Maternal immune activation leads to defective brain-blood vessels and intracerebral hemorrhages in male offspring.

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-ß1 active form and the consequent enhancement of pSMAD1-5 in males' brain endothelial cells. TGF-ß1, in combination with IL-1ß, 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.

Self-organizing maps of unbiased ligand-target binding pathways and kinetics.

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.

SCoV2-MD: a database for the dynamics of the SARS-CoV-2 proteome and variant impact predictions.

SCoV2-MD (www.scov2-md.org) is a new online resource that systematically organizes atomistic simulations of the SARS-CoV-2 proteome. The database includes simulations produced by leading groups using molecular dynamics (MD) methods to investigate the structure-dynamics-function relationships of viral proteins. SCoV2-MD cross-references the molecular data with the pandemic evolution by tracking all available variants sequenced during the pandemic and deposited in the GISAID resource. SCoV2-MD enables the interactive analysis of the deposited trajectories through a web interface, which enables users to search by viral protein, isolate, phylogenetic attributes, or specific point mutation. Each mutation can then be analyzed interactively combining static (e.g. a variety of amino acid substitution penalties) and dynamic (time-dependent data derived from the dynamics of the local geometry) scores. Dynamic scores can be computed on the basis of nine non-covalent interaction types, including steric properties, solvent accessibility, hydrogen bonding, and other types of chemical interactions. Where available, experimental data such as antibody escape and change in binding affinities from deep mutational scanning experiments are also made available. All metrics can be combined to build predefined or custom scores to interrogate the impact of evolving variants on protein structure and function.

Publisher Correction: GPCRmd uncovers the dynamics of the 3D-GPCRome.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

GPCRmd uncovers the dynamics of the 3D-GPCRome.

G-protein-coupled receptors (GPCRs) are involved in numerous physiological processes and are the most frequent targets of approved drugs. The explosion in the number of new three-dimensional (3D) molecular structures of GPCRs (3D-GPCRome) over the last decade has greatly advanced the mechanistic understanding and drug design opportunities for this protein family. Molecular dynamics (MD) simulations have become a widely established technique for exploring the conformational landscape of proteins at an atomic level. However, the analysis and visualization of MD simulations require efficient storage resources and specialized software. Here we present GPCRmd (http://gpcrmd.org/), an online platform that incorporates web-based visualization capabilities as well as a comprehensive and user-friendly analysis toolbox that allows scientists from different disciplines to visualize, analyze and share GPCR MD data. GPCRmd originates from a community-driven effort to create an open, interactive and standardized database of GPCR MD simulations.

Classification Model for the Second Extracellular Loop of Class A GPCRs.

The extracellular loop 2 (ECL2) is the longest and the most diverse loop among class A G protein-coupled receptors (GPCRs). It connects the transmembrane (TM) helices 4 and 5 and contains a highly conserved cysteine through which it is bridged with TM3. In this paper, experimental ECL2 structures were analyzed based on their sequences, shapes, and intramolecular contacts. To take into account the flexibility, we incorporated into our analyses information from the molecular dynamics trajectories available on the GPCRmd website. Despite the high sequence variability, shapes of the analyzed structures, defined by the backbone volume overlaps, can be clustered into seven main groups. Conformational differences within the clusters can be then identified by intramolecular interactions with other GPCR structural domains. Overall, our work provides a reorganization of the structural information of the ECL2 of class A GPCR subfamilies, highlighting differences and similarities on sequence and conformation levels.

The structure of N184K amyloidogenic variant of gelsolin highlights the role of the H-bond network for protein stability and aggregation properties.

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.

Computational and Experimental Characterization of NF023, A Candidate Anticancer Compound Inhibiting cIAP2/TRAF2 Assembly.

Protein-protein interactions are the basis of many important physiological processes and are currently promising, yet difficult, targets for drug discovery. In this context, inhibitor of apoptosis proteins (IAPs)-mediated interactions are pivotal for cancer cell survival; the interaction of the BIR1 domain of cIAP2 with TRAF2 was shown to lead the recruitment of cIAPs to the TNF receptor, promoting the activation of the NF-κB survival pathway. In this work, using a combined in silico-in vitro approach, we identified a drug-like molecule, NF023, able to disrupt cIAP2 interaction with TRAF2. We demonstrated in vitro its ability to interfere with the assembly of the cIAP2-BIR1/TRAF2 complex and performed a thorough characterization of the compound's mode of action through 248 parallel unbiased molecular dynamics simulations of 300 ns (totaling almost 75 µs of all-atom sampling), which identified multiple binding modes to the BIR1 domain of cIAP2 via clustering and ensemble docking. NF023 is, thus, a promising protein-protein interaction disruptor, representing a starting point to develop modulators of NF-κB-mediated cell survival in cancer. This study represents a model procedure that shows the use of large-scale molecular dynamics methods to typify promiscuous interactors.

High-resolution crystal structure of gelsolin domain 2 in complex with the physiological calcium ion.

The second domain of gelsolin (G2) hosts mutations responsible for a hereditary form of amyloidosis. The active form of gelsolin is Ca2+-bound; it is also a dynamic protein, hence structural biologists often rely on the study of the isolated G2. However, the wild type G2 structure that have been used so far in comparative studies is bound to a crystallographic Cd2+, in lieu of the physiological calcium. Here, we report the wild type structure of G2 in complex with Ca2+ highlighting subtle ion-dependent differences. Previous findings on different G2 mutations are also briefly revised in light of these results.

Synthesis, molecular modelling studies and biological evaluation of new oxoeicosanoid receptor 1 agonists.

The oxoeicosanoid receptor 1 (OXER1) is a member of the G-protein coupled receptors (GPCR) family, and is involved in inflammatory processes and oncogenesis. As such it is an attractive target for pharmacological intervention. The present study aimed to shed light on the molecular fundaments of OXER1 modulation using chemical probes structurally related to the natural agonist 5-oxo-ETE. In a first step, 5-oxo-ETE and its closely related derivatives (5-oxo-EPE and 4-oxo-DHA) were obtained by conducting concise and high-yielding syntheses. The biological activity of obtained compounds was assessed in terms of potency (EC50) and efficacy (Emax) for arrestin recruitment. Finally, molecular modelling and simulation were used to explore binding characteristics of 5-oxo-ETE and derivatives with the aim to rationalize biological activity. Our data suggest that the tested 5-oxo-ETE derivatives (i) insert quickly into the membrane, (ii) access the receptor via transmembrane helices (TMs) 5 and 6 from the membrane side and (iii) drive potency and efficacy by differential interaction with TM5 and 7. Most importantly, we found that the methyl ester of 5-oxo-ETE (1a) showed even a higher maximum response than the natural agonist (1). In contrast, shifting the 5-oxo group into position 4 results in inactive compounds (4-oxo DHA compounds (3) and (3a)). All in all, our study provides relevant structural data that help understanding better OXER1 functionality and its modulation. The structural information presented herein will be useful for designing new lead compounds with desired signalling profiles.

Impact of allogeneic stem cell transplantation on survival of patients less than 65 years of age with primary myelofibrosis.

Allogeneic hematopoietic stem cell transplantation (SCT) is the only curative option for patients with primary myelofibrosis (PMF), but information on its net advantage over conventional therapies is lacking. Using ad hoc statistical analysis, we determined outcomes in 438 patients <65 years old at diagnosis who received allogenic SCT (n = 190) or conventional therapies (n = 248). Among patients at low risk per the Dynamic International Prognostic Scoring System (DIPSS) model, the relative risk of death after allogenic SCT vs those treated with nontransplant modalities was 5.6 (95% CI, 1.7-19; P = .0051); for intermediate-1 risk it was 1.6 (95% CI, 0.79-3.2; P = .19), for intermediate-2 risk, 0.55 (95% CI, 0.36-0.83; P = .005), and for high risk, 0.37 (95% CI, 0.21-0.66; P = .0007). Thus, patients with intermediate-2 or high-risk PMF clearly benefit from allogenic SCT. Patients at low risk should receive nontransplant therapy, whereas individual counseling is indicated for patients at intermediate-1 risk.

PlayMolecule ProteinPrepare: A Web Application for Protein Preparation for Molecular Dynamics Simulations.

Protein preparation is a critical step in molecular simulations that consists of refining a Protein Data Bank (PDB) structure by assigning titration states and optimizing the hydrogen-bonding network. In this application note, we describe ProteinPrepare, a web application designed to interactively support the preparation of protein structures. Users can upload a PDB file, choose the solvent pH value, and inspect the resulting protonated residues and hydrogen-bonding network within a 3D web interface. Protonation states are suggested automatically but can be manually changed using the visual aid of the hydrogen-bonding network. Tables and diagrams provide estimated pKa values and charge states, with visual indication for cases where review is required. We expect the graphical interface to be a useful instrument to assess the validity of the preparation, but nevertheless, a script to execute the preparation offline with the High-Throughput Molecular Dynamics (HTMD) environment is also provided for noninteractive operations.

MEMBPLUGIN: studying membrane complexity in VMD.

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.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Membrane lipids are key modulators of the endocannabinoid-hydrolase FAAH.

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 FAAH dimer is stabilized by the lipid bilayer and shows a higher membrane-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.

Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations.

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 k(on) = (1.5 ± 0.2) × 10(8) M(-1) s(-1) and k(off) = (9.5 ± 3.3) × 10(4) 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.

AlloViz: A tool for the calculation and visualisation of protein allosteric communication networks.

Allostery, the presence of functional interactions between distant parts of proteins, is a critical concept in the field of biochemistry and molecular biology, particularly in the context of protein function and regulation. Understanding the principles of allosteric regulation is essential for advancing our knowledge of biology and developing new therapeutic strategies. This paper presents AlloViz, an open-source Python package designed to quantitatively determine, analyse, and visually represent allosteric communication networks on the basis of molecular dynamics (MD) simulation data. The software integrates well-known techniques for understanding allosteric properties simplifying the process of accessing, rationalising, and representing protein allostery and communication routes. It overcomes the inefficiency of having multiple methods with heterogeneous implementations and showcases the advantages of using MD simulations and multiple replicas to obtain statistically sound information on protein dynamics; it also enables the calculation of "consensus-like" scores aggregating methods that consider multiple structural aspects of allosteric networks. We demonstrate the features of AlloViz on two proteins: ß-arrestin 1, a key player for regulating G protein-coupled receptor (GPCR) signalling, and the protein tyrosine phosphatase 1B, an important pharmaceutical target for allosteric inhibitors. The software includes comprehensive documentation and examples, tutorials, and a user-friendly graphical interface.

Identification of slow molecular order parameters for Markov model construction.

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.

Machine learning coarse-grained potentials of protein thermodynamics.

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.