Proteins in action : simulations of conformational changes in small proteins

The initiation of de novo protein folding on the ribosome

How the earliest structure within the unfolded state is formed during biosynthesis on the ribosome and whether it has any consequences for downstream folding remain open questions. Here, we combine 15N paramagnetic relaxation enhancement NMR with all-atom molecular dynamics simulations to characterise the unfolded state of a folding-competent immunoglobulin-like domain on the ribosome at the cusp of folding initiation. We identify three structurally distinct sub-ensembles that differ in compaction and ribosome interactions. Non-native contacts, together with ribosome interactions, likely delay folding, yet their persistence alongside early native-like contacts within a sparsely populated compact sub-ensemble suggests they may also facilitate the formation of a co-translational folding nucleus, whose contacts overlap with those of the downstream intermediates. From these findings we infer a mechanistic model of de novo folding initiation during biosynthesis and, by linking the folding nucleus to downstream partially structured intermediates and the native state, provide a complete atomistic description of a co-translational folding pathway.

Intra-Protein Interfaces Control Folding Dynamics and Mechanical Stability in a De Novo Designed Repeat Protein

Protein function evolution through the lens of conformational dynamics: A single-molecule perspective.

Understanding how proteins evolve requires moving beyond the classical sequence-structure paradigm toward a framework that incorporates conformational dynamics as a substrate of innovation. Within this framework, function emerges not only from single structures but from an ensemble encoded in the sequence. In this context, single-molecule Förster resonance energy transfer, which resolves heterogeneous and transient conformations inaccessible to ensemble methods, has shown that dynamics can tune activity, specificity, and regulation. Importantly, disordered proteins - which maintain biochemical properties rather than precise sequences - represent the limiting case of this principle, where dynamics become the primary substrate for evolution. Recent advances have extended these principles to protein design, demonstrating that dynamics are not only evolvable but also programmable. Together, these insights position conformational dynamics at the center of understanding and engineering the evolutionary logic of protein function, opening the door to study how proteins are tuned to operate under the nonequilibrium conditions of living cells.

Rho (𝛒) Analysis to Dissect RNA Folding and Assembly Pathways

The modular structure and energetics of RNA simplifies its folding. Leveraging this modularity, we introduce Rho (ρ) analysis to systematically dissect RNA conformational pathways. ρ analysis uses isolated RNA secondary or tertiary contacts as external standards to provide insights not possible via the “internal” comparisons of traditional ϕ analysis. Equivalent effects of a mutation on the folding rate constant of the RNA of interest and the thermodynamic stability of the isolated contact indicate that the mutated interaction is fully formed prior to the rate-limiting transition state; the absence of a kinetic effect indicates that the interaction is formed after this transition state. Comparisons with properties of the isolated contact provide additional insights about conformational pathways. We demonstrate ρ analysis by dissecting Tetrahymena group I intron folding pathways, using a split intron in which the P5abc subdomain assembles with the intron core through three tertiary contacts. We uncover multiple folding pathways and modulation in pathway flux that are readily understood from the energetic properties of the constituent RNA motifs. Extending these concepts to RNA-guided DNA recognition by CRISPR-Cas12a, crRNA–DNA mismatches give substantial ϕ values across much of the target, indicating a late transition state in binding. Thermodynamic penalties from mismatches support modular base-pairing energetics and define an upper bound on DNA target specificity. Our results establish ρ analysis as a general framework to probe RNA conformational pathways and function. It is straightforward to implement and can be readily applied in vitro and in cells.

Benchmarking AI-generated structural ensembles of membrane proteins against physics-based modelling

Proteins dynamically switch between a continuum of interconverting conformational states, and understanding these structural dynamics is important for understanding protein function and for developing therapeutics. Molecular dynamics (MD) simulations can provide insight into protein conformational ensembles, but sampling rare conformational states can require substantial computational resources. The recent development of AI-based approaches for generating protein conformational ensembles, such as the Biomolecular Emulator (BioEmu), offers a potential alternative, although it remains unclear whether these approaches can accurately capture the conformational landscapes, especially for special cases such as membrane proteins. Here, we assess the ability of BioEmu to model the conformational dynamics of a model membrane protein, the bacterial rhomboid intramembrane proteases GlpG. We find that BioEmu generates a range of conformations corresponding to both open and closed states of the rhomboid lateral gate, including states associated with different stages of the catalytic cycle. These conformations broadly correspond to states sampled during microsecond-timescale MD simulations, although BioEmu does not reproduce the full conformational landscape observed using MD. BioEmu also samples substantial conformational heterogeneity within the soluble domains of rhomboids, which are highly flexible and poorly represented in experimental structures. Overall, our findings demonstrate that BioEmu can generate plausible conformational ensembles for relatively large, six-and seven-pass membrane proteins, sampling rare states at a fraction of the computational cost of conventional MD simulations. These results suggest that AI-based ensemble generation could provide an accessible approach for exploring membrane protein dynamics and complement conventional molecular modelling approaches.

Biomolecular Condensates Dictate the Folding Landscape of Protein Alpha-Helices

This work investigates how protein folding landscapes are altered inside condensates, using the protein α-helix as a model folded domain and develops a chemically specific, residue-resolution model for quantification of α-helical folding and applies it to characterize diverse helices within condensates of varying physicochemical properties.