Diffusion mechanisms for spinel ferrite NiFe2O4 by using kinetic activation-relaxation technique.

Mass transport in bulk spinel ferrites NiFe2O4 is studied computationally using the kinetic activation-relaxation technique (k-ART), an off-lattice kinetic Monte Carlo algorithm. Diffusion mechanisms-difficult to observe with molecular dynamics-are described by k-ART. Point defects are assumed to be responsible for ionic diffusion; thus, both cation and anion defects are investigated. This work focuses on vacancies and interstitials by comparing their properties with two Buckingham potential parameterizations: one with nominal charges and the other with partial charges. Both potentials are corrected at short distances, thus allowing interstitial diffusion and avoiding the catastrophic infinite energies appearing with Buckingham at short distances. The energy landscape along different pathways is described in detail. Both potentials predict the same mechanisms but different migration energies. Mechanisms by which a normal spinel is transformed to an inverse spinel via cation diffusion are unveiled, and diffusion coefficients are predicted. We find that interstitial Ni diffusion involves the movement of two Ni ions and that O interstitials trigger a collective diffusion of O ions, while an O vacancy diffuses by an O ion moving to the center of a cuboctahedron.

Evaluating approaches for on-the-fly machine learning interatomic potentials for activated mechanisms sampling with the activation-relaxation technique nouveau.

In the last few years, much effort has gone into developing general machine-learning potentials capable of describing interactions for a wide range of structures and phases. Yet, as attention turns to more complex materials, including alloys and disordered and heterogeneous systems, the challenge of providing reliable descriptions for all possible environments becomes ever more costly. In this work, we evaluate the benefits of using specific vs general potentials for the study of activated mechanisms in solid-state materials. More specifically, we test three machine-learning fitting approaches using the moment-tensor potential to reproduce a reference potential when exploring the energy landscape around a vacancy in Stillinger-Weber silicon crystal and silicon-germanium zincblende structures using the activation-relaxation technique nouveau (ARTn). We find that a targeted on-the-fly approach specific to and integrated into ARTn generates the highest precision on the energetics and geometry of activated barriers while remaining cost-effective. This approach expands the types of problems that can be addressed with high-accuracy ML potential.

The molecular origin of the electrostatic gating of single-molecule field-effect biosensors investigated by molecular dynamics simulations.

Field-effect biosensors (bioFETs) offer a novel way to measure the kinetics of biomolecular events such as protein function and DNA hybridization at the single-molecule level on a wide range of time scales. These devices generate an electrical current whose fluctuations are correlated to the kinetics of the biomolecule under study. BioFETs are indeed highly sensitive to changes in the electrostatic potential (ESP) generated by the biomolecule. Here, using all-atom solvent explicit molecular dynamics simulations, we further investigate the molecular origin of the variation of this ESP for two prototypical cases of proteins or nucleic acids attached to a carbon nanotube bioFET: the function of the lysozyme protein and the hybridization of a 10-nt DNA sequence, as previously done experimentally. Our results show that the ESP changes significantly on the surface of the carbon nanotube as the state of these two biomolecules changes. More precisely, the ESP distributions calculated for these molecular states explain well the magnitude of the conductance fluctuations measured experimentally. The dependence of the ESP with salt concentration is found to agree with the reduced conductance fluctuations observed experimentally for the lysozyme, but to differ for the case of DNA, suggesting that other mechanisms might be at play in this case. Furthermore, we show that the carbon nanotube does not impact significantly the structural stability of the lysozyme, corroborating that the kinetic rates measured using bioFETs are similar to those measured by other techniques. For DNA, we find that the structural ensemble of the single-stranded DNA is significantly impacted by the presence of the nanotube, which, combined with the ESP analysis, suggests a stronger DNA-device interplay. Overall, our simulations strengthen the comprehension of the inner working of field-effect biosensors used for single-molecule kinetics measurements on proteins and nucleic acids.

Molecular Interactions of Tannic Acid with Proteins Associated with SARS-CoV-2 Infectivity.

The overall impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on our society is unprecedented. The identification of small natural ligands that could prevent the entry and/or replication of the coronavirus remains a pertinent approach to fight the coronavirus disease (COVID-19) pandemic. Previously, we showed that the phenolic compounds corilagin and 1,3,6-tri-O-galloyl-ß-D-glucose (TGG) inhibit the interaction between the SARS-CoV-2 spike protein receptor binding domain (RBD) and angiotensin-converting enzyme 2 (ACE2), the SARS-CoV-2 target receptor on the cell membrane of the host organism. Building on these promising results, we now assess the effects of these phenolic ligands on two other crucial targets involved in SARS-CoV-2 cell entry and replication, respectively: transmembrane protease serine 2 (TMPRSS2) and 3-chymotrypsin like protease (3CLpro) inhibitors. Since corilagin, TGG, and tannic acid (TA) share many physicochemical and structural properties, we investigate the binding of TA to these targets. In this work, a combination of experimental methods (biochemical inhibition assays, surface plasmon resonance, and quartz crystal microbalance with dissipation monitoring) confirms the potential role of TA in the prevention of SARS-CoV-2 infectivity through the inhibition of extracellular RBD/ACE2 interactions and TMPRSS2 and 3CLpro activity. Moreover, molecular docking prediction followed by dynamic simulation and molecular mechanics Poisson-Boltzmann surface area (MMPBSA) free energy calculation also shows that TA binds to RBD, TMPRSS2, and 3CLpro with higher affinities than TGG and corilagin. Overall, these results suggest that naturally occurring TA is a promising candidate to prevent and inhibit the infectivity of SARS-CoV-2.

Corilagin and 1,3,6-Tri-O-galloy-ß-D-glucose: potential inhibitors of SARS-CoV-2 variants.

The COVID-19 disease caused by the virus SARS-CoV-2, first detected in December 2019, is still emerging through virus mutations. Although almost under control in some countries due to effective vaccines that are mitigating the worldwide pandemic, the urgency to develop additional vaccines and therapeutic treatments is imperative. In this work, the natural polyphenols corilagin and 1,3,6-tri-O-galloy-ß-d-glucose (TGG) are investigated to determine the structural basis of inhibitor interactions as potential candidates to inhibit SARS-CoV-2 viral entry into target cells. First, the therapeutic potential of the ligands are assessed on the ACE2/wild-type RBD. We first use molecular docking followed by molecular dynamics, to take into account the conformational flexibility that plays a significant role in ligand binding and that cannot be captured using only docking, and then analyze more precisely the affinity of these ligands using MMPBSA binding free energy. We show that both ligands bind to the ACE2/wild-type RBD interface with good affinities which might prevent the ACE2/RBD association. Second, we confirm the potency of these ligands to block the ACE2/RBD association using a combination of surface plasmon resonance and biochemical inhibition assays. These experiments confirm that TGG and, to a lesser extent, corilagin, inhibit the binding of RBD to ACE2. Both experiments and simulations show that the ligands interact preferentially with RBD, while weak binding is observed with ACE2, hence, avoiding potential physiological side-effects induced by the inhibition of ACE2. In addition to the wild-type RBD, we also study numerically three RBD mutations (E484K, N501Y and E484K/N501Y) found in the main SARS-CoV-2 variants of concerns. We find that corilagin could be as effective for RBD/E484K but less effective for the RBD/N501Y and RBD/E484K-N501Y mutants, while TGG strongly binds at relevant locations to all three mutants, demonstrating the significant interest of these molecules as potential inhibitors for variants of SARS-CoV-2.

Is Hydrogen Diffusion along Grain Boundaries Fast or Slow? Atomistic Origin and Mechanistic Modeling.

We perform comprehensive first-principles calculations and kinetic Monte Carlo simulations to explicitly elucidate the distinct roles of grain boundaries (GBs) in affecting hydrogen (H) diffusion in fcc nickel (Ni). We demonstrate the transition between slow and fast H diffusion along the GB with an abrupt change in H diffusivity. Low-angle GBs are shown to comprise isolated high-barrier regions to trap and inhibit H diffusion, with H diffusivity well prescribed by the classical trapping model, while high-angle GBs are shown to provide interconnected low-barrier channels to facilitate H transport. On the basis of the dislocation description of the GB and the Frank-Bilby model, the slow-fast diffusion transition is identified to result from dislocation core overlapping and is accurately predicted. The present Letter provides key mechanistic insights towards interpreting various experimental studies of H diffusion in metals, new critical knowledge for predictive modeling of H embrittlement, and better understanding of the kinetics of H and other interstitial impurities in microstructures.

Algorithmic developments of the kinetic activation-relaxation technique: Accessing long-time kinetics of larger and more complex systems.

In spite of the considerable computer speed increase of the last decades, long-time atomic simulations remain a challenge and most molecular dynamical simulations are limited to 1 µs at the very best in condensed matter and materials science. There is a need, therefore, for accelerated methods that can bridge the gap between the full dynamical description of molecular dynamics and experimentally relevant time scales. This is the goal of the kinetic Activation-Relaxation Technique (k-ART), an off-lattice kinetic Monte-Carlo method with on-the-fly catalog building capabilities based on the topological tool NAUTY and the open-ended search method Activation-Relaxation Technique (ART nouveau) that has been applied with success to the study of long-time kinetics of complex materials, including grain boundaries, alloys, and amorphous materials. We present a number of recent algorithmic additions, including the use of local force calculation, two-level parallelization, improved topological description, and biased sampling and show how they perform on two applications linked to defect diffusion and relaxation after ion bombardement in Si.

Free-Energy Landscape of the Amino-Terminal Fragment of Huntingtin in Aqueous Solution.

The first exon of Huntingtin-a protein with multiple biological functions whose misfolding is related to Huntington's disease-modulates its localization, aggregation, and function within the cell. It is composed of a 17-amino-acid amphipathic segment (Htt17), an amyloidogenic segment of consecutive glutamines (QN), and a proline-rich segment. Htt17 is of fundamental importance: it serves as a membrane anchor to control the localization of huntingtin, it modulates huntingtin's function through posttranslational modifications, and it controls the self-assembly of the amyloidogenic QN segment into oligomers and fibrils. Experimentally, the conformational ensemble of the Htt17 monomer, as well as the impact of the polyglutamine and proline-rich segments, remains, however, mostly uncharacterized at the atomic level due to its intrinsic flexibility. Here, we unveil the free-energy landscape of Htt17, Htt17Q17, and Htt17Q17P11 using Hamiltonian replica exchange combined with well-tempered metadynamics. We characterize the free-energy landscape of these three fragments in terms of a few selected collective variables. Extensive simulations reveal that the free energy of Htt17 is dominated by a broad ensemble of configurations that agree with solution NMR chemical shifts. Addition of Q17 at its carboxy-terminus reduces the extent of the main basin to more extended configurations of Htt17 with lower helix propensity. Also, the aliphatic carbons of Q17 partially sequester the nonpolar amino acids of Htt17. For its part, addition of Q17P11 shifts the overall landscape to a more extended and helical Htt17 stabilized by interactions with Q17 and P11, which almost exclusively form a PPII-helix, as well as by intramolecular H-bonds and salt bridges. Our characterization of Huntingtin's amino-terminus provides insights into the structural origin of its ability to oligomerize and interact with phospholipid bilayers, processes closely linked to the biological functions of this protein.

Influence of surface vacancy defects on the carburisation of Fe 110 surface by carbon monoxide.

Adsorption and dissociation of gaseous carbon monoxide (CO) on metal surfaces is one of the most frequently occurring processes of carburisation, known as primary initiator of metal dusting corrosion. Among the various factors that can significantly influence the carburisation process are the intrinsic surface defects such as single surface vacancies occurring at high concentrations due to their low formation energy. Intuitively, adsorption and dissociation barriers of CO are expected to be lowered in the vicinity of a surface vacancy, due to the strong attractive interaction between the vacancy and the C atom. Here the adsorption energies and dissociation pathways of CO on clean and defective Fe 110 surface are explored by means of density functional theory. Interestingly, we find that the O adatom, resulting from the CO dissociation, is unstable in the electron-deficit neighbourhood of the vacancy due to its large electron affinity, and raises the barrier of the carburisation pathway. Still, a full comparative study between the clean surface and the vacancy-defected surface reveals that the complete process of carburisation, starting from adsorption to subsurface diffusion of C, is more favourable in the vicinity of a vacancy defect.

Probing the Huntingtin 1-17 membrane anchor on a phospholipid bilayer by using all-atom simulations.

Mislocalization and aggregation of the huntingtin protein are related to Huntington's disease. Its first exon-more specifically the first 17 amino acids (Htt17)-is crucial for the physiological and pathological functions of huntingtin. It regulates huntingtin's activity through posttranslational modifications and serves as an anchor to membrane-containing organelles of the cell. Recently, structure and orientation of the Htt17 membrane anchor were determined using a combined solution and solid-state NMR approach. This prompted us to refine this model by investigating the dynamics and thermodynamics of this membrane anchor on a POPC bilayer using all-atom, explicit solvent molecular dynamics and Hamiltonian replica exchange. Our simulations are combined with various experimental measurements to generate a high-resolution atomistic model for the huntingtin Htt17 membrane anchor on a POPC bilayer. More precisely, we observe that the single α-helix structure is more stable in the phospholipid membrane than the NMR model obtained in the presence of dodecylphosphocholine detergent micelles. The resulting Htt17 monomer has its hydrophobic plane oriented parallel to the bilayer surface. Our results further unveil the key residues interacting with the membrane in terms of hydrogen bonds, salt-bridges, and nonpolar contributions. We also observe that Htt17 equilibrates at a well-defined insertion depth and that it perturbs the physical properties-order parameter, thickness, and area per lipid-of the bilayer in a manner that could favor its dimerization. Overall, our observations reinforce and refine the NMR measurements on the Htt17 membrane anchor segment of huntingtin that is of fundamental importance to its biological functions.

The OPEP protein model: from single molecules, amyloid formation, crowding and hydrodynamics to DNA/RNA systems.

The OPEP coarse-grained protein model has been applied to a wide range of applications since its first release 15 years ago. The model, which combines energetic and structural accuracy and chemical specificity, allows the study of single protein properties, DNA-RNA complexes, amyloid fibril formation and protein suspensions in a crowded environment. Here we first review the current state of the model and the most exciting applications using advanced conformational sampling methods. We then present the current limitations and a perspective on the ongoing developments.

Atomistic mechanisms of huntingtin N-terminal fragment insertion on a phospholipid bilayer revealed by molecular dynamics simulations.

The huntingtin protein is characterized by a segment of consecutive glutamines (Q(N)) that is responsible for its fibrillation. As with other amyloid proteins, misfolding of huntingtin is related to Huntington's disease through pathways that can involve interactions with phospholipid membranes. Experimental results suggest that the N-terminal 17-amino-acid sequence (htt(NT)) positioned just before the Q(N) region is important for the binding of huntingtin to membranes. Through all-atom explicit solvent molecular dynamics simulations, we unveil the structure and dynamics of the htt(NT)Q(N) fragment on a phospholipid membrane at the atomic level. We observe that the insertion dynamics of this peptide can be described by four main steps-approach, reorganization, anchoring, and insertion-that are very diverse at the atomic level. On the membrane, the htt(NT) peptide forms a stable α-helix essentially parallel to the membrane with its nonpolar side-chains-mainly Leu-4, Leu-7, Phe-11 and Leu-14-positioned in the hydrophobic core of the membrane. Salt-bridges involving Glu-5, Glu-12, Lys-6, and Lys-15, as well as hydrogen bonds involving Thr-3 and Ser-13 with the phospholipids also stabilize the structure and orientation of the htt(NT) peptide. These observations do not significantly change upon adding the Q(N) region whose role is rather to provide, through its hydrogen bonds with the phospholipids' head group, a stable scaffold facilitating the partitioning of the htt(NT) region in the membrane. Moreover, by staying accessible to the solvent, the amyloidogenic Q(N) region could also play a key role for the oligomerization of htt(NT)Q(N) on phospholipid membranes.

Early oligomerization stages for the non-amyloid component of α-synuclein amyloid.

In recent years, much effort has focused on the early stages of aggregation and the formation of amyloid oligomers. Aggregation processes for these proteins are complex and their non-equilibrium nature makes any experimental study very difficult. Under these conditions, simulations provide a useful alternative for understanding the dynamics of the early stages of oligomerization. Here, we focus on the non-Aß amyloid component (NAC) of the monomer, dimer, and trimer of α-synuclein, an important 35-residue sequence involved in the aggregation and fibrillation of this protein associated with Parkinson's disease. Using Hamiltonian and temperature replica exchange molecular dynamics simulations combined with the coarse grained Optimized Potential for Efficient peptide structure Prediction potential, we identify the role of the various regions and the secondary structures for the onset of oligomerization. For this sequence, we clearly observe the passage from α-helix to ß-sheet, a characteristic transition of amyloid proteins. More precisely, we find that the NAC monomer is highly structured with two α-helical regions, between residues 2-13 and 19-25. As the dimer and trimer form, ß-sheet structures between residues 2-14 and 26-34 appear and rapidly structure the system. The resulting conformations are much more structured than similar dimers and trimers of ß-amyloid and amylin proteins and yet display a strong polymorphism at these early stages of aggregation. In addition to its inherent experimental interest, comparison with other sequences shows that NAC could be a very useful numerical model for understanding the onset of aggregation.

Replenish and relax: explaining logarithmic annealing in ion-implanted c-Si.

We study ion-damaged crystalline silicon by combining nanocalorimetric experiments with an off-lattice kinetic Monte Carlo simulation to identify the atomistic mechanisms responsible for the structural relaxation over long time scales. We relate the logarithmic relaxation, observed in a number of disordered systems, with heat-release measurements. The microscopic mechanism associated with this logarithmic relaxation can be described as a two-step replenish and relax process. As the system relaxes, it reaches deeper energy states with logarithmically growing barriers that need to be unlocked to replenish the heat-releasing events leading to lower-energy configurations.

Kinetics of amyloid aggregation: a study of the GNNQQNY prion sequence.

The small amyloid-forming GNNQQNY fragment of the prion sequence has been the subject of extensive experimental and numerical studies over the last few years. Using unbiased molecular dynamics with the OPEP coarse-grained potential, we focus here on the onset of aggregation in a 20-mer system. With a total of 16.9 µs of simulations at 280 K and 300 K, we show that the GNNQQNY aggregation follows the classical nucleation theory (CNT) in that the number of monomers in the aggregate is a very reliable descriptor of aggregation. We find that the critical nucleus size in this finite-size system is between 4 and 5 monomers at 280 K and 5 and 6 at 300 K, in overall agreement with experiment. The kinetics of growth cannot be fully accounted for by the CNT, however. For example, we observe considerable rearrangements after the nucleus is formed, as the system attempts to optimize its organization. We also clearly identify two large families of structures that are selected at the onset of aggregation demonstrating the presence of well-defined polymorphism, a signature of amyloid growth, already in the 20-mer aggregate.

Large loop conformation sampling using the activation relaxation technique, ART-nouveau method.

We present an adaptation of the ART-nouveau energy surface sampling method to the problem of loop structure prediction. This method, previously used to study protein folding pathways and peptide aggregation, is well suited to the problem of sampling the conformation space of large loops by targeting probable folding pathways instead of sampling exhaustively that space. The number of sampled conformations needed by ART nouveau to find the global energy minimum for a loop was found to scale linearly with the sequence length of the loop for loops between 8 and about 20 amino acids. Considering the linear scaling dependence of the computation cost on the loop sequence length for sampling new conformations, we estimate the total computational cost of sampling larger loops to scale quadratically compared to the exponential scaling of exhaustive search methods.

A multiscale approach to characterize the early aggregation steps of the amyloid-forming peptide GNNQQNY from the yeast prion sup-35.

The self-organization of peptides into amyloidogenic oligomers is one of the key events for a wide range of molecular and degenerative diseases. Atomic-resolution characterization of the mechanisms responsible for the aggregation process and the resulting structures is thus a necessary step to improve our understanding of the determinants of these pathologies. To address this issue, we combine the accelerated sampling properties of replica exchange molecular dynamics simulations based on the OPEP coarse-grained potential with the atomic resolution description of interactions provided by all-atom MD simulations, and investigate the oligomerization process of the GNNQQNY for three system sizes: 3-mers, 12-mers and 20-mers. Results for our integrated simulations show a rich variety of structural arrangements for aggregates of all sizes. Elongated fibril-like structures can form transiently in the 20-mer case, but they are not stable and easily interconvert in more globular and disordered forms. Our extensive characterization of the intermediate structures and their physico-chemical determinants points to a high degree of polymorphism for the GNNQQNY sequence that can be reflected at the macroscopic scale. Detailed mechanisms and structures that underlie amyloid aggregation are also provided.

Understanding the EF-hand closing pathway using non-biased interatomic potentials.

The EF-hand superfamily of proteins is characterized by the presence of calcium binding helix-loop-helix structures. Many of these proteins undergo considerable motion responsible for a wide range of properties upon binding but the exact mechanism at the root of this motion is not fully understood. Here, we use an unbiased accelerated multiscale simulation scheme, coupled with two force fields - CHARMM-EEF1 and the extended OPEP - to explore in details the closing pathway, from the unbound holo state to the closed apo state, of two EF-hand proteins, the Calmodulin and Troponin C N-terminal nodules. Based on a number of closing simulations for these two sequences, we show that the EF-hand ß-scaffold, identified as crucial by Grabarek for the EF-hand opening driven by calcium binding, is also important in closing the EF-hand. We also show the crucial importance of the phenylalanine situated at the end of first EF-hand helix, and identify an intermediate state modulating its behavior, providing a detailed picture of the closing mechanism for these two representatives of EF-hand proteins.

A Generalized Attraction-Repulsion Potential and Revisited Fragment Library Improves PEP-FOLD Peptide Structure Prediction.

Fast and accurate structure prediction is essential to the study of peptide function, molecular targets, and interactions and has been the subject of considerable efforts in the past decade. In this work, we present improvements to the popular simplified PEP-FOLD technique for small peptide structure prediction. PEP-FOLD originality is threefold: (i) it uses a predetermined structural alphabet, (ii) it uses a sequential algorithm to reconstruct the tridimensional structures of these peptides in a discrete space using a fragment library, and (iii) it assesses the energy of these structures using a coarse-grained representation in which all of the backbone atoms but the α-hydrogen are present, and the side chain corresponds to a unique bead. In former versions of PEP-FOLD, a van der Waals formulation was used for non-bonded interactions, with each side chain being associated with a fixed radius. Here, we explore the relevance of using instead a generalized formulation in which not only the optimal distance of interaction and the energy at this distance are parameters but also the distance at which the potential is zero. This allows each side chain to be associated with a different radius and potential energy shape, depending on its interaction partner, and in principle to make more effective the coarse-grained representation. In addition, the new PEP-FOLD version is associated with an updated library of fragments. We show that these modifications lead to important improvements for many of the problematic targets identified with the former PEP-FOLD version while maintaining already correct predictions. The improvement is in terms of both model ranking and model accuracy. We also compare the PEP-FOLD enhanced version to state-of-the-art techniques for both peptide and structure predictions: APPTest, RaptorX, and AlphaFold2. We find that the new predictions are superior, in particular with respect to the prediction of small ß-targets, to those of APPTest and RaptorX and bring, with its original approach, additional understanding on folded structures, even when less precise than AlphaFold2. With their strong physical influence, the revised structural library and coarse-grained potential offer, however, the means for a deeper understanding of the nature of folding and open a solid basis for studying flexibility and other dynamical properties not accessible to IA structure prediction approaches.