arXiv subjects

Pietro Faccioli

Publications and source records attributed to Pietro Faccioli.

At least 19 recordsLinked to original sources

arXiv2025

De Novo Design of Protein-Binding Peptides by Quantum Computing

In silico de novo design can drastically cut the costs and time of drug development. In particular, a key advantage of bottom-up physics-based approaches is their independence from training datasets, unlike generative models. However, they require the simultaneous exploration of chemical and conformational space. In this study, we address this formidable challenge leveraging quantum annealers. Focusing on peptide de novo design, we introduce a multi-scale framework that integrates classical and quantum computing for atomically resolved predictions. We assess this scheme by designing binders for several protein targets. The D-Wave quantum annealer rapidly generates a chemically diverse set of binders with primary structures and binding poses that correlate well with experiments. These results demonstrate that, even in their current early stages, quantum technologies can already empower physics-based drug design.

quant-ph↗

arXiv2024

Statistical Mechanics of Heteropolymers from Lattice Gauge Theory

Lattice models are valuable tools to gain insight into the statistical physics of heteropolymers. We rigorously map the partition function of these models into a vacuum expectation value of a $\mathbb{Z}_2$ lattice gauge theory (LGT), with both fermionic and bosonic degrees of freedom. Because the associated path integral expression is not affected by a sign problem, it is amenable to Monte Carlo (MC) sampling in both the sequence and structure space, unlike conventional polymer field theory. At the same time, since the LGT encoding relies on qubits, it provides a framework for future efforts to capitalize on the development of quantum computing hardware. We discuss two illustrative applications of our formalism: first, we use it to characterize the thermodynamically stable sequences and structures of small heteropolymers consisting of two types of residues. Next, we assess its efficiency to sample ensembles of compact structures, finding that the MC decorrelation time scales only linearly with the chain length.

cond-mat.stat-mech↗

arXiv2024

Protein Design by Integrating Machine Learning with Quantum Annealing and Quantum-inspired Optimization

The protein design problem involves finding polypeptide sequences folding into a given threedimensional structure. Its rigorous algorithmic solution is computationally demanding, involving a nested search in sequence and structure spaces. Structure searches can now be bypassed thanks to recent machine learning breakthroughs, which have enabled accurate and rapid structure predictions. Similarly, sequence searches might be entirely transformed by the advent of quantum annealing machines and by the required new encodings of the search problem, which could be performative even on classical machines. In this work, we introduce a general protein design scheme where algorithmic and technological advancements in machine learning and quantum-inspired algorithms can be integrated, and an optimal physics-based scoring function is iteratively learned. In this first proof-of-concept application, we apply the iterative method to a lattice protein model amenable to exhaustive benchmarks, finding that it can rapidly learn a physics-based scoring function and achieve promising design performances. Strikingly, our quantum-inspired reformulation outperforms conventional sequence optimization even when adopted on classical machines. The scheme is general and can be easily extended, e.g., to encompass off-lattice models, and it can integrate progress on various computational platforms, thus representing a new paradigm approach for protein design.

quant-ph↗

arXiv2023

Sampling a rare protein transition with a hybrid classical-quantum computing algorithm

Simulating spontaneous structural rearrangements in macromolecules with classical Molecular Dynamics (MD) is an outstanding challenge. Conventional supercomputers can access time intervals up to tens of $\mu$s, while many key events occur on exponentially longer time scales. Transition path sampling techniques have the advantage of focusing the computational power on barrier-crossing trajectories, but generating uncorrelated transition paths that explore diverse conformational regions remains an unsolved problem. We employ a path-sampling paradigm combining machine learning (ML) with quantum computing (QC) to address this issue. We use ML on a classical computer to perform a preliminary uncharted exploration of the conformational space. The data set generated in this exploration is then post-processed to obtain a network representation of the reactive kinetics. Quantum annealing machines can exploit quantum superposition to encode all the transition pathways in this network in the initial quantum state and ensure the generation of completely uncorrelated transition paths. In particular, we resort to the DWAVE quantum computer to perform an all-atom simulation of a protein conformational transition that occurs on the ms timescale. Our results match those of a special purpose supercomputer designed to perform MD simulations. These results highlight the role of biomolecular simulation as a ground for applying, testing, and advancing quantum technologies.

physics.bio-ph↗

arXiv2022

Quarkonium polarization in low-$p_{\rm T}$ hadro-production: from past data to future opportunities

Several fixed-target experiments reported J/$\psi$ and $\Upsilon$ polarization measurements, as functions of Feynman $x$ ($x_{\rm F}$) and transverse momentum ($p_{\rm T}$), in three different polarization frames, using different combinations of beam particles, target nuclei and collision energies. The data form such a diverse and heterogeneous picture that, at first sight, no clear trends can be observed. A more detailed look, however, allows us to discern qualitative physical patterns that inspire and support a simple interpretation: the directly-produced quarkonia result from either gluon-gluon fusion or from quark-antiquark annihilation, with the former mesons being fully longitudinally polarized and the latter being fully transversely polarized. This hypothesis provides a reasonable quantitative description of the J/$\psi$ and $\Upsilon$(1S) polarizations measured in the $x_{\rm F} \lesssim 0.5$ kinematical domain. We provide predictions that can be experimentally tested, using proton and/or pion beams, and show that improved J/$\psi$ and $\psi$(2S) polarization measurements in pion-nucleus collisions can provide significant constraints on the poorly known parton distribution functions of the pion.

hep-ph↗

arXiv2022

On the polarization of the non-prompt contribution to inclusive J/$\psi$ production in pp collisions

Of the J/$\psi$ mesons (inclusively) produced in pp collisions, a big fraction results from B decays, increasing with transverse momentum and exceeding 50\% for $p_{\rm T} > 20$ GeV. These events must be subtracted in measurements of the polarization of prompt J/$\psi$ mesons. While several studies have addressed the $\psi$(2S) and $\chi_c$ impact on the determination of the polarization of the directly-produced J/$\psi$ mesons, the theoretical and experimental knowledge of the non-prompt polarization is very poor. Furthermore, non-prompt J/$\psi$ polarization measurements can provide interesting information on quarkonium hadroproduction, complementing the studies of prompt production. We review the method of measuring the polarization of non-prompt J/$\psi$ mesons (produced in decays of unreconstructed B mesons and detected in the dilepton channel), in conditions typical of LHC experiments studying J/$\psi$ production. Realistic model-independent scenarios are validated with data from experiments studying $e^+e^- \to \Upsilon$(4S) interactions, converted to the high-momentum regime using B differential cross sections measured at the LHC. The non-prompt J/$\psi$ polarization measurements are seen to remain dependent on the event selection criteria, even after correcting for the dilepton acceptance and efficiencies. This implies that reproducible definitions of all relevant analysis choices must be reported with the polarization result, for rigorous comparisons with other measurements and/or theoretical calculations. We also discuss how the non-prompt J/$\psi$ polarization significantly depends on the relative importance of two complementary $\mathrm{B}\to {\rm J}/\psi$ decay topologies, two-body (reasonably dominated by singlet production) and multi-body (including octet contributions), providing, hence, valuable information for studies of the charmonium formation mechanisms.

hep-ph↗

arXiv2022

RNA folding landscapes from explicit solvent all-atom simulations

Atomically detailed simulations of RNA folding have proven very challenging in view of the difficulties of developing realistic force fields and the intrinsic computational complexity of sampling rare conformational transitions. To tackle both these issues, we extend to RNA an enhanced path sampling method previously successfully applied to proteins. In this scheme, the information about the RNA's native structure is harnessed by a soft history-dependent biasing force, which is added to the atomistic force field, thus promoting the generation of productive folding trajectories. Here, we report on the results of simulations in explicit solvent of RNA molecules from 20 to 47 nucleotides long and increasing topological complexity. From a statistical analysis of the folding pathways we infer that, differently from proteins, the underlying free energy landscape is significantly frustrated, even for relatively small chains with a simple topology. The folding mechanism and the thermodynamics are in agreement with the available experiments and some of the existing coarse-grained models. This scheme provides a fully microscopic characterization of RNA folding, relating the kinetics and dynamics of the transition to the chemistry of the chain and its solvent. Therefore, it provides a transferable framework that sets the stage for future translational applications.

cond-mat.soft↗

arXiv2022

Sampling Rare Conformational Transitions with a Quantum Computer

Spontaneous structural rearrangements play a central role in the organization and function of complex biomolecular systems. In principle, physics-based computer simulations like Molecular Dynamics (MD) enable us to investigate these thermally activated processes with an atomic level of resolution. However, rare conformational transitions are intrinsically hard to investigate with MD, because an exponentially large fraction of computational resources must be invested to simulate thermal fluctuations in metastable states. Path sampling methods like Transition Path Sampling hold the great promise of focusing the available computational power on sampling the rare stochastic transition between metastable states. In these approaches, one of the outstanding limitations is to generate paths that visit significantly different regions of the conformational space at a low computational cost. To overcome these problems we introduce a rigorous approach that integrates a machine learning algorithm and MD simulations implemented on a classical computer with adiabatic quantum computing. First, using functional integral methods, we derive a rigorous low-resolution representation of the system's dynamics, based on a small set of molecular configurations generated with machine learning. Then, a quantum annealing machine is employed to explore the transition path ensemble of this low-resolution theory, without introducing un-physical biasing forces to steer the system's dynamics. Using the D-Wave quantum computer, we validate our scheme by simulating a benchmark conformational transition in a state-of-the-art atomistic description. We show that the quantum computing step generates uncorrelated trajectories, thus facilitating the sampling of the transition region in configuration space. Our results provide a new paradigm for MD simulations to integrate machine learning and quantum computing.

quant-ph↗

arXiv2021

Stochastic Dynamics and Bound States of Heavy Impurities in a Fermi Bath

We investigate the dynamics of heavy impurities embedded in an ultra-cold Fermi gas by using a Generalized Langevin equation. The latter -- derived by means of influence functional theory -- describes the stochastic classical dynamics of the impurities and the quantum nature of the fermionic bath manifests in the emergent interaction between the impurities and in the viscosity tensor. By focusing on the two-impurity case, we predict the existence of bound states, in different conditions of coupling and temperature, and whose life-time can be analytically estimated. Our predictions should be testable using cold-gases platforms within current technology.

cond-mat.quant-gas↗

arXiv2021

Optimal navigation strategy of active Brownian particles in target-search problems

We investigate exploration patterns of a microswimmer, modeled as an active Brownian particle, searching for a target region located in a well of an energy landscape and separated from the initial position of the particle by high barriers. We find that the microswimmer can enhance its success rate in finding the target by tuning its activity and its persistence in response to features of the environment. The target-search patterns of active Brownian particles are counterintuitive and display characteristics robust to changes of the energy landscape. On the contrary, the transition rates and transition-path times are sensitive to the details of the specific energy landscape. In striking contrast to the passive case, the presence of additional local minima does not significantly slow down the active target-search dynamics.

cond-mat.soft↗

arXiv2021

Polymer Physics by Quantum Computing

Sampling equilibrium ensembles of dense polymer mixtures is a paradigmatically hard problem in computational physics, even in lattice-based models. Here, we develop a formalism based on interacting binary tensors that allows for tackling this problem using quantum annealing machines. Our approach is general in that properties such as self-avoidance, branching, and looping can all be specified in terms of quadratic interactions of the tensors. Microstates realizations of different lattice polymer ensembles are then seamlessly generated by solving suitable discrete energy-minimization problems. This approach enables us to capitalize on the strengths of quantum annealing machines, as we demonstrate by sampling polymer mixtures from low to high densities, using the D-Wave quantum computer. Our systematic approach offers a promising avenue to harness the rapid development of quantum computers for sampling discrete models of filamentous soft-matter systems.

cond-mat.soft↗

arXiv2020

Dominant Reaction Pathways by Quantum Computing

Characterizing thermally activated transitions in high-dimensional rugged energy surfaces is a very challenging task for classical computers. Here, we develop a quantum annealing scheme to solve this problem. First, the task of finding the most probable transition paths in configuration space is reduced to a shortest-path problem defined on a suitable weighted graph. Next, this optimization problem is mapped into finding the ground state of a generalized Ising model. A finite-size scaling analysis suggests this task may be solvable efficiently by a quantum annealing machine. Our approach leverages on the quantized nature of qubits to describe transitions between different system's configurations. Since it does not involve any lattice space discretization, it paves the way towards future biophysical applications of quantum computing based on realistic all-atom models.

quant-ph↗

arXiv2020

Target search of active agents crossing high energy barriers

Target search by active agents in rugged energy landscapes has remained a challenge because standard enhanced sampling methods do not apply to irreversible dynamics. We overcome this non-equilibrium rare-event problem by developing an algorithm generalizing transition-path sampling to active Brownian dynamics. This method is exemplified and benchmarked for a paradigmatic two-dimensional potential with a high barrier. We find that even in such a simple landscape the structure and kinetics of the ensemble of transition paths change drastically in the presence of activity. Indeed, active Brownian particles reach the target more frequently than passive Brownian particles, following longer and counterintuitive search patterns.

cond-mat.soft↗

arXiv2020

From prompt to direct J/$\psi$ production: new insights on the $\chi_{c1}$ and $\chi_{c2}$ polarizations and feed-down contributions from a global-fit analysis of mid-rapidity LHC data

While the prompt J/$\psi$ cross section and polarization have been measured with good precision as a function of transverse momentum, $p_{\rm T}$, those of the directly produced J/$\psi$ are practically unknown, given that the cross sections and polarizations of the $\chi_{c1}$ and $\chi_{c2}$ mesons, large indirect contributors to J/$\psi$ production, are only known with rather poor accuracy. The lack of precise measurements of the $\chi_{cJ}$ polarizations induces large uncertainties in the level of their feed-down contributions to the prompt J/$\psi$ yield, because of the polarization-dependent acceptance corrections. The experimental panorama of charmonium production can be significantly improved through a consistent and model-independent global analysis of existing measurements of J/$\psi$, $\psi$(2S) and $\chi_{c}$ cross sections and polarizations, faithfully respecting all the correlations and uncertainties. In particular, it is seen that the $\chi_{cJ}$ polarizations and feed-down fractions to J/$\psi$ production have a negligible dependence on the J/$\psi$ $p_{\rm T}$, with average values $\lambda_\vartheta^{\chi_{c1}} = 0.55 \pm 0.23$, $\lambda_\vartheta^{\chi_{c2}} = -0.39 \pm 0.22$, $R^{\chi_{c1}} = (18.8 \pm 1.4)\%$ and $R^{\chi_{c2}} = (6.5 \pm 0.5)\%$. The analysis also shows that $(67.2 \pm 1.9)\%$ of the prompt J/$\psi$ yield is due to directly-produced mesons, of polarization constrained to remarkably small values, $\lambda_\vartheta^{{\rm J}/\psi} = 0.04 \pm 0.06$.

hep-ph↗

arXiv2020

Antimalarial Artefenomel Inhibits Human SARS-CoV-2 Replication in Cells while Suppressing the Receptor ACE2

The steep climbing of victims caused by the new coronavirus disease 2019 (COVID-19) throughout the planet is sparking an unprecedented effort to identify effective therapeutic regimens to tackle the pandemic. The SARS-CoV-2 virus is known to gain entry into various cell types through the binding of one of its surface proteins (spike) to the host Angiotensin-Converting Enzyme 2 (ACE2). Thus, spike-ACE2 interaction represents a major target for vaccines and antiviral drugs. A novel method has been recently described by some of the authors to pharmacologically downregulate the expression of target proteins at the post-translational level. This technology builds on computational advancements in the simulation of folding mechanisms to rationally block protein expression by targeting folding intermediates, hence hampering the folding process. Here, we report the all-atom simulations of the entire sequence of events underlying the folding pathway of ACE2. Our data revealed the existence of a folding intermediate showing two druggable pockets hidden in the native conformation. Both pockets were targeted by a virtual screening repurposing campaign aimed at quickly identifying drugs capable to decrease the expression of ACE2. We identified four compounds capable of lowering ACE2 expression in Vero cells in a dose-dependent fashion. All these molecules were found to inhibit the entry into cells of a pseudotyped retrovirus exposing the SARS-CoV-2 spike protein. Importantly, the antiviral activity has been tested against live SARS-CoV-2 (MEX-BC2/2020 strain). One of the selected drugs (Artefenomel) could completely prevent cytopathic effects induced by the presence of the virus, thus showing antiviral activity against SARS-CoV-2. Ongoing studies are further evaluating the possibility of repurposing these drugs for the treatment of COVID-19.

q-bio.BM↗

arXiv2019

NRQCD colour-octet expansion vs LHC quarkonium production: signs of a hierarchy puzzle?

The observation of unpolarized quarkonium production in high energy pp collisions, at mid rapidity, implies a significant violation of the non-relativistic QCD (NRQCD) velocity scaling rules. A precise experimental confirmation of this picture could definitely rule out the current formulation of the factorization expansion. This conclusion relies on current perturbative determinations of the short-distance kinematic factors and may be reverted if improved calculations would modify, in a very specific way, their transverse momentum dependences. That solution would result, however, in a full degeneracy in the presently assumed basis of $^{2S+1}L_J$ Fock states. Therefore, whatever the outcome, improved polarization measurements will challenge and improve our fundamental understanding of quarkonium production.

hep-ph↗

arXiv2018

The fate of quarkonia in heavy-ion collisions at LHC energies: a unified description of the sequential suppression patterns

Measurements made at the LHC have shown that the production of the ${\rm J}/ψ$, $ψ$(2S), $Υ$(1S) and $Υ$(2S) quarkonia is suppressed in Pb-Pb collisions, with respect to the extrapolation of the pp production yields. The $ψ$(2S) and $Υ$(2S) states are more strongly suppressed than the ground states and the level of the suppression changes with the centrality of the collision. We show that the measured patterns can be reproduced by a simple model, where all quarkonia are treated in a unified way, starting from the recent realisation that, in pp collisions, the probability of quarkonium formation has a universal dependence on the binding-energy of the bound state. The hot-medium suppression effect is parametrized by a penalty factor in the binding energy, identical for all (S- and P-wave) charmonium and bottomonium states, including those that indirectly contribute to the measured results through feed-down decays. This single parameter, computed through a global fit of all available suppression patterns, fully determines the hierarchy of nuclear effects, for all states and centrality bins. The resulting faithful description of the data provides convincing evidence in favour of the conjecture of sequential quarkonium suppression induced by QGP formation.

hep-ph↗

arXiv2018

All-atom simulations reveal how single point mutations promote serpin misfolding

Protein misfolding is implicated in many diseases, including the serpinopathies. For the canonical inhibitory serpin α1-antitrypsin (A1AT), mutations can result in protein deficiencies leading to lung disease, and misfolded mutants can accumulate in hepatocytes leading to liver disease. Using all-atom simulations based on the recently developed Bias Functional algorithm we elucidate how wild-type A1AT folds and how the disease-associated S (Glu264Val) and Z (Glu342Lys) mutations lead to misfolding. The deleterious Z mutation disrupts folding at an early stage, while the relatively benign S mutant shows late stage minor misfolding. A number of suppressor mutations ameliorate the effects of the Z mutation and simulations on these mutants help to elucidate the relative roles of steric clashes and electrostatic interactions in Z misfolding. These results demonstrate a striking correlation between atomistic events and disease severity and shine light on the mechanisms driving chains away from their correct folding routes.

q-bio.BM↗