Biology subjects

Genz, L. R.

Publications and source records attributed to Genz, L. R..

3 recordsLinked to original sources

bioRxiv2026

ProNA3D: Distance-Based Analysis of Nucleic Acid-Containing Interfaces

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. In addition, the repository contains the results of the large-scale analyses.

bioinformatics↗

bioRxiv2025

Assessing scoring metrics for AlphaFold2 and AlphaFold3 protein complex predictions

1.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 analyse 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. Impact of this workMany essential cellular functions rely on protein complexes, which are now predominantly predicted using AlphaFold by both experts and non-experts. This study systematically evaluates the performance of multiple widely used scoring metrics for distinguishing accurate from poor predictions. The new C2Qscore developed in this study improves the reliability of AlphaFold model assessments, enabling a more consistent and accessible evaluation of protein structures. These advancements support downstream applications in biomedicine, drug discovery, and computational protein design.

bioinformatics↗

bioRxiv2023

PICKLUSTER: A protein-interface clustering and analysis plug-in for UCSF ChimeraX

MotivationThe identification and characterization of interfaces in protein complexes is crucial for understanding the mechanisms of molecular recognition. These interfaces are also attractive targets for protein inhibition. However, targeting protein interfaces can be challenging for large interfaces that consist of multiple interacting regions. We present PICKLUSTER -a program for identifying sub-interfaces in protein-protein complexes using distance clustering. The division of the interface into smaller "sub-interfaces" offers a more focused approach for targeting protein-protein interfaces. Availability and implementationThe plug-in PICKLUSTER is implemented for the molecular visualization program UCSF ChimeraX 1.4 and subsequent versions and and is freely available in the ChimeraX toolshed or, together with the source code, from https://gitlab.com/topf-lab/pickluster.git). Contactmaya.topf@cssb-hamburg.de

bioinformatics↗