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.

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.

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.

In Silico Analysis of Nanoplastics' and β-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 β-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 β-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 β-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.

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.

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 β-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 β-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 β-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 β-sheet packing.

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.

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.

Molecular dynamics simulations provide insights into the substrate specificity of FAOX family members

Enzymatic assays based on Fructosyl Amino Acid Oxidases (FAOX) represent a potential, rapid and economical strategy to measure glycated hemoglobin (HbA1c), which is in turn a reliable method to monitor the insurgence and the development of diabetes mellitus.

The role of collagen crosslinks in ageing and diabetes - the good, the bad, and the ugly

The non-enzymatic reaction of proteins with glucose (glycation) is a topic of rapidly growing importance in human health and medicine. There is increasing evidence that this reaction plays a central role in ageing and disease of connective tissues. Of particular interest are changes in type-I collagens, long-lived proteins that form the mechanical backbone of connective tissues in nearly every human organ. Despite considerable correlative evidence relating extracellular matrix (ECM) glycation to disease, little is known of how ECM modification by glucose impacts matrix mechanics and damage, cell-matrix interactions, and matrix turnover during aging. More daunting is to understand how these factors interact to cumulatively affect local repair of matrix damage, progression of tissue disease, or systemic health and longevity. This focused review will summarize what is currently known regarding collagen glycation as a potential driver of connective tissue disease. We concentrate attention on tendon as an affected connective tissue with large clinical relevance, and as a tissue that can serve as a useful model tissue for investigation into glycation as a potentially critical player in tissue fibrosis related to ageing and diabetes.

Response of osteoblast-like MG63 on neoglycosylated collagen matrices

Collagen matrices modified in order to expose galactose residues to cells were studied for their interaction with osteosarcoma-derived cell line MG63.

Nanomechanics of collagen microfibrils

Collagen constitutes one third of the human proteome, providing mechanical stability, elasticity and strength to organisms and is thus the prime construction material in biology. Collagen is also the dominating material in the extracellular matrix where its stiffness controls cell differentiation, growth and pathology. We use atomistic-based hierarchical multiscale modeling to describe this complex biological material from the bottom up. This includes the use and development of large-scale computational modeling tools to investigate several aspects related to collagen-based tissues, including source of visco-elasticity and deformation mechanisms at the nanoscale level. The key innovation of this research is that until now, collagen materials have primarily been described at macroscopic scales, without explicitly understanding the mechanical contributions at the molecular and fibrillar levels. The major impact of this research will be the development of fundamental models of collagenous tissues, important to the design of new scaffolding biomaterials for regenerative medicine as well as for the understanding of collagen-related diseases.

Thickness of hydroxyapatite nanocrystal controls mechanical properties of the collagen-hydroxyapatite interface

Collagen-hydroxyapatite interfaces compose an important building block of bone structures. While it is known that the nanoscale structure of this elementary building block can affect the mechanical properties of bone, a systematic understanding of the effect of the geometry on the mechanical properties of this interface between protein and mineral is lacking. Here we study the effect of geometry, different crystal surfaces, and hydration on the mechanical properties of collagen-hydroxyapatite interfaces from an atomistic perspective, and discuss underlying deformation mechanisms. We find that the presence of hydroxyapatite significantly enhances the tensile modulus and strength compared with a tropocollagen molecule alone. The stiffening effect is strongly dependent on the thickness of the mineral crystal until a plateau is reached at 2 nm crystal thickness. We observe no significant differences due to the mineral surface (Ca surface vs OH surface) or due to the presence of water. Our result shows that the hydroxyapatite crystal with its thickness confined to the nanometer size efficiently increases the tensile modulus and strength of the collagen-hydroxyapatite composite, agreeing well with experimental observations that consistently show the existence of extremely thin mineral flakes in various types of bones. We also show that the collagen-hydroxyapatite interface can be modeled with an elastic network model which, based on the results of atomistic simulations, provides a good estimate of the surface energy and other mechanical features.

Hierarchical structure and nanomechanics of collagen microfibrils from the atomistic scale up

Collagen constitutes one-third of the human proteome, providing mechanical stability, elasticity, and strength to organisms and is the prime construction material in biology. Collagen is also the dominating material in the extracellular matrix and its stiffness controls cell differentiation, growth, and pathology. However, the origin of the unique mechanical properties of collagenous tissues, and in particular its stiffness, extensibility, and nonlinear mechanical response at large deformation, remains unknown. By using X-ray diffraction data of a collagen fibril (Orgel, J. P. R. O. et al. Proc. Natl. Acad. Sci. 2006, 103, 9001) here we present an experimentally validated model of the nanomechanics of a collagen microfibril that incorporates the full biochemical details of the amino acid sequence of constituting molecules and the nanoscale molecular arrangement. We demonstrate by direct mechanical testing that hydrated (wet) collagen microfibrils feature a Young's modulus of ≈300 MPa at small, and ≈1.2 GPa at larger deformation in excess of 10% strain, which is in excellent agreement with experimental data. We find that dehydrated (dry) collagen microfibrils show a significantly increased Young's modulus of ≈1.8-2.25 GPa, which is in agreement with experimental measurements and owing to tighter molecular packing. Our results show that the unique mechanical properties of collagen microfibrils arise due to their hierarchical structure at the nanoscale, where key deformation mechanisms are straightening of twisted triple-helical molecules at small strains, followed by axial stretching and eventual molecular uncoiling. The establishment of a model of hierarchical deformation mechanisms explains the striking difference of the elastic modulus of collagen fibrils compared with single molecules, which is found in the range of 4.8 ± 2 GPa, or ≈10-20 times greater. We find that collagen molecules alone are not capable of providing the broad range of mechanical functionality required for physiological function of collagenous tissues. Rather, the existence of an array of deformation mechanisms, derived from the hierarchical makeup of the material, is critical to the material's ability to confer key mechanical properties, specifically large extensibility, strain hardening, and toughness, despite the limitation that collagenous materials are constructed from only few distinct amino acids. The atomistic model of collagen microfibril mechanics now enables the bottom-up elucidation of structure-property relationships in a broader class of collagen materials (e.g., tendon, bone, cornea), including studies of genetic disease where the incorporation of biochemical details is essential. The availability of a molecular-based model of collagen tissues may eventually result in novel nanomedicine approaches to develop treatments for a broad class of collagen diseases and the design of de novo biomaterials for regenerative medicine.

How to predict diffusion of medium-sized molecules in polymer matrices. From atomistic to coarse grain simulations

The normal diffusion regime of many small and medium-sized molecules occurs on a time scale that is too long to be studied by atomistic simulations. Coarse-grained (CG) molecular simulations allow to investigate length and time scales that are orders of magnitude larger compared to classical molecular dynamics simulations, hence providing a valuable approach to span time and length scales where normal diffusion occurs. Here we develop a novel multi-scale method for the prediction of diffusivity in polymer matrices which combines classical and CG molecular simulations. We applied an atomistic-based method in order to parameterize the CG MARTINI force field, providing an extension for the study of diffusion behavior of penetrant molecules in polymer matrices. As a case study, we found the parameters for benzene (as medium sized penetrant molecule whose diffusivity cannot be determined through atomistic models) and Poly (vinyl alcohol) (PVA) as polymer matrix. We validated our extended MARTINI force field determining the self diffusion coefficient of benzene (2.27·10⁻⁹m² s⁻¹) and the diffusion coefficient of benzene in PVA (0.263·10⁻¹² m² s⁻¹). The obtained diffusion coefficients are in remarkable agreement with experimental data (2.20·10⁻⁹m² s⁻¹ and 0.25·10⁻¹² m² s⁻¹, respectively). We believe that this method can extend the application range of computational modeling, providing modeling tools to study the diffusion of larger molecules and complex polymeric materials.

Molecular and mesoscale mechanisms of osteogenesis imperfecta disease in collagen fibrils

Osteogenesis imperfecta (OI) is a genetic disorder in collagen characterized by mechanically weakened tendon, fragile bones, skeletal deformities, and in severe cases, prenatal death. Although many studies have attempted to associate specific mutation types with phenotypic severity, the molecular and mesoscale mechanisms by which a single point mutation influences the mechanical behavior of tissues at multiple length scales remain unknown. We show by a hierarchy of full atomistic and mesoscale simulation that OI mutations severely compromise the mechanical properties of collagenous tissues at multiple scales, from single molecules to collagen fibrils. Mutations that lead to the most severe OI phenotype correlate with the strongest effects, leading to weakened intermolecular adhesion, increased intermolecular spacing, reduced stiffness, as well as a reduced failure strength of collagen fibrils. We find that these molecular-level changes lead to an alteration of the stress distribution in mutated collagen fibrils, causing the formation of stress concentrations that induce material failure via intermolecular slip. We believe that our findings provide insight into the microscopic mechanisms of this disease and lead to explanations of characteristic OI tissue features such as reduced mechanical strength and a lower cross-link density. Our study explains how single point mutations can control the breakdown of tissue at much larger length scales, a question of great relevance for a broad class of genetic diseases.

Single molecule effects of osteogenesis imperfecta mutations in tropocollagen protein domains

Osteogenesis imperfecta (OI) is a genetic disease characterized by fragile bones, skeletal deformities and, in severe cases, prenatal death that affects more than 1 in 10,000 individuals. Here we show by full atomistic simulation in explicit solvent that OI mutations have a significant influence on the mechanical properties of single tropocollagen molecules, and that the severity of different forms of OI is directly correlated with the reduction of the mechanical stiffness of individual tropocollagen molecules. The reduction of molecular stiffness provides insight into the molecular-scale mechanisms of the disease. The analysis of the molecular mechanisms reveals that physical parameters of side-chain volume and hydropathy index of the mutated residue control the loss of mechanical stiffness of individual tropocollagen molecules. We propose a model that enables us to predict the loss of stiffness based on these physical characteristics of mutations. This finding provides an atomistic-level mechanistic understanding of the role of OI mutations in defining the properties of the basic protein constituents, which could eventually lead to new strategies for diagnosis and treatment the disease. The focus on material properties and their role in genetic diseases is an important, yet so far only little explored, aspect in studying the mechanisms that lead to pathological conditions. The consideration of how material properties change in diseases could lead to a new paradigm that may expand beyond the focus on biochemical readings alone and include a characterization of material properties in diagnosis and treatment, an effort referred to as materiomics.

Deformation rate controls elasticity and unfolding pathway of single tropocollagen molecules

Collagen is an important structural protein in vertebrates and is responsible for the integrity of many tissues like bone, teeth, cartilage and tendon. The mechanical properties of these tissues are primarily determined by their hierarchical arrangement and the role of the collagen matrix in their structures. Here we report a series of Steered Molecular Dynamics (SMD) simulations in explicit solvent, used to elucidate the influence of the pulling rate on the Young's modulus of individual tropocollagen molecules. We stretch a collagen peptide model sequence [(Gly-Pro-Hyp)(10)](3) with pulling rates ranging from 0.01 to 100 m/s, reaching much smaller deformation rates than reported in earlier SMD studies. Our results clearly demonstrate a strong influence of the loading velocity on the observed mechanical properties. Most notably, we find that Young's modulus converges to a constant value of approximately 4 GPa tangent modulus at 8% tensile strain when the initially crimped molecule is straightened out, for pulling rates below 0.5 m/s. This enables us for the first time to predict the elastic properties of a single tropocollagen molecule at physiologically and experimentally relevant pulling rates, directly from atomistic-level calculations. At deformation rates larger than 0.5 m/s, Young's modulus increases continuously and approaches values in excess of 15 GPa for deformation rates larger than 100 m/s. The analyses of the molecular deformation mechanisms show that the tropocollagen molecule unfolds in distinctly different ways, depending on the loading rate, which explains the observation of different values of Young's modulus at different loading rates. For low pulling rates, the triple helix first uncoils completely at 10%-20% strain, then undergoes some recoiling in the opposite direction, and finally straightens for strains larger than 30%. At intermediate rates, the molecule uncoils linearly with increasing strain up to 35% strain. Finally, at higher velocities the triple helix does not uncoil during stretching.