Evaluating Polarizable Biomembrane Simulations against Experiments.

Owing to the increase of available computational capabilities and the potential for providing a more accurate description, polarizable molecular dynamics force fields are gaining popularity in modeling biomolecular systems. It is, however, crucial to evaluate how much precision is truly gained with increasing cost and complexity of the simulation. Here, we leverage the NMRlipids open collaboration and Databank to assess the performance of available polarizable lipid models─the CHARMM-Drude and the AMOEBA-based parameters─against high-fidelity experimental data and compare them to the top-performing nonpolarizable models. While some improvement in the description of ion binding to membranes is observed in the most recent CHARMM-Drude parameters, and the conformational dynamics of AMOEBA-based parameters are excellent, the best nonpolarizable models tend to outperform their polarizable counterparts for each property we explored. The identified shortcomings range from inaccuracies in describing the conformational space of lipids to excessively slow conformational dynamics. Our results provide valuable insights for the further refinement of polarizable lipid force fields and for selecting the best simulation parameters for specific applications.

Overlay databank unlocks data-driven analyses of biomolecules for all.

Tools based on artificial intelligence (AI) are currently revolutionising many fields, yet their applications are often limited by the lack of suitable training data in programmatically accessible format. Here we propose an effective solution to make data scattered in various locations and formats accessible for data-driven and machine learning applications using the overlay databank format. To demonstrate the practical relevance of such approach, we present the NMRlipids Databank-a community-driven, open-for-all database featuring programmatic access to quality-evaluated atom-resolution molecular dynamics simulations of cellular membranes. Cellular membrane lipid composition is implicated in diseases and controls major biological functions, but membranes are difficult to study experimentally due to their intrinsic disorder and complex phase behaviour. While MD simulations have been useful in understanding membrane systems, they require significant computational resources and often suffer from inaccuracies in model parameters. Here, we demonstrate how programmable interface for flexible implementation of data-driven and machine learning applications, and rapid access to simulation data through a graphical user interface, unlock possibilities beyond current MD simulation and experimental studies to understand cellular membranes. The proposed overlay databank concept can be further applied to other biomolecules, as well as in other fields where similar barriers hinder the AI revolution.

Effects of ion type and concentration on the structure and aggregation of the amyloid peptide A ß 16 - 22 $$ {\boldsymbol{\beta}}_{16-22} $$.

Among the various factors controlling the amyloid aggregation process, the influences of ions on the aggregation rate and the resulting structures are important aspects to consider, which can be studied by molecular simulations. There is a wide variety of protein force fields and ion models, raising the question of which model to use in such studies. To address this question, we perform molecular dynamics simulations of Aß16-22 , a fragment of the Alzheimer's amyloid ß peptide, using different protein force fields, AMBER99SB-disp (A99-d) and CHARMM36m (C36m), and different ion parameters. The influences of NaCl and CaCl2 at various concentrations are studied and compared with the systems without the addition of ions. Our results indicate a sensitivity of the peptide-ion interactions to the different ion models. In particular, we observe a strong binding of Ca2+ to residue E22 with C36m and also with the Åqvist ion model used together with A99-d, which slightly affects the monomeric Aß16-22 structures and the aggregation rate, but significantly affects the oligomer structures formed in the aggregation simulations. For example, at high Ca2+ concentrations, there was a switch from an antiparallel to a parallel ß-sheet. Such ionic influences are of biological relevance because local ion concentrations can change in vivo and could help explain the polymorphism of amyloid fibrils.

Measuring pico-Newton Forces with Lipid Anchors as Force Sensors in Molecular Dynamics Simulations.

Binding forces between biomolecules are ubiquitous in nature but sometimes as weak as a few pico-Newtons (pN). In many cases, the binding partners are attached to biomembranes with the help of a lipid anchor. One important example are glycolipids that promote membrane adhesion through weak carbohydrate-carbohydrate binding between adjacent membranes. Here, we use molecular dynamics (MD) simulations to quantify the forces generated by bonds involving membrane-anchored molecules. We introduce a method in which the protrusion of the lipid anchors from the membrane acts as the force sensor. Our results with two different glycolipids reveal binding forces of up to 20 pN and corroborate the recent notion that carbohydrate-carbohydrate interactions are generic rather than specific.

Does the inclusion of electronic polarisability lead to a better modelling of peptide aggregation?

Simulating the process of amyloid aggregation with atomic detail is a challenging task for various reasons. One of them is that it is difficult to parametrise a force field such that all protein states ranging from the folded through the unfolded to the aggregated state are represented with the same level of accuracy. Here, we test whether the consideration of electronic polarisability improves the description of the different states of Aß16-22. Surprisingly, the CHARMM Drude polarisable force field is found to perform worse than its unpolarisable counterpart CHARMM36m. Sources for this failure of the Drude model are discussed.

Probing the Link between Pancratistatin and Mitochondrial Apoptosis through Changes in the Membrane Dynamics on the Nanoscale.

Pancratistatin (PST) is a natural antiviral alkaloid that has demonstrated specificity toward cancerous cells and explicitly targets the mitochondria. PST initiates apoptosis while leaving healthy, noncancerous cells unscathed. However, the manner by which PST induces apoptosis remains elusive and impedes the advancement of PST as a natural anticancer therapeutic agent. Herein, we use neutron spin-echo (NSE) spectroscopy, molecular dynamics (MD) simulations, and supporting small angle scattering techniques to study PST's effect on membrane dynamics using biologically representative model membranes. Our data suggests that PST stiffens the inner mitochondrial membrane (IMM) by being preferentially associated with cardiolipin, which would lead to the relocation and release of cytochrome c. Second, PST has an ordering effect on the lipids and disrupts their distribution within the IMM, which would interfere with the maintenance and functionality of the active forms of proteins in the electron transport chain. These previously unreported findings implicate PST's effect on mitochondrial apoptosis.

Disorder-to-order transition of the amyloid-ß peptide upon lipid binding.

There is mounting evidence that Alzheimer's disease progression and severity are linked to neuronal membrane damage caused by aggregates of the amyloid-ß (Aß) peptide. However, the detailed mechanism behind the membrane damage is not well understood yet. Recently, the lipid-chaperone hypothesis has been put forward, based on which the formation of complexes between Aß and free lipids enables an easy insertion of Aß into membranes. In order to test this hypothesis, we performed numerous all-atom molecular dynamics simulations. We studied the complex formation between individual lipids, considering both POPC and DPPC, and Aß and examined whether the resulting complexes would be able to insert into lipid membranes. Complex formation at a one-to-one ratio was readily observed, yet with minimal effects on Aß's characteristics. Most importantly, the peptide remains largely disordered in 1:1 complexes, and the complex does not insert into the membrane; instead, it is adsorbed to the membrane surface. The results change considerably once Aß forms a complex with a POPC cluster composed of three lipid molecules. The hydrophobic interactions between Aß and the lipid tails cause the peptide to fold into either a helical or a ß-sheet structure. These observations provide atomic insight into the disorder-to-order transition that is needed for membrane insertion or amyloid aggregation to proceed.

Interplay of Trans- and Cis-Interactions of Glycolipids in Membrane Adhesion.

Glycolipids mediate stable membrane adhesion of potential biological relevance. In this article, we investigate the trans- and cis-interactions of glycolipids in molecular dynamics simulations and relate these interactions to the glycolipid-induced average separations of membranes obtained from neutron scattering experiments. We find that the cis-interactions between glycolipids in the same membrane leaflet tend to strengthen the trans-interactions between glycolipids in apposing leaflets. The trans-interactions of the glycolipids in our simulations require local membrane separations that are significantly smaller than the average membrane separations in the neutron scattering experiments, which indicates an important role of membrane shape fluctuations in glycolipid trans-binding. Simulations at the experimentally measured average membrane separations provide a molecular picture of the interplay between glycolipid attraction and steric repulsion of the fluctuating membranes probed in the experiments.

Different Force Fields Give Rise to Different Amyloid Aggregation Pathways in Molecular Dynamics Simulations.

The progress toward understanding the molecular basis of Alzheimers's disease is strongly connected to elucidating the early aggregation events of the amyloid-ß (Aß) peptide. Molecular dynamics (MD) simulations provide a viable technique to study the aggregation of Aß into oligomers with high spatial and temporal resolution. However, the results of an MD simulation can only be as good as the underlying force field. A recent study by our group showed that none of the common force fields can distinguish between aggregation-prone and nonaggregating peptide sequences, producing a similar and in most cases too fast aggregation kinetics for all peptides. Since then, new force fields specially designed for intrinsically disordered proteins such as Aß were developed. Here, we assess the applicability of these new force fields to studying peptide aggregation using the Aß16-22 peptide and mutations of it as test case. We investigate their performance in modeling the monomeric state, the aggregation into oligomers, and the stability of the aggregation end product, i.e., the fibrillar state. A main finding is that changing the force field has a stronger effect on the simulated aggregation pathway than changing the peptide sequence. Also the new force fields are not able to reproduce the experimental aggregation propensity order of the peptides. Dissecting the various energy contributions shows that AMBER99SB-disp overestimates the interactions between the peptides and water, thereby inhibiting peptide aggregation. More promising results are obtained with CHARMM36m and especially its version with increased protein-water interactions. It is thus recommended to use this force field for peptide aggregation simulations and base future reparameterizations on it.

Weak carbohydrate-carbohydrate interactions in membrane adhesion are fuzzy and generic.

Carbohydrates such as the trisaccharide motif LeX are key constituents of cell surfaces. Despite intense research, the interactions between carbohydrates of apposing cells or membranes are not well understood. In this article, we investigate carbohydrate-carbohydrate interactions in membrane adhesion as well as in solution with extensive atomistic molecular dynamics simulations that exceed the simulation times of previous studies by orders of magnitude. For LeX, we obtain association constants of soluble carbohydrates, adhesion energies of lipid-anchored carbohydrates, and maximally sustained forces of carbohydrate complexes in membrane adhesion that are in good agreement with experimental results in the literature. Our simulations thus appear to provide a realistic, detailed picture of LeX-LeX interactions in solution and during membrane adhesion. In this picture, the LeX-LeX interactions are fuzzy, i.e. LeX pairs interact in a large variety of short-lived, bound conformations. For the synthetic tetrasaccharide Lac 2, which is composed of two lactose units, we observe similarly fuzzy interactions and obtain association constants of both soluble and lipid-anchored variants that are comparable to the corresponding association constants of LeX. The fuzzy, weak carbohydrate-carbohydrate interactions quantified in our simulations thus appear to be a generic feature of small, neutral carbohydrates such as LeX and Lac 2.

Headgroup Structure and Cation Binding in Phosphatidylserine Lipid Bilayers.

Phosphatidylserine (PS) is a negatively charged lipid type commonly found in eukaryotic membranes, where it interacts with proteins via nonspecific electrostatic interactions as well as via specific binding. Moreover, in the presence of calcium ions, PS lipids can induce membrane fusion and phase separation. Molecular details of these phenomena remain poorly understood, partly because accurate models to interpret the experimental data have not been available. Here we gather a set of previously published experimental NMR data of C-H bond order parameter magnitudes, |SCH|, for pure PS and mixed PS:PC (phosphatidylcholine) lipid bilayers and augment this data set by measuring the signs of SCH in the PS headgroup using S-DROSS solid-state NMR spectroscopy. The augmented data set is then used to assess the accuracy of the PS headgroup structures in, and the cation binding to, PS-containing membranes in the most commonly used classical molecular dynamics (MD) force fields including CHARMM36, Lipid17, MacRog, Slipids, GROMOS-CKP, Berger, and variants. We show large discrepancies between different force fields and that none of them reproduces the NMR data within experimental accuracy. However, the best MD models can detect the most essential differences between PC and PS headgroup structures. The cation binding affinity is not captured correctly by any of the PS force fields-an observation that is in line with our previous results for PC lipids. Moreover, the simulated response of the PS headgroup to bound ions can differ from experiments even qualitatively. The collected experimental data set and simulation results will pave the way for development of lipid force fields that correctly describe the biologically relevant negatively charged membranes and their interactions with ions. This work is part of the NMRlipids open collaboration project ( nmrlipids.blogspot.fi ).

Function changing mutations in glucocorticoid receptor evolution correlate with their relevance to mode coupling.

Nonlinear effects in protein dynamics are expected to play role in function, particularly of allosteric nature, by facilitating energy transfer between vibrational modes. A recently proposed method focusing on the non-Gaussian shape of the configurational population near equilibrium projects this information onto real space in order to identify the aminoacids relevant to function. We here apply this method to three ancestral proteins in glucocorticoid receptor (GR) family and show that the mutations that restrict functional activity during GR evolution correlate significantly with locations that are highlighted by the nonlinear contribution to the near-native configurational distribution. Our findings demonstrate that the analysis of nonlinear effects in protein dynamics can be harnessed into a predictive tool for functional site determination.

Force generation by the growth of amyloid aggregates.

The generation of mechanical forces are central to a wide range of vital biological processes, including the function of the cytoskeleton. Although the forces emerging from the polymerization of native proteins have been studied in detail, the potential for force generation by aberrant protein polymerization has not yet been explored. Here, we show that the growth of amyloid fibrils, archetypical aberrant protein polymers, is capable of unleashing mechanical forces on the piconewton scale for individual filaments. We apply microfluidic techniques to measure the forces released by amyloid growth for two systems: insulin and lysozyme. The level of force measured for amyloid growth in both systems is comparable to that observed for actin and tubulin, systems that have evolved to generate force during their native functions and, unlike amyloid growth, rely on the input of external energy in the form of nucleotide hydrolysis for maximum force generation. Furthermore, we find that the power density released from growing amyloid fibrils is comparable to that of high-performance synthetic polymer actuators. These findings highlight the potential of amyloid structures as active materials and shed light on the criteria for regulation and reversibility that guide molecular evolution of functional polymers.