Decoding Spliceosome Dynamics through Computation and Experiment

RNA splicing is orchestrated by a complex and exceptionally dynamic RNA–protein machine, called the spliceosome. Stepwise, large-scale structural and compositional remodeling of the spliceosome enables splicing and ensures its fidelity. While cryogenic electron microscopy provided structural information on numerous splicing cycle intermediates, allowing large-scale rearrangements to be inferred on a comparative basis, all-atom simulations complement and enrich structural studies by capturing the dynamic nature of the spliceosome on a finer but equally important scale. Here, we review the current understanding of the spliceosome’s function attained by enriching experimental insights with computation. We focus on splicing factors mediating the spliceosome’s dynamic behavior, key for splicing cycle progression, and discuss computational challenges on the path toward more accurate large-scale simulations that could further bridge the gap between computational and experimental data. A synergistic interplay between experiment and computation is vital for obtaining high-accuracy structural ensembles of the spliceosome and its components and for addressing unresolved mechanistic and biological questions related to splicing. Such integrative approaches also hold promise for advancing the design of splicing-targeted therapeutics and gene modulation technologies for treating diseases linked to splicing dysregulation.