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.

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.

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.

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.

A prognostic model to predict survival after 6 months of ruxolitinib in patients with myelofibrosis.

Ruxolitinib (RUX) is extensively used in myelofibrosis (MF). Despite its early efficacy, most patients lose response over time and, after discontinuation, have a worse overall survival (OS). Currently, response criteria able to predict OS in RUX-treated patients are lacking, leading to uncertainty regarding the switch to second-line treatments. In this study, we investigated predictors of survival collected after 6 months of RUX in 209 MF patients participating in the real-world ambispective observational RUXOREL-MF study (NCT03959371). Multivariable analysis identified the following risk factors: (1) RUX dose <20 mg twice daily at baseline, months 3 and 6 (hazard ratio [HR], 1.79; 95% confidence interval [CI], 1.07-3.00; P = .03), (2) palpable spleen length reduction from baseline ≤30% at months 3 and 6 (HR, 2.26; 95% CI, 1.40-3.65; P = .0009), (3) red blood cell (RBC) transfusion need at months 3 and/or 6 (HR, 1.66; 95% CI, 0.95-2.88; P = .07), and (4) RBC transfusion need at all time points (ie, baseline and months 3 and 6; HR, 2.32; 95% CI, 1.19-4.54; P = .02). Hence, we developed a prognostic model, named Response to Ruxolitinib After 6 Months (RR6), dissecting 3 risk categories: low (median OS, not reached), intermediate (median OS, 61 months; 95% CI, 43-80), and high (median OS, 33 months; 95% CI, 21-50). The RR6 model was validated and confirmed in an external cohort comprised of 40 MF patients. In conclusion, the RR6 prognostic model allows for the early identification of RUX-treated MF patients with impaired survival who might benefit from a prompt treatment shift.

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.

A novel hotspot of gelsolin instability triggers an alternative mechanism of amyloid aggregation.

Gelsolin comprises six homologous domains, named G1 to G6. Single point substitutions in this protein are responsible for AGel amyloidosis, a hereditary disease causing progressive corneal lattice dystrophy, cutis laxa, and polyneuropathy. Although several different amyloidogenic variants of gelsolin have been identified, only the most common mutants present in the G2 domain have been thoroughly characterized, leading to clarification of the functional mechanism. The molecular events underlying the pathological aggregation of 3 recently identified mutations, namely A551P, E553K and M517R, all localized at the interface between G4 and G5, are here explored for the first time. Structural studies point to destabilization of the interface between G4 and G5 due to three structural determinants: ß-strand breaking, steric hindrance and/or charge repulsion, all implying impairment of interdomain contacts. Such rearrangements decrease the temperature and pressure stability of gelsolin but do not alter its susceptibility to furin cleavage, the first event in the canonical aggregation pathway. These variants also have a greater tendency to aggregate in the unproteolysed forms and exhibit higher proteotoxicity in a C. elegans-based assay. Our data suggest that aggregation of G4G5 variants follows an alternative, likely proteolysis-independent, pathway.

TorchMD: A Deep Learning Framework for Molecular Simulations.

Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved leveraging data-driven models derived with machine learning approaches. Here, we present TorchMD, a framework for molecular simulations with mixed classical and machine learning potentials. All force computations including bond, angle, dihedral, Lennard-Jones, and Coulomb interactions are expressed as PyTorch arrays and operations. Moreover, TorchMD enables learning and simulating neural network potentials. We validate it using standard Amber all-atom simulations, learning an ab initio potential, performing an end-to-end training, and finally learning and simulating a coarse-grained model for protein folding. We believe that TorchMD provides a useful tool set to support molecular simulations of machine learning potentials. Code and data are freely available at github.com/torchmd.

Different mechanisms of GIP and GLP-1 action explain their different therapeutic efficacy in type 2 diabetes.

BACKGROUND AND AIMS: The reduced action of incretin hormones in type 2 diabetes (T2D) is mainly attributed to GIP insensitivity, but efficacy estimates of GIP and GLP-1 differ among studies, and the negligible effects of pharmacological GIP doses remain unexplained. We aimed to characterize incretin action in vivo in subjects with normal glucose tolerance (NGT) or T2D and provide an explanation for the different insulinotropic activity of GIP and GLP-1 in T2D subjects. METHODS: We used in vivo data from ten studies employing hormone infusion or an oral glucose test (OGTT). To homogeneously interpret and compare the results of the studies we performed the analysis using a mathematical model of the ß-cell incorporating the effects of incretins on the triggering and amplifying pathways. The effect on the amplifying pathway was quantified by a time-dependent factor that is greater than one when insulin secretion (ISR) is amplified by incretins. To validate the model results for GIP in NGT subjects, we performed an extensive literature search of the available data. RESULTS: a) the stimulatory effects of GIP and GLP-1 differ markedly: ISR potentiation increases linearly with GLP-1 over the whole dose range, while with GIP infusion it reaches a plateau at ~100 pmol/L GIP, with ISR potentiation of ~2 fold; b) ISR potentiation in T2D is reduced by ~50% for GIP and by ~40% for GLP-1; c) the literature search of GIP in NGT subjects confirmed the saturative effect on insulin secretion. CONCLUSION: We show that incretin potentiation of ISR is reduced in T2D, but not abolished, and that the lack of effects of pharmacological GIP doses is due to saturation of the GIP effect more than insensitivity to GIP in T2D.

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.

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.

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.

Analysis Libraries for Molecular Trajectories: A Cross-Language Synopsis.

Analyzing the results of molecular dynamics (MD)-based simulations usually entails extensive manipulations of file formats encoding both the topology (e.g., the chemical connectivity) and configurations (the trajectory) of the simulated system. This chapter reviews a number of software libraries developed to facilitate interactive and batch analysis of MD results with scripts written in high-level, interpreted languages. It provides a beginners' introduction to MD analysis presenting a side-by-side comparison of major scripting languages used in MD and shows how to perform common analysis tasks within the Visual Molecular Dynamics (VMD), Bio3D, MDTraj, MDAnalysis, and High-Throughput Molecular Dynamics (HTMD) environments.

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.

Exploring the role of interdisciplinarity in physics: Success, talent and luck.

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.

Second primary malignancies in postpolycythemia vera and postessential thrombocythemia myelofibrosis: A study on 2233 patients.

Patients with myeloproliferative neoplasms (MPN) are known to have higher incidence of nonhematological second primary malignancies (SPM) compared to general population. In the MYSEC study on 781 secondary myelofibrosis (SMF) patients, the incidence of SPM after SMF diagnosis resulted 0.98/100 patient-years. When including non-melanoma skin cancers (NMSC), the incidence arose to 1.56/100 patient-years. In SMF, JAK inhibitor treatment was associated only with NMSC occurrence. Then, we merged the MYSEC cohort with a large dataset of PV and ET not evolving into SMF. In this subanalysis, we did not find any correlation between SPM and SMF occurrence. These findings highlight the need of studies aimed at identifying MPN patients at higher risk of SPM.

Nanobody interaction unveils structure, dynamics and proteotoxicity of the Finnish-type amyloidogenic gelsolin variant.

AGel amyloidosis, formerly known as familial amyloidosis of the Finnish-type, is caused by pathological aggregation of proteolytic fragments of plasma gelsolin. So far, four mutations in the gelsolin gene have been reported as responsible for the disease. Although D187N is the first identified variant and the best characterized, its structure has been hitherto elusive. Exploiting a recently-developed nanobody targeting gelsolin, we were able to stabilize the G2 domain of the D187N protein and obtained, for the first time, its high-resolution crystal structure. In the nanobody-stabilized conformation, the main effect of the D187N substitution is the impairment of the calcium binding capability, leading to a destabilization of the C-terminal tail of G2. However, molecular dynamics simulations show that in the absence of the nanobody, D187N-mutated G2 further misfolds, ultimately exposing its hydrophobic core and the furin cleavage site. The nanobody's protective effect is based on the enhancement of the thermodynamic stability of different G2 mutants (D187N, G167R and N184K). In particular, the nanobody reduces the flexibility of dynamic stretches, and most notably decreases the conformational entropy of the C-terminal tail, otherwise stabilized by the presence of the Ca2+ ion. A Caenorhabditis elegans-based assay was also applied to quantify the proteotoxic potential of the mutants and determine whether nanobody stabilization translates into a biologically relevant effect. Successful protection from G2 toxicity in vivo points to the use of C. elegans as a tool for investigating the mechanisms underlying AGel amyloidosis and rapidly screen new therapeutics.