Network Models of BACE-1 Inhibitors: Exploring Structural and Biochemical Relationships
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ABSTRACT:
SUBMITTER: Akguller O
PROVIDER: S-EPMC11240958 | biostudies-literature | 2024 Jun
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe inhibition of the activity of β-secretase (BACE-1) is a potentially important approach for the treatment of Alzheimer disease. To explore the mechanism of inhibition, we describe the use of 46 X-ray crystallographic BACE-1/inhibitor complexes to derive quantitative structure-activity relationship (QSAR) models. The inhibitors were aligned by superimposing 46 X-ray crystallographic BACE-1/inhibitor complexes, and gCOMBINE software was used to perform COMparative BINding Energy (COMBINE) analysis on these 46 minimized BACE-1/inhibitor complexes. The major advantage of the COMBINE analysis is that it can quantitatively extract key residues involved in binding the ligand and identify the nature of the interactions between the ligand and receptor.ResultsBy considering the contributions of the protein residues to the electrostatic and van der Waals intermolecular interaction energies, two predictive and robust COMBINE models were developed: (i) the 3-PC distance-dependent dielectric constant model (built from a single X-ray crystal structure) with a q2 value of 0.74 and an SDEC value of 0.521; and (ii) the 5-PC sigmoidal electrostatic model (built from the actual complexes present in the Brookhaven Protein Data Bank) with a q2 value of 0.79 and an SDEC value of 0.41.ConclusionsThese QSAR models and the information describing the inhibition provide useful insights into the design of novel inhibitors via the optimization of the interactions between ligands and those key residues of BACE-1.
| S-EPMC3533579 | biostudies-literature
Project description:BackgroundTo enable automatic searches, alignments, and model combination, the elements of systems biology models need to be compared and matched across models. Elements can be identified by machine-readable biological annotations, but assigning such annotations and matching non-annotated elements is tedious work and calls for automation.ResultsA new method called "semantic propagation" allows the comparison of model elements based not only on their own annotations, but also on annotations of surrounding elements in the network. One may either propagate feature vectors, describing the annotations of individual elements, or quantitative similarities between elements from different models. Based on semantic propagation, we align partially annotated models and find annotations for non-annotated model elements.ConclusionsSemantic propagation and model alignment are included in the open-source library semanticSBML, available on sourceforge. Online services for model alignment and for annotation prediction can be used at http://www.semanticsbml.org.
| S-EPMC3340317 | biostudies-literature
Project description: Not available
| S-EPMC7609629 | biostudies-literature
Project description:Peptides are increasingly used as inhibitors of various disease specific targets. Several naturally occurring and synthetically developed peptides are undergoing clinical trials. Our work explores the possibility of reusing the non-expressing DNA sequences to predict potential drug-target specific peptides. Recently, we experimentally demonstrated the artificial synthesis of novel proteins from non-coding regions of Escherichia coli genome. In this study, a library of synthetic peptides (Synpeps) was constructed from 2500 intergenic E. coli sequences and screened against Beta-secretase 1 protein, a known drug target for Alzheimer's disease (AD). Secondary and tertiary protein structure predictions followed by protein-protein docking studies were performed to identify the most promising enzyme inhibitors. Interacting residues and favorable binding poses of lead peptide inhibitors were studied. Though initial results are encouraging, experimental validation is required in future to develop efficient target specific inhibitors against AD.
| S-EPMC4427579 | biostudies-literature
Project description:BackgroundCurrently, several systems have been proposed to classify viruses and indicate the relationships between different ones, though each system has its limitations because of the complexity of viral origins and their rapid evolution rate. We hereby propose a new method to explore the relationships between different viruses.MethodA new method, which is based on the virus-host protein-protein interaction network, is proposed in this paper to categorize viruses. The distances between 114 human viruses, including 48 HIV-1 and HIV-2 viruses, are estimated according to the protein-protein interaction network between these viruses and humans.Conclusions/significanceThe results demonstrated that our method can disclose not only relationships consistent with the taxonomic results of currently used systems of classification but also the potential relationships that the current virus classification systems have not revealed. Moreover, the method points to a new direction where the functional relationships between viruses and hosts can be used to explore the virus relationships on a systematic level.
| S-EPMC3287566 | biostudies-literature
Project description:We report the design and synthesis of a series of BACE1 inhibitors incorporating mono- and bicyclic 6-substituted 2-oxopiperazines as novel P1' and P2' ligands and isophthalamide derivative as P2-P3 ligands. Among mono-substituted 2-oxopiperazines, inhibitor 5a with N-benzyl-2-oxopiperazine and isophthalamide showed potent BACE1 inhibitory activity (Ki=2nM). Inhibitor 5g, with N-benzyl-2-oxopiperazine and substituted indole-derived P2-ligand showed a reduction in potency. The X-ray crystal structure of 5g-bound BACE1 was determined and used to design a set of disubstituted 2-oxopiperazines and bicyclic derivatives that were subsequently investigated. Inhibitor 6j with an oxazolidinone derivative showed a BACE1 inhibitory activity of 23nM and cellular EC50 of 80nM.
| S-EPMC5479133 | biostudies-literature
Project description:BackgroundThe major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines.ResultsIn this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity.ConclusionThe proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.
| S-EPMC2694766 | biostudies-literature
Project description:Advanced brain imaging techniques make it possible to measure individuals' structural connectomes in large cohort studies non-invasively. Given the availability of large scale data sets, it is extremely interesting and important to build a set of advanced tools for structural connectome extraction and statistical analysis that emphasize both interpretability and predictive power. In this paper, we developed and integrated a set of toolboxes, including an advanced structural connectome extraction pipeline and a novel tensor network principal components analysis (TN-PCA) method, to study relationships between structural connectomes and various human traits such as alcohol and drug use, cognition and motion abilities. The structural connectome extraction pipeline produces a set of connectome features for each subject that can be organized as a tensor network, and TN-PCA maps the high-dimensional tensor network data to a lower-dimensional Euclidean space. Combined with classical hypothesis testing, canonical correlation analysis and linear discriminant analysis techniques, we analyzed over 1100 scans of 1076 subjects from the Human Connectome Project (HCP) and the Sherbrooke test-retest data set, as well as 175 human traits measuring different domains including cognition, substance use, motor, sensory and emotion. The test-retest data validated the developed algorithms. With the HCP data, we found that structural connectomes are associated with a wide range of traits, e.g., fluid intelligence, language comprehension, and motor skills are associated with increased cortical-cortical brain structural connectivity, while the use of alcohol, tobacco, and marijuana are associated with decreased cortical-cortical connectivity. We also demonstrated that our extracted structural connectomes and analysis method can give superior prediction accuracies compared with alternative connectome constructions and other tensor and network regression methods.
| S-EPMC6613218 | biostudies-literature
Project description:The sulfonamide function is used extensively as a general building block in various inhibitory scaffolds and, more specifically, as a zinc-binding group (ZBG) of metalloenzyme inhibitors. Here, we provide biochemical, structural, and computational characterization of a metallopeptidase in complex with inhibitors, where the mono- and bisubstituted sulfamide functions are designed to directly engage zinc ions of a bimetallic enzyme site. Structural data showed that while monosubstituted sulfamides coordinate active-site zinc ions via the free negatively charged amino group in a canonical manner, their bisubstituted counterparts adopt an atypical binding pattern divergent from expected positioning of corresponding tetrahedral reaction intermediates. Accompanying quantum mechanics calculations revealed that electroneutrality of the sulfamide function is a major factor contributing to the markedly lower potency of bisubstituted compounds by considerably lowering their interaction energy with the enzyme. Overall, while bisubstituted uncharged sulfamide functions can bolster favorable pharmacological properties of a given inhibitor, their use as ZBGs in metalloenzyme inhibitors might be less advantageous due to their suboptimal metal-ligand properties.
| S-EPMC10865363 | biostudies-literature
Project description:Splicing and expression analysis of a collection of 80 different mutations to the gene expression pathway in yeast
2010-06-10 | E-GEOD-1684 | biostudies-arrayexpress