Antibody binding modulates the dynamics of the membrane-bound prion protein.

Misfolding of the cellular prion protein (PrPC) is associated with lethal neurodegeneration. PrPC consists of a flexible tail (residues 23-123) and a globular domain (residues 124-231) whose C-terminal end is anchored to the cell membrane. The neurotoxic antibody POM1 and the innocuous antibody POM6 recognize the globular domain. Experimental evidence indicates that POM1 binding to PrPC emulates the influence on PrPC of the misfolded prion protein (PrPSc) while the binding of POM6 has the opposite biological response. Little is known about the potential interactions between flexible tail, globular domain, and the membrane. Here, we used atomistic simulations to investigate how these interactions are modulated by the binding of the Fab fragments of POM1 and POM6 to PrPC and by interstitial sequence truncations to the flexible tail. The simulations show that the binding of the antibodies restricts the range of orientations of the globular domain with respect to the membrane and decreases the distance between tail and membrane. Five of the six sequence truncations influence only marginally this distance and the contact patterns between tail and globular domain. The only exception is a truncation coupled to a charge inversion mutation of four N-terminal residues, which increases the distance of the flexible tail from the membrane. The interactions of the flexible tail and globular domain are modulated differently by the two antibodies.

On the removal of initial state bias from simulation data.

Classical atomistic simulations of biomolecules play an increasingly important role in molecular life science. The structure of current computing architectures favors methods that run multiple trajectories at once without requiring extensive communication between them. Many advanced sampling strategies in the field fit this mold. These approaches often rely on an adaptive logic and create ensembles of comparatively short trajectories whose starting points are not distributed according to the correct Boltzmann weights. This type of bias is notoriously difficult to remove, and Markov state models (MSMs) are one of the few strategies available for recovering the correct kinetics and thermodynamics from these ensembles of trajectories. In this contribution, we analyze the performance of MSMs in the thermodynamic reweighting task for a hierarchical set of systems. We show that MSMs can be rigorous tools to recover the correct equilibrium distribution for systems of sufficiently low dimensionality. This is conditional upon not tampering with local flux imbalances found in the data. For a real-world application, we find that a pure likelihood-based inference of the transition matrix produces the best results. The removal of the bias is incomplete, however, and for this system, all tested MSMs are outperformed by an alternative albeit less general approach rooted in the ideas of statistical resampling. We conclude by formulating some recommendations for how to address the reweighting issue in practice.

Focused conformational sampling in proteins.

A detailed understanding of the conformational dynamics of biological molecules is difficult to obtain by experimental techniques due to resolution limitations in both time and space. Computer simulations avoid these in theory but are often too short to sample rare events reliably. Here we show that the progress index-guided sampling (PIGS) protocol can be used to enhance the sampling of rare events in selected parts of biomolecules without perturbing the remainder of the system. The method is very easy to use as it only requires as essential input a set of several features representing the parts of interest sufficiently. In this feature space, new states are discovered by spontaneous fluctuations alone and in unsupervised fashion. Because there are no energetic biases acting on phase space variables or projections thereof, the trajectories PIGS generates can be analyzed directly in the framework of transition networks. We demonstrate the possibility and usefulness of such focused explorations of biomolecules with two loops that are part of the binding sites of bromodomains, a family of epigenetic "reader" modules. This real-life application uncovers states that are structurally and kinetically far away from the initial crystallographic structures and are also metastable. Representative conformations are intended to be used in future high-throughput virtual screening campaigns.

A molecular simulation protocol to avoid sampling redundancy and discover new states.

BACKGROUND: For biomacromolecules or their assemblies, experimental knowledge is often restricted to specific states. Ambiguity pervades simulations of these complex systems because there is no prior knowledge of relevant phase space domains, and sampling recurrence is difficult to achieve. In molecular dynamics methods, ruggedness of the free energy surface exacerbates this problem by slowing down the unbiased exploration of phase space. Sampling is inefficient if dwell times in metastable states are large. METHODS: We suggest a heuristic algorithm to terminate and reseed trajectories run in multiple copies in parallel. It uses a recent method to order snapshots, which provides notions of "interesting" and "unique" for individual simulations. We define criteria to guide the reseeding of runs from more "interesting" points if they sample overlapping regions of phase space. RESULTS: Using a pedagogical example and an α-helical peptide, the approach is demonstrated to amplify the rate of exploration of phase space and to discover metastable states not found by conventional sampling schemes. Evidence is provided that accurate kinetics and pathways can be extracted from the simulations. CONCLUSIONS: The method, termed PIGS for Progress Index Guided Sampling, proceeds in unsupervised fashion, is scalable, and benefits synergistically from larger numbers of replicas. Results confirm that the underlying ideas are appropriate and sufficient to enhance sampling. GENERAL SIGNIFICANCE: In molecular simulations, errors caused by not exploring relevant domains in phase space are always unquantifiable and can be arbitrarily large. Our protocol adds to the toolkit available to researchers in reducing these types of errors. This article is part of a Special Issue entitled "Recent developments of molecular dynamics".

A comparison of numerical approaches for statistical inference with stochastic models.

Due to our limited knowledge about complex environmental systems, our predictions of their behavior under different scenarios or decision alternatives are subject to considerable uncertainty. As this uncertainty can often be relevant for societal decisions, the consideration, quantification and communication of it is very important. Due to internal stochasticity, often poorly known influence factors, and only partly known mechanisms, in many cases, a stochastic model is needed to get an adequate description of uncertainty. As this implies the need to infer constant parameters, as well as the time-course of stochastic model states, a very high-dimensional inference problem for model calibration has to be solved. This is very challenging from a methodological and a numerical perspective. To illustrate aspects of this problem and show options to successfully tackle it, we compare three numerical approaches: Hamiltonian Monte Carlo, Particle Markov Chain Monte Carlo, and Conditional Ornstein-Uhlenbeck Sampling. As a case study, we select the analysis of hydrological data with a stochastic hydrological model. We conclude that the performance of the investigated techniques is comparable for the analyzed system, and that also generality and practical considerations may be taken into account to guide the choice of which technique is more appropriate for a particular application. Supplementary Information: The online version contains supplementary material available at 10.1007/s00477-023-02434-z.

Cognition in COVID-19 infected patients undergoing invasive ventilation: results from a multicenter retrospective study.

A growing number of scientific contributions suggest that COVID-19 infection can lead to impairment of cognition, mainly in executive functions and memory domains, even in the absence of frank neurological pathologies.The primary objective of this retrospective study is to evaluate the frequency and type of inefficiencies in a selection of cognitive tests administered to a sample of subjects who, following infection, required invasive assisted ventilation and were admitted to rehabilitation wards for the treatment of functional impairment.Fifty-seven subjects were enrolled. The recruited patients undergone an assessment of verbal and visuospatial memory and executive functions, upon entry into the rehabilitation department, after discharge from intensive care. The following tests were administered: Rey Auditory Verbal Learning Test (AVLT) (immediate and delayed recall), Rey-Osterrieth Complex Figure Test (ROCFT) (copy and delayed recall), Stroop Color-Word Test, and Trail Making Test (TMT, A and B).Deficient scores, in beyond 25% of subjects, were found in the copy of the ROCFT (32.1% of subjects), and in the delayed recall of ROCFT (27.2%). Between 10 and 20% of patients presented an abnormal result in delayed recall of AVLT (16.07%), and Stroop Test (time, 15.6%, error, 11.5%). Less than 10% of the sample had abnormal performances on TMT (A, 3.5%, and B, 9.4%), and in AVLT immediate recall (8.9%). Correlations of the performances with age, sex, and education were also found.This paper highlights the high incidence of abnormal cognitive performances in this specific subpopulation of patients with COVID-19 infection.

Persistent dysexecutive syndrome after pneumococcal meningitis complicated by recurrent ischemic strokes: A case report.

BACKGROUND: Meningitis is a possible complication of pneumococcal infection concerning acute otitis media and sinusitis. It might compromise cognitive function, both for the infection itself and the vascular events that sometimes follow the acute phase. CASE SUMMARY: Here we describe the case of a 32-year-old female patient admitted to the emergency room due to extensive pneumococcal meningitis as a consequence of sinus outbreak. She presented with extensive laminar ischemic damage in the acute phase, resulting in severe cognitive and behavioural impairment. Four years of follow-up, through neuropsychological assessments and neuroradiological investigations, demonstrated the presence of subsequent vascular events, 3 months and 2 years after onset. CONCLUSION: The case is discussed in light of scientific knowledge of the long-term outcomes of this pathology in order to potentially improve diagnosis and promote better outcomes.

A discrete-to-continuum model of protein complexes.

On the basis of a tensor representation of protein shape, obtained by an affine decomposition of residue velocity, we show how to identify actions at continuum scale for both single proteins and their complexes in terms of power equivalence. The approach constructs and justifies a continuum modeling of protein complexes, which avoids a direct, atomistic-based, simulation of the whole complex, rather it focuses (in a statistical sense) on a single protein and its interactions with the neighbors. In the resulting setting we also prove the existence of equilibrium configurations (native states) under large strains.

Amyloid ß Fibril Elongation by Monomers Involves Disorder at the Tip.

The growth of amyloid fibrils from Aß1-42 peptide, one of the key pathogenic players in Alzheimer's disease, is believed to follow a nucleation-elongation mechanism. Fibril elongation is often described as a "dock-lock" procedure, where a disordered monomer adsorbs to an existing fibril in a relatively fast process (docking), followed by a slower conformational transition toward the ordered state of the template (locking). Here, we use molecular dynamics simulations of an ordered pentamer of Aß42 at fully atomistic resolution, which includes solvent, to characterize the elongation process. We construct a Markov state model from an ensemble of short trajectories generated by an advanced sampling algorithm that efficiently diversifies a subset of the system without any bias forces. This subset corresponds to selected dihedral angles of the peptide chain at the fibril tip favored to be the fast growing one experimentally. From the network model, we extract distinct locking pathways covering time scales in the high microsecond regime. Slow steps are associated with the exchange of hydrophobic contacts, between nonnative and native intermolecular contacts as well as between intra- and intermolecular ones. The N-terminal segments, which are disordered in fibrils and typically considered inert, are able to shield the lateral interfaces of the pentamer. We conclude by discussing our findings in the context of a refined dock-lock model of Aß fibril elongation, which involves structural disorder for more than one monomer at the growing tip.

Protein transport across nanopores: a statistical mechanical perspective from coarse-grained modeling and approaches.

We survey the transport of proteins across nanopores in the framework of coarse-grained modeling. The advantage of a reduced complexity with respect to full-atomistic techniques lies in the possibility of massive sampling of events, thus allowing a statistical mechanical description of translocation in terms of ensemble averages. Often, protein transport through narrow channels tightly couples with unfolding pathways causing a richer phenomenology compared to unstructured polymer translocation. This reflects into a process controlled by the presence of protein-specific free-energy barriers which can be conveniently estimated by statistical mechanical methods implemented in coarse-grained simulations. We illustrate how protein transport dynamics can be characterized by the statistical properties of trajectories and sometimes interpreted as driven diffusion of a single collective coordinate over a free-energy landscape. We also discuss, through selected examples, the connection between reduced-model simulations and recent experimental results.

Protein translocation in narrow pores: inferring bottlenecks from native structure topology.

Coarse-grained simulations of protein translocation across narrow pores suggest that the transport is characterized by long stall events. The translocation bottlenecks and the associated free-energy barriers are found to be strictly related to the structural properties of the protein native structure. The ascending ramps of the free-energy profile systematically correspond to regions of the chain denser in long range native contacts formed with the untranslocated portion of the protein. These very regions are responsible for the stalls occurring during the protein transport along the nanopore. The decomposition of the free energy in internal energyand entropic terms shows that the dominant energetic contribution can be estimated on the base of the protein native structure only. Interestingly, the essential features of the dynamics are retained in a reduced phenomenological model of the process describing the evolution of a suitable collective variable in the associated free-energy landscape.

Role of denaturation in maltose binding protein translocation dynamics.

We present a computational study on the driven transport of the Maltose Binding Protein (MBP) across nanochannels in the framework of coarse-grained modeling. The work is motivated by recent experiments on voltage-driven transport of MBP across nanopores exploring the influence of denaturation on translocation pathways. Our simplified approach allows a statistical mechanical interpretation of the process which may be convenient also to the experiments. Specifically, we identify and characterize short and long channel blockades, associated to the translocation of denaturated and folded MBP conformations, respectively. We show that long blockades are related to long stall events where MBP undergoes specific and reproducible structural rearrangements. To clarify the origin of the stalls, the stick-and-slip translocation is compared to mechanical unfolding pathways obtained via steered molecular dynamics. This comparison clearly shows the translocation pathway to significantly differ from free-space unfolding dynamics and strongly suggests that stalling events are preferentially determined by the MBP regions with higher density of long-range native interactions. This result might constitute a possible criterion to predict a priori some statistical features of protein translocation from the structural analysis.