Biology subjects

Taskova, K.

Publications and source records attributed to Taskova, K..

3 recordsLinked to original sources

bioRxiv2026

Capturing secondary structure in coarse grained intrinsically disordered proteins with simulations driven by chemical shifts.

A major challenge when investigating intrinsically disordered proteins (IDPs) pertains to understanding how secondary structure formation across otherwise disordered ensembles, relates to function. While coarse-grained (CG) simulation methods are instrumental to describe ensemble-to-function relationships in IDPs, they remain largely incapable of sampling secondary structure. We here present NapshiftC, an artificial neural network capable of modelling NMR chemical shifts from C-mapped CG protein conformations, leveraging its predictions to restrain simulations and yield experimentally-informed IDP ensembles that contain secondary structure. We applied NapshiftC on a large set of proteins in isolation or condensed phase, and found that, when secondary structure is present, CS incorporation yields improved agreements with smFRET spectroscopy and resolves the over-reliance of CG models on near linear conformers. By incorporating observables that are routinely collected for IDPs but never coupled to CG simulations, NapshiftC ultimately informs on both local and global conformational features of function-defining IDP ensembles.

biophysics↗

bioRxiv2025

Integrating NMR restraints into coarse-grained simulations: toward accurate conformational ensembles of complex protein systems

Structural dynamics play critical roles for the biological activity of protein molecules. Characterising the inherent conformational landscapes of these macromolecules remains a major experimental and computational challenge, particularly for heterogeneous and transient systems such as intrinsically disordered proteins, membrane-associated assemblies and disordered fuzzy coats of amyloid aggregates. In this context, coarse-grained (CG) molecular dynamics simulations have enabled accessing to extended timescales and large system sizes, however, their reduced resolution and simplified interaction potentials often limit the structural accuracy. Here, we introduce Martini3-NMR, an integrative framework that incorporates nuclear magnetic resonance (NMR) observables directly into CG protein force fields. Using artificial neural networks to model NMR chemical shifts at the CG level, and integrating these data with NOE restraints, we define an approach to significantly enhance the accuracy of CG simulations while maintaining their elevated sampling efficiency, thereby resulting in a substantially improved description of protein conformational ensembles. We demonstrate the broad applicability of Martini3-NMR by generating CG ensembles for a diverse range of systems and complex processes such as protein folding from misfolded states, oligomer disassembly within lipid bilayers and the topology of disordered fuzzy regions at amyloid fibril surfaces, which were found to display condensate-like structural and dynamical properties. By enabling an experimentally driven and computationally efficient exploration of protein conformational landscapes, Martini3-NMR provides a novel general framework for investigating dynamic, heterogeneous and multiscale biomolecular processes. This approach opens to significant new opportunities for extending CG simulations towards a more quantitative understanding of the relationship between molecular structure, dynamics and biological function.

biophysics↗

bioRxiv2018

Evaluating Cell Identity from Transcription Profiles

Induced pluripotent stem cells (iPS) and direct lineage programming offer promising autologous and patient-specific sources of cells for personalized drug-testing and cell-based therapy. Before these engineered cells can be widely used, it is important to evaluate how well the engineered cell types resemble their intended target cell types. We have developed a method to generate CellScore, a cell identity score that can be used to evaluate the success of an engineered cell type in relation to both its initial and desired target cell type, which are used as references. Of 20 cell transitions tested, the most successful transitions were the iPS cells (CellScore > 0.9), while other transitions (e.g. induced hepatocytes or motor neurons) indicated incomplete transitions (CellScore < 0.5). In principle, the method can be applied to any engineered cell undergoing a cell transition, where transcription profiles are available for the reference cell types and the engineered cell type.\n\nHighlightsO_LIA curated standard dataset of transcription profiles from normal cell types was created.\nC_LIO_LICellScore evaluates the cell identity of engineered cell types, using the curated dataset.\nC_LIO_LICellScore considers the initial and desired target cell type.\nC_LIO_LICellScore identifies the most successfully engineered clones for further functional testing.\nC_LI

bioinformatics↗