Characterizing thermally activated transitions in high-dimensional rugged energy surfaces is a very challenging task for classical computers. Here, we develop a quantum annealing scheme to solve this problem. First, the task of finding the most probable transition paths in configuration space is reduced to a shortest-path problem defined on a suitable weighted graph. Next, this optimization problem is mapped into finding the ground state of a generalized Ising model. A finite-size scaling analysis suggests this task may be solvable efficiently by a quantum annealing machine. Our approach leverages on the quantized nature of qubits to describe transitions between different system’s configurations. Since it does not involve any lattice space discretization, it paves the way towards future biophysical applications of quantum computing based on realistic all-atom models.

The cowpea chlorotic mottle virus (CCMV) has emerged as a model system to assess the balance between electrostatic and topological features of single‐stranded RNA viruses, specifically in the context of the viral self‐assembly. Yet, despite its biophysical significance, little structural data on the RNA content of the CCMV virion is available. Here, the conformational dynamics of the RNA2 fragment of CCMV was assessed via coarse‐grained molecular dynamics simulations, employing the oxRNA2 force field. The behavior of RNA2 was characterized both as a freely‐folding molecule and within a mean‐field depiction of the capsid. For the former, the role of the salt concentration, the temperature and of ad hoc constraints on the RNA termini was verified on the equilibrium properties of RNA2. For the latter, a multi‐scale approach was employed to derive a potential profile of the viral cavity from atomistic structures of the CCMV capsid in solution. The conformational ensembles of the encapsidated RNA2 were significantly altered with respect to the freely‐folding counterparts, as shown by the emergence of long‐range motifs and pseudoknots. Finally, the role of the N‐terminal tails of the CCMV subunits is highlighted as a critical feature in the construction of a proper electrostatic model of the CCMV capsid.

The paradigmatic sequence–structure–dynamics–function relation in proteins is currently well established in the scientific community; in particular, a large effort has been made to probe the first connection, indeed providing convincing evidence of its strength and rationalizing it in a quantitative and general framework. In contrast, however, the role of dynamics as a link between structure and function has eluded a similarly clear-cut verification and description. In this work, we propose a pipeline aimed at building a basis for the quantitative characterization of the large-scale dynamics of a set of proteins, starting from the sole knowledge of their native structures. The method hinges on a dynamics-based clusterization, which allows a straightforward comparison with structural and functional protein classifications. The resulting basis set, obtained through the application to a group of related proteins, is shown to reproduce the salient large-scale dynamical features of the dataset. Most interestingly, the basis set is shown to encode the fluctuation patterns of homologous proteins not belonging to the initial dataset, thus highlighting the general applicability of the pipeline used to build it.

The paradigmatic sequence-structure-dynamics-function relation in proteins is nowadays well established in the scientific community; in particular, large effort has been spent to probe the first connection, indeed providing convincing evidence of its strength and rationalising it in a quantitative and general framework. In contrast, however, the role of dynamics as a link between structure and function has eluded a similarly clear-cut verification and description. In this work, we propose a pipeline aimed at building a basis for the quantitative characterisation of the large-scale dynamics of a set of proteins, starting from the sole knowledge of their native structures. The method hinges on a dynamics-based clusterization, which allows a straightforward comparison with structural and functional protein classifications. The resulting basis set, obtained through the application to a group of related proteins, is shown to reproduce the salient large-scale dynamical features of the dataset. Most interestingly, the basis set is shown to encode the fluctuation patterns of homologous proteins not belonging to the initial dataset, thus highlighting the general applicability of the pipeline used to build it.

Shwachman–Diamond syndrome (SDS) is a rare autosomal recessive disorder characterized by pleiotropic phenotypes, including pancreatic insufficiency, skeletal abnormalities, and bone marrow dysfunction. Notably, patients with SDS exhibit an increased risk of developing myelodysplastic syndrome and leukemia. In this study, we employed a combination of comparative molecular dynamics (MD) simulations and small-angle X-ray scattering (SAXS)-based analysis to investigate the Shwachman–Bodian–Diamond syndrome protein (SBDS). Specifically, we explored the molecular basis of the syndrome by examining the conformational dynamics of a set of missense mutants of SBDS in comparison to those of the wild-type (WT) protein. Our observations suggest that different mutations may impact (i) the interaction of SBDS with the ribosome, (ii) the binding of SBDS to Elongation Factor-Like 1 (EFL1), and (iii) the SBDS rearrangements coupled to EFL1 binding. Extensive MD simulations, with a total simulation time of 17 μs, revealed variations in the interdomain flexibility of SBDS, which are consistent with previously published affinity data and the new SAXS experimental data presented here. We propose a structural rationale behind the previously reported weak interaction of mutants I167T, R175W, and I212T with EFL1. Additionally, SAXS data indicate that R19Q, I167T, and R175W mutants exhibit altered relative abundances of SBDS conformational states in solution, further supporting our computational results. Overall, our integrated computational and experimental approach provides a comprehensive understanding of how specific mutations in SBDS alter its structural dynamics and binding interactions. These insights enhance our broader understanding of SBDS function and its role in ribosome biogenesis.