Biology subjects

Quoika, P. K.

Publications and source records attributed to Quoika, P. K..

3 recordsLinked to original sources

bioRxiv2026

FRET-guided integrative modeling resolves Mg2+-dependent RNA tertiary-contact formation

Tertiary contact interactions between GNRA tetraloops and their receptors are recurrent motifs that stabilize complex RNA folds and in the context of ribosome biogenesis contribute to ribosomal subunit maturation. High-resolution structure determination methods typically yield single structural snapshots, which may not fully describe the conformational collection populated by such contacts in solution. Here, we investigate a ribosomal RNA tertiary contact formed by a GAAA tetraloop and a kissing loop acting as its receptor using single-molecule FRET-guided integrative modeling. We used a minimal RNA model construct in which the tetraloop and its kissing loop receptor are connected by flexible poly(A)-linkers, assembled its bound state from the native ribosomal cryo-EM geometry, and combined restrained and unrestrained all-atom molecular dynamics simulations with in silico FRET and dynamic fluorescence anisotropy predictions to compare with the respective experiments under varying salt conditions. The modeled bound state matches the smFRET distribution only at non-physiologically high Mg2+ concentrations. At 10 mM Mg2+, however, the single-molecule FRET distribution remains heterogeneous and is reproduced by mixtures of bound and unbound conformations, indicating their coexistence under intermediate Mg2+ conditions. Thus, smFRET-guided integrative modeling resolves subpopulations invisible to static high-resolution structures and ensemble FRET, providing a route to validate and refine RNA structural models and conformational ensembles in solution.

biophysics↗

bioRxiv2026

Estimation of Protein Melting Temperatures Using Small-Ladder Replica Exchange Simulations

The unfolding or melting temperature (TM) is a central quantity to characterize the stability of proteins and other biopolymers. The accurate prediction of protein melting temperatures by molecular mechanics force field simulations is highly desirable for many biophysical and biotechnological applications. Since the time scales for protein (un-)folding are hardly accessible in conventional MD (cMD) simulations, enhanced sampling techniques such as Temperature Replica Exchange Molecular Dynamics (TREMD) are typically employed. However, TREMD simulations are computationally very demanding especially if large temperature ranges need to be covered. Additionally, if the TM is initially unknown, setting up TREMD simulations is often challenging. To find the optimal initial conditions for such simulations, we describe their performance based on a theoretical model, which we validate on a minimalistic Markov Chain Monte Carlo (MCMC) simulation setup. In an effort to reduce the computational demand, we have investigated the possibility to use small sets of TREMD temperature ladders placed iteratively in the vicinity of a TM estimate. Different TREMD setups were extensively tested on the fast-folding protein Chignolin. We found that appropriate starting conformations lead to significantly faster convergence. Furthermore, we found that, in practice, combining multiple small temperature ladders can be advantageous in comparison to one single temperature ladder. Based on our findings, we formulate practical recommendations on how to set up TREMD for protein melting with optimal efficiency.

biophysics↗

bioRxiv2024

Assessing AF2's ability to predict structural ensembles of proteins

Recent breakthroughs in protein structure prediction have enhanced the precision and speed at which protein configurations can be determined, setting new benchmarks for accuracy and efficiency in the field. However, the fundamental mechanisms of biological processes at a molecular level are often connected to conformational changes of proteins. Molecular dynamics (MD) simulations serve as a crucial tool for capturing the conformational space of proteins, providing valuable insights into their structural fluctuations. However, the scope of MD simulations is often limited by the accessible timescales and the computational resources available, posing challenges to comprehensively exploring protein behaviors. Recently emerging approaches have focused on expanding the capability of AlphaFold2 (AF2) to predict conformational substates of protein structures by manipulating the input multiple sequence alignment (MSA). These approaches operate under the assumption that the MSA also contains information about the heterogeneity of protein structures. Here, we benchmark the performance of various workflows that have adapted AF2 for ensemble prediction focusing on the subsampling of the MSA as implemented in ColabFold and compare the obtained structures with ensembles obtained from MD simulations and NMR. As test cases, we chose four proteins namely the bovine pancreatic inhibitor protein (BPTI), thrombin and two antigen binding fragments (antibody Fv and nanobody), for which reliable experimentally validated structural information (X-ray and/or NMR) was available. Thus, we provide an overview of the levels of performance and accessible timescales that can currently be achieved with machine learning (ML) based ensemble generation. In three out of the four test cases, we find structural variations fall within the predicted ensembles. Nevertheless, significant minima of the free energy surfaces remain undetected. This study highlights the possibilities and pitfalls when generating ensembles with AF2 and thus may guide the development of future tools while informing upon the results of currently available applications.

biophysics↗