Biology subjects

Viliuga, V.

Publications and source records attributed to Viliuga, V..

4 recordsLinked to original sources

bioRxiv2026

Small-molecule activators of the Staphylococcus aureus ClpC/ClpP AAA+ protease

Summary/AbstractThe central AAA+ ClpC/ClpP protease in Gram-positive bacteria is crucial for virulence and stress resistance and has been recognized as drug target. Natural cyclic peptides deregulate the essential Mycobacterium tuberculosis ClpC1 and cause cell death. Similarly, overactivated mutants of the non-essential Staphylococcus aureus ClpC homologue cause uncontrolled proteolysis and severe toxicity in vivo. However, no chemical modulators of S. aureus ClpC have been described. Here, using a biochemical high-throughput screen we identify eight chemically distinct bona fide small molecules that robustly stimulate ClpC ATPase and proteolytic activity in vitro. Structural, computational, and mutational analyses define two ligandable regulatory sites within the ClpC N-terminal domain (NTD) as compound targets: a conserved hydrophobic groove and an allosteric pArg1 pocket, both engaged in substrate recognition. These findings establish S. aureus ClpC as chemically targetable and provide mechanistic insight into its regulatory architecture, enabling future development and optimization of chemical probes to deregulate AAA+ protease control.

biochemistry↗

bioRxiv2026

ABAG-Rank: Improving Model Selection of AlphaFold Antibody-Antigen Complexes by Learning to Rank

MotivationAlphaFold has transformed structural biology with an unprecedented accuracy in modeling protein structures and their interactions with biomolecules, with AlphaFold3 (AF3) achieving state-of-the-art performance. However, AF3 and other methods often struggle to accurately predict the structure of protein complexes that lack strong co-evolutionary information, such as antibody-antigen (Ab-Ag) complexes. One of the fundamental issues is that AF3 often generates accurate predictions, but fails to reliably distinguish them from the much larger set of incorrect ones. ResultsTo address this, we propose ABAG-Rank, a deep neural network that provides an efficient and robust solution for model selection of Ab-Ag interactions from a pool of structural ensembles predicted with AlphaFold. Built on the permutation-invariant DeepSets architecture, ABAG-Rank can process variable-sized ensembles of structural decoys and is directly applicable to prediction settings in which the number of candidates may vary. We train a model on a redundancy-reduced set of all known antibody-antigen complexes and find that simple geometric descriptors, along with confidence scores from AlphaFold, provide rich information about interface quality without requiring intensive physics-based calculations. Our experiments demonstrate that ABAG-Rank significantly outperforms AF3 internal scoring and the ranking performance of existing deep learning baselines. ImplementationSource code can be found at: https://github.com/tadteo/ABAG-Rank

bioinformatics↗

bioRxiv2025

Limitations of the refolding pipeline for de novo protein design

With the emergence of powerful deep learning-based tools, computational protein design has become a widely accessible technique. Nowadays, it is possible to perform both sequence and structure design in a matter of minutes, making the technology attractive to the broader scientific community. In protein design campaigns, one of the most common in silico strategies to evaluate how well a sequence encodes a target structure is the so-called self-consistency or refolding pipeline. In this approach, a structure prediction model is used to refold the designed sequence to probe whether it is compatible with the intended structure, and is evaluated via two metrics linked to experimental success: the confidence score of the predicted structure (pLDDT) and the self-consistency root-mean-square deviation (scRMSD), which measures how closely the refolded structure matches the target. In this work, we systematically evaluate how different models and structure prediction settings impact these metrics, and to what extent they can be used to reliably filter sequence design candidates. We show that evolutionary information can obscure folding models abilities to assess sequence-structure compatibility, reducing the predictive performance of refolding metrics for experimental success, particularly for designs that share homology with natural sequences. We further highlight limitations of refolding metrics, including their sensitivity to structural features, such as flexibility. Our findings raise awareness of potential pitfalls in refolding-based evaluation and support more informed use of these metrics in protein design campaigns.

bioinformatics↗

bioRxiv2024

ColBuilder: Flexible structure generation of crosslinked collagen fibrils

Collagen fibrils are fundamental building blocks of connective tissues, yet generating accurate molecular models of their structure remains challenging due to their hierarchical organization and complex crosslinking patterns. ColBuilder has been developed to automate the generation of atomistic models of crosslinked collagen fibrils and facilitate the setup of molecular simulations. The tool integrates homology modeling, higher-order structure generation and optimization to build complete fibril structures with precise control over sequence composition, crosslinking patterns, and dimensions. Users can explore different collagen sequences, manipulate crosslink chemistry through mixed ratios and densities, and generate fibrils of varying diameter and length. All-atom molecular dynamics simulations of 335 nm-long fibrils validate the generated structures, showing excellent agreement with experimental measurements of D-band periodicity and force-extension behavior. ColBuilder is available both as an open-source command-line application and through a web interface at colbuilder.mpip-mainz.mpg.de.

biophysics↗