Biology subjects

Schnapka, V.

Publications and source records attributed to Schnapka, V..

5 recordsLinked to original sources

bioRxiv2026

NMR-measured Per-residue Electrostatic Potentials Reveal the Importance of Arginine-Lysine Balance on the Conformational Ensemble of Disordered Histone H3 Tails in Nucleosome Core Particles

Biophysical studies of chromatin have provided key insights into how the genome is regulated. Many atomic resolution structures of the fundamental building block of chromatin, the nucleosome core particle (NCP), as well as larger NCP arrays, both in isolation and in complex with effectors, or with post-translational modifications, have been elucidated. These provide in-depth pictures of key functional interactions. Considerably less information is available on the intrinsically disordered nucleosome tails that are critically important for higher order interactions in chromatin, and for transcription, DNA repair, and phase separation, and that serve as hubs for post-translational modifications. Here we focus on the tail of one of the four histones, histone-3 (H3), and measure electrostatic potentials at each of its amide proton positions in isolated NCPs via solution NMR spectroscopy. Using these potentials, in concert with molecular dynamics simulations, an ensemble of structures is calculated for WT H3 tails, as well as variants where all tail arginines are replaced by lysines (R2K) or vice versa (K2R). Our ensembles reveal that K2R tails have a higher propensity to occupy the most negatively charged region of the NCP between the DNA gyres than R2K or WT tails, while lysine enriched tails have an increased relative preference for the less negative regions near the DNA entry and exit points. The structural ensembles provide insight into differences in interactions between neighboring particles that lead to changes in diffusion and phase separation propensities between the NCP variants.

biophysics↗

bioRxiv2026

Integrative Ensemble Modeling reveals RNA conformations targetable by small molecules

RNA molecules explore heterogeneous conformational ensembles that are essential for their biological function and molecular recognition, yet this intrinsic flexibility poses a major challenge for structure-based drug discovery. In particular, the absence of well-defined binding pockets in static structures limits the identification of ligandable sites. Here, we present an integrative ensemble-based approach that combines enhanced-sampling molecular dynamics simulations with Nuclear Magnetic Resonance data to characterize the conformational landscape of the HIV-1 TAR RNA at atomic resolution. Starting from extensive sampling, we refined the resulting conformational distribution through maximum-entropy reweighting to achieve quantitative agreement with experimental data. Analysis of the reweighted ensemble reveals a diverse set of conformational substates, including compact arrangements that exhibit pocket features compatible with ligand recognition and overlap with known ligand-bound structures. At the same time, highly ligandable conformations, which are only marginally populated, might nonetheless be critical for RNA recognition. Our results demonstrate that integrative ensemble modeling can reveal pharmacologically relevant RNA conformations that are not apparent from experimental static structures, providing a framework for ensemble-based strategies in RNA-targeted drug discovery.

biophysics↗

bioRxiv2025

Advancing in silico drug design with Bayesian refinement of AlphaFold models

Virtual screening has become an indispensable tool in modern structure-based drug discovery, enabling the identification of candidate molecules by computationally evaluating their potential to bind target proteins. The accuracy of such screenings critically depends on the quality of the target structures employed. Recent advances in protein structure prediction, particularly AlphaFold2, have revolutionized this field with unprecedented accuracy. However, AlphaFold2 models often exhibit limitations in local structural details, especially within binding pockets, which limit their utility for small molecule docking. In contrast, molecular dynamics simulations with accurate atomistic force fields can refine protein structures, but lack the ability to leverage the structural information provided by deep learning approaches. Here, we introduce bAIes, an integrative method that bridges this gap by combining physics-based force fields with data-driven predictions through Bayesian inference. Crucially, bAIes demonstrates a superior ability to discriminate between binders and non-binders in virtual screening campaigns, outperforming both AlphaFold2 and molecular dynamics-refined models. By enhancing the usability of AlphaFold2 models without requiring extensive experimental or computational resources, bAIes offers a convenient solution to a longstanding challenge in structure-based drug design, potentially accelerating the early phases of drug discovery.

bioinformatics↗

bioRxiv2025

Atomic resolution ensembles of intrinsically disordered and multi-domain proteins with Alphafold

Intrinsically disordered proteins are ubiquitous in biological systems and play essential roles in a wide range of biological processes and diseases. Despite recent advances in high-resolution structural biology techniques and breakthroughs in deep learning-based protein structure prediction, accurately determining structural ensembles of IDPs at atomic resolution remains a major challenge. Here we introduce bAIes, a Bayesian framework that integrates AlphaFold2 predictions with physico-chemical molecular mechanics force fields to generate accurate atomic-resolution ensembles of IDPs. We show that bAIes produces structural ensembles that match a wide range of high- and low-resolution experimental data across diverse systems, achieving accuracy comparable to atomistic molecular dynamics simulations but at a fraction of their computational cost. Furthermore, bAIes outperforms state-of-the-art IDP models based on coarse-grained potentials as well as deep-learning approaches. Our findings pave the way for integrating structural information from modern deep-learning approaches with molecular simulations, advancing ensemble-based understanding of disordered proteins.

bioinformatics↗

bioRxiv2021

Desiccation-induced fibrous condensation of CAHS protein from an anhydrobiotic tardigrade

Anhydrobiosis is one of the most extensively studied forms of cryptobiosis that is induced in certain organisms as a response to desiccation. Anhydrobiotic species has been hypothesized to produce substances that can protect their biological components and/or cell membranes without water. In extremotolerant tardigrades, highly hydrophilic and heat-soluble protein families, cytosolic abundant heat-soluble (CAHS) proteins, have been identified, which are postulated to be integral parts of the tardigrades response to desiccation. However, the molecular mechanisms underlying these protein functions remain to be fully elucidated. In this study, we perfomed in vitro and in vivo characterizations of the self-assembling property of CAHS1 protein, a major isoform of CAHS proteins from Ramazzottius varieornatus, using a series of spectroscopic and microscopic techniques. Our in vitro observations showed that CAHS1 proteins homo-oligomerized via the C-terminal -helical region and formed a hydrogel as their concentration increased, and that these molecular assembling processes were reversible. Furthermore, our in vivo observations demonstrated that the overexpressed CAHS1 proteins formed condensates under desiccation-mimicking conditions. These data strongly suggested that, upon drying, the CAHS1 proteins form oligomers and eventually underwent sol-gel transition in tardigrade cytosols. Thus, it is proposed that the CAHS1 proteins form the cytosolic fibrous condensates, which presumably have variable mechanisms for the desiccation tolerance of tardigrades. These findings provide insights into the protective mechanisms involved in the anhydrobiosis of tardigrades.

biochemistry↗