Biology subjects

Zechner, A.

Publications and source records attributed to Zechner, A..

2 recordsLinked to original sources

bioRxiv2026

Paleogenomic time series reveal past population dynamics and their relevance for the conservation of the Alpine ibex

Endangered species often suffer from genetic threats such as inbreeding depression, the accumulation of mutation load and the general loss of genetic diversity, which may deprive them from future adaptation. Since the genetic make-up of a species is strongly affected by its evolutionary past, however, interspecific comparisons are difficult and absolute levels of genetic diversity may be a relatively poor predictor of extinction risk. To better interpret current levels of genetic diversity of actively managed species, the comparison of present-day genomic data to that of ancient material pre-dating recent anthropogenic population declines has been advocated, but remains rarely done at the evolutionary time scales necessary to understand past population dynamics. Here we demonstrate the usefulness of deep genomic time series through 40 high quality ancient genomes of the Alpine ibex, which faced near-extinction in the 19th century but has since been reintroduced across the Alps and recovered remarkably. The samples, which span the last 14,000 years, evidence strong and stable population structure at the continental scale, paired with frequent extinction and recolonization events at the local scale. These metapopulation dynamics resulted in small and largely isolated populations, with present-day, reintroduced populations only having 20% to 40% less neutral genetic diversity and 3% higher mutation load, likely due to efficient purging in the past. At the species level, however, the loss of genetic diversity was dramatic and reduced to the small fraction that segregated within the only surviving population in the Gran Paradiso area. Our analyses of ancient metapopulation dynamics in Alpine ibex underscore the importance of a healthy genetic pool for successful recolonization and long-term survival, as well as the real possibility of regional extinctions.

evolutionary biology↗

bioRxiv2025

Predicting Loop Quality in Protein Structure Models

1.Typically, sequences designed de novo are assessed in silico using deep learning-based protein structure prediction methods prior to wetlab testing. While these deep learning (DL) models excel at predicting well-ordered regions, accurate prediction of loop regions, which often are flexible and crucial for protein function, remains a significant challenge. To address this, we introduce the Equivariant Loop Evaluation Network (ELEN), a local model quality assessment (MQA) method that is tailored towards evaluating the accuracy of protein loops at the per-residue level. ELEN jointly predicts three quality metrics, local Distance Difference Test (lDDT), Contact Area Difference Score (CAD-score), and Root Mean Squared Deviation (RMSD), by comparing predicted to experimental reference structures. Learning these metrics simultaneously enables ELEN to capture complementary structural insights, providing a richer assessment of model accuracy. The network operates at all-atom resolution and employs 3D equivariant group convolutions to learn the local geometric environment of each atom. By incorporating sequence embeddings from large language models (LLMs), such as SaProt, we enhance the sequence and evolutionary awareness of the model. Furthermore, by informing ELEN with per-residue physicochemical features, the model achieves competitive accuracy relative to state-of-the-art MQA methods on the Continuous Automated Model EvaluatiOn (CAMEO) benchmark. Although ELEN was primarily developed for assessing loop quality, its architecture also demonstrates strong potential for general MQA tasks. We used ELEN to perform detailed analysis, including identification of flexible or disordered regions and assessment of structural effects from single-residue mutations on three sets of redesigned enzymes. We show that for all sets ELEN successfully identifies poor design positions and thus serves as a powerful tool for advancing both the study and modeling of loops in protein structures.

bioinformatics↗