Search bioRxiv⌕ Search

bioRxiv · 10.1101/2025.06.26.661701

Molecular characterization of unique multi-domain harbouring fungal rhodopsin for establishing their novel opto-synthetic biological usages

Abstract

Organisms employ light as an external stimulus for regulating cellular functions. The light-sensitive photoreceptors detect light at varying wavelengths, activating signaling cascades and triggering a range of physiological responses. Rhodopsin is a transmembrane heptahelical protein that functions as an ion channel, or a pump, and sensory receptor, respectively. It consists of a light-sensing chromophore, a retinal that upon absorbing light, initiates a series of signaling pathways of sensory perception, growth and survival. Modular rhodopsin (Different from Rhodopsin-Cyclase Module) has been reported in lower eukaryotes, its identification, characterisation and functional significance in the Fungal Kingdom largely unknown. Here, we report the identification of novel modular rhodopsins in fungi, which highlights their potential usages towards the unexplored opto-biotechnological applications (e.g., biomanufacturing of terpenoids, cytoskeleton regulation, DNA metabolism, light-controlled acetyltransferase, etc.) simply by illumination. Furthermore, identification of novel modular rhodopsins augments the expansion of the new optogenetic tools for a wide range of relevant applications. The structural and homology analysis of these identified domains sheds light on their evolutionary lineage and relatedness with the well-characterised bacteriorhodopsin, sensory and channelrhodopsin. The interactome analysis effector domain coupled with the microbial rhodopsin (Rh) reveals RPEL-mediated gene expression and metabolite regulation, which further modulates the retinol synthesis pathway. The role of the fungal Rh-RPEL effector domain in modulating the terpenoid and sphingolipid metabolism in response to light was successfully elucidated via protein-protein interaction and Biosynthesis Gene Cluster (BGC) analysis. This highlights the potential of these novel opto-synthetic biological usages that can induce the light-dependent production of commercially relevant fungal bioactive(s).

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kumari, A., Kumar, A., Sharma, K., Pati, S. R., Mohanty, S., KATERIYA, S.. 2025-06-30. Molecular characterization of unique multi-domain harbouring fungal rhodopsin for establishing their novel opto-synthetic biological usages. https://doi.org/10.1101/2025.06.26.661701

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Targeted finetuning enables co-folding models to learn ligand-induced protein conformational states

Advances in protein structure prediction have enabled all-atom protein-ligand co-folding models that predict bound conformations directly from sequence and small-molecule structure. However, these models often fail to generalize to novel binding sites or alternative protein conformational states, limiting their utility for chemical biology and drug discovery. Here we show this limitation reflects training data bias rather than architectural constraints and can be overcome through targeted finetuning. Using ten previously unseen X-ray structures of Werner (WRN) helicase from a drug discovery program, we finetune Boltz-1 to learn both an allosteric binding site and a large conformational change locking the enzyme in an inactive state, while preserving accuracy on the ATP-bound state. The finetuned model generalizes to different chemical series and transfers the conformational logic across RecQ-family helicases in a binding-site sequence-dependent manner. This approach provides a blueprint for adapting foundation models as new structural and mechanistic data emerge, enabling co-folding networks to capture ligand-induced conformational switches and binding poses absent from their training data but central to biological regulation and therapeutic intervention.

bioinformatics↗

Benchmarking single-cell foundation models for aging biology

Single cell foundation models (scFMs) provide representations of cellular states, but their utility across biological questions in aging research remains unclear. We established a benchmark of cellular representations for aging research, evaluating ten general-purpose scFMs, three aging-specific models and conventional methods across five biological questions using more than 2.5 million single cell transcriptomes. Using frozen pretrained representations, Geneformer performed best among scFMs for chronological age prediction and age pseudotime concordance, although 2,000 highly variable genes achieved higher mean performance. Several scFMs captured positive molecular age shifts across three disease contexts, consistent with reported aging-associated changes. SCimilarity performed well for rare cellular state identification across out-of-distribution datasets, exceeding aging specific models and conventional baselines. At the gene level, scGPT showed the highest recovery of reference TF target interactions, including aging-related regulatory hubs. Overall, scFMs supported diverse aging analyses, but performance depended on the biological question, highlighting their utility for rare cellular state identification and regulatory analysis.

bioinformatics↗

CryoMV: Structure-Prior-Guided Modeling and Real-Particle Validation of Continuous Conformational Transitions in Cryo-EM

Continuous protein conformations are essential for understanding fundamental biological processes and supporting drug discovery. Although cryo-EM can resolve individual states at high resolution, recovering continuous heterogeneity from 2D particle images remains challenging. High noise, motion blur, and limited structural priors make it difficult to accurately generate and validate high-resolution continuous conformations using raw particle data. Here, we introduce cryoMV, a framework that integrates structure-prior-guided modeling with real-particle validation for continuous conformational transitions. CryoMV uses reference density maps to establish structural anchors and motion priors, models candidate transition paths between selected conformations, and transfers the learned representation to raw 2D cryo-EM particle images. Each candidate conformation is subsequently evaluated using the estimated particle poses and contrast transfer functions. Supported conformations are reconstructed through raw particle back-projection and assessed using canonical half-maps and Fourier shell correlation. On EMPIAR-10516 and EMPIAR-10345, cryoMV achieves excellent performance in terms of robustness, verifiability, and reconstruction resolution. By incorporating structure-prior modeling and evidence from the raw particles, cryoMV offers an explicit mechanism for assessing whether generated conformations are supported by experimental data and provides a practical approach to reducing model-induced artifacts in continuous cryo-EM heterogeneity analysis.

bioinformatics↗