Joint Publication – VirusREvolution

Joint Publications

319 entries « ‹ 1 of 7 › »

2026

Grigson, Susanna R; Geliashvili, Natia; Schubert, Torsten; Bouras, George; Mallawaarachchi, Vijini; Bogacz, Marta; Hellmich, Ute A; Edwards, Robert A; Dutilh, Bas E

Computational prediction resolves thousands of homooligomeric phage protein structures Journal Article

In: bioRxiv, 2026, ISSN: 2692-8205.

Abstract | Links | BibTeX

@article{pmid42244735,

title = {Computational prediction resolves thousands of homooligomeric phage protein structures},

author = {Susanna R Grigson and Natia Geliashvili and Torsten Schubert and George Bouras and Vijini Mallawaarachchi and Marta Bogacz and Ute A Hellmich and Robert A Edwards and Bas E Dutilh},

doi = {10.64898/2026.05.24.727406},

issn = {2692-8205},

year  = {2026},

date = {2026-05-01},

urldate = {2026-05-01},

journal = {bioRxiv},

abstract = {Bacteriophages (phages) play essential roles in microbial systems, yet most phage proteins remain poorly characterised. Protein tertiary and quaternary structure information contributes valuable information about protein function. As many phage proteins function as homooligomers, complexes that consist of multiple identical subunits, there is great interest in computationally predicting their configurations. Here we present a computational framework, the Phage Homomer Level Estimate and Generation Method (PHLEGM) for inferring homooligomeric states directly from the protein sequence by combining AlphaFold-Multimer modelling with inter-subunit interface quality assessment. We proceeded to experimentally validate two out of nine predicted homooligomers using size exclusion chromatography and complementary hydrodynamic techniques. These efforts confirmed our predictions for a dimer and a trimer, highlighting the value of experimentally benchmarked computational predictions and showing the challenges of heterologous phage protein production. Applied to >22,000 phage protein sequences in the PHROGs database, our approach revealed extensive diversity in phage homooligomeric protein complexes. Benchmarking against protein language model-based predictors on a curated reference set of known phage homooligomers demonstrated superior accuracy of our structure-based method, achieving robust performance in classifying protein homooligomeric states, with the highest accuracy observed for trimers and higher-order complexes. These results highlight the value of computational predictions to decipher the complexities of the vast viral sequence space. All predicted complex structures and functional inferences are made publicly available to support structural and functional studies of phage proteins.},

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pubstate = {published},

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Daodu, Richard Olumide; Riccabona, Jakob R; Peter, Antonia Sophia; Ulrich, Jens-Uwe; von Creytz, Isabel; Prescott, Joseph B; Reinert, Knut; Schoeder, Clara T; Kühnert, Denise

Sequence to structure insights into Lassa virus population-level biophysical properties and glycoprotein structure catalogue Journal Article

In: Npj Viruses, vol. 4, no. 1, 2026, ISSN: 2948-1767.

Abstract | Links | BibTeX

@article{pmid42135491,

title = {Sequence to structure insights into Lassa virus population-level biophysical properties and glycoprotein structure catalogue},

author = {Richard Olumide Daodu and Jakob R Riccabona and Antonia Sophia Peter and Jens-Uwe Ulrich and Isabel von Creytz and Joseph B Prescott and Knut Reinert and Clara T Schoeder and Denise Kühnert},

doi = {10.1038/s44298-026-00196-3},

issn = {2948-1767},

year  = {2026},

date = {2026-05-01},

urldate = {2026-05-01},

journal = {Npj Viruses},

volume = {4},

number = {1},

abstract = {Lassa virus (LASV) remains a major public health threat in West Africa, with recurrent outbreaks, exported cases, and no licensed vaccine. LASV lineages are geographically separated and differ in immunogenicity and pathogenicity; however, the fundamental biophysical properties that may explain these differences remain poorly defined. Here, we analyse LASV protein properties at the population scale across lineages, focusing on the glycoprotein (GP), the principal target of humoral immunity. Across hundreds of curated sequences, protein length variation is driven primarily by short indels, with pronounced variation in the RNA polymerase and a recurrent one-amino-acid difference in GP. In parallel, population-scale analyses reveal subtle lineage- and protein-specific differences in amino-acid composition across the LASV proteins. Despite co-circulation in Nigeria, S-segment-encoded proteins from lineage III are consistently heavier than those from lineage II. An integrative framework combining random forest feature importance, Manhattan-distance profiling, Pearson correlation, and amino-acid composition analyses reveals that lineage III GPs are ~180 Da heavier on average, driven by shifts toward the use of heavier residues at specific sites. Population-scale computational structural modelling and flow-cytometric assays indicate that the N-terminal GP1 indel is structurally and functionally tolerated. Together, these findings define lineage-specific biophysical patterns in LASV and provide a catalogue of GP structures to inform vaccine and therapeutic design.},

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pubstate = {published},

tppubtype = {article}

}

Meijer, Jeroen; Skiadas, Petros; Rainey, Paul B; Hogeweg, Paulien; Dutilh, Bas E

Eco-evolutionary dynamics of massive, parallel bacteriophage outbreaks in compost communities Journal Article

In: Sci Adv, vol. 12, no. 22, pp. eaeb8246, 2026, ISSN: 2375-2548.

Abstract | Links | BibTeX

Soltaninezhad, Mohammad; Rouzbahani, Yashar; Contreras, Jhonatan; Larios, Francisco Paez; Jordan, Paul M; Werz, Oliver; Chippalkatti, Rohan; Abankwa, Daniel Kwaku; Eggeling, Christian; Bocklitz, Thomas

Lightweight CycleGAN models for cross-modality image transformation and experimental quality assessment in fluorescence microscopy Journal Article

In: Biomed Opt Express, vol. 17, no. 3, pp. 1476–1498, 2026, ISSN: 2156-7085.

Abstract | Links | BibTeX

@article{pmid41970592,

title = {Lightweight CycleGAN models for cross-modality image transformation and experimental quality assessment in fluorescence microscopy},

author = {Mohammad Soltaninezhad and Yashar Rouzbahani and Jhonatan Contreras and Francisco Paez Larios and Paul M Jordan and Oliver Werz and Rohan Chippalkatti and Daniel Kwaku Abankwa and Christian Eggeling and Thomas Bocklitz},

doi = {10.1364/BOE.578297},

issn = {2156-7085},

year  = {2026},

date = {2026-03-01},

urldate = {2026-03-01},

journal = {Biomed Opt Express},

volume = {17},

number = {3},

pages = {1476--1498},

abstract = {With the growing integration of artificial intelligence in scientific and medical applications, lightweight deep learning models have become increasingly important. These models offer substantial reductions in memory usage and computational time. Given that GPU-based model training and inference contribute significantly to carbon emissions, lightweight architectures with comparable performance to parameter-rich models present a more environmentally friendly alternative. Specifically, we build upon CycleGAN with a fixed-channel lightweight U-Net generator for modality transfer from standard confocal to super-resolution STED and deconvolved STED images, and systematically compare it against Pix2Pix and standard CycleGAN baselines. Obtaining paired datasets in medical imaging and super-resolution microscopy is often infeasible due to the need for additional experiments and the intrinsic complexity of biological sample preparation. To address this, we investigate the performance of lightweight CycleGAN models, demonstrating their ability to achieve high-fidelity modality transfer despite reduced model complexity. We introduce a fixed channel strategy within the U-Net-based generator, in contrast to the traditional channel-doubling approach. This modification significantly reduces the number of trainable parameters from 41.8 million to approximately 9 thousand, while achieving comparable or slightly improved performance. We explore the utility of GAN models as a qualitative marker for assessing experimental and labeling quality. When trained on high-quality microscopy images, the GAN implicitly learns the characteristics of optimal imaging. Deviations between GAN-generated outputs trained on high-quality data and low-quality experimental images can highlight potential issues such as photobleaching, experimental artifacts, or inaccurate labeling. In this way, the model can support qualitative assessment of experimental consistency and image fidelity in fluorescence microscopy workflows.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

With the growing integration of artificial intelligence in scientific and medical applications, lightweight deep learning models have become increasingly important. These models offer substantial reductions in memory usage and computational time. Given that GPU-based model training and inference contribute significantly to carbon emissions, lightweight architectures with comparable performance to parameter-rich models present a more environmentally friendly alternative. Specifically, we build upon CycleGAN with a fixed-channel lightweight U-Net generator for modality transfer from standard confocal to super-resolution STED and deconvolved STED images, and systematically compare it against Pix2Pix and standard CycleGAN baselines. Obtaining paired datasets in medical imaging and super-resolution microscopy is often infeasible due to the need for additional experiments and the intrinsic complexity of biological sample preparation. To address this, we investigate the performance of lightweight CycleGAN models, demonstrating their ability to achieve high-fidelity modality transfer despite reduced model complexity. We introduce a fixed channel strategy within the U-Net-based generator, in contrast to the traditional channel-doubling approach. This modification significantly reduces the number of trainable parameters from 41.8 million to approximately 9 thousand, while achieving comparable or slightly improved performance. We explore the utility of GAN models as a qualitative marker for assessing experimental and labeling quality. When trained on high-quality microscopy images, the GAN implicitly learns the characteristics of optimal imaging. Deviations between GAN-generated outputs trained on high-quality data and low-quality experimental images can highlight potential issues such as photobleaching, experimental artifacts, or inaccurate labeling. In this way, the model can support qualitative assessment of experimental consistency and image fidelity in fluorescence microscopy workflows.

Winfried Goettsch Daria Meyer, Jannes Spangenberg

Unlocking the full potential of nanopore sequencing: tips, tricks, and advanced data analysis techniques Journal Article

In: Nucleic Acids Research, vol. 54, iss. 3, pp. gkag023, 2026, ISSN: 0305-1048.

Joint Publications

2026

2025

2024