Molecular insights into nanoplastics-peptides binding and their interactions with the lipid membrane.

Micro- and nanoplastics have become a significant concern, due to their ubiquitous presence in the environment. These particles can be internalized by the human body through ingestion, inhalation, or dermal contact, and then they can interact with environmental or biological molecules, such as proteins, resulting in the formation of the protein corona. However, information on the role of protein corona in the human body is still missing. Coarse-grain models of the nanoplastics and pentapeptides were created and simulated at the microscale to study the role of protein corona. Additionally, a lipid bilayer coarse-grain model was reproduced to investigate the behavior of the coronated nanoplastics in proximity of a lipid bilayer. Hydrophobic and aromatic amino acids have a high tendency to create stable bonds with all nanoplastics. Moreover, polystyrene and polypropylene establish bonds with polar and charged amino acids. When the coronated nanoplastics are close to a lipid bilayer, different behaviors can be observed. Polyethylene creates a single polymeric chain, while polypropylene tends to break down into its single chains. Polystyrene can both separate into its individual chains and remain aggregated. The protein corona plays an important role when interacting with the nanoplastics and the lipid membrane. More studies are needed to validate the results and to enhance the complexity of the systems.

Computation meets experiment: identification of highly efficient fibrillating peptides.

Self-assembling peptides are of huge interest for biological, medical and nanotechnological applications. The enormous chemical variety that is available from the 20 amino acids offers potentially unlimited peptide sequences, but it is currently an issue to predict their supramolecular behavior in a reliable and cheap way. Herein we report a computational method to screen and forecast the aqueous self-assembly propensity of amyloidogenic pentapeptides. This method was found also as an interesting tool to predict peptide crystallinity, which may be of interest for the development of peptide based drugs.

Unsupervised deep learning for molecular dynamics simulations: a novel analysis of protein-ligand interactions in SARS-CoV-2 Mpro.

Molecular dynamics (MD) simulations, which are central to drug discovery, offer detailed insights into protein-ligand interactions. However, analyzing large MD datasets remains a challenge. Current machine-learning solutions are predominantly supervised and have data labelling and standardisation issues. In this study, we adopted an unsupervised deep-learning framework, previously benchmarked for rigid proteins, to study the more flexible SARS-CoV-2 main protease (Mpro). We ran MD simulations of Mpro with various ligands and refined the data by focusing on binding-site residues and time frames in stable protein conformations. The optimal descriptor chosen was the distance between the residues and the center of the binding pocket. Using this approach, a local dynamic ensemble was generated and fed into our neural network to compute Wasserstein distances across system pairs, revealing ligand-induced conformational differences in Mpro. Dimensionality reduction yielded an embedding map that correlated ligand-induced dynamics and binding affinity. Notably, the high-affinity compounds showed pronounced effects on the protein's conformations. We also identified the key residues that contributed to these differences. Our findings emphasize the potential of combining unsupervised deep learning with MD simulations to extract valuable information and accelerate drug discovery.

Tailoring FPOX enzymes for enhanced stability and expanded substrate recognition.

Fructosyl peptide oxidases (FPOX) are deglycating enzymes that find application as key enzymatic components in diabetes monitoring devices. Indeed, their use with blood samples can provide a measurement of the concentration of glycated hemoglobin and glycated albumin, two well-known diabetes markers. However, the FPOX currently employed in enzymatic assays cannot directly detect whole glycated proteins, making it necessary to perform a preliminary proteolytic treatment of the target protein to generate small glycated peptides that can act as viable substrates for the enzyme. This is a costly and time consuming step. In this work, we used an in silico protein engineering approach to enhance the overall thermal stability of the enzyme and to improve its catalytic activity toward large substrates. The final design shows a marked improvement in thermal stability relative to the wild type enzyme, a distinct widening of its access tunnel and significant enzymatic activity towards a range of glycated substrates.

Unbiased in silico design of pH-sensitive tetrapeptides.

We used coarse-grain molecular dynamics simulations to screen all possible histidine-bearing tetrapeptide sequences, finding novel peptide sequences with pH-tunable assembly properties. These tetrapeptides could be used for various biological applications, such as triggered delivery of bioactive molecules.

In Silico Analysis of Nanoplastics' and ß-amyloid Fibrils' Interactions.

Plastic pollution has become a global environmental threat, which leads to an increasing concern over the consequences of plastic exposition on global health. Plastic nanoparticles have been shown to influence the folding of proteins and influence the formation of aberrant amyloid proteins, therefore potentially triggering the development of systemic and local amyloidosis. This work aims to study the interaction between nanoplastics and ß-amyloid fibrils to better understand the potential role of nanoplastics in the outbreak of neurodegenerative disorders. Using microsecond-long coarse-grained molecular dynamics simulations, we investigated the interactions between neutral and charged nanoparticles made of the most common plastic materials (i.e., polyethylene, polypropylene, and polystyrene) and ß-amyloid fibrils. We observe that the occurrence of contacts, region of amyloid fibril involved, and specific amino acids mediating the interaction depend on the type and charge of the nanoparticles.

Micro- and Nanoplastics' Effects on Protein Folding and Amyloidosis.

A significant portion of the world's plastic is not properly disposed of and, through various processes, is degraded into microscopic particles termed micro- and nanoplastics. Marine and terrestrial faunae, including humans, inevitably get in contact and may inhale and ingest these microscopic plastics which can deposit throughout the body, potentially altering cellular and molecular functions in the nervous and other systems. For instance, at the cellular level, studies in animal models have shown that plastic particles can cross the blood-brain barrier and interact with neurons, and thus affect cognition. At the molecular level, plastics may specifically influence the folding of proteins, induce the formation of aberrant amyloid proteins, and therefore potentially trigger the development of systemic and local amyloidosis. In this review, we discuss the general issue of plastic micro- and nanoparticle generation, with a focus on their effects on protein folding, misfolding, and their possible clinical implications.

Emergence of Elastic Properties in a Minimalist Resilin-Derived Heptapeptide upon Bromination.

Bromination is herein exploited to promote the emergence of elastic behavior in a short peptide-SDSYGAP-derived from resilin, a rubber-like protein exerting its role in the jumping and flight systems of insects. Elastic and resilient hydrogels are obtained, which also show self-healing behavior, thanks to the promoted non-covalent interactions that limit deformations and contribute to the structural recovery of the peptide-based hydrogel. In particular, halogen bonds may stabilize the ß-sheet organization working as non-covalent cross-links between nearby peptide strands. Importantly, the unmodified peptide (i.e., wild type) does not show such properties. Thus, SDSY(3,5-Br)GAP is a novel minimalist peptide elastomer.

Composite Peptide-Agarose Hydrogels for Robust and High-Sensitivity 3D Immunoassays.

Canonical immunoassays rely on highly sensitive and specific capturing of circulating biomarkers by interacting biomolecular baits. In this frame, bioprobe immobilization in spatially discrete three-dimensional (3D) spots onto analytical surfaces by hydrogel encapsulation was shown to provide relevant advantages over conventional two-dimensional (2D) platforms. Yet, the broad application of 3D systems is still hampered by hurdles in matching their straightforward fabrication with optimal functional properties. Herein, we report on a composite hydrogel obtained by combining a self-assembling peptide (namely, Q3 peptide) with low-temperature gelling agarose that is proved to have simple and robust application in the fabrication of microdroplet arrays, overcoming hurdles and limitations commonly associated with 3D hydrogel assays. We demonstrate the real-case scenario feasibility of our 3D system in the profiling of Covid-19 patients' serum IgG immunoreactivity, which showed remarkably improved signal-to-noise ratio over canonical assays in the 2D format and exquisite specificity. Overall, the new two-component hydrogel widens the perspectives of hydrogel-based arrays and represents a step forward towards their routine use in analytical practices.

In Silico Engineering of Enzyme Access Tunnels.

Enzyme engineering is a tailoring process that allows the modification of naturally occurring enzymes to provide them with improved catalytic efficiency, stability, or specificity. By introducing partial modifications to their sequence and to their structural features, enzyme engineering can transform natural enzymes into more efficient, specific and resistant biocatalysts and render them suitable for virtually countless industrial processes. Current enzyme engineering methods mostly target the active site of the enzyme, where the catalytic reaction takes place. Nonetheless, the tunnel that often connects the surface of an enzyme with its buried active site plays a key role in the activity of the enzyme as it acts as a gatekeeper and regulates the access of the substrate to the catalytic pocket. Hence, there is an increasing interest in targeting the sequence and the structure of substrate entrance tunnels in order to fine-tune enzymatic activity, regulate substrate specificity, or control reaction promiscuity.In this chapter, we describe the use of a rational in silico design and screening method to engineer the access tunnel of a fructosyl peptide oxidase with the aim to facilitate access to its catalytic site and to expand its substrate range. Our goal is to engineer this class of enzymes in order to utilize them for the direct detection of glycated proteins in diabetes monitoring devices. The design strategy involves remodeling of the backbone structure of the enzyme , a feature that is not possible with conventional enzyme engineering techniques such as single-point mutagenesis and that is highly unlikely to occur using a directed evolution approach.The proposed strategy, which results in a significant reduction in cost and time for the experimental production and characterization of candidate enzyme variants, represents a promising approach to the expedited identification of novel and improved enzymes. Rational enzyme design aims to provide in silico strategies for the fast, accurate, and inexpensive development of biocatalysts that can meet the needs of multiple industrial sectors, thus ultimately promoting the use of green chemistry and improving the efficiency of chemical processes.

In silico prediction of the in vitro behavior of polymeric gene delivery vectors.

Non-viral gene delivery vectors have increasingly come under the spotlight, but their performaces are still far from being satisfactory. Therefore, there is an urgent need for forecasting tools and screening methods to enable the development of ever more effective transfectants. Here, coarse-grained (CG) models of gold standard transfectant poly(ethylene imine)s (PEIs) have been profitably used to investigate and highlight the effect of experimentally-relevant parameters, namely molecular weight (2 vs. 10 kDa) and topologies (linear vs. branched), protonation state, and ammine-to-phosphate ratios (N/Ps), on the complexation and the gene silencing efficiency of siRNA molecules. The results from the in vitro screening of cationic polymers and conditions were used to validate the in silico platform that we developed, such that the hits which came out of the CG models were of high practical relevance. We show that our in silico platform enables to foresee the most suitable conditions for the complexation of relevant siRNA-polycation assemblies, thereby providing a reliable predictive tool to test bench transfectants in silico, and foster the design and development of gene delivery vectors.

Rational backbone redesign of a fructosyl peptide oxidase to widen its active site access tunnel.

Fructosyl peptide oxidases (FPOXs) are enzymes currently used in enzymatic assays to measure the concentration of glycated hemoglobin and albumin in blood samples, which serve as biomarkers of diabetes. However, since FPOX are unable to work directly on glycated proteins, current enzymatic assays are based on a preliminary proteolytic digestion of the target proteins. Herein, to improve the speed and costs of the enzymatic assays for diabetes testing, we applied a rational design approach to engineer a novel enzyme with a wider access tunnel to the catalytic site, using a combination of Rosetta design and molecular dynamics simulations. Our final design, L3_35A, shows a significantly wider and shorter access tunnel, resulting from the deletion of five-amino acids lining the gate structures and from a total of 35 point mutations relative to the wild-type (WT) enzyme. Indeed, upon experimental testing, our engineered enzyme shows good structural stability and maintains significant activity relative to the WT.

The Anti-Amyloidogenic Action of Doxycycline: A Molecular Dynamics Study on the Interaction with Aß42.

The pathological aggregation of amyloidogenic proteins is a hallmark of many neurological diseases, including Alzheimer's disease and prion diseases. We have shown both in vitro and in vivo that doxycycline can inhibit the aggregation of Aß42 amyloid fibrils and disassemble mature amyloid fibrils. However, the molecular mechanisms of the drug's anti-amyloidogenic property are not understood. In this study, a series of molecular dynamics simulations were performed to explain the molecular mechanism of the destabilization of Aß42 fibrils by doxycycline and to compare the action of doxycycline with those of iododoxorubicin (a toxic structural homolog of tetracyclines), curcumin (known to have anti-amyloidogenic activity) and gentamicin (an antibiotic with no experimental evidence of anti-amyloidogenic properties). We found that doxycycline tightly binds the exposed hydrophobic amino acids of the Aß42 amyloid fibrils, partly leading to destabilization of the fibrillar structure. Clarifying the molecular determinants of doxycycline binding to Aß42 may help devise further strategies for structure-based drug design for Alzheimer's disease.

Molecular dynamics investigation of halogenated amyloidogenic peptides.

Besides their biomolecular relevance, amyloids, generated by the self-assembly of peptides and proteins, are highly organized structures useful for nanotechnology applications. The introduction of halogen atoms in these peptides, and thus the possible formation of halogen bonds, allows further possibilities to finely tune the amyloid nanostructure. In this work, we performed molecular dynamics simulations on different halogenated derivatives of the ß-amyloid peptide core-sequence KLVFF, by using a modified AMBER force field in which the σ-hole located on the halogen atom is modeled with a positively charged extra particle. The analysis of equilibrated structures shows good agreement with crystallographic data and experimental results, in particular concerning the formation of halogen bonds and the stability of the supramolecular structures. The modified force field described here allows describing the atomistic details contributing to peptides aggregation, with particular focus on the role of halogen bonds. This framework can potentially help the design of novel halogenated peptides with desired aggregation propensity. Graphical abstract Molecular dynamics investigation of halogenated amyloidogenic peptides.

Thermal stabilization of the deglycating enzyme Amadoriase I by rational design.

Amadoriases are a class of FAD-dependent enzymes that are found in fungi, yeast and bacteria and that are able to hydrolyze glycated amino acids, cleaving the sugar moiety from the amino acidic portion. So far, engineered Amadoriases have mostly found practical application in the measurement of the concentration of glycated albumin in blood samples. However, these engineered forms of Amadoriases show relatively low absolute activity and stability levels, which affect their conditions of use. Therefore, enzyme stabilization is desirable prior to function-altering molecular engineering. In this work, we describe a rational design strategy based on a computational screening method to evaluate a library of potentially stabilizing disulfide bonds. Our approach allowed the identification of two thermostable Amadoriase I mutants (SS03 and SS17) featuring a significantly higher T50 (55.3 °C and 60.6 °C, respectively) compared to the wild-type enzyme (52.4 °C). Moreover, SS17 shows clear hyperstabilization, with residual activity up to 95 °C, whereas the wild-type enzyme is fully inactive at 55 °C. Our computational screening method can therefore be considered as a promising approach to expedite the design of thermostable enzymes.

Review: Engineering of thermostable enzymes for industrial applications.

The catalytic properties of some selected enzymes have long been exploited to carry out efficient and cost-effective bioconversions in a multitude of research and industrial sectors, such as food, health, cosmetics, agriculture, chemistry, energy, and others. Nonetheless, for several applications, naturally occurring enzymes are not considered to be viable options owing to their limited stability in the required working conditions. Over the years, the quest for novel enzymes with actual potential for biotechnological applications has involved various complementary approaches such as mining enzyme variants from organisms living in extreme conditions (extremophiles), mimicking evolution in the laboratory to develop more stable enzyme variants, and more recently, using rational, computer-assisted enzyme engineering strategies. In this review, we provide an overview of the most relevant enzymes that are used for industrial applications and we discuss the strategies that are adopted to enhance enzyme stability and/or activity, along with some of the most relevant achievements. In all living species, many different enzymes catalyze fundamental chemical reactions with high substrate specificity and rate enhancements. Besides specificity, enzymes also possess many other favorable properties, such as, for instance, cost-effectiveness, good stability under mild pH and temperature conditions, generally low toxicity levels, and ease of termination of activity. As efficient natural biocatalysts, enzymes provide great opportunities to carry out important chemical reactions in several research and industrial settings, ranging from food to pharmaceutical, cosmetic, agricultural, and other crucial economic sectors.

Herringbone-like hydrodynamic structures in microchannels: A CFD model to evaluate the enhancement of surface binding.

Selected adsorption efficiency of a molecule in solution in a microchannel is strongly influenced by the convective/diffusive mass transport phenomena that supply the target molecule to the adsorption surface. In a standard microchannel with a rectangular cross section, laminar flow regime limits the fluid mixing, thus suggesting that mass transport conditions can be improved by the introduction of herringbone-like structures. Tuning of these geometrical patterns increases the concentration gradient of the target molecule at the adsorption surface. A computational fluid dynamic (CFD) study was performed to evaluate the relation between the geometrical herringbone patterns and the concentration gradient improvement in a 14 mm long microchannel. The results show that the inhomogeneity of the concentration gradient can provide an improved and localized adsorption under specific geometrical features, which can be tuned in order to adapt the adsorption pattern to the specific assay requirements.

Catch-and-Release of Target Cells Using Aptamer-Conjugated Electroactive Zwitterionic Oligopeptide SAM.

Nucleic acid aptamers possess attractive features such as specific molecular recognition, high-affinity binding, and rapid acquisition and replication, which could be feasible components for separating specific cells from other cell types. This study demonstrates that aptamers conjugated to an oligopeptide self-assembled monolayer (SAM) can be used to selectively trap human hepatic cancer cells from cell mixtures containing normal human hepatocytes or human fibroblasts. Molecular dynamics calculations have been performed to understand how the configurations of the aptamers are related to the experimental results of selective cell capture. We further demonstrate that the captured hepatic cancer cells can be detached and collected along with electrochemical desorption of the oligopeptide SAM, and by repeating these catch-and-release processes, target cells can be enriched. This combination of capture with aptamers and detachment with electrochemical reactions is a promising tool in various research fields ranging from basic cancer research to tissue engineering applications.

Nanostructure and stability of calcitonin amyloids.

Calcitonin is a 32-amino acid thyroid hormone that can form amyloid fibrils. The structural basis of the fibril formation and stabilization is still debated and poorly understood. The reason is that NMR data strongly suggest antiparallel ß-sheet calcitonin assembly, whereas modeling studies on the short DFNKF peptide (corresponding to the sequence from Asp15 to Phe19 of human calcitonin and reported as the minimal amyloidogenic module) show that it assembles with parallel ß-sheets. In this work, we first predict the structure of human calcitonin through two complementary molecular dynamics (MD) methods, finding that human calcitonin forms an α-helix. We use extensive MD simulations to compare previously proposed calcitonin fibril structures. We find that two conformations, the parallel arrangement and one of the possible antiparallel structures (with Asp15 and Phe19 aligned), are highly stable and ordered. Nonetheless, fibrils with parallel molecules show bulky loops formed by residues 1 to 7 located on the same side, which could limit or prevent the formation of larger amyloids. We investigate fibrils formed by the DFNKF peptide by simulating different arrangements of this amyloidogenic core sequence. We show that DFNKF fibrils are highly stable when assembled in parallel ß-sheets, whereas they quickly unfold in antiparallel conformation. Our results indicate that the DFNKF peptide represents only partially the full-length calcitonin behavior. Contrary to the full-length polypeptide, in fact, the DFNKF sequence is not stable in antiparallel conformation, suggesting that the residue flanking the amyloidogenic peptide contributes to the stabilization of the experimentally observed antiparallel ß-sheet packing.

Molecular dynamics simulations of the intrinsically disordered protein amelogenin.

Amelogenin refers to a class of intrinsically disordered proteins that are the major constituents of enamel matrix derivative (EMD), an extract of porcine fetal teeth used in regenerative periodontal therapy. Modifications in molecular conformation induced by external stresses, such as changes in temperature or pH, are known to reduce the effectiveness of EMD. However, detailed descriptions of the conformational behavior of native amelogenin are lacking in the open literature. In the present work, a molecular model for the secondary and tertiary structure of the full-length major porcine amelogenin P173 was constructed from its primary sequence by replica exchange molecular dynamics (REMD) simulations. The REMD results for isolated amelogenin molecules at different temperatures were shown to be consistent with the available spectroscopic data. They therefore represent an important first step toward the simulation of the intra- and intermolecular interactions that mediate self-organization in amelogenin and its behavior in the presence of other EMD components under conditions representative of its therapeutic application.