Biology subjects

Zamuner, S.

Publications and source records attributed to Zamuner, S..

6 recordsLinked to original sources

bioRxiv2022

The individual and combined benefits of differentnon-equilibrium proofreading mechanisms

Genome duplication, transcription and translation are among many crucial cellular processes that need to be performed with high fidelity. However those extremely low error rates cannot be explained with simple equilibrium thermodynamic considerations. They instead require considering irreversible, energy consuming reactions in the overall mechanism. We develop here a model of substrates selection comprising energy consuming steps and which aims at selecting right substrates among wrong ones. With this model, we investigate the impact of energy consumption on the accuracy and the speed of the selection, as well as different selection strategies. The model presented here encompasses the classic kinetic proofreading scheme and a different mechanism whereby the rates of the energy consuming step are modulated by the nature of the substrate. We show that, in our framework, the fastest and most accurate selection strategy relies on a combination of both mechanisms. A structurally and biochemically informed coarse-grained description of real biological processes such as DNA replication and protein translation, traditionally used as examples of kinetic proofreading at work, shows that, as a matter of fact, a combination of both mechanisms explored here is exploited.

biophysics↗

bioRxiv2022

Data-driven large-scale genomic analysis reveals an intricate phylogenetic and functional landscape in J-domain proteins

The 70 kDalton Heat shock protein (Hsp70) chaperone system is emerging as a central hub of the proteostasis network that helps maintain protein homeostasis in all organisms. The recruitment of Hsp70 to perform a vast array of different cellular functions is regulated by a family of co-chaperones known as J-domain proteins (JDP) that bear a small namesake J-domain, which is required to interact and drive the ATPase cycle of Hsp70s. Both prokaryotic and eukaryotic JDPs display staggering diversity in domain architecture (besides the ubiquitous J-domain), function, and cell localization. On the contrary, a relatively small number of Hsp70 paralogs exist in cells, suggesting a high degree of specificity, but also promiscuity, in the partnering between JDPs and Hsp70s. Very little is known about the JDP family, despite their essential role in cellular proteostasis, development, and the link to a broad range of human diseases. The number of JDP gene sequences identified across all kingdoms as a consequence of advancements in sequencing technology has exponentially increased, where it is now beyond the ability of careful manual curation. In this work, we first provide a broad overview of the JDP repertoire accessible from public databases, and then we use an automated classification scheme, based on Artificial Neural Networks (ANNs), to demonstrate that the sequences of J-domains carry sufficient discriminatory information to recover with high reliability the phylogeny, localization, and domain composition of the corresponding full-length JDP. By harnessing the interpretability of the ANNs, we find that many of the discriminatory sequence positions match to residues that form the interaction interface between the J-domain and Hsp70. This reveals that key residues within the J-domains have coevolved with their obligatory Hsp70 partners to build chaperone circuits for specific functions in cells.

bioinformatics↗

bioRxiv2021

ABC Transporters are billion-year-old Maxwell Demons

ABC transporters are a broad family of biological machines, found in most prokaryotic and eukaryotic cells, performing the crucial import or export of substrates through both plasma and organellar membranes, and maintaining a steady concentration gradient driven by ATP hydrolysis. Building upon the present biophysical and biochemical characterization of ABC transporters, we propose here a model whose solution reveals that these machines are an exact molecular realization of the Maxwell Demon, a century-old abstract device that uses an energy source to drive systems away from thermodynamic equilibrium. In particular, the Maxwell Demon does not perform any direct mechanical work on the system, but simply selects which spontaneous processes to allow and which ones to forbid based on information that it collects and processes. In the molecular model introduced here, the different information-processing steps that characterize Maxwell Demons (measurement, feedback and resetting) are features that emerge from the biochemical and structural properties of ABC transporters, allowing us to develop an explicit bridge between the molecular level description and the higher-level language of information theory.

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↗

bioRxiv2021

Relief of ParB autoinhibition by parS DNA catalysis and ParB recycling by CTP hydrolysis promote bacterial centromere assembly.

Three-component ParABS systems are widely distributed factors for plasmid partitioning and chromosome segregation in bacteria. ParB protein acts as an adaptor between the 16 bp centromeric parS DNA sequences and the DNA segregation ATPase ParA. It accumulates at high concentrations at and near a parS site by assembling a partition complex. ParB dimers form a DNA sliding clamp whose closure at parS requires CTP binding. The mechanism underlying ParB loading and the role of CTP hydrolysis however remain unclear. We show that CTP hydrolysis is dispensable for Smc recruitment to parS sites in Bacillus subtilis but is essential for chromosome segregation by ParABS in the absence of Smc. Our results suggest that CTP hydrolysis contributes to partition complex assembly via two mechanisms. It recycles off-target ParB clamps to allow for new attempts at parS targeting and it limits the extent of spreading from parS by promoting DNA unloading. We also propose a model for how parS DNA catalyzes ParB clamp closure involving a steric clash between ParB protomers binding to opposing parS half sites.

biochemistry↗

bioRxiv2019

Mechanics of allostery: contrasting the induced fit and population shift scenarios

In allosteric proteins, binding a ligand can affect function at a distant location, for example by changing the binding affinity of a substrate at the active site. The induced fit and population shift models, which differ by the assumed number of stable configurations, explain such cooperative binding from a thermodynamic viewpoint. Yet, understanding what mechanical principles constrain these models remains a challenge. Here we provide an empirical study on 34 proteins supporting the idea that allosteric conformational change generally occurs along a soft elastic mode presenting extended regions of high shear. We argue, based on a detailed analysis of how the energy profile along such a mode depends on binding, that in the induced fit scenario there is an optimal stiffness [Formula] for cooperative binding, where N is the number of residues involved in the allosteric response. We find that the population shift scenario is more robust to mutation affecting stiffness, as binding becomes more and more cooperative with stiffness up to the same characteristic value [Formula], beyond which cooperativity saturates instead of decaying. We confirm numerically these findings in a non-linear mechanical model. Dynamical considerations suggest that a stiffness of order [Formula] is favorable in that scenario as well, supporting that for proper function proteins must evolve a functional elastic mode that is softer as their size increases. In consistency with this view, we find a significant anticorrelation between the stiffness of the allosteric response and protein size in our data set.

biophysics↗