Search

The interpretation of ligand–target interactions at atomistic resolution is central to most efforts in computational drug discovery and optimization. However, the highly dynamic nature of protein targets, as well as possible induced fit effects, makes difficult to sample many interactions effectively with docking studies or even with large-scale molecular dynamics (MD) simulations. We propose a novel application of Self-Organizing Maps (SOMs) to address the sampling and dynamic mapping tasks, particularly in cases involving ligand flexibility and induced fit. The SOM approach offers a data-driven strategy to create a map of the interaction process and pathways based on unbiased MD. Furthermore, we show how the preliminary SOM mapping is complementary to kinetic analysis, with the employment of both network-based approaches and Markov state models. We demonstrate the method by comprehensively mapping a large dataset of 640 μs of unbiased trajectories sampling the recognition process between the phosphorylated YEEI peptide and its high-specificity target lck-SH2. The integration of SOM into unbiased simulation protocols significantly advances our understanding of the ligand binding mechanism. This approach serves as a potent tool for mapping intricate ligand–target interactions with unprecedented detail, thereby enhancing the characterization of kinetic properties crucial to drug design. [ABSTRACT FROM AUTHOR]

Coronaviruses (CoVs) share key genomic elements critical for viral replication, suggesting the feasibility of developing therapeutics with efficacy across different viruses. In a previous work, we demonstrated the antiviral activity of the antipsychotic drug lurasidone against both SARS-CoV-2 and HCoV-OC43. In this study, our investigations on the mechanism of action of lurasidone suggested that the drug exhibits antiviral activity by targeting the papain-like protease (PL-Pro) of both viruses, and the Spike protein of SARS-CoV-2, thereby hampering both the entry and the viral replication. In vitro assays demonstrate that lurasidone significantly reduces viral load in infected cells, showing that the drug is a promising candidate for further development as a dual-action antiviral, offering a potential new strategy in the fight against COVID-19 and other coronavirus-related diseases. [ABSTRACT FROM AUTHOR]

Recent advancements in protein structure determination are revolutionizing our understanding of proteins. Still, a significant gap remains in the availability of comprehensive datasets that focus on the dynamics of proteins, which are crucial for understanding protein function, folding, and interactions. To address this critical gap, we introduce mdCATH, a dataset generated through an extensive set of all-atom molecular dynamics simulations of a diverse and representative collection of protein domains. This dataset comprises all-atom systems for 5,398 domains, modeled with a state-of-the-art classical force field, and simulated in five replicates each at five temperatures from 320 K to 450 K. The mdCATH dataset records coordinates and forces every 1 ns, for over 62 ms of accumulated simulation time, effectively capturing the dynamics of the various classes of domains and providing a unique resource for proteome-wide statistical analyses of protein unfolding thermodynamics and kinetics. We outline the dataset structure and showcase its potential through four easily reproducible case studies, highlighting its capabilities in advancing protein science. [ABSTRACT FROM AUTHOR]

A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins with multiple secondary structure arrangements. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems. Coarse-grained simulations identify relevant structural states in the ensemble with comparable energetics to the all-atom systems. Furthermore, we show that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics. Understanding protein dynamics is a complex scientific challenge. Here, authors construct coarse-grained molecular potentials using artificial neural networks, significantly accelerating protein dynamics simulations while preserving their thermodynamics. [ABSTRACT FROM AUTHOR]

Intracerebral hemorrhages are recognized risk factors for neurodevelopmental disorders and represent early biomarkers for cognitive dysfunction and mental disability, but the pathways leading to their occurrence are not well defined. We report that a single intrauterine exposure of the immunostimulant Poly I:C to pregnant mice at gestational day 9, which models a prenatal viral infection and the consequent maternal immune activation, induces the defective formation of brain vessels and causes intracerebral hemorrhagic events, specifically in male offspring. We demonstrate that maternal immune activation promotes the production of the TGF‐β1 active form and the consequent enhancement of pSMAD1‐5 in males' brain endothelial cells. TGF‐β1, in combination with IL‐1β, reduces the endothelial expression of CD146 and claudin‐5, alters the endothelium–pericyte interplay resulting in low pericyte coverage, and increases hemorrhagic events in the adult offspring. By showing that exposure to Poly I:C at the beginning of fetal cerebral angiogenesis results in sex‐specific alterations of brain vessels, we provide a mechanistic framework for the association between intragravidic infections and anomalies of the neural vasculature, which may contribute to neuropsychiatric disorders. Synopsis: A mouse model of viral infection during pregnancy shows that maternal immune activation results in male‐specific defects in the formation of brain vasculature in the offspring leading to fragile and hemorrhage‐prone blood vessels.A single intrauterine exposure to Poly I:C in pregnant mice leads to disruption of endothelial cell–pericyte interplay in the brain–blood vessels of the offspring.Breakdown of the neurovascular unit results in hemorrhage‐prone vessels.Maternal immune activation leads to the induction of TGF‐β and reduced endothelial expression of CD146 selectively in male mice.Male brain–blood vessels show a reduced number of pericytes and concomitant disruption of the tight junction protein CLND5. [ABSTRACT FROM AUTHOR]

Mutations in the gelsolin protein are responsible for a rare conformational disease known as AGel amyloidosis. Four of these mutations are hosted by the second domain of the protein (G2): D187N/Y, G167R and N184K. The impact of the latter has been so far evaluated only by studies on the isolated G2. Here we report the characterization of full-length gelsolin carrying the N184K mutation and compare the findings with those obtained on the wild type and the other variants. The crystallographic structure of the N184K variant in the Ca2+-free conformation shows remarkable similarities with the wild type protein. Only minimal local rearrangements can be observed and the mutant is as efficient as the wild type in severing filamentous actin. However, the thermal stability of the pathological variant is compromised in the Ca2+-free conditions. These data suggest that the N to K substitution causes a local disruption of the H-bond network in the core of the G2 domain. Such a subtle rearrangement of the connections does not lead to significant conformational changes but severely affects the stability of the protein. [ABSTRACT FROM AUTHOR]

Although interdisciplinarity is often touted as a necessity for modern research, the evidence on the relative impact of sectorial versus to interdisciplinary science is qualitative at best. In this paper we leverage the bibliographic data set of the American Physical Society to quantify the role of interdisciplinarity in physics, and that of talent and luck in achieving success in scientific careers. We analyze a period of 30 years (1980-2009) tagging papers and their authors by means of the Physics and Astronomy Classification Scheme (PACS), to show that some degree of interdisciplinarity is quite helpful to reach success, measured as a proxy of either the number of articles or the citations score. We also propose an agent-based model of the publication-reputation-citation dynamics which reproduces the trends observed in the APS data set. On the one hand, the results highlight the crucial role of randomness and serendipity in real scientific research; on the other, they shed light on a counterintuitive effect indicating that the most talented authors are not necessarily the most successful ones. [ABSTRACT FROM AUTHOR]

In vivo studies have investigated the role of β-cell dysfunction in type 2 diabetes (T2D), whereas in vitro research on islets has elucidated key mechanisms that control the insulin secretion rate. However, the relevance of the cellular mechanisms identified in vitro (i.e., the triggering and amplifying pathways) has not been established in vivo. Furthermore, the mechanisms underpinning β-cell dysfunction in T2D remain undetermined. We propose a unifying explanation of several characteristic features of insulin secretion both in vitro and in vivo by using a mathematical model. The model describes the triggering and amplifying pathways and reproduces a variety of in vitro and in vivo tests in subjects with and without T2D, identifies the mechanisms modulating first-phase insulin secretion rate in response to basal hyperglycemia or insulin resistance, and shows that β-cell dysfunction in T2D can be explained by an impaired amplifying pathway with no need to postulate defects in intracellular calcium handling. [ABSTRACT FROM AUTHOR]

Substantial data indicate that amyloid-β (Aβ), the major component of senile plaques, plays a central role in Alzheimer’s Disease and indeed the assembly of naturally occurring amyloid peptides into cytotoxic aggregates is linked to the disease pathogenesis. Although Aβ42 is a highly aggregating form of Aβ, the co-occurrence of shorter Aβ peptides might affect the aggregation potential of the Aβ pool. In this study we aimed to assess whether the structural behavior of human Aβ42 peptide inside the brain is influenced by the concomitant presence of N-terminal fragments produced by the proteolytic activity of glial cells. We show that the occurrence of the human C-terminal truncated 1–24 Aβ fragment impairs Aβ42 clearance through blood brain barrier and promotes the formation of Aβ42 aggregates even in the healthy brain. By showing that Aβ1-24 has seeding properties for aggregate formation in intracranially injected wild type mice, our study provide the proof-of-concept that peptides produced upon Aβ42 cleavage by activated glial cells may cause phenotypic defects even in the absence of genetic mutations associated with Alzheimer’s Disease, possibly contributing to the development of the sporadic form of the pathology. [ABSTRACT FROM AUTHOR]

Lipid composition is expected to play an important role in modulating membrane enzyme activity, in particular if the substrates are themselves lipid molecules. A paradigmatic case is FAAH (fatty acid amide hydrolase), an enzyme critical in terminating endocannabinoid signalling and an important therapeutic target. In the present study, using a combined experimental and computational approach, we show that membrane lipids modulate the structure, subcellular localization and activity of FAAH.We report that the FAAHdimer is stabilized by the lipid bilayer and shows a highermembrane-binding affinity and enzymatic activity within membranes containing both cholesterol and the natural FAAH substrate AEA (anandamide). Additionally, co-localization of cholesterol, AEA and FAAH in mouse neuroblastoma cells suggests a mechanism through which cholesterol increases the substrate accessibility of FAAH. [ABSTRACT FROM AUTHOR]

The understanding of protein-ligand binding is of critical importance for biomedical research, yet the process itself has been very difficult to study because of its intrinsically dynamic character. Here, we have been able to quantitatively reconstruct the complete binding process of the enzyme-inhibitor complex trypsin-benzamidine by performing 495 molecular dynamics simulations of free ligand binding of 100 ns each, 187 of which produced binding events with an rmsd less than 2 Å compared to the crystal structure. The binding paths obtained are able to capture the kinetic pathway of the inhibitor diffusing from solvent (S0) to the bound (S4) state passing through two metastable intermediate states S2 and S3. Rather than directly entering the binding pocket the inhibitor appears to roll on the surface of the protein in its transition between S3 and the final binding pocket, whereas the transition between S2 and the bound pose requires rediffusion to S3. An estimation of the standard free energy of binding gives ΔG° = -5.2 ± 0.4 kcal/mol (cf. the experimental value -6.2 kcal/mol), and a two-states kinetic model kon = (1.5 ± 0.2) × 108 M-1 s-1 and koff = (9.5 ± 3.3) × 104 s-1 for unbound to bound transitions. The ability to reconstruct by simple diffusion the binding pathway of an enzyme-inhibitor binding process demonstrates the predictive power of unconventional high-throughput molecular simulations. Moreover, the methodology is directly applicable to other molecular systems and thus of general interest in biomedical and pharmaceutical research. [ABSTRACT FROM AUTHOR]

Recent studies suggest that the quality of recovery after a stroke can be increased by early and more intensive rehabilitation. Portable unobtrusive devices are promising candidates for the realization of stroke-rehabilitation systems that complement care in the post-acute rehabilitation phase, both in the clinic and at home. The proposed system allows patients to increase the amount of motor exercise they can perform in autonomy, providing them with a real-time feedback based on wearable sensors embedded in the garment's tissue across the upper limb and trunk. A dynamic time warping algorithm allows for the recognition of correct and incorrect motor exercises. After the feedback phase, data are stored in a central location for review and statistics. Workstations can be installed either at home or at the hospital to support patients, independent of their location. The performance of the system on healthy subjects was quantified for seven types of motor exercises for upper limb's rehabilitation. Finally, we present the preliminary results of a pilot clinical study to test the system's acceptability and usability by post-stroke patients, and physicians in a clinical setting. [ABSTRACT FROM AUTHOR]

The system described in this paper is aimed at improving the clinical workflow of post-stroke patients under oral anticoagulant therapy (OAT). The system helps both physicians and patients during the periodic control visits necessary to assess the anticoagulation status and the next therapeutic plan. Controls represent a burden for both patients, which after blood drawing must wait for the result, and for physicians, that, after assessing the therapy plan, must communicate it to patients, face-to-face or by telephone. A system is proposed, which embeds an algorithm for the patient-tailored calculation of the drug dosage and scheduling, and an automatic telephone dialogue for the communication of the therapy plan, once it has been validated or adjusted by the physician. [ABSTRACT FROM AUTHOR]

Conductive elastomers are a novel strain sensing technology which can be unobtrusively embedded into a garment's fabric, allowing a new type of sensorized cloths for motion analysis. A possible application for this technology is remote monitoring and control of motor rehabilitation exercises. The present work describes a sensorized shirt for upper limb posture recognition. Supervised learning techniques have been employed to compare classification models for the analysis of strains, simultaneously measured at multiple points of the shirt. The instantaneous position of the limb was classified into a finite set of predefined postures, and the movement was decomposed in an ordered sequence of discrete states. The amount of information given by the observation of each sensor during the execution of a specific exercise was quantitatively estimated by computing the information gain for each sensor, which in turn allows the data-driven optimization of the garment. Real-time feedback on exercise progress can also be provided by reconstructing the sequence of consecutive positions assumed by the limb. [ABSTRACT FROM AUTHOR]

Stroke is a serious neurological accident which accounts for a wide fraction of the healthcare costs in industrialized societies. Recovery from stroke and other neurological accidents usually include motor rehabilitation, maintained for several months, and logopedic training for the recovery of cognitive and speech abilities. The MyHeart consortium is addressing several aspects of cardiovascular diseases' management by combining clothes with embedded biomedical sensors and information technologies. One of the application areas is especially devoted to supporting Neurological Rehabilitation (NR). This article describes how MyHeart's Concept NR is structured and how technologies are leveraged to support both motor rehabilitation and speech/cognitive training. Information technology and garment-embedded sensors, combined, permit assisted training both at the clinic and at home, after discharge from the intensive care unit. [ABSTRACT FROM AUTHOR]

Summary: Computer simulations are giving way to more complex and accurate studies of biological membranes by molecular dynamics (MD) simulations. The analysis of MD trajectories comprises the biophysical characterization of membrane properties or the study of protein–lipid interactions and dynamics. However, there is a lack of automated tools to analyse MD simulations of complex membrane or membrane-protein systems. Here we present MEMBPLUGIN, a plugin for the Visual Molecular Dynamics package that provides algorithms to measure a host of essential biophysical properties in simulated membranes. MEMBPLUGIN features are accessible both through a user-friendly graphical interface and as command-line procedures to be invoked in analysis scripts.Availability and implementation: MEMBPLUGIN is a VMD extension written in Tcl. Multi-platform source code, documentation and tutorials are freely available at http://membplugin.sourceforge.net.Contact: toni.giorgino@isib.cnr.it or jana.selent@upf.eduSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

A goal in the kinetic characterization of a macromolecular system is the description of its slow relaxation processes via (i) identification of the structural changes involved in these processes and (ii) estimation of the rates or timescales at which these slow processes occur. Most of the approaches to this task, including Markov models, master-equation models, and kinetic network models, start by discretizing the high-dimensional state space and then characterize relaxation processes in terms of the eigenvectors and eigenvalues of a discrete transition matrix. The practical success of such an approach depends very much on the ability to finely discretize the slow order parameters. How can this task be achieved in a high-dimensional configuration space without relying on subjective guesses of the slow order parameters? In this paper, we use the variational principle of conformation dynamics to derive an optimal way of identifying the 'slow subspace' of a large set of prior order parameters - either generic internal coordinates or a user-defined set of parameters. Using a variational formulation of conformational dynamics, it is shown that an existing method-the time-lagged independent component analysis-provides the optional solution to this problem. In addition, optimal indicators-order parameters indicating the progress of the slow transitions and thus may serve as reaction coordinates-are readily identified. We demonstrate that the slow subspace is well suited to construct accurate kinetic models of two sets of molecular dynamics simulations, the 6-residue fluorescent peptide MR121-GSGSW and the 30-residue intrinsically disordered peptide kinase inducible domain (KID). The identified optimal indicators reveal the structural changes associated with the slow processes of the molecular system under analysis. [ABSTRACT FROM AUTHOR]