Biology subjects

Trovato, A.

Publications and source records attributed to Trovato, A..

5 recordsLinked to original sources

bioRxiv2023

Folding kinetics of an entangled protein

The possibility of the protein backbone adopting lasso-like entangled motifs has attracted increasing attention. After discovering the surprising abundance of natively entangled single-domain proteins, it was shown that misfolded entangled subpopulations might become thermosensitive or escape the homeostasis network just after translation. To investigate the role of entanglement in shaping folding kinetics, we introduce a novel indicator and analyze simulations of a coarse-grained, structure-based model for two small single-domain proteins. The model recapitulates the well-known two-state folding mechanism of a non-entangled SH3 domain. However, despite its small size, a natively entangled antifreeze RD1 protein displays a rich refolding behavior, populating two distinct kinetic intermediates: a short-lived, entangled, near-unfolded state and a long-lived, non-entangled, near-native state. The former directs refolding along a fast pathway, whereas the latter is a kinetic trap, consistently with known experimental evidence of two different characteristic times. Upon trapping, the natively entangled loop forms without being threaded by the N-terminal residues. After trapping, the native entangled structure emerges by either backtracking to the unfolded state or threading through the already formed but not yet entangled loop. Along the fast pathway, the earlier the native contacts form, the more their formation time may fluctuate. Trapping does not occur because the native contacts at the closure of the lasso-like loop form after those involved in the N-terminal thread, confirming previous predictions. Despite this, entanglement may appear already in unfolded configurations. Remarkably, a long-lived, near-native intermediate, with non-native entanglement properties, recalls what was observed in cotranslational folding. Author summaryRecently, a surprisingly large fraction of protein structures was shown to host topologically entangled motifs, whereby one protein chain portion is lassoed by a second portion, that loops between two residues in non-covalent contact with each other. Moreover, there is growing evidence that failure in adopting the correct entangled motifs may produce misfolded structures with impaired biological functions. Such structures are otherwise similar to the correct ones and can escape the cell quality control system for protein expression, leading to soluble and less functional protein species. Here, we study in detail the folding kinetics of an entangled small anti-freeze protein, using a simplified representation of the protein chain. We find a very rich folding behavior, unusual for small proteins, with different folding pathways. A fast pathway is followed if a crucial set of contacts is formed before lassoing takes place. If not, a misfolded structure which acts as a kinetic trap is formed, slowing down folding; in such structure, most of the contacts are correctly in place yet the lasso is not formed. The detailed understanding that we provide for a small protein may pave the way for similar studies for larger entangled proteins.

biophysics↗

bioRxiv2023

A scale-invariant log-normal droplet size distribution below the critical concentration for protein phase separation

Many proteins have been recently shown to undergo a process of phase separation that leads to the formation of biomolecular condensates. Intriguingly, it has been observed that some of these proteins form dense droplets of sizeable dimensions already below the critical concentration, which is the concentration at which phase separation occurs. To understand this phenomenon, which is not readily compatible with classical nucleation theory, we investigated the properties of the droplet size distributions as a function of protein concentration. We found that these distributions can be described by a scale-invariant log-normal function with an average that increases progressively as the concentration approaches the critical concentration from below. The results of this scaling analysis suggest the existence of a universal behaviour independent of the sequences and structures of the proteins undergoing phase separation. While we refrain from proposing a theoretical model here, we suggest that any model of protein phase separation should predict the scaling exponents that we reported here from the fitting of experimental measurements of droplet size distributions. Furthermore, based on these observations, we show that it is possible to use the scale invariance to estimate the critical concentration for protein phase separation.

biophysics↗

bioRxiv2021

Sequence based prediction of protein phase separation into disordered condensates using machine learning

Several proteins which are responsible for neuro-degenrerative disorders (Alzheimers, Parkinsons etc) are shown to undergo a mechanism known as liquid liquid phase separation (LLPS). We in this research build a predictor which would answer whether a protein molecule would undergo LLPS or not. For this we used some protein sequences for which we already knew the answer. The ones who undergo LLPS were considered as the positive set and the ones who do not, were taken as the negative set. Depending on the knowledge of amino-acid sequences we identified some relevant variables in the context of LLPS e.g. number of amino acids, length of the best pairings, average register shifts. Using these variables we built a number of scoring functions which were basically analytic functions involving these variables and we also combined some scores already existing in the literature. We considered a total of 43636 protein sequences, among them only 121 were positive. We applied logistic regression and performed cross validation, where 25% of the data were used as the training set and the performance of the obtained results were tested on the remaining 75% of the data. In the training process, we used Simplex algorithm to maximize area under the curve (AUC) in receiver operator characteristics (ROC) space for each of the scores we defined. The optimised parameters were then used to evaluate AUC on the test set to check the accuracy. The best performing score was identified as the predicting model to answer the question whether a protein chain would undergo phase separating behavior or not. Author summaryLiquid-liquid phase separation (LLPS) is a classic subject in polymer physics. The essen- tial physics is aptly captured within the Flory-Huggins (FH) approach, a simple lattice mean-field theory. Evidence has been mounting in the last decade that protein LLPS underlies the formation of membrane-less organelles (MLOs) in living cells, explaining why proteins and other bio-molecules can remain in a dense liquid condensate without diffusing away. Intrinsically disordered regions (IDRs), with a sequence-intrinsic prefer- ence for conformational heterogeneity or disorder under native conditions, are enriched in proteins that have the ability of switching on LLPS. The detailed understanding of the biological function of disordered bio-molecular condensates, whose formation is driven by LLPS, is currently the focus of a major effort in cell biology. Several key proteins in neuro-degenerative disorders are components of MLOs, and a further liquid-to-solid transition to unsoluble amyloid aggregates may be triggered by pathogenic mutants. Non-equilibrium active processes are also known to drive non trivial spatio-temporal organization patterns in MLOs. In this research we aim to predict which proteins can undergo LLPS in physiological conditions in living cells, and the corresponding phase behavior, based on theoretical tools and on the knowledge of the amino-acid sequence alone. We derive specific knowledge-based potentials for the different kind of short-range interactions that are believed to drive protein LLPS and use them to build a simple yes/no predictor of phase separation in physiological conditions.

biophysics↗

bioRxiv2021

Statistical potentials from the Gaussian scaling behaviour of chain fragments buried within protein globules

Knowledge-based approaches use the statistics collected from protein data-bank structures to estimate effective interaction potentials between amino acid pairs. Empirical relations are typically employed that are based on the crucial choice of a reference state associated to the null interaction case. Despite their significant effectiveness, the physical interpretation of knowledge-based potentials has been repeatedly questioned, with no consensus on the choice of the reference state. Here we use the fact that the Flory theorem, originally derived for chains in a dense polymer melt, holds also for chain fragments within the core of globular proteins, if the average over buried fragments collected from different non-redundant native structures is considered. After verifying that the ensuing Gaussian statistics, a hallmark of effectively non-interacting polymer chains, holds for a wide range of fragment lengths, we use it to define a bona fide reference state. Notably, despite the latter does depend on fragment length, deviations from it do not. This allows to estimate an effective interaction potential which is not biased by the presence of correlations due to the connectivity of the protein chain. We show how different sequence-independent effective statistical potentials can be derived using this approach by coarse-graining the protein representation at varying levels. The possibility of defining sequence-dependent potentials is explored.

biophysics↗

bioRxiv2019

Acquisition of cross-resistance to bedaquiline and clofazimine following treatment for tuberculosis in Pakistan

We report on the first six cases of acquired resistance to bedaquiline in Pakistan. Seventy sequential culture isolates from 30 drug-resistant tuberculosis patients on bedaquiline-containing regimen were tested for minimum inhibitory concentration (MIC) and whole genome sequencing. An increase in MICs associated with cross-resistance to clofazimine and appearance of specific mutations was documented in six cases. The study underlines that appropriate monitoring is mandatory for the introduction of new drugs.

microbiology↗