Maya Topf

Drebrin modulates F-actin networks and links them to other intracellular components, regulating crucial processes including neuritogenesis, synaptic plasticity, virus internalisation and cancer invasion. Using single-particle cryo-EM we characterise drebrin’s interaction with F-actin through two separate conserved actin binding domains (ABD1 and ABD2), revealing structural bases for its F-actin-modulating properties. We describe a multimodal interaction where drebrin’s ABD1 can adopt two conformations and a long flexible loop connecting to ABD2 allows the two ABDs to occupy multiple relative positions along F-actin. The flexible loop connecting the two ABDs also confers some propensity to loosely bundle F-actin. Drebrin’s ABDs bind across multiple actin protomers and their subdomains and modify the longitudinal inter-protomer interface, explaining its F-actin stabilising properties. Furthermore, we show drebrin’s binding site on F-actin is shared with other critical actin-binding and regulatory proteins, explaining their competitive displacement.

The Bunyaviricetes class includes several human pathogens, such as the Lassa virus (LASV), which pose a significant threat to global health and impact the society and economy in affected countries of the Global South. At present, there are no antiviral treatments or vaccines approved to treat or prevent, for example, potentially fatal human Lassa fever. Relevant processes for the development of antiviral strategies include viral genome replication and transcription, which are catalysed by the multifunctional large (L) protein. Specific binding of the 3‘ and 5‘ viral genome termini to the L protein has been proven to be essential for these processes. Therefore, we investigated the tractability of the 3’ and 5’ RNA binding sites as drug targets. To this end, in vitro assays for high-throughput screening (HTS) of small molecule libraries were used to identify inhibitors of 3‘ and 5‘ RNA binding to the L protein. Two assays per binding site based on fluorescence polarization (FP) and homogenous time-resolved fluorescence resonance energy transfer (HTRF) were established and used for the orthogonal screening of a repurposing collection. Selected compounds were profiled in biochemical experiments and analysed for antiviral activity and cytotoxicity in cell-based assays. In silico analyses support the proposed mechanism of action of the hit molecule micafungin to inhibit 5’ RNA binding to L. We established the specific viral promoter RNA–polymerase interfaces as druggable targets and demonstrate the first pharmacological inhibition of a bunyaviral promoter RNA-binding site. This established early-stage drug discovery pipeline paves the way for the development of antiviral therapeutics against bunyaviruses and could also translate to influenza viruses.

1. Biomolecular interactions are central to many essential cellular processes, but RNA-containing complexes remain challenging to resolve structurally, even as experimental methods and AI-based prediction have expanded structural coverage. Tools for the integrated analysis of complex interfaces remain limited. We present ProNA3D, a tool that provides a unified platform for analyzing protein-nucleic acid and nucleic acid-only complexes, bridging the gap between structure prediction and functional interpretation. ProNA3D supports both experimental and computationally predicted structures, incorporating scoring metrics for AlphaFold3 predictions. It also offers interactive two-dimensional interface visualization and secondary-structure topology plots for RNA and DNA. An interface-based density zoning feature facilitates structure analysis in cryo-EM maps, allowing evaluation of dynamic complexes in the context of heterogeneous density. We demonstrate ProNA3D on diverse complexes solved by X-ray crystallography or cryo-EM, as well as on computational models. For example, in a trimeric complex of HIV-1 RNA and a human antibody, ProNA3D identified a high-connectivity nucleotide with potential functional relevance. Applying ProNA3D to the entire Protein Data Bank revealed distinct interface connectivity trends and interaction modes characteristic of specific complexes (e.g., in methyltransferase-DNA and CRISPR-associated) in nucleic acid-containing interfaces. The method is available as both a UCSF ChimeraX plug-in for visualization and a command-line tool at https://gitlab.com/topf-lab/ProNA3D .

Biomolecular interactions are central to many essential cellular processes, but RNA-containing complexes remain challenging to resolve structurally, even as experimental methods and AI-based prediction have expanded structural coverage. Tools for the integrated analysis of interfaces within such complexes remain limited. We present ProNA3D, a tool that provides a unified platform for analyzing protein–nucleic acid and nucleic acid-only complexes, bridging the gap between structure prediction and functional interpretation. ProNA3D supports both experimental and computationally predicted structures, incorporating scoring metrics for AlphaFold 3 predictions. It also offers interactive 2-dimensional interface visualization and secondary-structure topology plots for RNA and DNA. An interface-based density zoning feature facilitates structure analysis of cryo-electron microscopy maps, allowing the evaluation of dynamic complexes in the context of heterogeneous density. We demonstrate ProNA3D on diverse complexes solved by x-ray crystallography or cryo-electron microscopy, as well as on computational models. In one example, ProNA3D identified a high-connectivity nucleotide with potential functional relevance in a trimeric complex of HIV-1 RNA and a human antibody. Applying ProNA3D to the entire Protein Data Bank revealed distinct interface connectivity trends and interaction modes characteristic of specific classes of nucleic acid-containing complexes (e.g., methyltransferase-DNA and CRISPR-associated). The method is available as both a UCSF ChimeraX plug-in for visualization and a command-line tool at https://gitlab.com/topf-lab/ProNA3D . In addition, the repository contains the results of the large-scale analyses.

Since CASP13, experimentalists have been encouraged to provide their cryo‐EM data along with the derived atomic models to the CASP organizers to aid assessment. In CASP16, 38 cryo‐EM datasets were provided for assessment, which represented most cryo‐EM targets. The corresponding targets typically comprised a single derived atomic structure; however, that model may be only one of several valid conformations. Flexibility often manifests as low‐resolution regions in a cryo‐EM reconstruction, particularly in RNA but often also in protein complexes. We show that local resolution in the reconstruction correlates well with the root‐mean‐square fluctuations (RMSF) of residues of accurate CASP predictions. The correlation between the local resolution and pLDDT was less clear, especially when mobile domains were present. When the resolution allowed, assessment of features such as sidechains, using our variant of SMOC with local fragment alignment, indicated that even high‐quality predictions have room for improvement; on the other hand, some predictions fitted the density better in specific regions, indicating modeling discrepancies in the target. In one extreme case, a submitted target had regions of low‐resolution that limited unambiguous model building. In such cases, part of the target structure is essentially a prediction itself, with implications for the assessment. Experimental data remain essential for model‐free assessment of predictions and offer unique analyses such as comparisons to local resolution and thus flexibility.

Many RNAs rely on their 3D structures for function. While acquiring functional 3D structures, certain RNAs form misfolded, non-functional states (‘kinetic traps’). Instead, other RNAs sequentially assemble into their functional conformations over pre-folded scaffolds. Elucidating the principles of RNA sequential assembly is thus important to understand how RNAs avoid the formation of misfolded, non-functional states. Integrating single-particle electron cryomicroscopy (cryo-EM), image processing, in solution small-angle X-ray scattering (SAXS), EM-driven molecular dynamics (MD) simulations, structure-based mutagenesis, and enzymatic assays, we have visualized the sequential multidomain assembly of a self-splicing ribozyme of biomedical and bioengineering significance. Our work reveals a distinct dynamic interplay of helical subdomains in the ribozyme’s 5’-terminal scaffold, which acts as a gate to control the docking of 3’-terminal domains. We identify specific conserved and functionally important secondary structure motifs as the key players for orchestrating the energetically inexpensive conformational changes that lead to the productive formation of the catalytic pocket. Our work provides a near-atomic resolution molecular movie of a large multidomain RNA assembling into its functionally active conformation and establishes a basis for understanding how RNA avoids the formation of non-functional ‘kinetic traps’.

CASP16 is the most recent in a series of community experiments to rigorously assess the state of the art in areas of computational structural biology. The field has advanced enormously in recent years: in early CASPs, the assessments centered around whether the methods were at all useful. Now they mostly focus on how near we are to not needing experiments. In most areas, deep learning methods dominate, particularly AlphaFold variants and associated technology. In this round, there is no significant change in overall agreement between calculated monomer protein structures and their experimental counterparts, not because of method deficiencies but because, for most proteins, agreement is likely as high as can be obtained given experimental uncertainty. For protein complexes, huge gains in accuracy were made in the previous CASP, but there still appears to be room for further improvement. In contrast to these encouraging results, for RNA structures, the deep learning methods are notably unsuccessful at present and are not superior to traditional approaches. Both approaches still produce very poor results in the absence of structural homology. For macromolecular ensembles, the small CASP target set limits conclusions, but generally, in the absence of structural templates, results tend to be poor and detailed structures of alternative conformations are usually of relatively low accuracy. For organic ligand–protein structures and affinities (important for aspects of drug design), deep learning methods are substantially more successful than traditional ones on the relatively easy CASP target set, though the results often fall short of experimental accuracy. In the less glamorous but essential area of methods for estimating the accuracy, previous results found reliable accuracy estimates at the amino acid level. The present CASP results show that the best methods are also largely effective in selecting models of protein complexes with high interface accuracy. Will upcoming method improvements overcome the remaining barriers to reaching experimental accuracy in all categories? We will have to wait until the next CASP to find out, but there are two promising trends. One is the combination of traditional physics‐inspired methods and deep learning, and the other is the expected increase in training data, especially for ligand–protein complexes.

Recent breakthroughs in AI‐driven protein structure prediction have revolutionized structural biology, unlocking new possibilities to model complex biomolecular interactions. We evaluated widely used scoring metrics for assessing models predicted by ColabFold with templates, ColabFold without templates, and AlphaFold3. We benchmarked the optimal cutoffs for these assessment scores using a set of 223 heterodimeric, high‐resolution protein structures and their predictions. Our results show that ColabFold with templates and AlphaFold3 perform similarly, and both outperform ColabFold without templates. However, the assessment scores perform best on ColabFold without templates. Furthermore, interface‐specific scores are more reliable for evaluating protein complex predictions compared to the corresponding global scores. Notably, ipTM and model confidence achieve the best discrimination between correct and incorrect predictions. Based on our results, we developed a weighted combined score, C2Qscore, to improve model quality assessment. We used C2Qscore to analyze dimers from large assemblies solved by cryoEM, revealing potential limitations of the existing metrics when multiple configurations of heterodimers are possible. This study provides insights into the strengths and weaknesses of current scores and offers guidance for improving protein complex model assessment under realistic use case conditions. C2Qscore has been integrated as a tool into our ChimeraX plug‐in PICKLUSTER v.2.0 and is also available as a command‐line tool on https://gitlab.com/topf-lab/c2qscore .

This article presents an in‐depth analysis of selected CASP16 targets, with a focus on their biological and functional significance. The authors highlight the most relevant features of the target proteins and discuss how well these were reproduced in the submitted predictions. While the overall performance of structure prediction methods remains impressive, challenges persist, particularly in modeling rare structural motifs, flexible regions, small molecule interactions, posttranslational modifications, and biologically important interfaces. Addressing these limitations can strengthen the role of structure prediction in complementing experimental efforts and advancing both basic research and biomedical applications.