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Small molecule metabolites are the product of many enzymatic reactions. Metabolomics thus opens a window into enzyme activity and function, integrating effects at the post-translational, proteome, transcriptome and genome level. In addition, small molecules can themselves regulate enzyme activity, expression and function both via substrate availability mechanisms and through allosteric regulation. Metabolites are therefore at the nexus of infectious diseases, regulating nutrient availability to the pathogen, immune responses, tropism, and host disease tolerance and resilience. Analysis of metabolomics data is however complex, particularly in terms of metabolite annotation. An emerging valuable approach to extend metabolite annotations beyond existing compound libraries and to identify infection-induced chemical changes is molecular networking. In this chapter, we discuss the applications of molecular networking in the context of infectious diseases specifically, with a focus on considerations relevant to these biological systems.
Hepatocellular carcinoma (HCC) is a deadly disease, often unnoticed until the late stages, when treatment options become limited. Thus, there is a crucial need to identify biomarkers for early detection of developing HCC, as well as molecular pathways that would be amenable to therapeutic intervention. Although analysis of human HCC tissues and serum components may serve these purposes, inability of early detection also precludes possibilities of identification of biomarkers or pathways that are sequentially perturbed at earlier phases of disease progression. We have therefore explored the option of utilizing mouse models to understand in a systematic and longitudinal manner the molecular pathways that are progressively deregulated by various etiological factors in contributing to HCC formation, and we report the initial findings in characterizing their validity. Hepatitis B surface antigen transgenic mice, which had been exposed to aflatoxin B1 at various stages in life, were used as a hepatitis model. Our findings confirm a synergistic effect of both these etiological factors, with a gender bias towards males for HCC predisposition. Time-based aflatoxin B1 treatment also demonstrated the requirement of non-quiescent liver for effective transformation. Tumors from these models with various etiologies resemble human HCCs histologically and at the molecular level. Extensive molecular characterization revealed the presence of an 11-gene HCC-expression signature that was able to discern transformed human hepatocytes from primary cells, regardless of etiology, and from other cancer types. Moreover, distinct molecular pathways appear to be deregulated by various etiological agents en route to formation of HCCs, in which common pathways converge, highlighting the existence of etiology-specific as well as common HCC-specific molecular perturbations. This study therefore highlights the utility of these mouse models, which provide a rich resource for the longitudinal analysis of molecular changes and biomarkers associated with HCC that could be exploited further for therapeutic targeting.
Independent surveys of human gene promoter regions have demonstrated an overrepresentation of G(3)X(n1)G3X(n2)G(3)X(n3)G(3) motifs which are known to be capable of forming intrastrand quadruple helix structures. In spite of the widely recognized importance of G-quadruplex structures in gene regulation and growing interest around this unusual DNA structure, there are at present only few such structures available in the Nucleic Acid Database. In the present work we generate by molecular modeling feasible G-quadruplex structures which may be useful for interpretation of experimental data.
Statistical models and methods that associate changes in the physicochemical properties of amino acids with natural selection at the molecular level typically do not take into account the correlations between such properties. We propose a Bayesian hierarchical regression model with a generalization of the Dirichlet process prior on the distribution of the regression coefficients that describes the relationship between the changes in amino acid distances and natural selection in protein-coding DNA sequence alignments.
Neuroblastoma (Nb), the most common extracranial tumor in children, exhibited remarkable phenotypic diversity and heterogeneous clinical behavior. Tumors with MYCN overexpression have a worse prognosis. MYCN promotes tumor progression by inducing cell proliferation, de-differentiation, and dysregulated mitochondrial metabolism. Cyclophosphamide (CFF) at minimum effective oral doses (metronomic therapy) exerts beneficial actions on chemoresistant cancers. Molecular iodine (I2) in coadministration with all-trans retinoic acid synergizes apoptosis and cell differentiation in Nb cells. This work analyzes the impact of I2 and CFF on the viability (culture) and tumor progression (xenografts) of Nb chemoresistant SK-N-BE(2) cells. Results showed that both molecules induce dose-response antiproliferative effects, and I2 increases the sensibility of Nb cells to CFF, triggering PPARγ expression and acting as a mitocan in mitochondrial metabolism. In vivo oral I2/metronomic CFF treatments showed significant inhibition in xenograft growth, decreasing proliferation (Survivin) and activating apoptosis signaling (P53, Bax/Bcl-2). In addition, I2 decreased the expression of master markers of malignancy (MYCN, TrkB), vasculature remodeling, and increased differentiation signaling (PPARγ and TrkA). Furthermore, I2 supplementation prevented loss of body weight and hemorrhagic cystitis secondary to CFF in nude mice. These results allow us to propose the I2 supplement in metronomic CFF treatments to increase the effectiveness of chemotherapy and reduce side effects.
In this article we present a model for molecularly imprinted polymers, which considers both complexation processes in the pre-polymerization mixture and adsorption in the imprinted structures within a single consistent framework. As a case study we investigate MAA/EGDMA polymers imprinted with pyrazine and pyrimidine. A polymer imprinted with pyrazine shows substantial selectivity towards pyrazine over pyrimidine, thus exhibiting molecular recognition, whereas the pyrimidine imprinted structure shows no preferential adsorption of the template. Binding sites responsible for the molecular recognition of pyrazine involve one MAA molecule and one EGDMA molecule, forming associations with the two functional groups of the pyrazine molecule. Presence of these specific sites in the pyrazine imprinted system and lack of the analogous sites in the pyrimidine imprinted system is directly linked to the complexation processes in the pre-polymerization solution. These processes are quite different for pyrazine and pyrimidine as a result of both enthalpic and entropic effects.
Colorectal cancer (CRC) primary tumours are molecularly classified into four consensus molecular subtypes (CMS1-4). Genetically engineered mouse models aim to faithfully mimic the complexity of human cancers and, when appropriately aligned, represent ideal pre-clinical systems to test new drug treatments. Despite its importance, dual-species classification has been limited by the lack of a reliable approach. Here we utilise, develop and test a set of options for human-to-mouse CMS classifications of CRC tissue.
Selecting genes and pathways indicative of disease is a central problem in computational biology. This problem is especially challenging when parsing multi-dimensional genomic data. A number of tools, such as L1-norm based regularization and its extensions elastic net and fused lasso, have been introduced to deal with this challenge. However, these approaches tend to ignore the vast amount of a priori biological network information curated in the literature.
The voltage-gated potassium channel KCNQ1 (KV7.1) assembles with the KCNE1 accessory protein to generate the slow delayed rectifier current, IKS, which is critical for membrane repolarization as part of the cardiac action potential. Loss-of-function (LOF) mutations in KCNQ1 are the most common cause of congenital long QT syndrome (LQTS), type 1 LQTS, an inherited genetic predisposition to cardiac arrhythmia and sudden cardiac death. A detailed structural understanding of KCNQ1 is needed to elucidate the molecular basis for KCNQ1 LOF in disease and to enable structure-guided design of new anti-arrhythmic drugs. In this work, advanced structural models of human KCNQ1 in the resting/closed and activated/open states were developed by Rosetta homology modeling guided by newly available experimentally-based templates: X. leavis KCNQ1 and various resting voltage sensor structures. Using molecular dynamics (MD) simulations, the capacity of the models to describe experimentally established channel properties including state-dependent voltage sensor gating charge interactions and pore conformations, PIP2 binding sites, and voltage sensor-pore domain interactions were validated. Rosetta energy calculations were applied to assess the utility of each model in interpreting mutation-evoked KCNQ1 dysfunction by predicting the change in protein thermodynamic stability for 50 experimentally characterized KCNQ1 variants with mutations located in the voltage-sensing domain. Energetic destabilization was successfully predicted for folding-defective KCNQ1 LOF mutants whereas wild type-like mutants exhibited no significant energetic frustrations, which supports growing evidence that mutation-induced protein destabilization is an especially common cause of KCNQ1 dysfunction. The new KCNQ1 Rosetta models provide helpful tools in the study of the structural basis for KCNQ1 function and can be used to generate hypotheses to explain KCNQ1 dysfunction.
Attention mechanisms have led to many breakthroughs in sequential data modeling but have yet to be incorporated into any generative algorithms for molecular design. Here we explore the impact of adding self-attention layers to generative β-VAE models and show that those with attention are able to learn a complex "molecular grammar" while improving performance on downstream tasks such as accurately sampling from the latent space ("model memory") or exploring novel chemistries not present in the training data. There is a notable relationship between a model's architecture, the structure of its latent memory and its performance during inference. We demonstrate that there is an unavoidable tradeoff between model exploration and validity that is a function of the complexity of the latent memory. However, novel sampling schemes may be used that optimize this tradeoff. We anticipate that attention will play an important role in future molecular design algorithms that can make efficient use of the detailed molecular substructures learned by the transformer.
Molecular dynamics (MD) simulation is a powerful technique for sampling the meta-stable and transitional conformations of proteins and other biomolecules. Computational data clustering has emerged as a useful, automated technique for extracting conformational states from MD simulation data. Despite extensive application, relatively little work has been done to determine if the clustering algorithms are actually extracting useful information. A primary goal of this paper therefore is to provide such an understanding through a detailed analysis of data clustering applied to a series of increasingly complex biopolymer models.
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a recent pandemic outbreak threatening human beings worldwide. This novel coronavirus disease-19 (COVID-19) infection causes severe morbidity and mortality and rapidly spreading across the countries. Therefore, there is an urgent need for basic fundamental research to understand the pathogenesis and druggable molecular targets of SARS-CoV-2. Recent sequencing data of the viral genome and X-ray crystallographic data of the viral proteins illustrate potential molecular targets that need to be investigated for structure-based drug design. Further, the SARS-CoV-2 viral pathogen isolated from clinical samples needs to be cultivated and titrated. All of these scenarios demand suitable laboratory experimental models. The experimental models should mimic the viral life cycle as it happens in the human lung epithelial cells. Recently, researchers employing primary human lung epithelial cells, intestinal epithelial cells, experimental cell lines like Vero cells, CaCo-2 cells, HEK-293, H1299, Calu-3 for understanding viral titer values. The human iPSC-derived lung organoids, small intestinal organoids, and blood vessel organoids increase interest among researchers to understand SARS-CoV-2 biology and treatment outcome. The SARS-CoV-2 enters the human lung epithelial cells using viral Spike (S1) protein and human angiotensin-converting enzyme 2 (ACE-2) receptor. The laboratory mouse show poor ACE-2 expression and thereby inefficient SARS-CoV-2 infection. Therefore, there was an urgent need to develop transgenic hACE-2 mouse models to understand antiviral agents' therapeutic outcomes. This review highlighted the viral pathogenesis, potential druggable molecular targets, and suitable experimental models for basic fundamental research.
Modelling the associations from high-throughput experimental molecular data has provided unprecedented insights into biological pathways and signalling mechanisms. Graphical models and networks have especially proven to be useful abstractions in this regard. Ad hoc thresholds are often used in conjunction with structure learning algorithms to determine significant associations. The present study overcomes this limitation by proposing a statistically motivated approach for identifying significant associations in a network.
Abelson kinase (c-Abl) is a non-receptor tyrosine kinase involved in several biological processes essential for cell differentiation, migration, proliferation, and survival. This enzyme's activation might be an alternative strategy for treating diseases such as neutropenia induced by chemotherapy, prostate, and breast cancer. Recently, a series of compounds that promote the activation of c-Abl has been identified, opening a promising ground for c-Abl drug development. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) methodologies have significantly impacted recent drug development initiatives. Here, we combined SBDD and LBDD approaches to characterize critical chemical properties and interactions of identified c-Abl's activators. We used molecular docking simulations combined with tree-based machine learning models-decision tree, AdaBoost, and random forest to understand the c-Abl activators' structural features required for binding to myristoyl pocket, and consequently, to promote enzyme and cellular activation. We obtained predictive and robust models with Matthews correlation coefficient values higher than 0.4 for all endpoints and identified characteristics that led to constructing a structure-activity relationship model (SAR).
Mammalian circadian timekeeping arises from a transcription-based feedback loop driven by a set of dedicated clock proteins. At its core, the heterodimeric transcription factor CLOCK:BMAL1 activates expression of Period, Cryptochrome, and Rev-Erb genes, which feed back to repress transcription and create oscillations in gene expression that confer circadian timing cues to cellular processes. The formation of different clock protein complexes throughout this transcriptional cycle helps to establish the intrinsic ∼24 h periodicity of the clock; however, current models of circadian timekeeping lack the explanatory power to fully describe this process. Recent studies confirm the presence of at least three distinct regulatory complexes: a transcriptionally active state comprising the CLOCK:BMAL1 heterodimer with its coactivator CBP/p300, an early repressive state containing PER:CRY complexes, and a late repressive state marked by a poised but inactive, DNA-bound CLOCK:BMAL1:CRY1 complex. In this review, we analyze high-resolution structures of core circadian transcriptional regulators and integrate biochemical data to suggest how remodeling of clock protein complexes may be achieved throughout the 24 h cycle. Defining these detailed mechanisms will provide a foundation for understanding the molecular basis of circadian timing and help to establish new platforms for the discovery of therapeutics to manipulate the clock.
Molecular evolutionary rate variation is a key aspect of the evolution of many organisms that can be modeled using molecular clock models. For example, fixed local clocks revealed the role of episodic evolution in the emergence of SARS-CoV-2 variants of concern. Like all statistical models, however, the reliability of such inferences is contingent on an assessment of statistical evidence. We present a novel Bayesian phylogenetic approach for detecting episodic evolution. It consists of computing Bayes factors, as the ratio of posterior and prior odds of evolutionary rate increases, effectively quantifying support for the effect size. We conducted an extensive simulation study to illustrate the power of this method and benchmarked it to formal model comparison of a range of molecular clock models using (log) marginal likelihood estimation, and to inference under a random local clock model. Quantifying support for the effect size has higher sensitivity than formal model testing and is straight-forward to compute, because it only needs samples from the posterior and prior distribution. However, formal model testing has the advantage of accommodating a wide range molecular clock models. We also assessed the ability of an automated approach, known as the random local clock, where branches under episodic evolution may be detected without their a priori definition. In an empirical analysis of a data set of SARS-CoV-2 genomes, we find "very strong" evidence for episodic evolution. Our results provide guidelines and practical methods for Bayesian detection of episodic evolution, as well as avenues for further research into this phenomenon.
Cannabidiol (CBD) is a non-psychoactive phytocannabinoid known for its beneficial effects including antioxidant and anti-inflammatory properties. Moreover, CBD is a compound with antidepressant, anxiolytic, anticonvulsant and antipsychotic effects. Thanks to all these properties, the interest of the scientific community for it has grown. Indeed, CBD is a great candidate for the management of neurological diseases. The purpose of our review is to summarize the in vitro and in vivo studies published in the last 15 years that describe the biochemical and molecular mechanisms underlying the effects of CBD and its therapeutic application in neurological diseases. CBD exerts its neuroprotective effects through three G protein coupled-receptors (adenosine receptor subtype 2A, serotonin receptor subtype 1A and G protein-coupled receptor 55), one ligand-gated ion channel (transient receptor potential vanilloid channel-1) and one nuclear factor (peroxisome proliferator-activated receptor γ). Moreover, the therapeutical properties of CBD are also due to GABAergic modulation. In conclusion, CBD, through multi-target mechanisms, represents a valid therapeutic tool for the management of epilepsy, Alzheimer's disease, multiple sclerosis and Parkinson's disease.
Pharmacophore models are widely used for the identification of promising primary hits in compound large libraries. Recent studies have demonstrated that pharmacophores retrieved from protein-ligand molecular dynamic trajectories outperform pharmacophores retrieved from a single crystal complex structure. However, the number of retrieved pharmacophores can be enormous, thus, making it computationally inefficient to use all of them for virtual screening. In this study, we proposed selection of distinct representative pharmacophores by the removal of pharmacophores with identical three-dimensional (3D) pharmacophore hashes. We also proposed a new conformer coverage approach in order to rank compounds using all representative pharmacophores. Our results for four cyclin-dependent kinase 2 (CDK2) complexes with different ligands demonstrated that the proposed selection and ranking approaches outperformed the previously described common hits approach. We also demonstrated that ranking, based on averaged predicted scores obtained from different complexes, can outperform ranking based on scores from an individual complex. All developments were implemented in open-source software pharmd.
Reconstructing protein structure based on contact maps leads to two types of models: properly oriented models and mirror models. This is due to the fact that contact maps do not include information on protein chirality. Therefore, both types of model orientations share the same contact map and are geometrically allowed. In this work, we verified the hypothesis that some of the energy terms calculated by PyRosetta could be useful to distinguish between properly oriented and mirror models. We studied 440 models of all-alpha protein domains reconstructed manually from their contact maps, where 50% of the models were properly oriented and 50% had mirror orientation. We showed that dihedral angles and energy terms, based on the probability of specific geometrical arrangement of the residues, differed significantly for properly oriented and mirror models.
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