Biology subjects

Korbeld, K. T.

Publications and source records attributed to Korbeld, K. T..

3 recordsLinked to original sources

bioRxiv2026

Cell cycle-dependent protein dynamics in budding yeast resolved by deconvolution of bulk proteomics

The cell division cycle is characterised by oscillatory dynamics in regulatory mechanisms and biosynthesis, coordinated with genome replication and segregation. To understand these dynamics, quantitative cell cycle-dependent protein concentration data is essential. Unfortunately, accurate resolution of cell cycle-dependent protein dynamics is challenging because single-cell proteomics is currently infeasible and bulk proteomics requires inherently imperfect cell synchronisation. Here, we developed a computational method to deconvolve cell cycle-dependent protein concentration dynamics and applied it to new budding yeast bulk proteome data. Key to this method was a yeast population model, parameterised with experimental cell cycle progression and volume growth data, for quantifying the desynchronisation in sampled populations. We performed deconvolution on 3373 proteins, using cross-validation to determine regularisation parameters, and identified 563 proteins with cell cycle-dependent dynamics. Many of these dynamics were consistent with known yeast biology and dynamic proteins were enriched for several metabolic process, extending previous observations and supporting the emerging picture of metabolic activity as varying substantially over cell cycle phases. We consider the generated cell cycle-resolved budding yeast proteome data a key resource.

systems biology↗

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

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↗