Biology subjects

Novev, J. K.

Publications and source records attributed to Novev, J. K..

5 recordsLinked to original sources

bioRxiv2026

Exploring the large-scale properties of a protein secondary structure genotype-to-phenotype map

We perform a large-scale computational characterization of the map of protein primary to secondary structure using an AVR3a class protein effector domain from the plant pathogen P. palmivora as a case study. We formulate a modified site-scanning approach for exploring the neutral component of secondary structure phenotypes based on predictions from the machine-learning algorithm Porter 5 and apply it to the AVR3a phenotype. We predict a set of sensitive sites within the effector domain that are generally located at or near the boundaries of structured regions, with restrictions on the possible amino acid residues at these sites dictated by the secondary structure type that they participate in within the WT. We characterize a set of mutated phenotypes derived through the exploration of the neutral component of the WT effector domain, selecting them so that they span a range including both very rarely and very commonly seen secondary structures, and that they include both secondary structures nearly identical to the WT and ones far removed from it. We find that all these diverse phenotypes have an estimated robustness of the same order as that of the WT, and that the robustness scales logarithmically phenotype frequency, as seen in other genotype-to-phenotype maps. Furthermore, we observe that the dependence of the estimated phenotype frequency on the Kolmogorov complexity indicates simplicity bias in the protein secondary structure map.

biophysics↗

bioRxiv2026

3D confinement reshapes RNA folding and enhances circularisation in the Zika virus

Many RNA molecules function within confined environments, but the effect of confinement on RNA folding remains poorly understood. Proximity ligation experiments reveal altered long-range contacts in confined versus unconfined states, yet they do not explain how spatial constraints give rise to these differences. Here, we develop a physical modeling approach that incorporates proximity ligation data into coarse-grained molecular dynamics simulations to reconstruct RNA 3D structures under confinement. We test our model on the[~] 11 kb genome of the Zika virus, comparing the folding in virions (confined) and in cells (unconfined). We observe that the probability of contact between two regions of the genome vs. linear distance follows different scaling laws in the confined and unconfined cases, in agreement with proximity ligation experiments. We find that genome circularization - an interaction that regulates replication in Zika - occurs more frequently under confinement in both experiments and simulations. Our model reveals that the formation of long double-stranded stems through stacking confers local nematic liquid crystalline order to the RNA, and predicts pseudoknot topologies consistent with those seen in crystallographic structures of shorter RNAs. These results provide insight into the mechanism through which confinement alters the ensemble of 3D structures accessible to a long RNA molecule. Significance statementSqueezing a long string into a tight ball changes how it behaves: distant segments are forced into close contact, making ends more likely to meet. Similar principles apply to biopolymers, such as RNA, when confined within virus particles or other enclosures smaller than the polymer contour length, but there is no framework to model the effects of confinement on RNA folding. Here we develop such a framework, combining molecular dynamics simulations with experimental measurements of contacts within unconfined and confined RNA molecules. We find that RNA is glassy and exhibits liquid crystalline order, and that genome circularization, a long-range interaction between the two ends of the viral RNA important for virus replication, is facilitated under confinement.

biophysics↗

bioRxiv2024

Changes in miRNA secondary structure can predict mutations associated with cancer and other diseases

MicroRNAs (miRNAs) are ubiquitous short RNAs regulating gene expression in many organisms, including humans. How the secondary structure (SS) of a mature miRNA affects its regulatory function remains an open question. Here we investigate this question through computational SS predictions of miRNA point mutants. We explore the mutational neighborhoods of miRNAs with association to human diseases, including cancer. We focus on possible SS changes independent of target-site complementarity, by leaving the seed region unchanged. We formulate metrics of the SS differences between such mutants and their wild types (WTs), and test whether these metrics predict disease association by comparing our results with the miRNASNP-v3 database. We find that disease-related mutants tend to have a higher probability of being fully unfolded than their WT; this and other SS-related measures are statistically significant at the database level. With the same approach, we identify a subset of individual miRNAs for which SS changes are most likely to predict disease-related mutations. These are hsa-miR-1269b, hsa-miR-4537, hsa-miR-4477b, hsa-miR-4641, and hsa-miR-6821-3p. In addition, we show that there are pairs of known miRNA WTs differing only by disease-related point mutations outside the seed region and exhibit very different SS. These pairs include hsa-miR-1269a--hsa-miR-1269b, and hsa-miR-3689a-3p--hsa-miR-3689b-3p.

bioinformatics↗

bioRxiv2022

Predicting phenotype transition probabilities via conditional algorithmic probability approximations

Unravelling the structure of genotype-phenotype (GP) maps is an important problem in biology. Recently, arguments inspired by algorithmic information theory (AIT) and Kolmogorov complexity have been invoked to uncover simplicity bias in GP maps, an exponentially decaying upper bound in phenotype probability with increasing phenotype descriptional complexity. This means that phenotypes with very many genotypes assigned via the GP map must be simple, while complex phenotypes must have few genotypes assigned. Here we use similar arguments to bound the probability P (x [->] y) that phenotype x, upon random genetic mutation, transitions to phenotype y. The bound is [Formula], where [Formula] is the estimated conditional complexity of y given x, quantifying how much extra information is required to make y given access to x. This upper bound is related to the conditional form of algorithmic probability from AIT. We demonstrate the practical applicability of our derived bound by predicting phenotype transition probabilities (and other related quantities) in simulations of RNA and protein secondary structures. Our work contributes to a general mathematical understanding of GP maps, and may facilitate the prediction of transition probabilities directly from examining phenotype themselves, without utilising detailed knowledge of the GP map.

evolutionary biology↗

bioRxiv2020

Spatiotemporal model of cellular mechanotransduction via Rho and YAP

How cells sense and respond to mechanical stimuli remains an open question. Recent advances have identified the translocation of Yes-associated protein (YAP) between nucleus and cytoplasm as a central mechanism for sensing mechanical forces and regulating mechanotransduction. We formulate a spatiotemporal model of the mechanotransduction signalling pathway that includes coupling of YAP with the cell force-generation machinery through the Rho family of GTPases. Considering the active and inactive forms of a single Rho protein (GTP/GDP-bound) and of YAP (non-phosphorylated/phosphorylated), we study the cross-talk between cell polarization due to active Rho and YAP activation through its nuclear localization. For fixed mechanical stimuli, our model predicts stationary nuclear-to-cytoplasmic YAP ratios consistent with experimental data at varying adhesive cell area. We further predict damped and even sustained oscillations in the YAP nuclear-to-cytoplasmic ratio by accounting for recently reported positive and negative YAP-Rho feedback. Extending the framework to time-varying mechanical stimuli that simulate cyclic stretching and compression, we show that the YAP nuclear-to-cytoplasmic ratios time dependence follows that of the cyclic mechanical stimulus. The model presents one of the first frameworks for understanding spatiotemporal YAP mechanotransduction, providing several predictions of possible YAP localization dynamics, and suggesting new directions for experimental and theoretical studies.

biophysics↗