scholarly journals A Comprehensive In Silico Method to Study the QSTR of the Aconitine Alkaloids for Designing Novel Drugs

Molecules ◽  
2018 ◽  
Vol 23 (9) ◽  
pp. 2385 ◽  
Author(s):  
Ming-Yang Wang ◽  
Jing-Wei Liang ◽  
Kamara Olounfeh ◽  
Qi Sun ◽  
Nan Zhao ◽  
...  
Keyword(s):  
Protein Interactions ◽  
In Silico ◽  
Dynamics Simulation ◽  
Protein Protein Interaction ◽  
Calcium Calmodulin Dependent ◽  
Qsar Models ◽  
Dependent Protein ◽  
Structure Toxicity Relationship ◽  
Toxicity Relationship ◽  
Aconitine Alkaloids

A combined in silico method was developed to predict potential protein targets that are involved in cardiotoxicity induced by aconitine alkaloids and to study the quantitative structure–toxicity relationship (QSTR) of these compounds. For the prediction research, a Protein-Protein Interaction (PPI) network was built from the extraction of useful information about protein interactions connected with aconitine cardiotoxicity, based on nearly a decade of literature and the STRING database. The software Cytoscape and the PharmMapper server were utilized to screen for essential proteins in the constructed network. The Calcium-Calmodulin-Dependent Protein Kinase II alpha (CAMK2A) and gamma (CAMK2G) were identified as potential targets. To obtain a deeper insight on the relationship between the toxicity and the structure of aconitine alkaloids, the present study utilized QSAR models built in Sybyl software that possess internal robustness and external high predictions. The molecular dynamics simulation carried out here have demonstrated that aconitine alkaloids possess binding stability for the receptor CAMK2G. In conclusion, this comprehensive method will serve as a tool for following a structural modification of the aconitine alkaloids and lead to a better insight into the cardiotoxicity induced by the compounds that have similar structures to its derivatives.

scholarly journals HUBUNGAN KUANTITATIF STRUKTUR TOKSISITAS : REVIEW

2020 ◽  
Vol 3 (2) ◽  
pp. 107-126
Author(s):  
Purwaniati Purwaniati
Keyword(s):  
In Silico ◽  
Toxicity Data ◽  
Quantitative Structure ◽  
Training Set ◽  
Drug Discovery And Development ◽  
Independent Variables ◽  
Drug Molecules ◽  
Structure Toxicity Relationship ◽  
Toxicity Relationship ◽  
In Silico Methods

AbstrakProses penemuan dan pengembangan obat merupakan proses panjang yang memerlukan banyak waktu dan biaya. Ada banyak calon molekul obat yang gagal mencapai pasaran karena alasan toksisitasnya yang tinggi, sehingga harus dapat diidentifikasi sedini mungkin. Hubungan kuantitatif struktur toksisitas (HKST) merupakan salah satu metode in silico yang cukup tangguh untuk memprediksi toksisitas. HKST merupakan persamaan matematis yang dibentuk dari variabel data endpoint toksisitas seperti LD50 sebagai variabel terikat dan sejumlah deskriptor sebagai variable bebas yang dihitung dari senyawa-senyawa dalam training set. Persamaan HKST kemudian digunakan untuk memprediksi toksisitas senyawa baru.Kata kunci : toksisitas, hubungan kuantitatif struktur toksisitas (HKST)AbstractThe process of drug discovery and development is a long process that requires a lot of time and costly. There are many prospective drug molecules that fail to reach the market due to high toxicity reasons, so they must be identified as early as possible. The quantitative structure toxicity relationship  (QSTR) is one of the in silico methods that is strong enough to predict toxicity. QSTR is a mathematical equation formed from endpoint toxicity data variables such as LD50 as a bound variable and a number of descriptors as independent variables calculated from the compounds in the training set. The QSTR equation is then used to predict the toxicity of new compounds.Keywords: toxicity, quantitative structure toxicity relationship (QSTR)

scholarly journals Predicting virus-receptor mutant binding by molecular dynamics simulation

2013 ◽  
Author(s):  
Austin G Meyer ◽  
Sara L Sawyer ◽  
Andrew D Ellington ◽  
Claus O Wilke
Keyword(s):  
Protein Interactions ◽  
Tight Binding ◽  
Dynamics Simulation ◽  
Virus Protein ◽  
Protein Protein Interactions ◽  
Distance Curve ◽  
Protein Protein Interaction ◽  
Human Transferrin Receptor ◽  
Machupo Virus ◽  
Receptor Mutant

Existing computational methods to predict protein–protein interaction affinity often perform poorly in important test cases. In particular, the effects of multiple mutations, non-alanine substitutions, and flexible loops are difficult to predict with available tools and protocols. We present here a new method to interrogate affinity differences resulting from mutations in a host-virus protein–protein interface. Our method is based on extensive non-equilibrium all atom simulations: We computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1) and estimate affinity using the max imum applied force during a pulling simulation and the area under the force-versus-distance curve. We find that these quantities can provide novel biophysical insight into the GP1/hTfR1 interaction. First, with no prior knowledge of the system we can differentiate among wild type and mutant complexes. Second, although the static co-crystal structure shows two large hydrogen-bonding networks in the GP1/hTfR1 interface, our simulations indicate that one of them may not be important for tight binding. Third, one viral site known to be critical for infection may mark an important evolutionary suppressor site for infection-resistant hTfR1 mutants. Finally, our method provides an elegant framework to compare the effects of multi ple mutations, individually and jointly, on protein–protein interactions.

scholarly journals Dynamic rewiring of the human interactome by interferon signalling

2019 ◽  
Author(s):  
Craig H. Kerr ◽  
Michael A. Skinnider ◽  
Angel M. Madero ◽  
Daniel D.T. Andrews ◽  
R. Greg Stacey ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Interaction Network ◽  
Cell Types ◽  
Antiviral Response ◽  
Type I ◽  
Protein Protein Interactions ◽  
Human Interactome ◽  
Viral Pathogens ◽  
Protein Protein Interaction ◽  
Dependent Protein

ABSTRACTBackgroundThe type I interferon (IFN) response is an ancient pathway that protects cells against viral pathogens by inducing the transcription of hundreds of IFN-stimulated genes (ISGs). Transcriptomic and biochemical approaches have established comprehensive catalogues of ISGs across species and cell types, but their antiviral mechanisms remain incompletely characterized. Here, we apply a combination of quantitative proteomic approaches to delineate the effects of IFN signalling on the human proteome, culminating in the use of protein correlation profiling to map IFN-induced rearrangements in the human protein-protein interaction network.ResultsWe identified >27,000 protein interactions in IFN-stimulated and unstimulated cells, many of which involve proteins associated with human disease and are observed exclusively within the IFN-stimulated network. Differential network analysis reveals interaction rewiring across a surprisingly broad spectrum of cellular pathways in the antiviral response. We identify IFN-dependent protein-protein interactions mediating novel regulatory mechanisms at the transcriptional and translational levels, with one such interaction modulating the transcriptional activity of STAT1. Moreover, we reveal IFN-dependent changes in ribosomal composition that act to buffer ISG protein synthesis.ConclusionsOur map of the IFN interactome provides a global view of the complex cellular networks activated during the antiviral response, placing ISGs in a functional context, and serves as a framework to understand how these networks are dysregulated in autoimmune or inflammatory disease.

Exploring the Stability of Inhibitor Binding to SIK2 Using Molecular Dynamics Simulation and Binding Free Energy Calculation

2021 ◽  
Author(s):  
Mingsong Shi ◽  
Min Zhao ◽  
Lun Wang ◽  
Kongjun Liu ◽  
Penghui Li ◽  
...  
Keyword(s):  
Binding Free Energy ◽  
Dynamics Simulation ◽  
Free Energy Calculation ◽  
Energy Calculation ◽  
Inhibitor Binding ◽  
Dependent Protein Kinase ◽  
Calcium Calmodulin Dependent ◽  
Biological Phenomena ◽  
Dependent Protein ◽  
The Stability

Salt inducible kinase 2 (SIK2) is a calcium/calmodulin-dependent protein kinase-like kinase that is implicated in a variety of biological phenomena, including cellular metabolism, growth, and apoptosis. SIK2 is the key...

scholarly journals Histone Deacetylase 5 Acquires Calcium/Calmodulin-Dependent Kinase II Responsiveness by Oligomerization with Histone Deacetylase 4

2008 ◽  
Vol 28 (10) ◽  
pp. 3437-3445 ◽  
Author(s):  
Johannes Backs ◽  
Thea Backs ◽  
Svetlana Bezprozvannaya ◽  
Timothy A. McKinsey ◽  
Eric N. Olson
Keyword(s):  
Histone Deacetylase ◽  
Protein Interactions ◽  
Nuclear Export ◽  
Coiled Coil ◽  
Protein Protein Interactions ◽  
Specific Domain ◽  
Calcium Calmodulin Dependent ◽  
Histone Deacetylase 4 ◽  
Dependent Protein ◽  
Class Iia Hdacs

ABSTRACT Calcium/calmodulin-dependent protein kinase II (CaMKII) phosphorylates histone deacetylase 4 (HDAC4), a class IIa HDAC, resulting in the cytosolic accumulation of HDAC4 and the derepression of the transcription factor myocyte enhancer factor 2. Phosphorylation by CaMKII requires docking of the kinase to a specific domain of HDAC4 not present in other HDACs. Paradoxically, however, CaMKII signaling can also promote the nuclear export of other class IIa HDACs, such as HDAC5. Here, we show that HDAC4 and HDAC5 form homo- and hetero-oligomers via a conserved coiled-coil domain near their amino termini. Whereas HDAC5 alone is unresponsive to CaMKII, it becomes responsive to CaMKII in the presence of HDAC4. The acquisition of CaMKII responsiveness by HDAC5 is mediated by HDAC5's direct association with HDAC4 and can occur by phosphorylation of HDAC4 or by transphosphorylation by CaMKII bound to HDAC4. Thus, HDAC4 integrates upstream Ca2+-dependent signals via its association with CaMKII and transmits these signals to HDAC5 by protein-protein interactions. We conclude that HDAC4 represents a point of convergence for CaMKII signaling to downstream HDAC-regulated genes, and we suggest that modulation of the interaction of CaMKII and HDAC4 represents a means of regulating CaMKII-dependent gene programs.

scholarly journals Effect of pH on the structure, function, and stability of human calcium/calmodulin-dependent protein kinase IV: combined spectroscopic and MD simulation studies

2016 ◽  
Vol 94 (3) ◽  
pp. 221-228 ◽  
Author(s):  
Huma Naz ◽  
Mohd. Shahbaaz ◽  
Krishna Bisetty ◽  
Asimul Islam ◽  
Faizan Ahmad ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Protein Kinase ◽  
Dynamics Simulation ◽  
Dimensional Structure ◽  
Spectroscopic Techniques ◽  
Dependent Protein Kinase ◽  
Calcium Calmodulin Dependent ◽  
Structural And Functional Properties ◽  
Dependent Protein ◽  
Ph Conditions

Human calcium/calmodulin-dependent protein kinase IV (CAMKIV) is a member of Ser/Thr protein kinase family. It is regulated by the calcium–calmodulin dependent signal through a secondary messenger, Ca2+, which leads to the activation of its autoinhibited form. The over-expression and mutation in CAMKIV as well as change in Ca2+ concentration is often associated with numerous neurodegenerative diseases and cancers. We have successfully cloned, expressed, and purified a functionally active kinase domain of human CAMKIV. To observe the effect of different pH conditions on the structural and functional properties of CAMKIV, we have used spectroscopic techniques such as circular diachroism (CD) absorbance and fluorescence. We have observed that within the pH range 5.0–11.5, CAMKIV maintained both its secondary and tertiary structures, along with its function, whereas significant aggregation was observed at acidic pH (2.0–4.5). We have also performed ATPase activity assays under different pH conditions and found a significant correlation between the structure and enzymatic activities of CAMKIV. In-silico validations were further carried out by modeling the 3-dimensional structure of CAMKIV and then subjecting it to molecular dynamics (MD) simulations to understand its conformational behavior in explicit water conditions. A strong correlation between spectroscopic observations and the output of molecular dynamics simulation was observed for CAMKIV.

scholarly journals Prediction of antiviral drugs against African swine fever viruses based on protein–protein interaction analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8855
Author(s):  
Zhaozhong Zhu ◽  
Yunshi Fan ◽  
Yang Liu ◽  
Taijiao Jiang ◽  
Yang Cao ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Broad Spectrum ◽  
Antiviral Drug ◽  
African Swine Fever ◽  
Antiviral Drugs ◽  
Dynamics Simulation ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Spectrum Effect

The African swine fever virus (ASFV) has severely influenced the swine industry of the world. Unfortunately, there is currently no effective antiviral drug or vaccine against the virus. Identification of new anti-ASFV drugs is urgently needed. Here, an up-to-date set of protein–protein interactions between ASFV and swine were curated by integration of protein–protein interactions from multiple sources. Thirty-eight swine proteins were observed to interact with ASFVs and were defined as ASFV-interacting swine proteins. The ASFV-interacting swine proteins were found to play a central role in the swine protein–protein interaction network, with significant larger degree, betweenness and smaller shortest path length than other swine proteins. Some of ASFV-interacting swine proteins also interacted with several other viruses and could be taken as potential targets of drugs for broad-spectrum effect, such as HSP90AB1. Finally, the antiviral drugs which targeted ASFV-interacting swine proteins and ASFV proteins were predicted. Several drugs with either broad-spectrum effect or high specificity on ASFV-interacting swine proteins were identified, such as Polaprezinc and Geldanamycin. Structural modeling and molecular dynamics simulation showed that Geldanamycin could bind with swine HSP90AB1 stably. This work could not only deepen our understanding towards the ASFV-swine interactions, but also help for the development of effective antiviral drugs against the ASFVs.

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