Biology subjects

Daumiller, D.

Publications and source records attributed to Daumiller, D..

3 recordsLinked to original sources

bioRxiv2026

The Structural History of Eukarya

Comparative genomics has traditionally relied on sequence-based markers, yet the global evolutionary landscape of protein architecture remains largely unexplored. We present the Structural History of Eukarya (SHE), a phylogeny derived from all-vs-all structural comparisons of 1,542 eukaryotic proteomes, encompassing nearly 300 trillion protein-protein alignments. This proteome-scale analysis reveals a bipartite model of eukaryotic evolution: a rigid Strict Core dominated by cytoskeletal architecture, supporting a more plastic Operational Engine centred on translational machinery. We identify lineage-specific accelerations of structural evolution in species such as birds and ants that are decoupled from proteome expansion, and show that aggregate structural topology provides a quantitative diagnostic of reference proteome quality. Finally, we demonstrate that proteome-wide structural fidelity enables a data-driven framework for model organism selection, replacing heuristic choices with quantitative matching to human biological processes. SHE is freely accessible as an interactive portal: https://she-app.serve.scilifelab.se/

evolutionary biology↗

bioRxiv2025

RareFold: Structure prediction and design of proteins with noncanonical amino acids

Protein structure prediction and design have traditionally been confined to the 20 canonical amino acids. Expanding this chemical space to include non-canonical amino acids (ncAAs) is essential for engineering proteins with novel chemical and functional properties. However, existing methods are not designed to generalise across chemically diverse residue types. Here, we present RareFold, a deep learning architecture for structure prediction and design of proteins containing the 20 canonical amino acids and 29 ncAAs. By representing each residue as an independent token, RareFold learns context-dependent atomic interaction patterns across chemically diverse sequence spaces, enabling modelling of non-standard chemistries within a unified framework. We apply this capability in EvoBindRare, a generative framework for de novo design of linear and cyclic peptide binders with an efficient implementation that substantially reduces computational requirements compared to existing architectures. We demonstrate its performance by designing binders against Ribonuclease A, yielding novel linear and cyclic peptides incorporating ncAAs within predicted interfaces with low-micromolar affinities (KD [~]2-9 M), comparable to the native ligand (KD [~]2 M). Hydrogen-deuterium exchange mass spectrometry confirms that the designed peptides engage the target at regions consistent with predicted binding interfaces. In addition, immunogenicity profiling in human-derived organoid models shows no detectable immune activation. By extending deep learning-based protein design to non-canonical chemical spaces, RareFold enables programmable access to expanded amino acid alphabets and broadens the scope of de novo protein engineering.

bioinformatics↗

bioRxiv2025

Single-Shot Design of a Cyclic Peptide Inhibitor of HIV-1 Membrane Fusion with EvoBind

HIV evades the immune system through rapid mutation of its surface proteins, particularly the envelope glycoprotein. However, the core mechanism of viral entry, CD4 binding and co-receptor engagement remains conserved. While therapies such as Lenacapavir represent important advances, the continued emergence of resistant strains will demand new and more adaptable treatment strategies. This challenge is not unique to HIV; future pandemics will likely present similar pressures, highlighting the need for drug design methods that are not only effective but also fast and scalable. Recent advances in protein structure prediction have transformed the landscape of therapeutic design, enabling the accurate modelling of target structures from sequence alone and now facilitating the development of novel therapeutics without prior structural data. EvoBind leverages these advances to rapidly generate cyclic peptide binders in a single design round, using only the amino acid sequence of a target protein. Cyclic peptides offer several advantages over traditional linear protein molecules, including increased stability, while their small size improves oral bioavailability and enables access to challenging binding sites. Here, we demonstrate the use of EvoBind to generate cyclic peptide binders against the HIV envelope protein gp41, which is essential for viral-host membrane fusion. Cell-based assays confirm potent inhibition of two different HIV-1 strains with no detectable toxicity. The combination of artificial intelligence-guided design and streamlined experimental validation can significantly accelerate therapeutic development, reduce costs, and provide timely solutions to the challenges posed by viral evolution and emerging global health threats.

pharmacology and toxicology↗