RESUMEN
Senescence is a cellular state linked to ageing and age-onset disease across many mammalian species1,2. Acutely, senescent cells promote wound healing3,4 and prevent tumour formation5; but they are also pro-inflammatory, thus chronically exacerbate tissue decline. Whereas senescent cells are active targets for anti-ageing therapy6-11, why these cells form in vivo, how they affect tissue ageing and the effect of their elimination remain unclear12,13. Here we identify naturally occurring senescent glia in ageing Drosophila brains and decipher their origin and influence. Using Activator protein 1 (AP1) activity to screen for senescence14,15, we determine that senescent glia can appear in response to neuronal mitochondrial dysfunction. In turn, senescent glia promote lipid accumulation in non-senescent glia; similar effects are seen in senescent human fibroblasts in culture. Targeting AP1 activity in senescent glia mitigates senescence biomarkers, extends fly lifespan and health span, and prevents lipid accumulation. However, these benefits come at the cost of increased oxidative damage in the brain, and neuronal mitochondrial function remains poor. Altogether, our results map the trajectory of naturally occurring senescent glia in vivo and indicate that these cells link key ageing phenomena: mitochondrial dysfunction and lipid accumulation.
Asunto(s)
Envejecimiento , Encéfalo , Senescencia Celular , Drosophila melanogaster , Metabolismo de los Lípidos , Mitocondrias , Neuroglía , Animales , Femenino , Humanos , Masculino , Envejecimiento/metabolismo , Envejecimiento/patología , Encéfalo/metabolismo , Encéfalo/patología , Encéfalo/citología , Drosophila melanogaster/metabolismo , Drosophila melanogaster/citología , Fibroblastos/metabolismo , Fibroblastos/patología , Longevidad , Mitocondrias/metabolismo , Mitocondrias/patología , Neuroglía/metabolismo , Neuroglía/patología , Neuronas/metabolismo , Neuronas/patología , Estrés Oxidativo , Factor de Transcripción AP-1/metabolismo , Lípidos , Inflamación/metabolismo , Inflamación/patologíaRESUMEN
Astrocytes regulate the response of the central nervous system to disease and injury and have been hypothesized to actively kill neurons in neurodegenerative disease1-6. Here we report an approach to isolate one component of the long-sought astrocyte-derived toxic factor5,6. Notably, instead of a protein, saturated lipids contained in APOE and APOJ lipoparticles mediate astrocyte-induced toxicity. Eliminating the formation of long-chain saturated lipids by astrocyte-specific knockout of the saturated lipid synthesis enzyme ELOVL1 mitigates astrocyte-mediated toxicity in vitro as well as in a model of acute axonal injury in vivo. These results suggest a mechanism by which astrocytes kill cells in the central nervous system.
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Astrocitos/química , Astrocitos/metabolismo , Muerte Celular/efectos de los fármacos , Lípidos/química , Lípidos/toxicidad , Animales , Medios de Cultivo Condicionados/química , Medios de Cultivo Condicionados/toxicidad , Elongasas de Ácidos Grasos/deficiencia , Elongasas de Ácidos Grasos/genética , Elongasas de Ácidos Grasos/metabolismo , Femenino , Técnicas de Inactivación de Genes , Masculino , Ratones , Ratones Noqueados , Enfermedades Neurodegenerativas/metabolismo , Enfermedades Neurodegenerativas/patología , Neurotoxinas/química , Neurotoxinas/toxicidadRESUMEN
Ligation of the CD28 receptor on T cells provides a critical second signal alongside T cell receptor (TCR) ligation for naive T cell activation. Here, we discuss the expression, structure, and biochemistry of CD28 and its ligands. CD28 signals play a key role in many T cell processes, including cytoskeletal remodeling, production of cytokines, survival, and differentiation. CD28 ligation leads to unique epigenetic, transcriptional, and post-translational changes in T cells that cannot be recapitulated by TCR ligation alone. We discuss the function of CD28 and its ligands in both effector and regulatory T cells. CD28 is critical for regulatory T cell survival and the maintenance of immune homeostasis. We outline the roles that CD28 and its family members play in human disease and we review the clinical efficacy of drugs that block CD28 ligands. Despite the centrality of CD28 and its family members and ligands to immune function, many aspects of CD28 biology remain unclear. Translation of a basic understanding of CD28 function into immunomodulatory therapeutics has been uneven, with both successes and failures. Such real-world results might stem from multiple factors, including complex receptor-ligand interactions among CD28 family members, differences between the mouse and human CD28 families, and cell-type specific roles of CD28 family members.
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Enfermedades Autoinmunes/inmunología , Antígenos CD28/metabolismo , Antígeno CTLA-4/antagonistas & inhibidores , Inmunoterapia/métodos , Linfocitos T/inmunología , Abatacept/uso terapéutico , Animales , Enfermedades Autoinmunes/genética , Enfermedades Autoinmunes/terapia , Antígenos CD28/genética , Antígenos CD28/inmunología , Homeostasis , Humanos , Inmunoterapia/tendencias , Activación de Linfocitos , Ratones , Terapia Molecular Dirigida , Receptor Cross-Talk , Receptores de Antígenos de Linfocitos T/metabolismo , Transducción de SeñalRESUMEN
The growth of therapeutic monoclonal antibodies (mAbs) continues to accelerate due to their success as treatments for many diseases. As new therapeutics are developed, it is increasingly important to have robust bioanalytical methods to measure the pharmacokinetics (PK) of circulating therapeutic mAbs in serum. Ligand-binding assays such as enzyme-linked immunosorbent assays (ELISAs) with anti-idiotypic antibodies (anti-IDs) targeting the variable regions of the therapeutic antibody are sensitive and specific bioanalytical methods to measure levels of therapeutic antibodies in a biological matrix. However, soluble circulating drug mAb targets can interfere with the anti-IDs binding to the therapeutic mAb, thereby resulting in an underestimation of total drug concentration. Therefore, in addition to a high binding affinity for the mAb, the selection of anti-IDs and the assay format that are not impacted by soluble antigens and have low matrix interference is essential for developing a robust PK assay. Standardized automated approaches to screen and select optimal reagents and assay formats are critical to increase efficiency, quality, and PK assay robustness. However, there does not exist an integrated screening and analysis platform to develop robust PK assays across multiple formats. We have developed an automated workflow and scoring platform with multiple bioanalytical assay parameters that allow for ranking of candidate anti-IDs. A primary automated indirect electrochemiluminescence (ECL) was utilized to shortlist the anti-IDs that were selected for labeling and screening in pairs. A secondary screen using an ECL sandwich assay with labeled-anti-ID pairings was used to test multiple PK assay formats to identify the best anti-ID pairing/PK assay format. We developed an automated assay using fixed plate maps combined with a human-guided graphical user interface-based scoring system and compared it to a data-dependent scoring system using Gaussian mixture models for automated scoring and selection. Our approach allowed for screening of anti-IDs and identification of the most robust PK assay format with significantly reduced time and resources compared with traditional approaches. We believe that such standardized, automated, and integrated platforms that accelerate the development of PK assays will become increasingly important for supporting future human clinical trials.
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Anticuerpos Monoclonales , Antígenos , Humanos , Flujo de Trabajo , Ligandos , Anticuerpos Monoclonales/análisis , Ensayo de Inmunoadsorción Enzimática/métodosRESUMEN
Regulatory T cells (Treg cells) are required for immune homeostasis. Chromatin remodeling is essential for establishing diverse cellular identities, but how the epigenetic program in Treg cells is maintained throughout the dynamic activation process remains unclear. Here we have shown that CD28 co-stimulation, an extracellular cue intrinsically required for Treg cell maintenance, induced the chromatin-modifying enzyme, Ezh2. Treg-specific ablation of Ezh2 resulted in spontaneous autoimmunity with reduced Foxp3(+) cells in non-lymphoid tissues and impaired resolution of experimental autoimmune encephalomyelitis. Utilizing a model designed to selectively deplete wild-type Treg cells in adult mice co-populated with Ezh2-deficient Treg cells, Ezh2-deficient cells were destabilized and failed to prevent autoimmunity. After activation, the transcriptome of Ezh2-deficient Treg cells was disrupted, with altered expression of Treg cell lineage genes in a pattern similar to Foxp3-deficient Treg cells. These studies reveal a critical role for Ezh2 in the maintenance of Treg cell identity during cellular activation.
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Antígenos CD28/inmunología , Activación de Linfocitos/inmunología , Complejo Represivo Polycomb 2/inmunología , Linfocitos T Reguladores/inmunología , Animales , Autoinmunidad/genética , Autoinmunidad/inmunología , Linfocitos T CD8-positivos/inmunología , Ensamble y Desensamble de Cromatina , Encefalomielitis Autoinmune Experimental/inmunología , Proteína Potenciadora del Homólogo Zeste 2 , Femenino , Factores de Transcripción Forkhead/biosíntesis , Factores de Transcripción Forkhead/genética , Regulación de la Expresión Génica , Factor de Crecimiento Similar a EGF de Unión a Heparina/genética , Tolerancia Inmunológica/genética , Tolerancia Inmunológica/inmunología , Depleción Linfocítica , Ratones , Ratones Endogámicos C57BL , Ratones Transgénicos , Complejo Represivo Polycomb 2/genética , Regiones Promotoras Genéticas/genética , Linfocitos T Reguladores/citologíaRESUMEN
PURPOSE OR OBJECTIVE: Chemical and physical stabilities are two key features considered in pharmaceutical development. Chemical stability is typically reported as a combination of potency and degradation product. Moreover, fluorescent reporter Thioflavin-T is commonly used to measure physical stability. Executing stability studies is a lengthy process and requires extensive resources. To reduce the resources and shorten the process for stability studies during the development of a drug product, we introduce a machine learning-based model for predicting the chemical stability over time using both formulation conditions as well as aggregation curves. METHODS: In this work, we develop the relationships between the formulation, stability timepoint, and the chemical stability measurements and evaluated the performance on a random test set. We have developed a multilayer perceptron (MLP) for total degradation prediction and a random forest (RF) model for potency. RESULTS: The coefficient of determination (R2) of 0.945 and a mean absolute error (MAE) of 0.421 were achieved on the test set when using MLP for total degradation. Similarly, we achieved a R2 of 0.908 and MAE of 1.435 when predicting potency using the RF model. When physical stability measurements are included into the MLP model, the MAE of predicting TD decreases to 0.148. Using a similar strategy for potency prediction, the MAE decreases to 0.705 for the RF model. CONCLUSIONS: We conclude two important points: first, chemical stability can be modeled using machine learning techniques and second there is a relationship between the physical stability of a peptide and its chemical stability.
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Aprendizaje Automático , Redes Neurales de la Computación , Bosques Aleatorios , Máquina de Vectores de SoporteRESUMEN
Targeting live cell organelles is essential for imaging, understanding, and controlling specific biochemical processes. Typically, fluorescent probes with distinct structural scaffolds are used to target specific cell organelles. Here, we have designed a modular one-step synthetic strategy using a common reaction intermediate to develop new lysosomal, mitochondrial, and nucleus-targeting pH-activable fluorescent probes that are all based on a single boron dipyrromethane scaffold. The divergent cell organelle targeting was achieved by synthesizing probes with specific functional group changes to the central scaffold resulting in differential fluorescence and pKa . Specifically, we show that the functional group transformation of the same scaffold influences cellular localization and specificity of pH-activable fluorescent probes in live primary microglial cells with pKa values ranging from â¼3.2-6.0. We introduce a structure-organelle-relationship (SOR) framework to target nuclei (NucShine), lysosomes (LysoShine), and mitochondria (MitoShine) in live microglia. This work will result in future applications of SOR beyond imaging to target and control organelle-specific biochemical processes in disease-specific models.
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Colorantes Fluorescentes , Microglía , Colorantes Fluorescentes/química , Concentración de Iones de Hidrógeno , Lisosomas/química , Orgánulos/químicaRESUMEN
MOTIVATION: The Protein Data Bank (PDB) currently holds over 140 000 biomolecular structures and continues to release new structures on a weekly basis. The PDB is an essential resource to the structural bioinformatics community to develop software that mine, use, categorize and analyze such data. New computational biology methods are evaluated using custom benchmarking sets derived as subsets of 3D experimentally determined structures and structural features from the PDB. Currently, such benchmarking features are manually curated with custom scripts in a non-standardized manner that results in slow distribution and updates with new experimental structures. Finally, there is a scarcity of standardized tools to rapidly query 3D descriptors of the entire PDB. RESULTS: Our solution is the Lemon framework, a C++11 library with Python bindings, which provides a consistent workflow methodology for selecting biomolecular interactions based on user criterion and computing desired 3D structural features. This framework can parse and characterize the entire PDB in <10 min on modern, multithreaded hardware. The speed in parsing is obtained by using the recently developed MacroMolecule Transmission Format to reduce the computational cost of reading text-based PDB files. The use of C++ lambda functions and Python bindings provide extensive flexibility for analysis and categorization of the PDB by allowing the user to write custom functions to suite their objective. We think Lemon will become a one-stop-shop to quickly mine the entire PDB to generate desired structural biology features. AVAILABILITY AND IMPLEMENTATION: The Lemon software is available as a C++ header library along with a PyPI package and example functions at https://github.com/chopralab/lemon. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Biología Computacional , Bases de Datos de Proteínas , Programas Informáticos , Sustancias Macromoleculares , Flujo de TrabajoRESUMEN
Benchmarking is a crucial step in evaluating virtual screening methods for drug discovery. One major issue that arises among benchmarking data sets is a lack of a standardized format for representing the protein and ligand structures used to benchmark the virtual screening method. To address this, we introduce the Directory of Useful Benchmarking Sets (DUBS) framework, as a simple and flexible tool to rapidly create benchmarking sets using the protein databank. DUBS uses a simple input text based format along with the Lemon data mining framework to efficiently access and organize data to the protein databank and output commonly used inputs for virtual screening software. The simple input format used by DUBS allows users to define their own benchmarking data sets and access the corresponding information directly from the software package. Currently, it only takes DUBS less than 2 min to create a benchmark using this format. Since DUBS uses a simple python script, users can easily modify this to create more complex benchmarks. We hope that DUBS will be a useful community resource to provide a standardized representation for benchmarking data sets in virtual screening. The DUBS package is available on GitHub at https://github.com/chopralab/lemon/tree/master/dubs.
Asunto(s)
Benchmarking , Programas Informáticos , Bases de Datos de Proteínas , Descubrimiento de Drogas , LigandosRESUMEN
Traditional drug discovery methods focus on optimizing the efficacy of a drug against a single biological target of interest for a specific disease. However, evidence supports the multitarget theory, i.e., drugs work by exerting their therapeutic effects via interaction with multiple biological targets, which have multiple phenotypic effects. Analytics of drug-protein interactions on a large proteomic scale provides insight into disease systems while also allowing for prediction of putative therapeutics against specific indications. We present a Python package for analysis of drug-proteome and drug-disease relationships implementing the Computational Analysis of Novel Drug Opportunities (CANDO) platform. The CANDO package allows for rapid drug similarity assessment, most notably via an in-house interaction scoring protocol where billions of drug-protein interactions are rapidly scored and the similarity of drug-proteome interaction signatures is calculated. The package also implements a variety of benchmarking protocols for shotgun drug discovery and repurposing, i.e., to determine how every known drug is related to every other in the context of the indications/diseases for which they are approved. Drug predictions are generated through consensus scoring of the most similar compounds to drugs known to treat a particular indication. Support for comparing and ranking novel chemical entities, as well as machine learning modules for both benchmarking and putative drug candidate prediction is also available. The CANDO Python package is available on GitHub at https://github.com/ram-compbio/CANDO, through the Conda Python package installer, and at http://compbio.org/software/.
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Preparaciones Farmacéuticas , Proteómica , Descubrimiento de Drogas , Proteoma , Programas InformáticosRESUMEN
Small-molecule docking has proven to be invaluable for drug design and discovery. However, existing docking methods have several limitations such as improper treatment of the interactions of essential components in the chemical environment of the binding pocket (e.g., cofactors, metal ions, etc.), incomplete sampling of chemically relevant ligand conformational space, and the inability to consistently correlate docking scores of the best binding pose with experimental binding affinities. We present CANDOCK, a novel docking algorithm, that utilizes a hierarchical approach to reconstruct ligands from an atomic grid using graph theory and generalized statistical potential functions to sample biologically relevant ligand conformations. Our algorithm accounts for protein flexibility, solvent, metal ions, and cofactor interactions in the binding pocket that are traditionally ignored by current methods. We evaluate the algorithm on the PDBbind, Astex, and PINC proteins to show its ability to reproduce the binding mode of the ligands that is independent of the initial ligand conformation in these benchmarks. Finally, we identify the best selector and ranker potential functions such that the statistical score of the best selected docked pose correlates with the experimental binding affinities of the ligands for any given protein target. Our results indicate that CANDOCK is a generalized flexible docking method that addresses several limitations of current docking methods by considering all interactions in the chemical environment of a binding pocket for correlating the best-docked pose with biological activity. CANDOCK along with all structures and scripts used for benchmarking is available at https://github.com/chopralab/candock_benchmark.
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Algoritmos , Proteínas , Sitios de Unión , Diseño de Fármacos , Ligandos , Simulación del Acoplamiento Molecular , Unión Proteica , Conformación Proteica , Proteínas/metabolismoRESUMEN
N,N-dimethyl formamide (DMF) is an extensively used organic solvent but is also a potent pollutant. Certain bacterial species from genera such as Paracoccus, Pseudomonas, and Alcaligenes have evolved to use DMF as a sole carbon and nitrogen source for growth via degradation by a dimethylformamidase (DMFase). We show that DMFase from Paracoccus sp. strain DMF is a halophilic and thermostable enzyme comprising a multimeric complex of the α2 ß2 or (α2 ß2 )2 type. One of the three domains of the large subunit and the small subunit are hitherto undescribed protein folds of unknown evolutionary origin. The active site consists of a mononuclear iron coordinated by two Tyr side-chain phenolates and one carboxylate from Glu. The Fe3+ ion in the active site catalyzes the hydrolytic cleavage of the amide bond in DMF. Kinetic characterization reveals that the enzyme shows cooperativity between subunits, and mutagenesis and structural data provide clues to the catalytic mechanism.
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Amidohidrolasas/metabolismo , Dimetilformamida/metabolismo , Paracoccus/enzimología , Tirosina/metabolismo , Amidohidrolasas/química , Dominio Catalítico , Dimetilformamida/química , Estructura Molecular , Tirosina/químicaRESUMEN
Regulatory T cells (Tregs) play a central role in counteracting inflammation and autoimmunity. A more complete understanding of cellular heterogeneity and the potential for lineage plasticity in human Treg subsets may identify markers of disease pathogenesis and facilitate the development of optimized cellular therapeutics. To better elucidate human Treg subsets, we conducted direct transcriptional profiling of CD4(+)FOXP3(+)Helios(+) thymic-derived Tregs and CD4(+)FOXP3(+)Helios(-) T cells, followed by comparison with CD4(+)FOXP3(-)Helios(-) T conventional cells. These analyses revealed that the coinhibitory receptor T cell Ig and ITIM domain (TIGIT) was highly expressed on thymic-derived Tregs. TIGIT and the costimulatory factor CD226 bind the common ligand CD155. Thus, we analyzed the cellular distribution and suppressive activity of isolated subsets of CD4(+)CD25(+)CD127(lo/-) T cells expressing CD226 and/or TIGIT. We observed TIGIT is highly expressed and upregulated on Tregs after activation and in vitro expansion, and is associated with lineage stability and suppressive capacity. Conversely, the CD226(+)TIGIT(-) population was associated with reduced Treg purity and suppressive capacity after expansion, along with a marked increase in IL-10 and effector cytokine production. These studies provide additional markers to delineate functionally distinct Treg subsets that may help direct cellular therapies and provide important phenotypic markers for assessing the role of Tregs in health and disease.
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Antígenos de Diferenciación de Linfocitos T/inmunología , Fenotipo , Receptores Inmunológicos/inmunología , Linfocitos T Reguladores/inmunología , Transcriptoma/inmunología , Adulto , Antígenos de Diferenciación de Linfocitos T/genética , Antígenos CD4/genética , Antígenos CD4/inmunología , Diferenciación Celular , Linaje de la Célula/inmunología , Factores de Transcripción Forkhead/genética , Factores de Transcripción Forkhead/inmunología , Perfilación de la Expresión Génica , Humanos , Factor de Transcripción Ikaros/genética , Factor de Transcripción Ikaros/inmunología , Inmunofenotipificación , Interleucina-10/genética , Interleucina-10/inmunología , Ligandos , Activación de Linfocitos , Persona de Mediana Edad , Cultivo Primario de Células , Unión Proteica , Receptores Inmunológicos/genética , Receptores Virales/genética , Receptores Virales/inmunología , Linfocitos T Reguladores/citologíaRESUMEN
Ebola virus disease (EVD) is extremely virulent with an estimated mortality rate of up to 90%. However, the state-of-the-art treatment for EVD is limited to quarantine and supportive care. The 2014 Ebola epidemic in West Africa, the largest in history, is believed to have caused more than 11,000 fatalities. The countries worst affected are also among the poorest in the world. Given the complexities, time, and resources required for a novel drug development, finding efficient drug discovery pathways is going to be crucial in the fight against future outbreaks. We have developed a Computational Analysis of Novel Drug Opportunities (CANDO) platform based on the hypothesis that drugs function by interacting with multiple protein targets to create a molecular interaction signature that can be exploited for rapid therapeutic repurposing and discovery. We used the CANDO platform to identify and rank FDA-approved drug candidates that bind and inhibit all proteins encoded by the genomes of five different Ebola virus strains. Top ranking drug candidates for EVD treatment generated by CANDO were compared to in vitro screening studies against Ebola virus-like particles (VLPs) by Kouznetsova et al. and genetically engineered Ebola virus and cell viability studies by Johansen et al. to identify drug overlaps between the in virtuale and in vitro studies as putative treatments for future EVD outbreaks. Our results indicate that integrating computational docking predictions on a proteomic scale with results from in vitro screening studies may be used to select and prioritize compounds for further in vivo and clinical testing. This approach will significantly reduce the lead time, risk, cost, and resources required to determine efficacious therapies against future EVD outbreaks.
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Antivirales/uso terapéutico , Fiebre Hemorrágica Ebola/tratamiento farmacológico , Brotes de Enfermedades , Aprobación de Drogas/legislación & jurisprudencia , Descubrimiento de Drogas , Fiebre Hemorrágica Ebola/epidemiología , Humanos , Estados Unidos , United States Food and Drug AdministrationRESUMEN
The protein structure prediction problem continues to elude scientists. Despite the introduction of many methods, only modest gains were made over the last decade for certain classes of prediction targets. To address this challenge, a social-media based worldwide collaborative effort, named WeFold, was undertaken by 13 labs. During the collaboration, the laboratories were simultaneously competing with each other. Here, we present the first attempt at "coopetition" in scientific research applied to the protein structure prediction and refinement problems. The coopetition was possible by allowing the participating labs to contribute different components of their protein structure prediction pipelines and create new hybrid pipelines that they tested during CASP10. This manuscript describes both successes and areas needing improvement as identified throughout the first WeFold experiment and discusses the efforts that are underway to advance this initiative. A footprint of all contributions and structures are publicly accessible at http://www.wefold.org.
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Biología Computacional/métodos , Simulación por Computador , Conducta Cooperativa , Estructura Terciaria de Proteína , Proteínas/ultraestructura , Humanos , Modelos Moleculares , Proyectos de Investigación , Juegos de VideoRESUMEN
The KoBaMIN web server provides an online interface to a simple, consistent and computationally efficient protein structure refinement protocol based on minimization of a knowledge-based potential of mean force. The server can be used to refine either a single protein structure or an ensemble of proteins starting from their unrefined coordinates in PDB format. The refinement method is particularly fast and accurate due to the underlying knowledge-based potential derived from structures deposited in the PDB; as such, the energy function implicitly includes the effects of solvent and the crystal environment. Our server allows for an optional but recommended step that optimizes stereochemistry using the MESHI software. The KoBaMIN server also allows comparison of the refined structures with a provided reference structure to assess the changes brought about by the refinement protocol. The performance of KoBaMIN has been benchmarked widely on a large set of decoys, all models generated at the seventh worldwide experiments on critical assessment of techniques for protein structure prediction (CASP7) and it was also shown to produce top-ranking predictions in the refinement category at both CASP8 and CASP9, yielding consistently good results across a broad range of model quality values. The web server is fully functional and freely available at http://csb.stanford.edu/kobamin.
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Conformación Proteica , Programas Informáticos , Bases de Datos de Proteínas , Internet , Bases del Conocimiento , Modelos Moleculares , Interfaz Usuario-ComputadorRESUMEN
Accurate description of water structure affects simulation of protein folding, substrate binding, macromolecular recognition, and complex formation. We study the hydration of buckminsterfullerene, the smallest hydrophobic nanosphere, by molecular dynamics simulations using a state-of-the-art quantum mechanical polarizable force field (QMPFF3), derived from quantum mechanical data at the MP2/aug-cc-pVTZ(-hp) level augmented by CCSD(T). QMPFF3 calculation of the hydrophobic effect is compared to that obtained with empirical force fields. Using a novel and highly sensitive method, we see polarization increases ordered water structure so that the imprint of the hydrophobic surface atoms on the surrounding waters is stronger and extends to long-range. We see less water order for empirical force fields. The greater order seen with QMPFF3 will affect biological processes through a stronger hydrophobic effect.
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Fulerenos/química , Agua/química , Interacciones Hidrofóbicas e Hidrofílicas , Modelos Moleculares , Electricidad EstáticaRESUMEN
Scar formation is a process that occurs due to increased collagen deposition and uncontrolled inflammation. Previous studies have demonstrated that Pirfenidone (Pf), an FDA approved anti-inflammatory and antifibrotic drug can reduce inflammation in vivo as well as regulate activation of LPS-stimulated neutrophils. However, the molecular level mechanism of Pf's action is not well understood. Here, we used neural networks to identify new targets and molecular modeling methods to investigate the Pf's action pathways at the molecular level that are related to its ability to reduce both the inflammatory and remodeling phases of the wound healing process. Out of all the potential targets identified, both molecular docking and molecular dynamics results suggest that Pf has a noteworthy binding preference towards the active conformation of the p38 mitogen activated protein kinase-14 (MAPK14) and it is potentially a type I inhibitor-like molecule. In addition to p38 MAPK (MAPK14), additional potential targets of Pf include AKT1, MAP3K4, MAP2K3, MAP2K6, MSK2, MAP2K2, ERK1, ERK2, and PDK1. We conclude that several proteins/kinases, rather than a single target, are involved in Pf's wound healing ability to regulate signaling, inflammation, and proliferation.
RESUMEN
Statistical analysis and modeling of mass spectrometry (MS) data have a long and rich history with several modern MS-based applications using statistical and chemometric methods. Recently, machine learning (ML) has experienced a renaissance due to advents in computational hardware and the development of new algorithms for artificial neural networks (ANN) and deep learning architectures. Moreover, recent successes of new ANN and deep learning architectures in several areas of science, engineering, and society have further strengthened the ML field. Importantly, modern ML methods and architectures have enabled new approaches for tasks related to MS that are now widely adopted in several popular MS-based subdisciplines, such as mass spectrometry imaging and proteomics. Herein, we aim to provide an introductory summary of the practical aspects of ML methodology relevant to MS. Additionally, we seek to provide an up-to-date review of the most recent developments in ML integration with MS-based techniques while also providing critical insights into the future direction of the field.