Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments.

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

Modeling the instantaneous normal mode spectra of liquids as that of unstable elastic media.

We study the instantaneous normal mode (INM) spectrum of a simulated soft-sphere liquid at different equilibrium temperatures T We find that the spectrum of eigenvalues [Formula: see text] has a sharp maximum near (but not at) [Formula: see text] and decreases monotonically with [Formula: see text] on both the stable and unstable sides of the spectrum. The spectral shape strongly depends on temperature. It is rather asymmetric at low temperatures (close to the dynamical critical temperature) and becomes symmetric at high temperatures. To explain these findings we present a mean-field theory for [Formula: see text], which is based on a heterogeneous elasticity model, in which the local shear moduli exhibit spatial fluctuations, including negative values. We find good agreement between the simulation data and the model calculations, done with the help of the self-consistent Born approximation (SCBA), when we take the variance of the fluctuations to be proportional to the temperature T More importantly, we find an empirical correlation of the positions of the maxima of [Formula: see text] with the low-frequency exponent of the density of the vibrational modes of the glasses obtained by quenching to [Formula: see text] from the temperature T We discuss the present findings in connection to the liquid to glass transformation and its precursor phenomena.

Computational evidences of a misfolding event in an aggregation-prone light chain preceding the formation of the non-native pathogenic dimer.

Antibody light chain amyloidosis is a disorder in which protein aggregates, mainly composed of immunoglobulin light chains, deposit in diverse tissues impairing the correct functioning of organs. Interestingly, due to the high susceptibility of antibodies to mutations, AL amyloidosis appears to be strongly patient-specific. Indeed, every patient will display their own mutations that will make the proteins involved prone to aggregation thus hindering the study of this disease on a wide scale. In this framework, determining the molecular mechanisms that drive the aggregation could pave the way to the development of patient-specific therapeutics. Here, we focus on a particular patient-derived light chain, which has been experimentally characterized. We investigated the early phases of the aggregation pathway through extensive full-atom molecular dynamics simulations, highlighting a structural rearrangement and the exposure of two hydrophobic regions in the aggregation-prone species. Next, we moved to consider the pathological dimerization process through docking and molecular dynamics simulations, proposing a dimeric structure as a candidate pathological first assembly. Overall, our results shed light on the first phases of the aggregation pathway for a light chain at an atomic level detail, offering new structural insights into the corresponding aggregation process.

Dynamical changes of SARS-CoV-2 spike variants in the highly immunogenic regions impact the viral antibodies escaping.

The prolonged circulation of the SARS-CoV-2 virus resulted in the emergence of several viral variants, with different spreading features. Moreover, the increased number of recovered and/or vaccinated people introduced a selective pressure toward variants able to evade the immune system, developed against the former viral versions. This process results in reinfections. Aiming to study the latter process, we first collected a large structural dataset of antibodies in complex with the original version of SARS-CoV-2 Spike protein. We characterized the peculiarities of such antibodies population with respect to a control dataset of antibody-protein complexes, highlighting some statistically significant differences between these two sets of antibodies. Thus, moving our attention to the Spike side of the complexes, we identify the Spike region most prone to interaction with antibodies, describing in detail also the energetic mechanisms used by antibodies to recognize different epitopes. In this framework, fast protocols able to assess the effect of novel mutations on the cohort of developed antibodies would help establish the impact of the variants on the population. Performing a molecular dynamics simulation of the trimeric form of the SARS-CoV-2 Spike protein for the wild type and two variants of concern, that is, the Delta and Omicron variants, we described the physicochemical features and the conformational changes experienced locally by the variants with respect to the original version. Hence, combining the dynamical information with the structural study on the antibody-spike dataset, we quantitatively explain why the Omicron variant has a higher capability of escaping the immune system than the Delta variant, due to the higher conformational variability of the most immunogenic regions. Overall, our results shed light on the molecular mechanism behind the different responses the SARS-CoV-2 variants display against the immune response induced by either vaccines or previous infections. Moreover, our analysis proposes an approach that can be easily extended to both other SARS-CoV-2 variants or different molecular systems.

Thermometer: a webserver to predict protein thermal stability.

MOTIVATION: Thermal properties of proteins are of great importance for a number of theoretical and practical implications. Predicting the thermal stability of a protein is a difficult and still scarcely addressed task. RESULTS: Here, we introduce Thermometer, a webserver to assess the thermal stability of a protein using structural information. Thermometer is implemented as a publicly available, user-friendly interface. AVAILABILITY AND IMPLEMENTATION: Our server can be found at the following link (all major browser supported): http://service.tartaglialab.com/new_submission/thermometer_file. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Reference-less wavefront shaping in a Hopfield-like rough intensity landscape.

This study introduces a new digital-micromirror based binary-phase wavefront shaping technique, which allows the measurement of the full coupling matrix of a disordered medium without a reference and enables to focusing transmitted light. The coupling matrix takes on a bi-dyadic structure, similar to a Hopfield memory matrix containing two memory patterns. Sequential wavefront optimization in this configuration often stalls due to a rough intensity landscape, resulting in a non-optimal state. To overcome this issue, we propose the Complete Couplings Mapping method, which consistently reaches the theoretically expected maximum intensity.

Physics-informed deep neural network for image denoising.

Image enhancement deep neural networks (DNN) can improve signal to noise ratio or resolution of optically collected visual information. The literature reports a variety of approaches with varying effectiveness. All these algorithms rely on arbitrary data (the pixels' count-rate) normalization, making their performance strngly affected by dataset or user-specific data pre-manipulation. We developed a DNN algorithm capable to enhance images signal-to-noise surpassing previous algorithms. Our model stems from the nature of the photon detection process which is characterized by an inherently Poissonian statistics. Our algorithm is thus driven by distance between probability functions instead than relying on the sole count-rate, producing high performance results especially in high-dynamic-range images. Moreover, it does not require any arbitrary image renormalization other than the transformation of the camera's count-rate into photon-number.

Brillouin microscopy: an emerging tool for mechanobiology.

The role and importance of mechanical properties of cells and tissues in cellular function, development and disease has widely been acknowledged, however standard techniques currently used to assess them exhibit intrinsic limitations. Recently, Brillouin microscopy, a type of optical elastography, has emerged as a non-destructive, label- and contact-free method that can probe the viscoelastic properties of biological samples with diffraction-limited resolution in 3D. This led to increased attention amongst the biological and medical research communities, but it also sparked debates about the interpretation and relevance of the measured physical quantities. Here, we review this emerging technology by describing the underlying biophysical principles and discussing the interpretation of Brillouin spectra arising from heterogeneous biological matter. We further elaborate on the technique's limitations, as well as its potential for gaining insights in biology, in order to guide interested researchers from various fields.

Binding site identification of G protein-coupled receptors through a 3D Zernike polynomials-based method: application to C. elegans olfactory receptors.

Studying the binding processes of G protein-coupled receptors (GPCRs) proteins is of particular interest both to better understand the molecular mechanisms that regulate the signaling between the extracellular and intracellular environment and for drug design purposes. In this study, we propose a new computational approach for the identification of the binding site for a specific ligand on a GPCR. The method is based on the Zernike polynomials and performs the ligand-GPCR association through a shape complementarity analysis of the local molecular surfaces. The method is parameter-free and it can distinguish, working on hundreds of experimentally GPCR-ligand complexes, binding pockets from randomly sampled regions on the receptor surface, obtaining an Area Under ROC curve of 0.77. Given its importance both as a model organism and in terms of applications, we thus investigated the olfactory receptors of the C. elegans, building a list of associations between 21 GPCRs belonging to its olfactory neurons and a set of possible ligands. Thus, we can not only carry out rapid and efficient screenings of drugs proposed for GPCRs, key targets in many pathologies, but also we laid the groundwork for computational mutagenesis processes, aimed at increasing or decreasing the binding affinity between ligands and receptors.

Supervised perceptron learning vs unsupervised Hebbian unlearning: Approaching optimal memory retrieval in Hopfield-like networks.

The Hebbian unlearning algorithm, i.e., an unsupervised local procedure used to improve the retrieval properties in Hopfield-like neural networks, is numerically compared to a supervised algorithm to train a linear symmetric perceptron. We analyze the stability of the stored memories: basins of attraction obtained by the Hebbian unlearning technique are found to be comparable in size to those obtained in the symmetric perceptron, while the two algorithms are found to converge in the same region of Gardner's space of interactions, having followed similar learning paths. A geometric interpretation of Hebbian unlearning is proposed to explain its optimal performances. Because the Hopfield model is also a prototypical model of the disordered magnetic system, it might be possible to translate our results to other models of interest for memory storage in materials.

Lactoferrin Inhibition of the Complex Formation between ACE2 Receptor and SARS CoV-2 Recognition Binding Domain.

The present investigation focuses on the analysis of the interactions among human lactoferrin (LF), SARS-CoV-2 receptor-binding domain (RBD) and human angiotensin-converting enzyme 2 (ACE2) receptor in order to assess possible mutual interactions that could provide a molecular basis of the reported preventative effect of lactoferrin against CoV-2 infection. In particular, kinetic and thermodynamic parameters for the pairwise interactions among the three proteins were measured via two independent techniques, biolayer interferometry and latex nanoparticle-enhanced turbidimetry. The results obtained clearly indicate that LF is able to bind the ACE2 receptor ectodomain with significantly high affinity, whereas no binding to the RBD was observed up to the maximum "physiological" lactoferrin concentration range. Lactoferrin, above 1 µM concentration, thus appears to directly interfere with RBD-ACE2 binding, bringing about a measurable, up to 300-fold increase of the KD value relative to RBD-ACE2 complex formation.

Probing the non-Debye low-frequency excitations in glasses through random pinning.

We investigate the properties of the low-frequency spectrum in the density of states [Formula: see text] of a 3D model glass former. To magnify the non-Debye sector of the spectrum, we introduce a random pinning field that freezes a finite particle fraction to break the translational invariance and shifts all of the vibrational frequencies of the extended modes toward higher frequencies. We show that non-Debye soft localized modes progressively emerge as the fraction p of pinned particles increases. Moreover, the low-frequency tail of [Formula: see text] goes to zero as a power law [Formula: see text], with [Formula: see text] and [Formula: see text] above a threshold fraction [Formula: see text].

Bioprinting stem cells: building physiological tissues one cell at a time.

Bioprinting aims to direct the spatial arrangement in three dimensions of cells, biomaterials, and growth factors. The biofabrication of clinically relevant constructs for the repair or modeling of either diseased or damaged tissues is rapidly advancing, resulting in the ability to three-dimensional (3D) print biomimetic platforms which imitate a large number of tissues in the human body. Primary tissue-specific cells are typically isolated from patients and used for the fabrication of 3D models for drug screening or tissue repair purposes. However, the lack of resilience of these platforms, due to the difficulties in harnessing, processing, and implanting patient-specific cells can limit regeneration ability. The printing of stem cells obviates these hurdles, producing functional in vitro models or implantable constructs. Advancements in biomaterial science are helping the development of inks suitable for the encapsulation and the printing of stem cells, promoting their functional growth and differentiation. This review specifically aims to investigate the most recent studies exploring innovative and functional approaches for the printing of 3D constructs to model disease or repair damaged tissues. Key concepts in tissue physiology are highlighted, reporting stem cell applications in biofabrication. Bioprinting technologies and biomaterial inks are listed and analyzed, including recent advancements in biomaterial design for bioprinting applications, commenting on the influence of biomaterial inks on the encapsulated stem cells. Ultimately, most recent successful efforts and clinical potentials for the manufacturing of functional physiological tissue substitutes are reported here, with a major focus on specific tissues, such as vasculature, heart, lung and airways, liver, bone and muscle.

Simulated Epidemics in 3D Protein Structures to Detect Functional Properties.

The outcome of an epidemic is closely related to the network of interactions between individuals. Likewise, protein functions depend on the 3D arrangement of their residues and the underlying energetic interaction network. Borrowing ideas from the theoretical framework that has been developed to address the spreading of real diseases, we study for the first time the diffusion of a fictitious epidemic inside the protein nonbonded interaction network, aiming to study network features and properties. Our approach allows us to probe the overall stability and the capability of propagating information in complex 3D structures, proving to be very efficient in addressing different problems, from the assessment of thermal stability to the identification of functional sites.

Optonongenetic enhancement of activity in primary cortical neurons.

It has been recently demonstrated that the exposure of naive neuronal cells to light-at the basis of optogenetic techniques and calcium imaging measurements-may alter neuronal firing. Indeed, understanding the effect of light on nongenetically modified neurons is crucial for a correct interpretation of calcium imaging and optogenetic experiments. Here we investigated the effect of continuous visible LED light exposure (490 nm, $ 0.18 {-} 1.3\;{\rm mW}/{{\rm mm}^2} $0.18-1.3mW/mm2) on spontaneous activity of primary neuronal networks derived from the early postnatal mouse cortex. We demonstrated, by calcium imaging and patch clamp experiments, that illumination higher than $ 1.0\;{\rm mW}/{{\rm mm}^2} $1.0mW/mm2 causes an enhancement of network activity in cortical cultures. We investigated the possible origin of the phenomena by blocking the transient receptor potential vanilloid 4 (TRPV4) channel, demonstrating a complex connection between this temperature-dependent channel and the measured effect. The results presented here shed light on an exogenous artifact, potentially present in all calcium imaging experiments, that should be taken into account in the analysis of fluorescence data.

Reconstructing Nonparametric Productivity Networks.

Network models provide a general representation of inter-connected system dynamics. This ability to connect systems has led to a proliferation of network models for economic productivity analysis, primarily estimated non-parametrically using Data Envelopment Analysis (DEA). While network DEA models can be used to measure system performance, they lack a statistical framework for inference, due in part to the complex structure of network processes. We fill this gap by developing a general framework to infer the network structure in a Bayesian sense, in order to better understand the underlying relationships driving system performance. Our approach draws on recent advances in information science, machine learning and statistical inference from the physics of complex systems to estimate unobserved network linkages. To illustrate, we apply our framework to analyze the production of knowledge, via own and cross-disciplinary research, for a world-country panel of bibliometric data. We find significant interactions between related disciplinary research output, both in terms of quantity and quality. In the context of research productivity, our results on cross-disciplinary linkages could be used to better target research funding across disciplines and institutions. More generally, our framework for inferring the underlying network production technology could be applied to both public and private settings which entail spillovers, including intra- and inter-firm managerial decisions and public agency coordination. This framework also provides a systematic approach to model selection when the underlying network structure is unknown.

Relation between Heterogeneous Frozen Regions in Supercooled Liquids and Non-Debye Spectrum in the Corresponding Glasses.

Recent numerical studies on glassy systems provide evidence for a population of non-Goldstone modes (NGMs) in the low-frequency spectrum of the vibrational density of states D(ω). Similarly to Goldstone modes (GMs), i.e., phonons in solids, NGMs are soft low-energy excitations. However, differently from GMs, NGMs are localized excitations. Here we first show that the parental temperature T^{*} modifies the GM/NGM ratio in D(ω). In particular, the phonon attenuation is reflected in a parental temperature dependency of the exponent s(T^{*}) in the low-frequency power law D(ω)∼ω^{s(T^{*})}, with 2≤s(T^{*})≤4. Second, by comparing s(T^{*}) with s(p), i.e., the same quantity obtained by pinning a p particle fraction, we suggest that s(T^{*}) reflects the presence of dynamical heterogeneous regions of size ξ^{3}∝p. Finally, we provide an estimate of ξ as a function of T^{*}, finding a mild power law divergence, ξ∼(T^{*}-T_{d})^{-α/3}, with T_{d} the dynamical crossover temperature and α falling in the range α∈[0.8,1.0].

Beyond the Maximum Storage Capacity Limit in Hopfield Recurrent Neural Networks.

In a neural network, an autapse is a particular kind of synapse that links a neuron onto itself. Autapses are almost always not allowed neither in artificial nor in biological neural networks. Moreover, redundant or similar stored states tend to interact destructively. This paper shows how autapses together with stable state redundancy can improve the storage capacity of a recurrent neural network. Recent research shows how, in an N-node Hopfield neural network with autapses, the number of stored patterns (P) is not limited to the well known bound 0.14 N , as it is for networks without autapses. More precisely, it describes how, as the number of stored patterns increases well over the 0.14 N threshold, for P much greater than N, the retrieval error asymptotically approaches a value below the unit. Consequently, the reduction of retrieval errors allows a number of stored memories, which largely exceeds what was previously considered possible. Unfortunately, soon after, new results showed that, in the thermodynamic limit, given a network with autapses in this high-storage regime, the basin of attraction of the stored memories shrinks to a single state. This means that, for each stable state associated with a stored memory, even a single bit error in the initial pattern would lead the system to a stationary state associated with a different memory state. This thus limits the potential use of this kind of Hopfield network as an associative memory. This paper presents a strategy to overcome this limitation by improving the error correcting characteristics of the Hopfield neural network. The proposed strategy allows us to form what we call an absorbing-neighborhood of state surrounding each stored memory. An absorbing-neighborhood is a set defined by a Hamming distance surrounding a network state, which is an absorbing because, in the long-time limit, states inside it are absorbed by stable states in the set. We show that this strategy allows the network to store an exponential number of memory patterns, each surrounded with an absorbing-neighborhood with an exponentially growing size.

Hyperuniformity in amorphous speckle patterns.

Hyperuniform structures possess the ability to confine and drive light, although their fabrication is extremely challenging. Here we demonstrate that speckle patterns obtained by a superposition of randomly arranged sources of Bessel beams can be used to generate hyperunifrom scalar fields. By exploiting laser light tailored with a spatial filter, we experimentally produce (without requiring any computational power) a speckle pattern possessing maxima at locations corresponding to a hyperuniform distribution. By properly filtering out intensity fluctuation from the same speckle pattern, it is possible to retrieve an intensity profile satisfying the hyperuniformity requirements. Our findings are supported by extensive numerical simulations.