Laboratory of Statistical Biophysics

Laboratory

Related publications (93)

A fluorescent multi-domain protein reveals the unfolding mechanism of Hsp70

Salvatore Assenza, Paolo De Los Rios, Bruno Claude Daniel Fauvet, Pierre Goloubinoff

Detailed understanding of the mechanism by which Hsp70 chaperones protect cells against protein aggregation is hampered by the lack of a comprehensive characterization of the aggregates, which are typically heterogeneous. Here we designed a reporter chaperone substrate, MLucV, composed of a stress-labile luciferase flanked by stress-resistant fluorescent domains, which upon denaturation formed a discrete population of small aggregates. Combining Forster resonance energy transfer and enzymatic activity measurements provided unprecedented details on the aggregated, unfolded, Hsp70-bound and native MLucV conformations. The Hsp70 mechanism first involved ATP-fueled disaggregation and unfolding of the stable pre-aggregated substrate, which stretched MLucV beyond simply unfolded conformations, followed by native refolding. The ATP-fueled unfolding and refolding action of Hsp70 on MLucV aggregates could accumulate native MLucV species under elevated denaturing temperatures highly adverse to the native state. These results unambiguously exclude binding and preventing of aggregation from the non-equilibrium mechanism by which Hsp70 converts stable aggregates into metastable native proteins.

NATURE PORTFOLIO2022

Mutual information in changing environments: Nonlinear interactions, out-of-equilibrium systems, and continuously varying diffusivities

Daniel Maria Busiello

Biochemistry, ecology, and neuroscience are examples of prominent fields aiming at describing interacting systems that exhibit nontrivial couplings to complex, ever-changing environments. We have recently shown that linear interactions and a switching environment are encoded separately in the mutual information of the overall system. Here we first generalize these findings to a broad class of nonlinear interacting models. We find that a new term in the mutual information appears, quantifying the interplay between nonlinear interactions and environmental changes, and leading to either constructive or destructive information interference. Furthermore, we show that a higher mutual information emerges in out-of-equilibrium environments with respect to an equilibrium scenario. Finally, we generalize our framework to the case of continuously varying environments. We find that environmental changes can be mapped exactly into an effective spatially varying diffusion coefficient, shedding light on modeling of biophysical systems in inhomogeneous media.

AMER PHYSICAL SOC2022

Geometric Percolation of Spherically Symmetric Fractal Aggregates

Claudio Grimaldi

The connectedness percolation threshold (phi(c)) for spherically symmetric, randomly distributed fractal aggregates is investigated as a function of the fractal dimension (d(F)) of the aggregates through a mean-field approach. A pair of aggregates (each of radius R) are considered to be connected if a pair of primary particles (each of diameter delta), one from each assembly, are located within a prescribed distance of each other. An estimate for the number of such contacts between primary particles for a pair of aggregates is combined with a mapping onto the model for fully penetrable spheres to calculate phi(c). For sufficiently large aggregates, our analysis reveals the existence of two regimes for the dependence of fc upon R/delta namely: (i) when d(F) > 1.5 aggregates form contacts near to tangency, and phi(c) approximate to (R/delta)(dF-3), whereas (ii) when d(F) < 1.5 deeper interpenetration of the aggregates is required to achieve contact formation, and phi(c) approximate to (R/delta)(-dF). For a fixed (large) value of R/delta, a minimum for fc as a function of dF occurs when d(F) = 1.5. Taken together, these dependencies consistently describe behaviors observed over the domain 1

SPRINGER2022

Information-driven transitions in projections of underdamped dynamics

Daniel Maria Busiello, Amos Maritan

Low-dimensional representations of underdamped systems often provide useful insights and analytical tractability. Here, we build such representations via information projections, obtaining an optimal model that captures the most information on observed spatial trajectories. We show that, in paradigmatic systems, the minimization of the information loss drives the appearance of a discontinuous transition in the optimal model parameters. Our results raise serious warnings for general inference approaches, and they unravel fundamental properties of effective dynamical representations impacting several fields, from biophysics to dimensionality reduction.

AMER PHYSICAL SOC2022

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

Stefano Zamuner

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, although with significant deviations at short spatial scales, 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.

PUBLIC LIBRARY SCIENCE2022

An Efficient Signal-to-noise Approximation for Eccentric Inspiraling Binaries

Alexandra Shelest

Eccentricity has emerged as a potentially useful tool for helping to identify the origin of black hole mergers. However, eccentric templates can be computationally very expensive owing to the large number of harmonics, making statistical analyses to distinguish formation channels very challenging. We outline a method for estimating the signal-to-noise ratio (S/N) for inspiraling binaries at lower frequencies such as those proposed for LISA and DECIGO. Our approximation can be useful more generally for any quasi-periodic sources. We argue that surprisingly, the S/N evaluated at or near the peak frequency (of the power) is well approximated by using a constant-noise curve, even if in reality the noise strain has power-law dependence. We furthermore improve this initial estimate over our previous calculation to allow for frequency dependence in the noise to expand the range of eccentricity and frequency over which our approximation applies. We show how to apply this method to get an answer accurate to within a factor of 2 over almost the entire projected observable frequency range. We emphasize this method is not a replacement for detailed signal processing. The utility lies chiefly in identifying theoretically useful discriminators among different populations and providing fairly accurate estimates for how well they should work. This approximation can furthermore be useful for narrowing down parameter ranges in a computationally economical way when events are observed. We furthermore show a distinctive way to identify events with extremely high eccentricity where the signal is enhanced relative to naive expectations on the high-frequency end.

IOP Publishing Ltd2022

A birth-death-migration model for life in astrophysical environments

Claudio Grimaldi

To assess the number of life-bearing worlds in astrophysical environments, it is necessary to take the intertwined processes of abiogenesis (birth), extinction (death), and transfer of life (migration) into account. We construct a mathematical model that incorporates this trio of mechanisms and accordingly derive the probability distribution function and other statistical properties (e.g. mean) for the number of worlds with biospheres. We show that a given astrophysical setting may become eventually saturated with life if the rate of successful transfers of organisms is higher than the extinction rate of biospheres. Based on the available data, we suggest that this criterion might be fulfilled for star-forming clusters (and perhaps the Galactic bulge under optimal circumstances), thereby indicating that such regions could constitute promising abodes for hosting and detecting life.

OXFORD UNIV PRESS2021

Mutual Information Disentangles Interactions from Changing Environments

Daniel Maria Busiello

Real-world systems are characterized by complex interactions of their internal degrees of freedom, while living in ever-changing environments whose net effect is to act as additional couplings. Here, we introduce a paradigmatic interacting model in a switching, but unobserved, environment. We show that the limiting properties of the mutual information of the system allow for a disentangling of these two sources of couplings. Further, our approach might stand as a general method to discriminate complex internal interactions from equally complex changing environments.

AMER PHYSICAL SOC2021

Repair or Degrade: the Thermodynamic Dilemma of Cellular Protein Quality-Control

Paolo De Los Rios, Bruno Claude Daniel Fauvet, Pierre Goloubinoff

Life is a non-equilibrium phenomenon. Owing to their high free energy content, the macromolecules of life tend to spontaneously react with ambient oxygen and water and turn into more stable inorganic molecules. A similar thermodynamic picture applies to the complex shapes of proteins: While a polypeptide is emerging unfolded from the ribosome, it may spontaneously acquire secondary structures and collapse into its functional native conformation. The spontaneity of this process is evidence that the free energy of the unstructured state is higher than that of the structured native state. Yet, under stress or because of mutations, complex polypeptides may fail to reach their native conformation and form instead thermodynamically stable aggregates devoid of biological activity. Cells have evolved molecular chaperones to actively counteract the misfolding of stress-labile proteins dictated by equilibrium thermodynamics. HSP60, HSP70 and HSP100 can inject energy from ATP hydrolysis into the forceful unfolding of stable misfolded structures in proteins and convert them into unstable intermediates that can collapse into the native state, even under conditions inauspicious for that state. Aggregates and misfolded proteins may also be forcefully unfolded and degraded by chaperone-gated endo-cellular proteases, and in eukaryotes also by chaperone-mediated autophagy, paving the way for their replacement by new, unaltered functional proteins. The greater energy cost of degrading and replacing a polypeptide, with respect to the cost of its chaperone-mediated repair represents a thermodynamic dilemma: some easily repairable proteins are better to be processed by chaperones, while it can be wasteful to uselessly try recover overly compromised molecules, which should instead be degraded and replaced. Evolution has solved this conundrum by creating a host of unfolding chaperones and degradation machines and by tuning their cellular amounts and activity rates.

FRONTIERS MEDIA SA2021

Emergence of synchronised and amplified oscillations in neuromorphic networks with long-range interactions

Daniel Maria Busiello, Samir Suweis

Neuromorphic networks can be described in terms of coarse-grained variables, where emergent sus-tained behaviours spontaneously arise if stochasticity is properly taken into account. For example it has been recently found that a directed linear chain of connected patch of neurons amplifies an input sig-nal, also tuning its characteristic frequency. Here we study a generalization of such a simple model, intro-ducing heterogeneity and variability in the parameter space and long-range interactions, breaking, in turn, the preferential direction of information transmission of a directed chain. On one hand, enlarging the region of parameters leads to a more complex state space that we analytically characterise; moreover, we explicitly link the strength distribution of the non-local interactions with the frequency distribution of the network oscillations. On the other hand, we found that adding long-range interactions can cause the onset of novel phenomena, as coherent and synchronous oscillations among all the interacting units, which can also coexist with the amplification of the signal. (c) 2021 Published by Elsevier B.V.

ELSEVIER2021