectively, which share high sequence identity and similarity, to explore the possible mechanistic basis for their different inhibition efficacies on human MPO. Direct simulations of the unfolding and unbinding processes at 450 K reveal that these two SPIN/MPO complexes systems follow surprisingly different mechanisms of coupled binding and folding. While coupled binding and folding of SPIN- aureus NTD is highly cooperative, SPIN- delphini NTD appears to mainly utilize a conformational selection-like mechanism. These observations are in contrast to an overwhelming prevalence of induced folding-like mechanisms for intrinsically disordered proteins that fold into helical structures upon binding. Further simulations of unbound SPIN NTDs at room temperature reveal that SPIN- delphini NTD has a much stronger propensity of forming β-hairpin like structures, consistent with its preference to fold and then bind. These may help explain why the inhibition strength is not well correlated with binding affinity for different SPIN homologs. Altogether, our work establishes the relationship between the residual conformational stability of SPIN-NTD and their inhibitory function, which can help us develop new strategies towards treating Staphylococcal infections. 

Intrinsically disordered proteins (IDPs) are highly prevalent and play important roles in biology and human diseases. It is now also recognized that many IDPs remain dynamic even in specific complexes and functional assemblies. Computer simulations are essential for deriving a molecular description of the disordered protein ensembles and dynamic interactions for a mechanistic understanding of IDPs in biology, diseases, and therapeutics. Here, we provide an in-depth review of recent advances in the multi-scale simulation of disordered protein states, with a particular emphasis on the development and application of advanced sampling techniques for studying IDPs. These techniques are critical for adequate sampling of the manifold functionally relevant conformational spaces of IDPs. Together with dramatically improved protein force fields, these advanced simulation approaches have achieved substantial success and demonstrated significant promise towards the quantitative and predictive modeling of IDPs and their dynamic interactions. We will also discuss important challenges remaining in the atomistic simulation of larger systems and how various coarse-grained approaches may help to bridge the remaining gaps in the accessible time- and length-scales of IDP simulations.