Biology subjects

Fürst, M. J. L. J.

Publications and source records attributed to Fürst, M. J. L. J..

3 recordsLinked to original sources

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↗

bioRxiv2025

One-Pot Enzymatic ADDing of Click Chemistry Handles for Protein Immobilization and Bioconjugation of Small and Biomolecules

Site-specific attachment of biorthogonal handles to proteins is an essential tool in chemical biology research and diverse applications including imaging and protein immobilization, as well as for the development of next-generation therapeutics such as antibody drug-conjugates. Among the available methods, enzymatic post-translational modification of short protein tags offers precision, stability, and modularity. However, broader application is often limited by complex substrate syntheses, the requirement of long or rigid recognition tags, and limited reaction efficiencies. Here, we present ADDing, a straightforward enzymatic method for functionalizing proteins with click chemistry handles using the flavin transferase ApbE. We discovered that, given a dedicated adenine diphosphate derivative (ADD) substrate, the enzyme attaches a phosphoribosyl moiety bearing bioorthogonal handles to proteins featuring a DxxxGAT amino acid motif. As the substrates can easily be enzymatically synthesized from NAD and inexpensive precursors, ADDing click handles can be performed in a streamlined, one-pot workflow combining substrate synthesis and protein conjugation. ADDing allows rapid, high-yield functionalization of proteins featuring the recognition tag at either terminus or internal loops and is compatible with copper and copper free azide-alkyne cycloaddition reactions. To demonstrate its broad applicability, we performed a wide variety of protein functionalizations, including fluorescent labeling, protein-protein, protein-DNA conjugation, and protein immobilization. This versatile technology thus holds great potential for chemical biology and the production of biological therapeutics.

biochemistry↗

bioRxiv2025

Enriching stabilizing mutations through automated analysis of molecular dynamics simulations using BoostMut

Thermostability is a critical goal in protein engineering for applications of biocatalysts and biomedicines. Despite striking advances in biomolecular predictive modeling, reliably identifying stabilizing mutations remains challenging. Previously, molecular dynamics (MD) simulations and visual inspection have been used as secondary filter to improve the success rate of mutations pre-selected by thermostability algorithms. However, this approach suffers from low throughput and subjectivity. Here, we introduce BoostMut (Biophysical Overview of Optimal Stabilizing Mutations), a computational tool that standardizes and automates mutation filtering by analyzing dynamic structural features from MD. BoostMut formalizes the principles guiding manual verification, providing a consistent and reproducible stability assessment. Rigorous benchmarking across multiple datasets showed that integrating BoostMuts biophysical analysis improves prediction rate regardless of the initial thermostability predictor. Given a modest amount of existing mutant stability data, BoostMuts performance can be further enhanced with a lightweight machine learning model. Upon experimentally validating BoostMut predictions on the enzyme limonene-epoxide hydrolase, we identified stabilizing mutations previously overlooked by visual inspection, and achieved a higher overall success rate. We foresee BoostMut being used for mutation filtering, as an integrated step in thermostability prediction workflows, and for labelling data to train future predictors.

bioinformatics↗