Biology subjects

Bartels, S.

Publications and source records attributed to Bartels, S..

3 recordsLinked to original sources

bioRxiv2026

Conformational ensembles of flexible multidomain proteins: How close are we to accurate and reliable predictions?

Multidomain proteins connected by flexible linkers populate conformational ensembles that are challenging to characterize using conventional structural biology methods. In domain-linker-domain (DLD) proteins, linker-mediated inter-domain relative positions and orientations are functionally relevant, yet their dynamical behavior in solution normally remain poorly described. Small-angle X-ray scattering (SAXS) provides ensemble-averaged structural information for such systems; however, coupling with computational modeling is required to accurately describe the dynamic behavior of this family of proteins in solution. Here, we present a systematic evaluation of five ensemble-generation strategies applied to a set of eighteen proteins sharing the same two globular domains, connected by naturally occurring linkers of varying length and composition. Modeling methods based on different underlying principles are compared by assessing their agreement to experimental SAXS data, showing a large disparity and systematic structural biases among them. Furthermore, for each approach, we examine the effect of refinement against SAXS restraints and assess its capacity to describe the experimental data, as well as the induced biases in global dimensions and inter-domain distance distributions. This analysis underlines the importance of the initial conformational pool for deriving experimentally compatible ensembles. Overall, this work provides a high-quality benchmark for SAXS-driven ensemble modeling of flexible, multidomain proteins and establishes a framework for the critical interpretation of solution scattering data in systems with pronounced conformational heterogeneity.

biophysics↗

bioRxiv2023

Assessing the performance of protein regression models

To optimize proteins for particular traits holds great promise for industrial and pharmaceutical purposes. Machine Learning is increasingly applied in this field to predict properties of proteins, thereby guiding the experimental optimization process. A natural question is: How much progress are we making with such predictions, and how important is the choice of regressor and representation? In this paper, we demonstrate that different assessment criteria for regressor performance can lead to dramatically different conclusions, depending on the choice of metric, and how one defines generalization. We highlight the fundamental issues of sample bias in typical regression scenarios and how this can lead to misleading conclusions about regressor performance. Finally, we make the case for the importance of calibrated uncertainty in this domain.

bioinformatics↗

bioRxiv2021

Dual control of MAPK activities by AP2C1 and MKP1 MAPK phosphatases regulates defence responses in Arabidopsis

Mitogen-activated protein kinase (MAPK) cascades transmit environmental signals and induce stress and defence responses in plants. These signalling cascades are negatively controlled by specific phosphatases of the type 2C Ser/Thr protein phosphatase (PP2C) and dual-specificity phosphatase (DSP) families that inactivate stress-induced MAPKs; however, the interplay between phosphatases of these different types has remained unknown. Our work reveals that different Arabidopsis MAPK phosphatases, the PP2C-type AP2C1 and the DSP-type MKP1, exhibit both specific and overlapping functions in plant stress responses. Each single mutant and ap2c1 mkp1 double mutant displayed enhanced wound-induced activation of MAPKs MPK3, MPK4, and MPK6, as well as induction of a set of transcription factors. Moreover, ap2c1 mkp1 double mutants show an autoimmune-like response, associated with elevated levels the stress hormones salicylic acid and ethylene, and of the phytoalexin camalexin. Interestingly, this phenotype is reduced in ap2c1 mkp1 mpk6 triple mutants, suggesting that the autoimmune-like response is due to MPK6 misregulation. We conclude that the evolutionarily distant MAPK phosphatases AP2C1 and MKP1 contribute crucially to the tight control of MPK6 activity, ensuring appropriately balanced stress signalling and suppression of autoimmune-like responses during plant growth and development. HighlightDouble MAPK phosphatase mutant plants ap2c1 mkp1 exhibit constitutive, autoimmune-like stress responses, dependent on their substrate MAPK MPK6.

plant biology↗