The last years have witnessed remarkable advances in our understanding of the emergence and consequences of topological constraints in biological and soft matter. Examples are abundant in relation to (bio)polymeric systems and range from the characterization of knots in single polymers and proteins to that of whole chromosomes and polymer melts. At the same time, considerable advances have been made in the description of the interplay between topological and physical properties in complex fluids, with the development of techniques that now allow researchers to control the formation of and interaction between defects in diverse classes of liquid crystals. Thanks to technological progress and the integration of experiments with increasingly sophisticated numerical simulations, topological biological and soft matter is a vibrant area of research attracting scientists from a broad range of disciplines. However, owing to the high degree of specialization of modern science, many results have remained confined to their own particular fields, with different jargon making it difficult for researchers to share ideas and work together towards a comprehensive view of the diverse phenomena at play. Compelled by these motivations, here we present a comprehensive overview of topological effects in systems ranging from DNA and genome organization to entangled proteins, polymeric materials, liquid crystals, and theoretical physics, with the intention of reducing the barriers between different fields of soft matter and biophysics. Particular care has been taken in providing a coherent formal introduction to the topological properties of polymers and of continuum materials and in highlighting the underlying common aspects concerning the emergence, characterization, and effects of topological objects in different systems. The second half of the review is dedicated to the presentation of the latest results in selected problems, specifically, the effects of topological constraints on the viscoelastic properties of polymeric materials; their relation with genome organization; a discussion on the emergence and possible effects of knots and other entanglements in proteins; the emergence and effects of topological defects and solitons in complex fluids.This review is dedicated to the memory of Marek Cieplak.

The complex nature and structure of biomolecules and nanoparticles and their interactions make it challenging to achieve a deeper understanding of the dynamics at the nano-bio interface of enzymes and plasmonic nanoparticles subjected to light excitation. In this study, circular dichroism (CD) and Raman spectroscopic experiments and molecular dynamics (MD) simulations were used to investigate the potential changes at the nano-bio interface upon plasmonic excitation. Our data showed that photothermal and thermal heating induced distinct changes in the secondary structure of a model nanobioconjugate composed of lipase fromCandida antarcticafraction B (CALB) and gold nanoparticles (AuNPs). The use of a green laser led to a substantial decrease in the alpha-helix content of the lipase from 66% to 13% and an increase in the beta-sheet content from 5% to 31% compared to the initial conformation of the nanobioconjugate. In contrast, the differences under similar thermal heating conditions were only 55% and 11%, respectively. This study revealed important differences related to the enzyme secondary structure, enzyme-nanoparticle interactions, and the stability of the enzyme catalytic triad (Ser105-Asp187-His224), influenced by the instantaneous local temperature increase generated from photothermal heating compared to the slower rate of thermal heating of the bulk. These results provide valuable insights into the interactions between biomolecules and plasmonic nanoparticles induced by photothermal heating, advancing plasmonic biocatalysis and related fields.

Machine learning has been proposed as an alternative to theoretical modeling when dealing with complex problems in biological physics. However, in this perspective, we argue that a more successful approach is a proper combination of these two methodologies. We discuss how ideas coming from physical modeling neuronal processing led to early formulations of computational neural networks, e.g., Hopfield networks. We then show how modern learning approaches like Potts models, Boltzmann machines, and the transformer architecture are related to each other, specifically, through a shared energy representation. We summarize recent efforts to establish these connections and provide examples on how each of these formulations integrating physical modeling and machine learning have been successful in tackling recent problems in biomolecular structure, dynamics, function, evolution, and design. Instances include protein structure prediction; improvement in computational complexity and accuracy of molecular dynamics simulations; better inference of the effects of mutations in proteins leading to improved evolutionary modeling and finally how machine learning is revolutionizing protein engineering and design. Going beyond naturally existing protein sequences, a connection to protein design is discussed where synthetic sequences are able to fold to naturally occurring motifs driven by a model rooted in physical principles. We show that this model is “learnable” and propose its future use in the generation of unique sequences that can fold into a target structure.

This study presents an enhanced protein design algorithm that aims to emulate natural heterogeneity of protein sequences. Initial analysis revealed that natural proteins exhibit a permutation composition lower than the theoretical maximum, suggesting a selective utilization of the 20-letter amino acid alphabet. By not constraining the amino acid composition of the protein sequence but instead allowing random reshuffling of the composition, the resulting design algorithm generates sequences that maintain lower permutation compositions in equilibrium, aligning closely with natural proteins. Folding free energy computations demonstrated that the designed sequences refold to their native structures with high precision, except for proteins with large disordered regions. In addition, direct coupling analysis showed a strong correlation between predicted and actual protein contacts, with accuracy exceeding 82% for a large number of top pairs (>4L). The algorithm also resolved biases in previous designs, ensuring a more accurate representation of protein interactions. Overall, it not only mimics the natural heterogeneity of proteins but also ensures correct folding, marking a significant advancement in protein design and engineering.

This study presents the exploration of sequence-defined polyurethanes (PUs) as a new class of heteropolymers capable of precise conformational control. Utilizing molecular dynamics simulations, the folding behavior of polyurethane chains is investigated of varying lengths (11, 20, and 50 monomers) in both vacuum and aqueous environments. The simulations reveal that the heterogeneous chains systematically refold to approach the designed target structures better than non-designed chains or chains with artificially disrupted hydrogen-bond networks. The subsequent synthesis of an optimized 11-mer sequence (P1) is achieved through solid-phase chemistry, with thorough characterization via NMR, MS, and SEC confirming the accuracy of the predicted sequence and its controlled chain length. Solubility tests showed favorable results across multiple solvents, highlighting the versatility of the designed polymer. This research underscores the potential of sequence-defined polyurethanes to emulate the structural and functional attributes of biological macromolecules, opening new pathways for their application in catalysis, drug delivery, and advanced material design. The findings illustrate a promising direction for the development of synthetic polymers with tailored properties, emphasizing the transformative impact of sequence control in polymer chemistry. This study introduces a novel method for designing sequence-defined polyurethanes that fold into precise 3D structures, mimicking protein folding. Through iterative sequence design and molecular dynamics simulations, intricate hydrogen bond networks stabilize the polymers, enabling predictable folding. This work highlights the potential of these synthetic polymers to achieve controlled folding, paving the way for advanced materials that emulate biological functionalities. image

The urgency to find complementary therapies to current SARS-CoV-2 vaccines, whose effectiveness is preserved over time and not compromised by the emergence of new and emerging variants, has become a critical health challenge. We investigate the possibility of jamming the opening of the Receptor Binding Domain (RBD) of the spike protein of SARS-CoV-2 with small compounds. Through in silico screening, we identified two potential candidates that would lock the Receptor Binding Domain (RBD) in a closed configuration, preventing the virus from infecting the host cells. We show that two drugs already approved by the FDA, mithramycin and dihydroergotamine, can block infection using concentrations in the μM range in cell-based assays. Further STD-NMR experiments support dihydroergotamine's direct interaction with the spike protein. Overall, our results indicate that repurposing of these compounds might lead to potential clinical drug candidates for the treatment of SARS-CoV-2 infection.

Nanoparticles (NPs) and other engineered nanomaterials have great potential as nanodrugs or nanomedical devices for biomedical applications. However, the adsorption of proteins in blood circulation or similar physiological fluids can significantly alter the surface properties and therapeutic response induced by most nanomaterials. For example, interaction with proteins can change the bloodstream circulation time and availability of therapeutic NPs or hinder the accumulation in their desired target organs. Proteins can also trigger or prevent agglomeration. By combining experimental and computational approaches, we have developed NPs carrying polyethylene glycol (PEG) polymeric coatings that mimic the surface charge distribution of proteins typically found in blood, which are known to show low aggregation under normal blood conditions. Here, we show that NPs with coatings based on apoferritin or human serum albumin display better antifouling properties and weaker protein interaction compared to similar NPs carrying conventional PEG polymeric coatings.

COVID-19, caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), originated a global health crisis, causing over 2 million casualties and altering human daily life all over the world. This pandemic emergency revealed the limitations of current diagnostic tests, highlighting the urgency to develop faster, more precise and sensitive sensors. Graphene field effect transistors (GFET) are analytical platforms that enclose all these requirements. However, the design of a sensitive and robust GFET is not a straightforward objective. In this work, we report a GFET array biosensor for the detection of SARS-CoV-2 spike protein using the human membrane protein involved in the virus internalisation: angiotensin-converting enzyme 2 (ACE2). By finely controlling the graphene functionalisation, by tuning the Debye length, and by deeply characterising the ACE2-spike protein interactions, we have been able to detect the target protein with an extremely low limit of detection (2.94 aM). This work set the basis for a new class of analytical platforms, based on human membrane proteins, with the potential to detect a broad variety of pathogens, even before their isolation, being a powerful tool in the fight against future pandemics.

The coarse-grained MARTINI force field, initially developed for membranes, has proven to be an exceptional tool for investigating supramolecular peptide assemblies. Over the years, the force field underwent refinements to enhance accuracy, enabling, for example, the reproduction of protein-ligand interactions and constant pH behavior. However, these protein-focused improvements seem to have compromised its ability to model short peptide self-assembly. In this study, we assess the performance of MARTINI 3 in reproducing peptide self-assembly using the well-established diphenylalanine (FF) as our test case. Unlike its success in version 2.1, FF does not even exhibit aggregation in version 3. By systematically exploring parameters for the aromatic side chains and charged backbone beads, we established a parameter set that effectively reproduces tube formation. Remarkably, these parameter adjustments also replicate the self-assembly of other di- and tripeptides and coassemblies. Furthermore, our analysis uncovers pivotal insights for enhancing the performance of MARTINI in modeling short peptide self-assembly. Specifically, we identify issues stemming from overestimated hydrophilicity arising from charged termini and disruptions in pi-stacking interactions due to insufficient planarity in aromatic groups and a discrepancy in intermolecular distances between this and backbone-backbone interactions. This investigation demonstrates that strategic modifications can harness the advancements offered by MARTINI 3 for the realm of short peptide self-assembly.

Proteins are the workhorse of life. They are the building infrastructure of living systems; they are the most efficient molecular machines known, and their enzymatic activity is still unmatched in versatility by any artificial system. Perhaps proteins’ most remarkable feature is their modularity. The large amount of information required to specify each protein’s function is analogically encoded with an alphabet of just ∼20 letters. The protein folding problem is how to encode all such information in a sequence of 20 letters. In this review, we go through the last 30 years of research to summarize the state of the art and highlight some applications related to fundamental problems of protein evolution.

Hepatitis C virus (HCV) is the second viral agent that causes the majority of chronic hepatic infections worldwide, following Hepatitis B virus (HBV) infection. HCV infection comprises several steps, from the attachment to the receptors to the delivery of the viral genetic material and replication inside the cells. Tetraspanin CD81 is a key entry factor for HCV as it accompanies the virus during attachment and internalization through clathrin-mediated endocytosis. HCV-CD81 binding takes place through the viral glycoprotein E2. We performed full-atom molecular dynamics simulations reproducing the pH conditions that occur during the viral attachment to the hepatocytes (pH 7.4) and internalization (pH 6.2-4.6). We observed that changing the pH from 7.4 to 6.2 triggers a large conformational change in the binding orientation between E2core (E2core corresponds to residues 412-645 of the viral glycoprotein E2) and CD81LEL (CD81LEL corresponds to residues 112-204 of CD81) that occurs even more rapidly at low pH 4.6. This pH-induced switching mechanism has never been observed before and could allow the virus particles to sense the right moment during the maturation of the endosome to start fusion.

CD81 is a tetraspanin receptor that clusters into microdomains to mediate cell signalling processes. CD81 is also one of the four primary cellular receptors of the Hepatitis C virus (HCV). Previous structural studies on the α-helical CD81 large-extracellular-loop domain (CD81 LEL ) have shown that it can adopt different conformations (from closed to open), likely depending on the environmental conditions. This conformational plasticity has been implicated in the endosomal fusion of HCV upon entry. However, the precise mechanism governing the CD81 LEL plasticity has remained elusive so far. Here, by combining molecular dynamics simulations and circular dichroism experiments on wt-CD81 LEL and two mutants at different endosomal pH conditions, pH 5.5 and pH 4.6, we show that the modulation of the solvation shell governs the plasticity of CD81 LEL . The primarily implicated residues are D139 and E188, respectively, located near a loop preceded by a helix. At acidic conditions, their interaction with water is reduced, causing a re-ordering of the water molecules, and thus triggering the dynamics of CD81 LEL . However, mutations E188Q and D139N retain the solvation shell and restrict the conformational space that the head subdomain can explore. We propose that residues E188 and D139 control the solvent-induced allosteric transition of the CD81 LEL domain. This mechanism might play a role in other cellular receptors that function along the endosomal pathway. Popular Summary Understanding the cellular mechanisms that are exploited by viruses to infect their host is key for the development of therapeutics. Here, in the context of Hepatitis C Virus infection we report the mechanism that governs the plasticity of the extra-cellular domain of tetraspanin CD81, one of the major cellular receptors of this virus. The mechanism proposed here is a novel form of solvent-induced allosteric transition in proteins mediated by two antenna residues located in the head subdomain of CD81. We propose that it could serve as a pH sensing strategy to time the endosomal pathway and trigger a signal at the right time for HCV fusion.

By means of multiscale molecular simulation, we show that solvophilic-solvophobic AB diblock copolymer brushes in the semi-dilute regime present a re-entrant disorder/order/disorder transition. The latter is fully controllable through two parameters: the grafting density and the solvophobic to solvophilic ratio of the tethered macromolecules. Upon increasing density, chains first aggregate into patches, then further order into a crystalline phase and finally melt into a disordered phase. We demonstrate that the order/disorder transition can be explained through the peculiar properties of the aggregates: upon increasing density, the aggregation number grows as expected. On the contrary, their projection on the plane shrinks, thus melting the emergent ordered phase. Such a density dependent shrinkage, seen for the first time as the cause to an order/disorder phase transition, is as a consequence of the entropic/enthalpic competition that characterises the hierarchical self-assembly of the brush.

Water plays a fundamental role in protein stability. However, the effect of the properties of water on the behaviour of proteins is only partially understood. Several theories have been proposed to give insight into the mechanisms of cold and pressure denaturation, or the limits of temperature and pressure above which no protein has a stable, functional state, or how unfolding and aggregation are related. Here we review our results based on a theoretical approach that can rationalize the water contribution to protein solutions’ free energy. We show, using Monte Carlo simulations, how we can explain experimental data with our recent results. We discuss how our findings can help in developing new strategies for the design of novel synthetic biopolymers or new possible approaches for mitigating neurodegenerative pathologies.

Gold nanoparticles with various functionalities interact with the human serum albumin (HSA) leading to protein structural changes. HSA-nanoparticles bioconjugates display lower toxicity and slower cell uptake rates, compared to nanoparticles in the absence of protein.