Biology subjects

Löhr, T.

Publications and source records attributed to Löhr, T..

4 recordsLinked to original sources

bioRxiv2026

SpinForecast: chain-free probabilistic backbone assignment of intrinsically disordered proteins from NMR chemical shifts

Assigning peaks in NMR spectra to specific residues is an essential but time-consuming step in the study of intrinsically disordered proteins (IDPs). Conventional approaches rely on building chains of sequential connectivities between peaks, which are particularly prone to failure in disordered systems due to spectral overlap, missing peaks, and proline-rich sequences. Here we present SpinForecast, a tool that performs chain-free probabilistic backbone assignment of IDPs from chemical shifts alone, without requiring peaks to be linked into sequential chains. SpinForecast predicts residue-specific chemical shift distributions from a disorder-filtered subset of the Biomolecular Magnetic Resonance Data Bank (BMRB), incorporating nearest-neighbour residue effects and corrections for temperature and pH. Experimental chemical shifts are then assigned to residues by Bayes theorem, using residue-specific chemical shift distributions as likelihoods. We validate SpinForecast on three disordered proteins, IAPP (37 residues), NUPR1 (82 residues), and JPT2 (218 residues), achieving 100% confidence assignments for 74%, 57%, and 44% of in-distribution peaks, respectively, all with at least 99% accuracy. Where single assignments cannot be determined for these systems, the correct assignment was contained within the returned set of candidate assignments in greater than 97% of cases. SpinForecast is freely available at https://tools.bindresearch.org/bindbox/SpinForecast.

biophysics↗

bioRxiv2022

Determination of the structure and dynamics of the fuzzy coat of an amyloid fibril of IAPP using cryo-electron microscopy

In recent years, major advances in cryo-electron microscopy (cryo-EM) have enabled the routine determination of complex biomolecular structures at atomic resolution. An open challenge for this approach, however, concerns large systems that exhibit continuous dynamics. To address this problem, we developed the metadynamic electron-microscopy metainference (MEMMI) method, which incorporates metadynamics, an enhanced conformational sampling approach, into the metainference method of integrative structural biology. MEMMI enables the simultaneous determination of the structure and dynamics of large heterogeneous systems by combining cryo-EM density maps with prior information through molecular dynamics, while at the same time modelling the different sources of error. To illustrate the method, we apply it to elucidate the dynamics of an amyloid fibril of the islet amyloid polypeptide (IAPP). The resulting conformational ensemble provides an accurate description of the structural variability of the disordered region of the amyloid fibril, known as fuzzy coat. The conformational ensemble also reveals that in nearly half of the structural core of this amyloid fibril the side-chains exhibit liquid-like dynamics despite the presence of the highly ordered network backbone of hydrogen bonds characteristic of the cross-{beta} structure of amyloid fibrils.

biophysics↗

bioRxiv2021

A small molecule stabilises the disordered native state of the Alzheimer's Aβ peptide

The stabilisation of native states of proteins is a powerful drug discovery strategy. It is still unclear, however, whether this approach can be applied to intrinsically disordered proteins. Here we report a small molecule that stabilises the native state of the A{beta}42 peptide, an intrinsically disordered protein fragment associated with Alzheimers disease. We show that this stabilisation takes place by a dynamic binding mechanism, in which both the small molecule and the A{beta}42 peptide remain disordered. This disordered binding mechanism involves enthalpically favourable local {pi}-stacking interactions coupled with entropically advantageous global effects. These results indicate that small molecules can stabilise disordered proteins in their native states through transient non-specific interactions that provide enthalpic gain while simultaneously increasing the conformational entropy of the proteins.

biophysics↗

bioRxiv2020

A kinetic ensemble of the Alzheimer's Aβ peptide

The discovery that disordered proteins are widespread in the human proteome has prompted the quest for methods to characterize the conformational properties that determine their functional and dysfunctional behaviour. It has become customary to describe these proteins in terms of structural ensembles and free energy landscapes, which offer conformational and thermodynamic insight. However, a current major challenge is to generalize this description to kinetic ensembles, thereby also providing information on transition rates between states. Approaches based on the theory of stochastic processes can be particularly suitable for this purpose. Here, we develop a Markov state model and illustrate its application by determining a kinetic ensemble of the 42-residue form of the amyloid-{beta} peptide (A{beta}42), whose aggregation is associated with Alzheimers disease. Using the Google Compute Engine, we generated 315 s all-atom, explicit solvent molecular dynamics trajectories, validated with experimental data from nuclear magnetic resonance spectroscopy. Using a probabilistic-based definition of conformational states in a neural network approach, we found that A{beta}42 is characterized by inter-state transitions no longer than the microsecond timescale, exhibiting only fully unfolded or short-lived, partially-folded states. We contextualize our findings by performing additional simulations of the oxidized form of A{beta}42. Our results illustrate how the use of kinetic ensembles offers an effective means to provide information about the structure, thermodynamics, and kinetics of disordered proteins towards an understanding of these ubiquitous biomolecules.

biophysics↗