scholarly journals Hierarchical Graph Representation of Pharmacophore Models

2020 ◽  
Vol 7 ◽  
Author(s):  
Garon Arthur ◽  
Wieder Oliver ◽  
Bareis Klaus ◽  
Seidel Thomas ◽  
Ibis Gökhan ◽  
...  

For the investigation of protein-ligand interaction patterns, the current accessibility of a wide variety of sampling methods allows quick access to large-scale data. The main example is the intensive use of molecular dynamics simulations applied to crystallographic structures which provide dynamic information on the binding interactions in protein-ligand complexes. Chemical feature interaction based pharmacophore models extracted from these simulations, were recently used with consensus scoring approaches to identify potentially active molecules. While this approach is rapid and can be fully automated for virtual screening, additional relevant information from such simulations is still opaque and so far the full potential has not been entirely exploited. To address these aspects, we developed the hierarchical graph representation of pharmacophore models (HGPM). This single graph representation enables an intuitive observation of numerous pharmacophore models from long MD trajectories and further emphasizes their relationship and feature hierarchy. The resulting interactive depiction provides an easy-to-apprehend tool for the selection of sets of pharmacophores as well as visual support for analysis of pharmacophore feature composition and virtual screening results. Furthermore, the representation can be adapted to include information involving interactions between the same protein and multiple different ligands. Herein, we describe the generation, visualization and use of HGPMs generated from MD simulations of two x-ray crystallographic derived structures of the human glucokinase protein in complex with allosteric activators. The results demonstrate that a large number of pharmacophores and their relationships can be visualized in an interactive, efficient manner, unique binding modes identified and a combination of models derived from long MD simulations can be strategically prioritized for VS campaigns.

10.29007/z2wx ◽  
2020 ◽  
Author(s):  
Ankita Sahu ◽  
Dibyabhaba Pradhan ◽  
Khalid Raza ◽  
Sahar Qazi ◽  
A K Jain ◽  
...  

Health problems are increasing worldwide pertaining to cancer modalities.Cyclooxygenase enzyme is known to be involved in cancer biology, neurological disorders, cardiovascular and other diseases. It has been a promising target for developing novel anti-inflammatory drugs in breast cancer treatment. Hence, a computer-aided drug design strategy was applied to identify potent inhibitors of the COX-2 receptor. For this purpose, 12084 ligands from different databases to be tested based on similarity search criteria and were docked against our target protein COX-2 retrieved from the protein data bank. The high-throughput virtual screening protocol was performed and examined the compounds for its binding free energies. Eleven compounds were found out with better binding affinity by virtual screening results and showed interaction with the protein at the known active site. The selected compounds filtered through the Lipinski’s rule of five. The physicochemical properties and bioactivity scores were calculated. Molecular docking calculations, MD simulations, ADMET properties, and protein-ligand interaction were analyzed to determine the suitability of each ligand. Overall, the results from our study suggest that compound ZINC000039428234 could be a potent inhibitor for the COX-2 protein of breast cancer. We look forward to this result is of the enormous key in designing a potential drug candidate for breast cancer.


Author(s):  
Ekampreet Singh ◽  
Rameez Jabeer Khan ◽  
Rajat Kumar Jha ◽  
Gizachew Muluneh Amera ◽  
Monika Jain ◽  
...  

Abstract Background The COVID-19 pandemic caused by SARS-CoV-2 has shown an exponential trend of infected people across the planet. Crediting its virulent nature, it becomes imperative to identify potential therapeutic agents against the deadly virus. The 3-chymotrypsin-like protease (3CLpro) is a cysteine protease which causes the proteolysis of the replicase polyproteins to generate functional proteins, which is a crucial step for viral replication and infection. Computational methods have been applied in recent studies to identify promising inhibitors against 3CLpro to inhibit the viral activity. Main body of the abstract This review provides an overview of promising drug/lead candidates identified so far against 3CLpro through various in silico approaches such as structure-based virtual screening (SBVS), ligand-based virtual screening (LBVS) and drug-repurposing/drug-reprofiling/drug-retasking. Further, the drugs have been classified according to their chemical structures or biological activity into flavonoids, peptides, terpenes, quinolines, nucleoside and nucleotide analogues, protease inhibitors, phenalene and antibiotic derivatives. These are then individually discussed based on the various structural parameters namely estimated free energy of binding (ΔG), key interacting residues, types of intermolecular interactions and structural stability of 3CLpro-ligand complexes obtained from the results of molecular dynamics (MD) simulations. Conclusion The review provides comprehensive information of potential inhibitors identified through several computational methods thus far against 3CLpro from SARS-CoV-2 and provides a better understanding of their interaction patterns and dynamic states of free and ligand-bound 3CLpro structures.


2019 ◽  
Vol 15 (6) ◽  
pp. 588-601 ◽  
Author(s):  
Mahmoud A. Al-Sha'er ◽  
Rua'a A. Al-Aqtash ◽  
Mutasem O. Taha

<P>Background: PI3K&#948; is predominantly expressed in hematopoietic cells and participates in the activation of leukocytes. PI3K&#948; inhibition is a promising approach for treating inflammatory diseases and leukocyte malignancies. Accordingly, we decided to model PI3K&#948; binding. </P><P> Methods: Seventeen PI3K&#948; crystallographic complexes were used to extract 94 pharmacophore models. QSAR modelling was subsequently used to select the superior pharmacophore(s) that best explain bioactivity variation within a list of 79 diverse inhibitors (i.e., upon combination with other physicochemical descriptors). </P><P> Results: The best QSAR model (r2 = 0.71, r2 LOO = 0.70, r2 press against external testing list of 15 compounds = 0.80) included a single crystallographic pharmacophore of optimal explanatory qualities. The resulting pharmacophore and QSAR model were used to screen the National Cancer Institute (NCI) database for new PI3Kδ inhibitors. Two hits showed low micromolar IC50 values. </P><P> Conclusion: Crystallography-based pharmacophores were successfully combined with QSAR analysis for the identification of novel PI3K&#948; inhibitors.</P>


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anastasia Ryzhkova ◽  
Alena Taskina ◽  
Anna Khabarova ◽  
Veniamin Fishman ◽  
Nariman Battulin

AbstractGeneration of mature red blood cells, consisting mainly of hemoglobin, is a remarkable example of coordinated action of various signaling networks. Chromatin condensation is an essential step for terminal erythroid differentiation and subsequent nuclear expulsion in mammals. Here, we profiled 3D genome organization in the blood cells from ten species belonging to different vertebrate classes. Our analysis of contact maps revealed a striking absence of such 3D interaction patterns as loops or TADs in blood cells of all analyzed representatives. We also detect large-scale chromatin rearrangements in blood cells from mammals, birds, reptiles and amphibians: their contact maps display strong second diagonal pattern, representing an increased frequency of long-range contacts, unrelated to TADs or compartments. This pattern is completely atypical for interphase chromosome structure. We confirm that these principles of genome organization are conservative in vertebrate erythroid cells.


Author(s):  
Nicolas Fischer ◽  
Ean-Jeong Seo ◽  
Sara Abdelfatah ◽  
Edmond Fleischer ◽  
Anette Klinger ◽  
...  

SummaryIntroduction Differentiation therapy is a promising strategy for cancer treatment. The translationally controlled tumor protein (TCTP) is an encouraging target in this context. By now, this field of research is still at its infancy, which motivated us to perform a large-scale screening for the identification of novel ligands of TCTP. We studied the binding mode and the effect of TCTP blockade on the cell cycle in different cancer cell lines. Methods Based on the ZINC-database, we performed virtual screening of 2,556,750 compounds to analyze the binding of small molecules to TCTP. The in silico results were confirmed by microscale thermophoresis. The effect of the new ligand molecules was investigated on cancer cell survival, flow cytometric cell cycle analysis and protein expression by Western blotting and co-immunoprecipitation in MOLT-4, MDA-MB-231, SK-OV-3 and MCF-7 cells. Results Large-scale virtual screening by PyRx combined with molecular docking by AutoDock4 revealed five candidate compounds. By microscale thermophoresis, ZINC10157406 (6-(4-fluorophenyl)-2-[(8-methoxy-4-methyl-2-quinazolinyl)amino]-4(3H)-pyrimidinone) was identified as TCTP ligand with a KD of 0.87 ± 0.38. ZINC10157406 revealed growth inhibitory effects and caused G0/G1 cell cycle arrest in MOLT-4, SK-OV-3 and MCF-7 cells. ZINC10157406 (2 × IC50) downregulated TCTP expression by 86.70 ± 0.44% and upregulated p53 expression by 177.60 ± 12.46%. We validated ZINC10157406 binding to the p53 interaction site of TCTP and replacing p53 by co-immunoprecipitation. Discussion ZINC10157406 was identified as potent ligand of TCTP by in silico and in vitro methods. The compound bound to TCTP with a considerably higher affinity compared to artesunate as known TCTP inhibitor. We were able to demonstrate the effect of TCTP blockade at the p53 binding site, i.e. expression of TCTP decreased, whereas p53 expression increased. This effect was accompanied by a dose-dependent decrease of CDK2, CDK4, CDK, cyclin D1 and cyclin D3 causing a G0/G1 cell cycle arrest in MOLT-4, SK-OV-3 and MCF-7 cells. Our findings are supposed to stimulate further research on TCTP-specific small molecules for differentiation therapy in oncology.


Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2111
Author(s):  
Bo-Wei Zhao ◽  
Zhu-Hong You ◽  
Lun Hu ◽  
Zhen-Hao Guo ◽  
Lei Wang ◽  
...  

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.


2021 ◽  
Vol 17 (4) ◽  
pp. 1-21
Author(s):  
He Wang ◽  
Nicoleta Cucu Laurenciu ◽  
Yande Jiang ◽  
Sorin Cotofana

Design and implementation of artificial neuromorphic systems able to provide brain akin computation and/or bio-compatible interfacing ability are crucial for understanding the human brain’s complex functionality and unleashing brain-inspired computation’s full potential. To this end, the realization of energy-efficient, low-area, and bio-compatible artificial synapses, which sustain the signal transmission between neurons, is of particular interest for any large-scale neuromorphic system. Graphene is a prime candidate material with excellent electronic properties, atomic dimensions, and low-energy envelope perspectives, which was already proven effective for logic gates implementations. Furthermore, distinct from any other materials used in current artificial synapse implementations, graphene is biocompatible, which offers perspectives for neural interfaces. In view of this, we investigate the feasibility of graphene-based synapses to emulate various synaptic plasticity behaviors and look into their potential area and energy consumption for large-scale implementations. In this article, we propose a generic graphene-based synapse structure, which can emulate the fundamental synaptic functionalities, i.e., Spike-Timing-Dependent Plasticity (STDP) and Long-Term Plasticity . Additionally, the graphene synapse is programable by means of back-gate bias voltage and can exhibit both excitatory or inhibitory behavior. We investigate its capability to obtain different potentiation/depression time scale for STDP with identical synaptic weight change amplitude when the input spike duration varies. Our simulation results, for various synaptic plasticities, indicate that a maximum 30% synaptic weight change and potentiation/depression time scale range from [-1.5 ms, 1.1 ms to [-32.2 ms, 24.1 ms] are achievable. We further explore the effect of our proposal at the Spiking Neural Network (SNN) level by performing NEST-based simulations of a small SNN implemented with 5 leaky-integrate-and-fire neurons connected via graphene-based synapses. Our experiments indicate that the number of SNN firing events exhibits a strong connection with the synaptic plasticity type, and monotonously varies with respect to the input spike frequency. Moreover, for graphene-based Hebbian STDP and spike duration of 20ms we obtain an SNN behavior relatively similar with the one provided by the same SNN with biological STDP. The proposed graphene-based synapse requires a small area (max. 30 nm 2 ), operates at low voltage (200 mV), and can emulate various plasticity types, which makes it an outstanding candidate for implementing large-scale brain-inspired computation systems.


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