Katja Luck

Differential gene expression rarely explains why certain genes, e.g. associated with tissue-specific manifestations of human disease, exert tissue-specific functions. To understand how gene expression and molecular context act in concert to drive tissue-specific gene function, we focus on Neurodevelopmental disorders (NDDs), for which underlying disease mechanisms are still poorly understood. We developed a computational framework that harmonizes and integrates spatiotemporal transcriptome datasets enabling us to systematically quantify preferential gene expression across tissues and developmental stages. Applying this pipeline to approximately 1,000 NDD-associated genes, we find that only about 10% show brain-preferential expression, with distinct gene clusters instead exhibiting significant upregulation in non-brain tissues. Investigation of these clusters reveals regulatory mechanisms that govern mitochondrial function to meet neurons’ high energetic demands, which are mechanistically distinct from those employed by other energy-intensive tissues such as heart and skeletal muscle. Furthermore, by integrating tissue-agnostic protein-protein interaction data with our expression resource, we identify putative brain-specific interaction partners for 22 uniformly expressed NDD-associated genes. We experimentally validate these interactions for QRICH1 and demonstrate that disease-associated mutations disrupt them, providing a molecular hypothesis for how QRICH1 acquires brain-specific function and how its disruption might cause Ververi-Brady syndrome. Together, our findings highlight the importance of molecular context for brain-specific gene function in the context of neurodevelopmental disease. We provide a framework for dissecting molecular mechanisms of tissue-specific Mendelian-like disorders and for exploring how biological processes are adapted to tissue-specific needs.

Protein-protein interactions mediate a vast range of cellular functions, requiring diverse modes of binding. While recent years have seen major efforts to chart and classify the protein structure universe, we lack comparable methods to assess and cluster that diversity in interface structure at interactome scale. Here, we present Foldseek-Interface, a method that converts 3D interface structures into searchable sequences to enable fast alignment and clustering of protein interaction interfaces. It matches the accuracy of state-of-the-art tools while running up to 230 times faster. Applying it to all biological assemblies in the PDB, we cluster 3.1 million dimers into 77,167 interface clusters and use this resource to characterise interface diversity, evolution, and pathogen mimicry. Application of Foldseek-Interface to resources of predicted protein complex structures rapidly revealed putatively novel interface types worth further experimental interrogation. Foldseek-Interface and the interface cluster resource are freely available as webservers for search (https://search.foldseek.com/interface) and exploration (https://interface.foldseek.com).

In the mammalian circadian clock, the transcription factor CLOCK/BMAL1 cycles between an active state recruiting co-activators like MLL1 and repressed states associated with PER1/2 and CRY1/2 clock proteins. The MLL1 complex component WDR5 was also found in a repressive PER complex. Here we show that WDR5 directly binds to the CRY binding domain (CBD) regions of PER1 and PER2. PER2 binds WDR5 via a WBM motif within the PER2/CRY interface, imposing competition between PER2/WDR5-, PER2/CRY- and RbBP5/WDR5 complexes. PER1 binds WDR5 predominantly via a WIN motif outside the CBD, enabling formation of trimeric PER1 _WIN /WDR5/RbBP5 _WBM - and PER1/WDR5/CRY complexes. WDR5 WIN site inhibitors weaken PER1/WDR5 interactions and impact cellular Bmal1- and Per2 expression dependent on the PER1 WIN motif, likely modulating the primary BMAL1/CLOCK/PER/CRY- and secondary BMAL1/REV-ERB/ROR circadian feedback loop. Together, our studies revealed a role of WDR5 as a direct PER interaction partner with critical differences between PER1 and PER2.

Pathogens infect hosts by interacting with host proteins and exploiting their functions to their advantage. Short linear motifs, small functional regions within intrinsically disordered protein regions, are common mediators of host-pathogen protein interactions. While motifs have been more extensively studied in viruses and bacteria, the extent to which eukaryotic unicellular parasites use motifs during infection remains poorly explored. Toxoplasma gondii is a widespread intracellular apicomplexan parasite capable of infecting all warm-blooded animals and invading any of their nucleated cells. Toxoplasma ’s secreted proteins are key in interacting with host proteins during infection, making them potential sources for motifs. To study the role of motifs in Toxoplasma gondii infection, we curated 19 known motif instances in Toxoplasma proteins from the scientific literature. To identify more motifs in Toxoplasma- secreted proteins, we developed a computational pipeline that predicts and annotates putative motif matches with structural and functional features. Using this approach, we identified 24,097 motif matches within 295 proteins from secretory organelles. We highlight strategies to further prioritize likely functional motif matches by focusing on integrin motifs, degrons, TRAF6-binding motifs, and 42 confirmed secreted proteins. We subjected peptides containing four predicted TRAF6-binding motifs to experimental validation, supporting the predicted motifs in the Toxoplasma proteins RON10 and GRA15. Our motif predictions provide a valuable resource for generating hypotheses and designing experiments to study infection mechanisms. The characterization of motifs in Toxoplasma will be key to understanding the molecular principles underlying its broad host range and more comprehensive apicomplexan infection strategies. IMPORTANCE: Toxoplasma gondii is a widely distributed intracellular parasite that achieves successful infection by interacting with different host cell proteins. Short linear motifs are small functional modules found in unstructured protein regions and recognized by folded protein domains. Given that unstructured protein regions are a common feature of Toxoplasma ’s proteins, we hypothesize that motifs play important roles during its infection cycle. Here, we highlight the role of motifs during the Toxoplasma host cell invasion cycle through a curated set of motif examples. Through a computational pipeline, we predict thousands of motifs in secreted proteins, outline strategies for working with these predictions, and finally experimentally test peptides containing a motif involved in the innate immune response, successfully supporting the potential binding of two motifs. Our work provides a resource for further motif testing in Toxoplasma proteins, aiming at understanding the molecular mechanisms of the organism’s infection strategies and its broad host range.

Protein-protein interactions mediate a vast range of cellular functions, requiring diverse modes of binding. While recent years have seen major efforts to chart and classify the protein structure universe, we lack comparable methods to assess and cluster that diversity in interface structure at interactome scale. Here, we present Foldseek-Interface, a method that converts 3D interface structures into searchable sequences to enable fast alignment and clustering of protein interaction interfaces. It matches the accuracy of state-of-the-art tools while running up to 230 times faster. Applying it to all biological assemblies in the PDB, we cluster 3.1 million dimers into 77,167 interface clusters and use this resource to characterise interface diversity, evolution, and pathogen mimicry. Application of Foldseek-Interface to resources of predicted protein complex structures rapidly revealed putatively novel interface types worth further experimental interrogation. Foldseek-Interface and the interface cluster resource are freely available as webservers for search ( https://search.foldseek.com/interface ) and exploration ( https://interface.foldseek.com ). Contact: martin.steinegger@snu.ac.kr , k.luck@imb-mainz.de

Protein–protein interactions (PPIs) drive cellular function. Obtaining quantitative maps of protein interactomes is essential for linking genotype to phenotype and predicting cellular outcomes. A large fraction of PPIs are mediated by domain–motif interactions (DMIs), where motifs within intrinsically disordered regions bind to structured domains in partner proteins. These transient and low-affinity interactions play central roles in regulatory and cell signaling processes, and their disruption has been linked to disease. However, due to their transient and dynamic nature, systematic experimental characterization of DMIs has been challenging. Recent advances in high-throughput experimental technologies and machine learning are transforming our ability to map and predict DMIs on a proteome-wide scale. Here, we review the molecular characteristics of motifs, domains, and their interactions; discuss emerging experimental and computational approaches for their discovery; and outline current challenges and future directions toward quantitative DMI interactomes.

Genotype-phenotype relationships are mediated through intricate networks of physical and functional interactions among macromolecules. Knowledge of the interactome is vital to understand and model genetics and cellular biology. Recent advances in accurately predicting tertiary protein structures using artificial intelligence (AI) approaches such as AlphaFold ¹ have revived the vision that the protein-protein interactome might be fully predictable through computational modeling of quaternary structures. Here we present a comprehensive experimental framework to systematically assess the impact of AI-driven interactome predictions for yeast ² and human ³ . We find that the quality of high-confidence predictions is on par with established experimental approaches. However, in proteome-wide screening, the tested AI approaches underperform in the discovery of strictly novel protein-protein interactions (PPIs) compared to experimental reference interactome maps. In particular, the yeast interactome map described here identifies >40-fold more novel PPIs than its AI counterpart. Strikingly, AlphaFold provides structural models for a substantial number of experimentally identified PPIs missed by the virtual screens. Our results suggest that, at this stage, the main contribution of AI predictions is to provide quaternary structure models for experimentally identified PPIs.

Chromatin remodeling by BAF complexes regulates access to DNA with implications in transcription, replication and the sensing and repair of DNA lesions, processes that are closely coupled to cell growth control. BAF complexes exist in three major subtypes each composed of a unique subset of about a dozen subunits. While for individual BAF subunits direct functions in DNA repair and cell growth have been described, we lack a more systematic interrogation of BAF perturbation and its implications in these processes. To fill this need, we subjected an isogenic BAF knockout collection to treatment with diverse genotoxic and cytotoxic compounds quantifying proliferation, cell cycle phase distributions, cell fate decisions, DNA double-strand break signaling, and transcriptional responses. The screen uncovered vulnerabilities of selected BAF-deficient cells to MEK and EGFR pathway inhibition, as well as unexpected resistance toward checkpoint inhibitors and certain DNA-damaging agents. Notably, loss of distinct ARID paralogs produced markedly divergent phenotypes, indicating limited functional redundancy and subtype-specific contributions to genome stability and cell cycle regulation. Collectively, these results provide a comparative resource linking BAF complex composition to genotoxic and cytotoxic stress responses, highlighting candidate synthetic-lethal interactions and generating hypotheses for mechanistic studies of BAF complexes in genome maintenance and cell growth control.