arXiv subjects

Ugo Bastolla

Publications and source records attributed to Ugo Bastolla.

At least 19 recordsLinked to original sources

arXiv2021

Is Covid-19 severity associated with ACE2 degradation?

Covid-19 is particularly mild with children, and its severity escalates with age. Several theories have been proposed to explain these facts. In particular, it was proposed that the lower expression of the viral receptor ACE2 in children protects them from severe Covid. However, other works suggested an inverse relationship between ACE2 expression and disease severity. Here we try to reconcile seemingly contradicting observations noting that ACE2 is not monotonically related with age but it reaches a maximum at a young age that depends on the cell type and then decreases. This pattern is consistent with most existing data from humans and rodents and it is expected to be more marked for ACE2 cell protein than for mRNA because of the increase with age of the protease TACE/ADAM17 that sheds ACE2 from the cell membrane to the serum. The negative relation between ACE2 level and Covid-19 severity at old age is not paradoxical but it is consistent with a mathematical model of virus propagation that predicts that higher viral receptor does not necessarily favour virus propagation and it can even slow it down. More importantly, ACE2 is known to protect organs from chronic and acute inflammation, which are worsened by low ACE2 levels. Here we propose that ACE2 contributes essentially to reverse the inflammatory process by downregulating the pro-inflammatory peptides of the angiotensin and bradykinin system, and that failure to revert the inflammation triggered by SARS-COV-2 may underlie both severe CoViD-19 infection and its many post-infection manifestations, including the multi-inflammatory syndrome of children (MIS-C). Within this view, lower severity in children despite lower ACE2 expression may be consistent with their higher expression of the alternative angiotensin II receptor ATR2 and in general of the anti-inflammatory arm of the Renin-Angiotensin System (RAS) at young age.

q-bio.TO↗

arXiv2020

Mathematical model of SARS-Cov-2 propagation versus ACE2 fits COVID-19 lethality across age and sex and predicts that of SARS, supporting possible therapy

The fatality rate of Covid-19 escalates with age and is larger in men than women. I show that these variations correlate strongly with the level of the viral receptor protein ACE2 in rat lungs, which is consistent with the still limited and apparently contradictory data on human ACE2. Surprisingly, lower levels of the receptor correlate with higher fatality. However, a previous mathematical model predicts that the speed of viral progression in the organism has a maximum and then declines with the receptor level. Moreover, many manifestations of severe CoViD-19, such as severe lung injury, exacerbated inflammatory response and thrombotic problems may derive from increased Angiotensin II (Ang-II) level that results from degradation of ACE2 by the virus. I present here a mathematical model based on the influence of ACE2 on viral propagation and disease severity. The model fits Covid-19 fatality rate across age and sex with high accuracy ($r^2>0.9$) under the hypothesis that SARS-CoV-2 infections are in the dynamical regimes in which increased receptor slows down viral propagation. Moreover, rescaling the model parameters by the ratio of the binding rates of the spike proteins of SARS-CoV and SARS-CoV-2 allows predicting the fatality rate of SARS-CoV across age and sex, thus linking the molecular and epidemiological levels. The presented model opposes the fear that angiotensin receptor blockers (ARB), suggested as a therapy against the most adverse effects of CoViD-19, may favour viral propagation, and suggests that Ang-II and ACE2 are candidate prognostic factors for detecting population that needs stronger protection.

q-bio.PE↗

arXiv2015

The maximum penalty criterion for ridge regression: application to the calibration of the force constant in elastic network models

Multivariate regression is a widespread computational technique that may give meaningless results if the explanatory variables are too numerous or highly collinear. Tikhonov regularization, or ridge regression, is a popular approach to address this issue. We reveal here a formal analogy between ridge regression and statistical mechanics, where the objective function is comparable to a free energy and the ridge parameter plays the role of temperature. This analogy suggests two new criteria to select a suitable ridge parameter: the specific-heat (Cv) and the maximum penalty (MP) fits. We apply these methods to the calibration of the force constant in elastic network models (ENM). This key parameter determines the amplitude of the predicted atomic fluctuations, and is commonly obtained by fitting crystallographic B-factors. However, rigid-body motions are often partially neglected in such fits, even though their importance has been repeatedly stressed. Considering the full set of rigid-body and internal degrees of freedom bears significant risks of overfitting, due to strong correlations between explanatory variables, and requires thus careful regularization. Using simulated data, we show that ridge regression with the Cv or MP criterion markedly reduces the error of the estimated force constant, its across-protein variation, and the number of proteins with unphysical values of the fit parameters, in comparison with popular regularization schemes such as generalized cross-validation. When applied to protein crystals, the new methods provide a more robust calibration of ENM force constants, even though rigid-body motions account on average for more than 80% of the amplitude of B-factors. While MP emerges as the optimal choice for fitting crystallographic B-factors, the Cv fit is more robust to the nature of the data, and is thus an interesting candidate for other applications.

q-bio.BM↗

arXiv2014

Does mutualism hinder biodiversity?

A recent paper by James et al. finds that mutualistic interactions decrease the biodiversity of model ecosystems. However, this result can be reverted if we consider ecological trade-offs and choose parameters suitable for sparse mutualistic networks instead of fully connected networks.

q-bio.PE↗

arXiv2005

Biodiversity in model ecosystems, I: Coexistence conditions for competing species

This is the first of two papers where we discuss the limits imposed by competition to the biodiversity of species communities. In this first paper we study the coexistence of competing species at the fixed point of population dynamic equations. For many simple models, this imposes a limit on the width of the productivity distribution, which is more severe the more diverse the ecosystem is (Chesson, 1994). Here we review and generalize this analysis, beyond the ``mean-field''-like approximation of the competition matrix used in previous works, and extend it to structured food webs. In all cases analysed, we obtain qualitatively similar relations between biodiversity and competition: the narrower the productivity distribution is, the more species can stably coexist. We discuss how this result, considered together with environmental fluctuations, limits the maximal biodiversity that a trophic level can host.

q-bio.PE↗

arXiv2005

Biodiversity in model ecosystems, II: Species assembly and food web structure

This is the second of two papers dedicated to the relationship between population models of competition and biodiversity. Here we consider species assembly models where the population dynamics is kept far from fixed points through the continuous introduction of new species, and generalize to such models thecoexistence condition derived for systems at the fixed point. The ecological overlap between species with shared preys, that we define here, provides a quantitative measure of the effective interspecies competition and of the trophic network topology. We obtain distributions of the overlap from simulations of a new model based both on immigration and speciation, and show that they are in good agreement with those measured for three large natural food webs. As discussed in the first paper, rapid environmental fluctuations, interacting with the condition for coexistence of competing species, limit the maximal biodiversity that a trophic level can host. This horizontal limitation to biodiversity is here combined with either dissipation of energy or growth of fluctuations, which in our model limit the length of food webs in the vertical direction. These ingredients yield an effective model of food webs that produce a biodiversity profile with a maximum at an intermediate trophic level, in agreement with field studies.

q-bio.PE↗

arXiv2004

Looking at structure, stability, and evolution of proteins through the principal eigenvector of contact matrices and hydrophobicity profiles

We review and further develop an analytical model that describes how thermodynamic constraints on the stability of the native state influence protein evolution in a site-specific manner. To this end, we represent both protein sequences and protein structures as vectors: Structures are represented by the principal eigenvector (PE) of the protein contact matrix, a quantity that resembles closely the effective connectivity of each site; Sequences are represented through the ``interactivity'' of each amino acid type, using novel parameters that are correlated with hydropathy scales. These interactivity parameters are more strongly correlated than the other hydropathy scales that we examine with: (1) The change upon mutations of the unfolding free energy of proteins with two-states thermodynamics; (2) Genomic properties as the genome-size and the genome-wide GC content; (3) The main eigenvectors of the substitution matrices. The evolutionary average of the interactivity vector correlates very strongly with the PE of a protein structure. Using this result, we derive an analytic expression for site-specific distributions of amino acids across protein families in the form of Boltzmann distributions whose ``inverse temperature'' is a function of the PE component. We show that our predictions are in agreement with site-specific amino acid distributions obtained from the Protein Data Bank, and we determine the mutational model that best fits the observed site-specific amino acid distributions. Interestingly, the optimal model almost minimizes the rate at which deleterious mutations are eliminated by natural selection.

q-bio.BM↗

arXiv2004

Prediction of site-specific amino acid distributions and limits of divergent evolutionary changes in protein sequences

We derive an analytic expression for site-specific stationary distributions of amino acids from the Structurally Constrained Neutral (SCN) model of protein evolution with conservation of folding stability. The stationary distributions that we obtain have a Boltzmann-like shape, and their effective temperature parameter, measuring the limit of divergent evolutionary changes at a given site, can be predicted from a site-specific topological property, the principal eigenvector of the contact matrix of the native conformation of the protein. These analytic results, obtained without free parameters, are compared with simulations of the SCN model and with the site-specific amino acid distributions obtained from the Protein Data Bank. These results also provide new insights into how the topology of a protein fold influences its designability, i.e. the number of sequences compatible with that fold. The dependence of the effective temperature on the principal eigenvector decreases for longer proteins, a possible consequence of the fact that selection for thermodynamic stability becomes weaker in this case.

q-bio.BM↗

arXiv2004

The principal eigenvector of contact matrices and hydrophobicity profiles in proteins

With the aim to study the relationship between protein sequences and their native structures, we adopt vectorial representations for both sequence and structure. The structural representation is based on the Principal Eigenvector of the fold's contact matrix (PE). As recently shown, the latter encodes sufficient information for reconstructing the whole contact matrix. The sequence is represented through a Hydrophobicity Profile (HP), using a generalized hydrophobicity scale that we obtain from the principal eigenvector of a residue-residue interaction matrix and denote it as interactivity scale. Using this novel scale, we define the optimal HP of a protein fold, and predict, by means of stability arguments, that it is strongly correlated with the PE of the fold's contact matrix. This prediction is confirmed through an evolutionary analysis, which shows that the PE correlates with the HP of each individual sequence adopting the same fold and, even more strongly, with the average HP of this set of sequences. Thus, protein sequences evolve in such a way that their average HP is close to the optimal one, implying that neutral evolution can be viewed as a kind of motion in sequence space around the optimal HP. Our results indicate that the correlation coefficient between N-dimensional vectors constitutes a natural metric in the vectorial space in which we represent both protein sequences and protein structures, which we call Vectorial Protein Space. In this way, we define a unified framework for sequence to sequence, sequence to structure, and structure to structure alignments. We show that the interactivity scale is nearly optimal both for the comparison of sequences with sequences and sequences with structures.

q-bio.BM↗

arXiv2004

Reconstruction of protein structures from a vectorial representation

We show that the contact map of the native structure of globular proteins can be reconstructed starting from the sole knowledge of the contact map's principal eigenvector, and present an exact algorithm for this purpose. Our algorithm yields a unique contact map for all 221 globular structures of PDBselect25 of length $N \le 120$. We also show that the reconstructed contact maps allow in turn for the accurate reconstruction of the three-dimensional structure. These results indicate that the reduced vectorial representation provided by the principal eigenvector of the contact map is equivalent to the protein structure itself. This representation is expected to provide a useful tool in bioinformatics algorithms for protein structure comparison and alignment, as well as a promising intermediate step towards protein structure prediction.

q-bio.BM↗

arXiv2002

Lack of self-averaging in neutral evolution of proteins

We simulate neutral evolution of proteins imposing conservation of the thermodynamic stability of the native state in the framework of an effective model of folding thermodynamics. This procedure generates evolutionary trajectories in sequence space which share two universal features for all of the examined proteins. First, the number of neutral mutations fluctuates broadly from one sequence to another, leading to a non-Poissonian substitution process. Second, the number of neutral mutations displays strong correlations along the trajectory, thus causing the breakdown of self-averaging of the resulting evolutionary substitution process.

cond-mat↗

arXiv2002

Statistical properties of neutral evolution

Neutral evolution is the simplest model of molecular evolution and thus it is most amenable to a comprehensive theoretical investigation. In this paper, we characterize the statistical properties of neutral evolution of proteins under the requirement that the native state remains thermodynamically stable, and compare them to the ones of Kimura's model of neutral evolution. Our study is based on the Structurally Constrained Neutral (SCN) model which we recently proposed. We show that, in the SCN model, the substitution rate decreases as longer time intervals are considered, and fluctuates strongly from one branch of the evolutionary tree to another, leading to a non-Poissonian statistics for the substitution process. Such strong fluctuations are also due to the fact that neutral substitution rates for individual residues are strongly correlated for most residue pairs. Interestingly, structurally conserved residues, characterized by a much below average substitution rate, are also much less correlated to other residues and evolve in a much more regular way. Our results could improve methods aimed at distinguishing between neutral and adaptive substitutions as well as methods for computing the expected number of substitutions occurred since the divergence of two protein sequences.

cond-mat↗

arXiv2002

Connectivity of neutral networks and structural conservation in protein evolution

Protein structures are much more conserved than sequences during evolution. Based on this observation, we investigate the consequences of structural conservation on protein evolution. We study seven of the most studied protein folds, finding out that an extended neutral network in sequence space is associated to each of them. Within our model, neutral evolution leads to a non-Poissonian substitution process, due to the broad distribution of connectivities in neutral networks. The observation that the substitution process has non-Poissonian statistics has been used against the original Kimura's neutral theory, while our model shows that this is a generic property of neutral evolution with structural conservation. Our model also predicts that the substitution rate can strongly fluctuate from one branch to another of the evolutionary tree. The average sequence similarity within a neutral network is close to the threshold of randomness, as observed for families of sequences sharing the same fold. Nevertheless, some positions are more difficult to mutate than others. We compare such structurally conserved positions to positions conserved in protein evolution, suggesting that our model can be a valuable tool to distinguish structural from functional conservation in databases of protein families. These results indicate that a synergy between database analysis and structurally-based computational studies can increase our understanding of protein evolution.

cond-mat.soft↗

arXiv2001

Replica-symmetry breaking in dynamical glasses

Systems of globally coupled logistic maps (GCLM) can display complex collective behaviour characterized by the formation of synchronous clusters. In the dynamical clustering regime, such systems possess a large number of coexisting attractors and might be viewed as dynamical glasses. Glass properties of GCLM in the thermodynamical limit of large system sizes $N$ are investigated. Replicas, representing orbits that start from various initial conditions, are introduced and distributions of their overlaps are numerically determined. We show that for fixed-field ensembles of initial conditions, as used in previous numerical studies, all attractors of the system become identical in the thermodynamical limit up to variations of order $1/\sqrt{N}$ because the initial value of the coupling field is characterized by vanishing fluctuations, and thus replica symmetry is recovered for $N\to \infty $. In contrast to this, when random-field ensembles of initial conditions are chosen, replica symmetry remains broken in the thermodynamical limit.

cond-mat.dis-nn↗

arXiv2001

The shape of ecological networks

We study the statistics of ecosystems with a variable number of co-evolving species. The species interact in two ways: by prey-predator relationships and by direct competition with similar kinds. The interaction coefficients change slowly through successful adaptations and speciations. We treat them as quenched random variables. These interactions determine long-term topological features of the species network, which are found to agree with those of biological systems.

nlin.AO↗

arXiv2000

Exactness of the Annealed and the Replica Symmetric Approximations for Random Heteropolymers

We study a heteropolymer model with random contact interactions introduced some time ago as a simplified model for proteins. The model consists of self-avoiding walks on the simple cubic lattice, with contact interactions between nearest neighbor pairs. For each pair, the interaction energy is an independent Gaussian variable with mean value $B$ and variance $\D^2$. For this model the annealed approximation is expected to become exact for low disorder, at sufficiently high dimension and in the thermodynamic limit. We show that corrections to the annealed approximation in the 3-d high temperature phase are small, but do not vanish in the thermodynamic limit, and are in good agreement with our replica symmetric calculations. Such corrections derive from the fact that the overlap between two typical chains is nonzero. We explain why previous authors had come to the opposite conclusion, and discuss consequences for the thermodynamics of the model. Numerical results were obtained by simulating chains up to length 1400 by means of the recent PERM algorithm, in the coil and molten globular phases, well above the freezing temperature.

cond-mat.soft↗

arXiv2000

Structurally constrained protein evolution: results from a lattice simulation

We simulate the evolution of a protein-like sequence subject to point mutations, imposing conservation of the ground state, thermodynamic stability and fast folding. Our model is aimed at describing neutral evolution of natural proteins. We use a cubic lattice model of the protein structure and test the neutrality conditions by extensive Monte Carlo simulations. We observe that sequence space is traversed by neutral networks, i.e. sets of sequences with the same fold connected by point mutations. Typical pairs of sequences on a neutral network are nearly as different as randomly chosen sequences. The fraction of neutral neighbors has strong sequence to sequence variations, which influence the rate of neutral evolution. In this paper we study the thermodynamic stability of different protein sequences. We relate the high variability of the fraction of neutral mutations to the complex energy landscape within a neutral network, arguing that valleys in this landscape are associated to high values of the neutral mutation rate. We find that when a point mutation produces a sequence with a new ground state, this is likely to have a low stability. Thus we tentatively conjecture that neutral networks of different structures are typically well separated in sequence space. This results indicates that changing significantly a protein structure through a biologically acceptable chain of point mutations is a rare, although possible, event.

cond-mat.stat-mech↗

arXiv1998

Neutral evolution of model proteins: diffusion in sequence space and overdispersion

We simulate the evolution of model protein sequences subject to mutations. A mutation is considered neutral if it conserves 1) the structure of the ground state, 2) its thermodynamic stability and 3) its kinetic accessibility. All other mutations are considered lethal and are rejected. We adopt a lattice model, amenable to a reliable solution of the protein folding problem. We prove the existence of extended neutral networks in sequence space -- sequences can evolve until their similarity with the starting point is almost the same as for random sequences. Furthermore, we find that the rate of neutral mutations has a broad distribution in sequence space. Due to this fact, the substitution process is overdispersed (the ratio between variance and mean is larger than one). This result is in contrast with the simplest model of neutral evolution, which assumes a Poisson process for substitutions, and in qualitative agreement with biological data.

cond-mat.stat-mech↗