Biology subjects

Kaltenbrunner, S.

Publications and source records attributed to Kaltenbrunner, S..

3 recordsLinked to original sources

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↗

bioRxiv2024

Computational design of highly active de novo enzymes

Enzymes are broadly used as biocatalysts in industry and medicine due to their coverage of vast areas of chemical space, their exquisite selectivity and efficiency as well as the mild reaction conditions at which they operate. Custom designed enzymes can produce tailor-made biocatalysts with potential applications extending beyond natural reactions. However, current design methods require testing of high numbers of designs and mostly produce de novo enzymes with low catalytic activities. As a result, they require costly experimental optimization and high-throughput screening to be industrially viable. Here we present rotamer inverted fragment finder-diffusion (Riff-Diff), a hybrid machine learning and atomistic modelling strategy for scaffolding catalytic arrays in de novo proteins. We highlight the general applicability of Riff-Diff by designing enzymes for two mechanistically distinct chemical transformations, the retro-aldol reaction and the Morita-Baylis-Hillman reaction. We show that in both cases it is possible to generate catalysts exhibiting activities rivalling those optimized by in-vitro evolution, along with exquisite stereoselectivity. High resolution structures of six of the designs revealed an angstrom level of active site design precision. The design strategy can, in principle, be applied to any catalytically competent amino acid constellation. These findings enable the practical applicability of de novo protein catalysts in synthesis and shed light on fundamental principles of protein design and enzyme catalysis.

biochemistry↗

bioRxiv2021

Trypanosome morphogenesis involves recruitment of a pleckstrin homology domain protein by an orphan kinesin to the microtubule quartet

Kinesins are motor proteins found in all eukaryotic lineages that move along microtubules to mediate cellular processes such as mitosis and intracellular transport. In trypanosomatids, the kinesin superfamily has undergone a prominent expansion, resulting in one of the most diverse kinesin repertoires that includes the two kinetoplastid-restricted families X1 and X2. Here, we characterize in Trypanosoma brucei TbKifX2A, an orphaned X2 kinesin. TbKifX2A tightly interacts with TbPH1, a kinesin-like protein with a likely inactive motor domain, a rarely reported occurrence. Both TbKifX2A and TbPH1 localize to the microtubule quartet (MtQ), a characteristic but poorly understood cytoskeletal structure that wraps around the flagellar pocket as it extends to the cell body anterior. The proximal proteome of TbPH1 revealed two other interacting proteins, the flagellar pocket protein FP45 and intriguingly another X2 kinesin, TbKifX2C. Simultaneous ablation of TbKifX2A/TbPH1 results in the depletion of FP45 and TbKifX2C and also an expansion of the flagellar pocket, among other morphological defects. TbKifX2A is the first motor protein to be localized to the MtQ. The observation that TbKifX2C also associates with the MtQ suggests that the X2 kinesin family may have co-evolved with the MtQ, both kinetoplastid-specific traits.

molecular biology↗