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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.

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., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Baroni, Carletti, Donalisio, Arduino, Cazzaniga, Giorgino, Esposito, Porta, Diomede, De Luigi, Gobbi, Lembo, Marcello, Tramontano, Milani and Mastrangelo.)

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., (© 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC) license (https://creativecommons.org/licenses/by-nc/4.0/).)

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: β -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., Competing Interests: We have no conflicts of interest to disclose., (© 2024 The Author(s).)

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., (© 2023. Springer Nature Limited.)

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., (© 2022 The Authors. Published under the terms of the CC BY NC ND 4.0 license.)

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., (© 2022 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.)

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.

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., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

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: β-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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Author(s).)

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.

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 β-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., Competing Interests: Declaration of competing interest Ele Ferrannini: membership of scientific advisory board (Boehringer Ingelheim/Eli Lilly); ad hoc consulting (AstraZeneca); occasional speaking engagement (AstraZeneca, Novo Nordisk, Sanofi, Tanabe-Mitsubishi, Eli Lilly&Co, Boehringer Ingelheim; Merck Sharp&Dohme; research grant support (Boehringer Ingelheim, AstraZeneca). Andrea Natali: research grant support (Boehringer Ingelheim). The other authors have no conflict of interest to declare., (Copyright © 2020 Elsevier Inc. All rights reserved.)

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.

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.

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 Ca 2+ -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 Ca 2+ -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.

The second domain of gelsolin (G2) hosts mutations responsible for a hereditary form of amyloidosis. The active form of gelsolin is Ca 2+ -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 Cd 2+ , in lieu of the physiological calcium. Here, we report the wild type structure of G2 in complex with Ca 2+ highlighting subtle ion-dependent differences. Previous findings on different G2 mutations are also briefly revised in light of these results., (Copyright © 2019 Elsevier Inc. All rights reserved.)

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., (© 2019 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

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., Competing Interests: The authors have declared that no competing interests exist.

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 Ca 2+ 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., (Copyright © 2019 Elsevier B.V. All rights reserved.)

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.

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 (EC 50 ) and efficacy (E max ) 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., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

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., (© 2017 by the American Diabetes Association.)

HTMD is a programmable scientific platform intended to facilitate simulation-based research in molecular systems. This paper presents the functionalities of HTMD for the preparation of a molecular dynamics simulation starting from PDB structures, building the system using well-known force fields, and applying standardized protocols for running the simulations. We demonstrate the framework's flexibility for high-throughput molecular simulations by applying a preparation, building, and simulation protocol with multiple force-fields on all of the seven hundred eukaryotic membrane proteins resolved to-date from the orientation of proteins in membranes (OPM) database. All of the systems are available on www.playmolecule.org .

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 pK a 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.

Bio-molecular dynamics (MD) simulations based on graphical processing units (GPUs) were first released to the public in the early 2009 with the code ACEMD. Almost 8 years after, applications now encompass a broad range of molecular studies, while throughput improvements have opened the way to millisecond sampling timescales. Based on an extrapolation of the amount of sampling in published literature, the second timescale will be reached by the year 2022, and therefore we predict that molecular dynamics is going to become one of the main tools in drug discovery in both academia and industry. Here, we review successful applications in the drug discovery domain developed over these recent years of GPU-based MD. We also retrospectively analyse limitations that have been overcome over the years and give a perspective on challenges that remain to be addressed., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.)

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.

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., (© 2015 by The American Society of Hematology.)

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.

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.

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.

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.

Steered molecular dynamics (SMD) simulations for the calculation of free energies are well suited for high-throughput molecular simulations on a distributed infrastructure due to the simplicity of the setup and parallel granularity of the runs. However, so far, the computational cost limited the estimation of the free energy typically over just a few pullings, thus impeding the evaluation of statistical uncertainties involved. In this work, we performed two thousand pulls for the permeation of a potassium ion in the gramicidin A pore by all-atom molecular dynamics in order to assess the bidirectional SMD protocol with a proper amount of sampling. The estimated free energy profile still shows a statistical error of several kcal/mol, while the work distributions are estimated to be non-Gaussian at pulling speeds of 10 Å/ns. We discuss the methodology and the confidence intervals in relation to increasing amounts of computed trajectories and how different permeation pathways for the potassium ion, knock-on and sideways, affect the sampling and the free energy estimation.

The Virtual Physiological Human (VPH) is a major European e-Science initiative intended to support the development of patient-specific computer models and their application in personalized and predictive healthcare. The VPH Network of Excellence (VPH-NoE) project is tasked with facilitating interaction between the various VPH projects and addressing issues of common concern. A key deliverable is the 'VPH ToolKit'--a collection of tools, methodologies and services to support and enable VPH research, integrating and extending existing work across Europe towards greater interoperability and sustainability. Owing to the diverse nature of the field, a single monolithic 'toolkit' is incapable of addressing the needs of the VPH. Rather, the VPH ToolKit should be considered more as a 'toolbox' of relevant technologies, interacting around a common set of standards. The latter apply to the information used by tools, including any data and the VPH models themselves, and also to the naming and categorizing of entities and concepts involved. Furthermore, the technologies and methodologies available need to be widely disseminated, and relevant tools and services easily found by researchers. The VPH-NoE has thus created an online resource for the VPH community to meet this need. It consists of a database of tools, methods and services for VPH research, with a Web front-end. This has facilities for searching the database, for adding or updating entries, and for providing user feedback on entries. Anyone is welcome to contribute.

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.

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.

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.

Objective: The purpose of this study was to assess the performance of a real-time ("open-end") version of the dynamic time warping (DTW) algorithm for the recognition of motor exercises. Given a possibly incomplete input stream of data and a reference time series, the open-end DTW algorithm computes both the size of the prefix of reference which is best matched by the input, and the dissimilarity between the matched portions. The algorithm was used to provide real-time feedback to neurological patients undergoing motor rehabilitation., Methods and Materials: We acquired a dataset of multivariate time series from a sensorized long-sleeve shirt which contains 29 strain sensors distributed on the upper limb. Seven typical rehabilitation exercises were recorded in several variations, both correctly and incorrectly executed, and at various speeds, totaling a data set of 840 time series. Nearest-neighbour classifiers were built according to the outputs of open-end DTW alignments and their global counterparts on exercise pairs. The classifiers were also tested on well-known public datasets from heterogeneous domains., Results: Nonparametric tests show that (1) on full time series the two algorithms achieve the same classification accuracy (p-value =0.32); (2) on partial time series, classifiers based on open-end DTW have a far higher accuracy (kappa=0.898 versus kappa=0.447;p<10(-5)); and (3) the prediction of the matched fraction follows closely the ground truth (root mean square <10%). The results hold for the motor rehabilitation and the other datasets tested, as well., Conclusions: The open-end variant of the DTW algorithm is suitable for the classification of truncated quantitative time series, even in the presence of noise. Early recognition and accurate class prediction can be achieved, provided that enough variance is available over the time span of the reference. Therefore, the proposed technique expands the use of DTW to a wider range of applications, such as real-time biofeedback systems.

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.

Conductive elastomer elements can be industrially embedded into garments to form unobtrusive strain sensing stripes. The present article outlines the structure of a strain-sensor based gesture detection algorithm. Current sensing prototypes include several dozens of sensors; their redundancy with respect to the limb's degrees of freedom, and other artifacts implied by this measurement technique, call for the development of novel robust multivariate pattern-matching techniques. The algorithm's construction is explained, and its performances are evaluated in the context of motor rehabilitation exercises for both two-class and multi-class tasks.

There is a growing need for automated systems that can interview patients and consumers about their health and provide health education and behavior change interventions using natural language dialog. A number of these health dialog systems have been developed over the last two decades, many of which have been formally evaluated in clinical trials and shown to be effective. This article provides an overview of the theories, technologies and methodologies that are used in the construction and evaluation of these systems, along with a description of many of the systems developed and tested to date. The strengths and weaknesses of these approaches are also discussed, and the needs for future work in the field are delineated.

Stroke and other neurological accidents account for a wide fraction of the healthcare costs in industrialised societies. The last step in the chain of recovery from a neurological event often includes motor rehabilitation. While current motion-sensing technologies are inadequate for automated monitoring of rehabilitation exercises at home, conductive elastomers are a novel strain-sensing technology which can be embedded unobtrusively into a garment's fabric. A sensorized garment was realized to simultaneously measure the strains at multiple points of a shirt covering the thorax and upper limb. Supervised learning techniques were employed to analyse the strain measures in order to reconstruct upper-limb posture and provide real-time feedback on exercise progress.

Recent advances in automatic speech recognition and related technologies allow computers to carry on conversations by telephone. We developed an intelligent dialogue system that interacts with hypertensive patients to collect data about their health status. Patients thus avoid the inconvenience of traveling for frequent face to face visits to monitor the clinical variables they can easily measure at home; the physician is facilitated in acquiring patient information and cardiovascular risk, which is evaluated from the data according to noted guidelines. Controlled trials to assess the clinical efficacy are under way.