Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2303.02488

Multi class intracellular protein targeting predictions in diatoms and other algae with complex plastids: ASAFind 2.0

Ansgar Gruber·Cedar McKay·Miroslav Oborník·Gabrielle Rocap
View original source ↗Read source document ↗Export metadata ↓

Abstract

Cells of diatoms and related algae with complex plastids of red algal origin are highly compartmentalized. These plastids are surrounded by four envelope membranes, which also define the periplastidic compartment (PPC), the space between the second and third membranes. The PPC corresponds to the cytosol of the eukaryotic alga that was the ancestor of the complex plastid. Metabolic reactions as well as cell biological processes take place in this compartment; however, its exact function remains elusive. Automated predictions of protein locations proved useful for genome wide explorations of metabolism in the case of plastid proteins, but until now, no automated method for the prediction of PPC proteins was available. Here, we present an updated version of the plastid protein predictor ASAFind, which includes optional prediction of PPC proteins. The new ASAFind version also accepts the output of the most recent versions of SignalP (5.0) and TargetP (2.0) input data. Furthermore, we release a Python script to calculate custom scoring matrices for adjustment of the ASAFind method to other groups of algae, and included the option to run the predictions with custom scoring matrices in a simplified score cut-off mode.

Explore related subjects

q-bio.SC

Keep this discovery

BibTeXRIS

Ansgar Gruber, Cedar McKay, Miroslav Oborník, Gabrielle Rocap. 2023-03-04. Multi class intracellular protein targeting predictions in diatoms and other algae with complex plastids: ASAFind 2.0. https://doi.org/10.1111/tpj.70138

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Clustering versus sorting: a mass-conserving reaction-diffusion model of planar polarity puncta

Planar cell polarity is preceded by the clustering of polarity proteins into discrete, low-turnover membrane subdomains (puncta), yet the minimal interactions that nucleate puncta, set their number, and segregate opposite orientations remain unclear. We address these questions with a mass-conserving reaction-diffusion model in which two diffusible monomers bind reversibly across a cell-cell junction into trans-complexes of two orientations, with feedback entering only through concentration-dependent rates. Above a critical density, the uniform state undergoes a long-wavelength mass-redistribution instability rather than a finite-wavelength Turing bifurcation. The form of the feedback then selects between two morphologies: broad mesas fixed by a Maxwell construction under saturating feedback, and narrow mass-limited spikes under unbounded feedback. For spikes we obtain closed-form expressions exhibiting a clean separation of amplitude (mass and feedback), width (the complex diffusion length), and spacing (the monomer screening length). Within a fast-monomer reduction we prove, for any number and arrangement of puncta, that like-oriented arrays coarsen, so multiplicity is metastable and kinetically determined. The second monomer reservoir introduces a second screening length that rate-limits competition by the harmonic mean of the monomer diffusivities, and the spectrum of a punctum remains free of oscillatory ("blinking") instabilities throughout. Finally, sign-definite cross-modulation of turnover converts clustering into orientation sorting - the mutual exclusion of orientations along a single contact: mass redistribution sets puncta number, and the sign of the cross-coupling determines whether orientations segregate. Puncta number and orientation sorting are thus governed by mathematically separable ingredients.

q-bio.SC↗
arXiv2026

The role of estrogen receptor alpha on calcium transport during smooth muscle contractions

Reproductive hormones regulate a wide range of physiological processes throughout the human lifespan. Estrogen, in particular, varies substantially across the menstrual cycle and is widely used in contraceptives and hormone replacement therapies. Despite its physiological importance, few experimental studies and even fewer mathematical models explicitly investigate how estrogen regulates smooth muscle function. As smooth muscle lines our blood vessels, airways, uterus, and several other organs, understanding how estrogen impacts smooth muscle is important to improving the understanding of sex differences in lifelong health. Here we extend an established mathematical model of smooth muscle cell calcium signalling to incorporate estrogen-dependent modulation of intracellular calcium transport pathways. Numerical simulations, global sensitivity analysis, and numerical bifurcation analysis are then used to quantify the influence of estrogen on intracellular calcium dynamics and the resulting steady-state and oscillatory behaviours. Our results demonstrate that physiologically relevant changes in estrogen shift intracellular calcium concentrations while leaving the underlying bifurcation structure and qualitative dynamics largely unchanged, suggesting that estrogen acts primarily as a quantitative modulator of smooth muscle calcium signalling. This work also serves to establish a foundation for future mechanistic models of hormone-dependent cell physiology.

q-bio.SC↗
arXiv2026

A Persistent Random-Walk Model of Molecular Transport in Neuronal Dendritic Trees

A two-level analytical framework is presented for modeling random walk transport of messenger ribonucleic acid (mRNA) molecules along neuronal microtubules from soma to synapses. Motivated by empirical observations of mRNA cargo motion, the transport within a dendrite is modeled by a persistent telegraph process with pauses. Theoretical expressions for the probability of traversing the dendrite and the mean time for such travel are derived for different and equal probabilities of persistence. These results are used for the construction of a semi-Markov model of motion of mRNA cargo within the whole neuron. The semi-Markov model provides the probabilities of absorption at a given synapse and corresponding mean first-passage times (MFPTs) from the soma, where mRNA is transcribed. The theoretical expressions, together with experimentally obtained parameter values, are used to calculate MFPTs for neurons with empirically reconstructed morphology. The model predicts that when retrograde persistence is stronger, the MFPT to each synapse is effectively the same. Otherwise, when the persistence is more pronounced in the anterograde direction, the transport in the neuron resembles the motion along a single dendrite -- nearly linear dependence of MFPT on the distance between soma and synapse. These findings are theoretically justified when the lengths of dendrites are considerably longer than the distance traversed during a typical run.

q-bio.SC↗