Fitting Force Field Parameters to NMR Relaxation Data

We present an approach to optimize force field parameters using time-dependent data from NMR relaxation experiments. To do so, we scan parameters in the dihedral angle potential energy terms describing the rotation of the methyl groups in proteins and compare NMR relaxation rates calculated from molecular dynamics simulations with the modified force fields to deuterium relaxation measurements of T4 lysozyme. We find that a small modification of C^γ methyl groups improves the agreement with experiments both for the protein used to optimize the force field and when validating using simulations of CI2 and ubiquitin. We also show that these improvements enable a more effective a posteriori reweighting of the MD trajectories. The resulting force field thus enables more direct comparison between simulations and side-chain NMR relaxation data and makes it possible to construct ensembles that better represent the dynamics of proteins in solution.

Double Mutant of Chymotrypsin Inhibitor 2 Stabilized through Increased Conformational Entropy

The conformational heterogeneity of a folded protein can affect not only its function but also stability and folding. We recently discovered and characterized a stabilized double mutant (L49I/I57V) of the protein CI2 and showed that state-of-the-art prediction methods could not predict the increased stability relative to the wild-type protein. Here, we have examined whether changed native-state dynamics, and resulting entropy changes, can explain the stability changes in the double mutant protein, as well as the two single mutant forms. We have combined NMR relaxation measurements of the ps-ns dynamics of amide groups in the backbone and the methyl groups in the side chains with molecular dynamics simulations to quantify the native-state dynamics. The NMR experiments reveal that the mutations have different effects on the conformational flexibility of CI2: a reduction in conformational dynamics (and entropy estimated from this) of the native state of the L49I variant correlates with its decreased stability, while increased dynamics of the I57V and L49I/I57V variants correlates with their increased stability. These findings suggest that explicitly accounting for changes in native-state entropy might be needed to improve the predictions of the effect of mutations on protein stability.

Fitting Side-Chain NMR Relaxation Data Using Molecular Simulations

Proteins display a wealth of dynamical motions that can be probed using both experiments and simulations. We present an approach to integrate side-chain NMR relaxation measurements with molecular dynamics simulations to study the structure and dynamics of these motions. The approach, which we term ABSURDer (average block selection using relaxation data with entropy restraints), can be used to find a set of trajectories that are in agreement with relaxation measurements. We apply the method to deuterium relaxation measurements in T4 lysozyme and show how it can be used to integrate the accuracy of the NMR measurements with the molecular models of protein dynamics afforded by the simulations. We show how fitting of dynamic quantities leads to improved agreement with static properties and highlight areas needed for further improvements of the approach.

Structural and Biophysical Properties of Supercharged and Circularized Nanodiscs

Nanodiscs based on membrane scaffold proteins (MSPs) and phospholipids are used as membrane mimics to stabilize membrane proteins in solution for structural and functional studies. Combining small-angle X-ray scattering (SAXS), differential scanning calorimetry (DSC), and time-resolved small-angle neutron scattering (TR-SANS), we characterized the structure and lipid bilayer properties of five different nanodiscs made with dimyristoylphosphatidylcholine and different MSPs varying in size, charge, and circularization. Our SAXS modeling showed that the structural parameters of the embedded lipids are all similar, irrespective of the MSP properties. DSC showed that the lipid packing is not homogeneous in the nanodiscs and that a 20 Å wide boundary layer of lipids with perturbed packing is located close to the MSP, while the packing of central lipids is tighter than in large unilamellar vesicles. Finally, TR-SANS showed that lipid exchange rates in nanodiscs decrease with increasing nanodisc size and are lower for the nanodiscs made with supercharged MSPs compared to conventional nanodiscs. Altogether, the results provide a thorough biophysical understanding of the nanodisc as a model membrane system, which is important in order to carry out and interpret experiments on membrane proteins embedded in such systems.

Ab initio determination of the shape of membrane proteins in a nanodisc

New software, called Marbles, is introduced that employs SAXS intensities to predict the shape of membrane proteins embedded into membrane nanodiscs. To gain computational speed and efficient convergence, the strategy is based on a hybrid approach that allows one to account for the contribution of the nanodisc to the SAXS intensity through a semi-analytical model, while the embedded membrane protein is treated as a set of beads, similarly to as in well known ab initio methods. The reliability and flexibility of this approach is proved by benchmarking the code, implemented in C++ with a Python interface, on a toy model and two proteins with very different geometry and size.

Pharmacological inactivation of the prion protein by targeting a folding intermediate

Recent computational advancements in the simulation of biochemical processes allow investigating the mechanisms involved in protein regulation with realistic physics-based models, at an atomistic level of resolution. These techniques allowed us to design a drug discovery approach, named Pharmacological Protein Inactivation by Folding Intermediate Targeting (PPI-FIT), based on the rationale of negatively regulating protein levels by targeting folding intermediates. Here, PPI-FIT was tested for the first time on the cellular prion protein (PrP), a cell surface glycoprotein playing a key role in fatal and transmissible neurodegenerative pathologies known as prion diseases. We predicted the all-atom structure of an intermediate appearing along the folding pathway of PrP and identified four different small molecule ligands for this conformer, all capable of selectively lowering the load of the protein by promoting its degradation. Our data support the notion that the level of target proteins could be modulated by acting on their folding pathways, implying a previously unappreciated role for folding intermediates in the biological regulation of protein expression.

Spagnolli, Massignan, Astolfi et al. design a new drug discovery approach, termed Pharmacological Protein Inactivation by Folding Intermediate Targeting, in which folding intermediates of disease-causing proteins are targeted. They test it on the cellular prion protein, identifying ligands stabilizing a folding intermediate and consequently promoting its degradation by the cellular quality control machinery.

How to learn from inconsistencies: Integrating molecular simulations with experimental data

Molecular simulations and biophysical experiments can be used to provide independent and complementary insights into the molecular origin of biological processes. A particularly useful strategy is to use molecular simulations as a modeling tool to interpret experimental measurements, and to use experimental data to refine our biophysical models. Thus, explicit integration and synergy between molecular simulations and experiments is fundamental for furthering our understanding of biological processes. This is especially true in the case where discrepancies between measured and simulated observables emerge. In this chapter, we provide an overview of some of the core ideas behind methods that were developed to improve the consistency between experimental information and numerical predictions. We distinguish between situations where experiments are used to refine our understanding and models of specific systems, and situations where experiments are used more generally to refine transferable models. We discuss different philosophies and attempt to unify them in a single framework. Until now, such integration between experiments and simulations have mostly been applied to equilibrium data, and we discuss more recent developments aimed to analyze time-dependent or time-resolved data.

Assessment of groundwater geochemistry and diffusion of hexavalent chromium contamination in an industrial town of Italy

Groundwater contamination by hexavalent chromium (Cr (VI)) is currently a very serious and challenging issue. Therefore, in the present study, 108 shallow groundwater samples were collected during the years 2012 to 2015 (n = 27 samples each year) from an industrial town (Aosta) to assess the contamination status of the area. To evaluate the Cr (VI) concentration level, sources of pollutants and groundwater geochemistry of the study area, a combined approach of the multi-statistical and hydrogeochemical techniques were used. Furthermore, a geographic information system (GIS) was applied for the spatial distribution of Cr (VI) so that the most contaminated sites can be identified for a quick decision by policymakers. Results show that the groundwater chemistry was dominated by HCO₃^- and SO₄^2- in the anionic chemistry and Ca²⁺ and Na⁺ in the cationic chemistry, while Ca-Mg-HCO₃ and Ca-Mg-Cl-SO₄ were the main water types during the years 2012-2015 in the study area. The central part and some north-eastern parts of the Aosta town groundwater was contaminated with Cr (VI) and are unsafe for drinking use. Concentration levels of Cr (VI) in the groundwater were 0.09 μg/L (lowest value) and 165 μg/L (highest value) in the years studied, and the spatial analysis shows the diffusion of Cr (VI) to north-east direction from the source. Contamination of Cr (VI) in the groundwater was due to the superficial slag deposits from a steel company within the study area. This study would be helpful for current and future water resource management in the area concerned.

Full atomistic model of prion structure and conversion

Prions are unusual protein assemblies that propagate their conformationally-encoded information in absence of nucleic acids. The first prion identified, the scrapie isoform (PrPSc) of the cellular prion protein (PrPC), caused epidemic and epizootic episodes [1]. Most aggregates of other misfolding-prone proteins are amyloids, often arranged in a Parallel-In-Register-β-Sheet (PIRIBS) [2] or β-solenoid conformations [3]. Similar folding models have also been proposed for PrPSc, although none of these have been confirmed experimentally. Recent cryo-electron microscopy (cryo-EM) and X-ray fiber-diffraction studies provided evidence that PrPSc is structured as a 4-rung β-solenoid (4RβS) [4, 5]. Here, we combined different experimental data and computational techniques to build the first physically-plausible, atomic resolution model of mouse PrPSc, based on the 4RβS architecture. The stability of this new PrPSc model, as assessed by Molecular Dynamics (MD) simulations, was found to be comparable to that of the prion forming domain of Het-s, a naturally-occurring β-solenoid. Importantly, the 4RβS arrangement allowed the first simulation of the sequence of events underlying PrPC conversion into PrPSc. This study provides the most updated, experimentally-driven and physically-coherent model of PrPSc, together with an unprecedented reconstruction of the mechanism underlying the self-catalytic propagation of prions.

All-Atom Simulations Reveal How Single-Point Mutations Promote Serpin Misfolding

Protein misfolding is implicated in many diseases, including serpinopathies. For the canonical inhibitory serpin α₁-antitrypsin, mutations can result in protein deficiencies leading to lung disease, and misfolded mutants can accumulate in hepatocytes, leading to liver disease. Using all-atom simulations based on the recently developed bias functional algorithm, we elucidate how wild-type α₁-antitrypsin folds and how the disease-associated S (Glu264Val) and Z (Glu342Lys) mutations lead to misfolding. The deleterious Z mutation disrupts folding at an early stage, whereas the relatively benign S mutant shows late-stage minor misfolding. A number of suppressor mutations ameliorate the effects of the Z mutation, and simulations on these mutants help to elucidate the relative roles of steric clashes and electrostatic interactions in Z misfolding. These results demonstrate a striking correlation between atomistic events and disease severity and shine light on the mechanisms driving chains away from their correct folding routes.

Transition path theory from biased simulations

Transition Path Theory (TPT) provides a rigorous framework to investigate the dynamics of rare thermally activated transitions. In this theory, a central role is played by the forward committor function q⁺(x), which provides the ideal reaction coordinate. Furthermore, the reactive dynamics and kinetics are fully characterized in terms of two time-independent scalar and vector distributions. In this work, we develop a scheme which enables all these ingredients of TPT to be efficiently computed using the short non-equilibrium trajectories generated by means of a specific combination of enhanced path sampling techniques. In particular, first we further extend the recently introduced self-consistent path sampling algorithm in order to compute the committor q⁺(x). Next, we show how this result can be exploited in order to define efficient algorithms which enable us to directly sample the transition path ensemble.

Self-consistent calculation of protein folding pathways

We introduce an iterative algorithm to efficiently simulate protein folding and other conformational transitions, using state-of-the-art all-atom force fields. Starting from the Langevin equation, we obtain a self-consistent stochastic equation of motion, which directly yields the reaction pathways. From the solution of this set of equations we derive a stochastic estimate of the reaction coordinate. We validate this approach against the results of plain MD simulations of the folding of a small protein, which were performed on the Anton supercomputer. In order to explore the computational efficiency of this algorithm, we apply it to generate a folding pathway of a protein that consists of 130 amino acids and has a folding rate of the order of s^-1.

All-atom calculation of protein free-energy profiles

The Bias Functional (BF) approach is a variational method which enables one to efficiently generate ensembles of reactive trajectories for complex biomolecular transitions, using ordinary computer clusters. For example, this scheme was applied to simulate in atomistic detail the folding of proteins consisting of several hundreds of amino acids and with experimental folding time of several minutes. A drawback of the BF approach is that it produces trajectories which do not satisfy microscopic reversibility. Consequently, this method cannot be used to directly compute equilibrium observables, such as free energy landscapes or equilibrium constants. In this work, we develop a statistical analysis which permits us to compute the potential of mean-force (PMF) along an arbitrary collective coordinate, by exploiting the information contained in the reactive trajectories calculated with the BF approach. We assess the accuracy and computational efficiency of this scheme by comparing its results with the PMF obtained for a small protein by means of plain molecular dynamics.

Hepatic antipyrine metabolism in athletes

The aim of the present study was to investigate the influence of prolonged physical exercise on hepatic metabolism. An oral antipyrine loading test (15 mg/kg body weight) was carried out on 24 healthy male subjects: 8 controls, 8 sprinters (anaerobic activity), 8 long distance runners (aerobic activity). Antipyrine clearance resulted to be significantly higher in athletes vs. controls, without a significant difference between the two groups of athletes. These results put forward the importance of the degree of physical activity (1) for the evaluation of antipyrine-loading-test results as expression of liver function and (2) whenever drugs with a low therapeutic index are used in athletes.

Possible roles of insulin, glucagon, growth hormone and free fatty acids in the pathogenesis of insulin resistance of subjects with chronic liver diseases

In the present investigation, insulin sensitivity and fasting levels of insulin, C-peptide, glucagon, growth hormone and free fatty acids were estimated and correlated in a population of individuals suffering from liver cirrhosis or chronic hepatitis. Insulin sensitivity, assessed by glucose disappearance rate after intravenous bolus injection of insulin, was reduced but not significantly different from controls in subjects with chronic persistent hepatitis, while it was significantly reduced in individuals suffering from chronic active hepatitis or liver cirrhosis. Insulin, glucagon, growth hormone, and free fatty acid fasting levels were higher than in healthy subjects in individuals with liver cirrhosis or chronic active hepatitis but not in subjects with chronic persistent hepatitis. C-peptide concentrations did not differ from controls in subjects with liver disease. Significant negative correlations occurred between coefficients of insulin sensitivity and fasting concentrations of insulin, glucagon, growth hormone and free fatty acids, but not with fasting levels of C-peptide. Positive relationships were present between fasting levels of free fatty acids and both glucagon and growth hormone concentrations. These results show that, unlike subjects with liver cirrhosis and chronic active hepatitis, individuals suffering from chronic persistent hepatitis do not differ from healthy subjects in insulin sensitivity and fasting levels of insulin, glucagon, growth hormone, and free fatty acids. Moreover, they suggest that both hyperinsulinemia and high concentrations of counterregulatory substances might play a role in the pathogenesis of insulin resistance in subjects suffering from chronic liver disease.

The bromosulphthalein loading test in chronic alcoholic liver diseases

The BSP loading test was performed in 39 patients affected by alcoholic liver disease. The behaviour of some parameters of the hepatic BSP metabolism was observed(45th min retention percentage, plasma disappearance rate, K1 and K2 exponentials, K21, K12 and K32 transfer rates). The 45th min retention percentage was the most sensitive parameter in detecting liver disease. This parameter, PDR, K1 and K21 were the most reliable parameters in differentiating between the various forms of alcoholic liver disease(alcoholic steatosis, alcoholic chronic hepatitis, alcoholic cirrhosis).

Hyperinsulinemia of chronic active hepatitis: impaired insulin removal rather than pancreatic hypersecretion

Exaggerated insulin response to oral glucose was demonstrated in peripheral blood of patients with chronic hepatic diseases. High peripheral insulin levels may be the result of pancreatic hypersecretion or decreased hepatic removal of insulin. The simultaneous assay of insulin and C-Peptide concentrations in peripheral blood enables the determination of both beta-cell activity and hepatic fractional insulin extraction. We have measured peripheral insulin and C-Peptide levels during OGTT in a group of subjects with chronic active hepatitis (CAH). These subjects showed glucose levels and incremental areas significantly higher than controls, but still in the upper range of normality. Insulin response to oral glucose was significantly greater in CAH patients than in controls, whereas C-Peptide levels and areas were quite similar in the two groups. The C-Peptide to insulin molar ratios before and after glucose, and the relations between C-Peptide and insulin incremental areas were lower in CAH patients than in controls. We conclude that the peripheral hyperinsulinemia observed in subjects with CAH is due to diminished insulin removal by the diseased liver rather than pancreatic hypersecretion.