scholarly journals Scoring functions for drug-effect similarity

2020 ◽  
Author(s):  
Stephan Struckmann ◽  
Mathias Ernst ◽  
Sarah Fischer ◽  
Nancy Mah ◽  
Georg Fuellen ◽  
...  
Keyword(s):  
Cell Line ◽  
Drug Effect ◽  
Pearson Correlation ◽  
Source Code ◽  
New Drugs ◽  
Supplementary Information ◽  
Disease Genes ◽  
Scoring Functions ◽  
Profile Changes

Abstract Motivation The difficulty to find new drugs and bring them to the market has led to an increased interest to find new applications for known compounds. Biological samples from many disease contexts have been extensively profiled by transcriptomics, and, intuitively, this motivates to search for compounds with a reversing effect on the expression of characteristic disease genes. However, disease effects may be cell line-specific and also depend on other factors, such as genetics and environment. Transcription profile changes between healthy and diseased cells relate in complex ways to profile changes gathered from cell lines upon stimulation with a drug. Despite these differences, we expect that there will be some similarity in the gene regulatory networks at play in both situations. The challenge is to match transcriptomes for both diseases and drugs alike, even though the exact molecular pathology/pharmacogenomics may not be known. Results We substitute the challenge to match a drug effect to a disease effect with the challenge to match a drug effect to the effect of the same drug at another concentration or in another cell line. This is welldefined, reproducible in vitro and in silico and extendable with external data. Based on the Connectivity Map (CMap) dataset, we combined 26 different similarity scores with six different heuristics to reduce the number of genes in the model. Such gene filters may also utilize external knowledge e.g. from biological networks. We found that no similarity score always outperforms all others for all drugs, but the Pearson correlation finds the same drug with the highest reliability. Results are improved by filtering for highly expressed genes and to a lesser degree for genes with large fold changes. Also a network-based reduction of contributing transcripts was beneficial, here implemented by the FocusHeuristics. We found no drop in prediction accuracy when reducing the whole transcriptome to the set of 1000 landmark genes of the CMap’s successor project Library of Integrated Network-based Cellular Signatures. All source code to re-analyze and extend the CMap data, the source code of heuristics, filters and their evaluation are available to propel the development of new methods for drug repurposing. Availability https://bitbucket.org/ibima/moldrugeffectsdb Contact [email protected] Supplementary information Supplementary data are available at Briefings in Bioinformatics online.

scholarly journals Generation of High-Yield, Functional Oligodendrocytes from a c-myc Immortalized Neural Cell Line, Endowed with Staminal Properties

2021 ◽  
Vol 22 (3) ◽  
pp. 1124
Author(s):  
Mafalda Giovanna Reccia ◽  
Floriana Volpicelli ◽  
Eirkiur Benedikz ◽  
Åsa Fex Svenningsen ◽  
Luca Colucci-D’Amato
Keyword(s):  
Cell Line ◽  
Low Cost ◽  
Neural Cell ◽  
New Drugs ◽  
High Yield ◽  
Time Saving ◽  
Interacting Protein ◽  
Neuronal Marker ◽  
Differentiation Status

Neural stem cells represent a powerful tool to study molecules involved in pathophysiology of Nervous System and to discover new drugs. Although they can be cultured and expanded in vitro as a primary culture, their use is hampered by their heterogeneity and by the cost and time needed for their preparation. Here we report that mes-c-myc A1 cells (A1), a neural cell line, is endowed with staminal properties. Undifferentiated/proliferating and differentiated/non-proliferating A1 cells are able to generate neurospheres (Ns) in which gene expression parallels the original differentiation status. In fact, Ns derived from undifferentiated A1 cells express higher levels of Nestin, Kruppel-like factor 4 (Klf4) and glial fibrillary protein (GFAP), markers of stemness, while those obtained from differentiated A1 cells show higher levels of the neuronal marker beta III tubulin. Interestingly, Ns differentiation, by Epidermal Growth Factors (EGF) and Fibroblast Growth Factor 2 (bFGF) withdrawal, generates oligodendrocytes at high-yield as shown by the expression of markers, Galactosylceramidase (Gal-C) Neuron-Glial antigen 2 (NG2), Receptor-Interacting Protein (RIP) and Myelin Basic Protein (MBP). Finally, upon co-culture, Ns-A1-derived oligodendrocytes cause a redistribution of contactin-associated protein (Caspr/paranodin) protein on neuronal cells, as primary oligodendrocytes cultures, suggesting that they are able to form compact myelin. Thus, Ns-A1-derived oligodendrocytes may represent a time-saving and low-cost tool to study the pathophysiology of oligodendrocytes and to test new drugs.

scholarly journals In Vitro Inhibitory Effect of Recombinant Human Calprotectin on Nalm6 Leukemia Cell Line

2020 ◽  
Vol 20 (8) ◽  
pp. 951-962
Author(s):  
Samira Charkhizadeh ◽  
Mehdi Imani ◽  
Nematollah Gheibi ◽  
Fateme Shabaani ◽  
Akbar Nikpajouh ◽  
...  
Keyword(s):  
Cell Line ◽  
Leukemia Cell ◽  
Annexin V ◽  
Inhibitory Effect ◽  
New Drugs ◽  
Leukemia Cell Line ◽  
Cytotoxic Effects ◽  
Tumor Expression ◽  
Induced Apoptosis

Background & Purpose: In evaluating new drugs for the treatment of various types of cancer, investigations have been made to discover a variety of anti-tumor compounds with less side effects on normal cells. Investigations have shown that the heterodimers S100A8 and S100A9 inhibit the enzyme casein kinase 2 and then prevent the activation of the E7 oncoprotein. Therefore, the aim of this study was to evaluate the effect of calprotectin as an antitumor compound on the Nalm6 (B cell precursor leukemia cell line). Material & Methods: Transformation of genes encoding S100A8 and S100A9 human, designed in the pQE32 plasmid, was performed by the thermal shock method into E. coli M15 bacteria. After bacterial growth in LB medium, the expression of two S100A8 and S100A9 subunits, the solubility of the protein by SDS-PAGE method was determined. Finally, the S100A8 / A9 complex was equally placed in the microtube. In the next step, the cytotoxic effects of calprotectin produced on the Nalm6 cell line were evaluated using the wst1 test. Then, the apoptosis in these cells was measured using flow cytometry methods with Annexin-V coloration. Results: In the current study, the results showed that the cytotoxic effects of Calprotectin are time and concentration- dependent. Therefore, it can reduce the tumor expression and had a beneficial effect by induced apoptosis in Nalm6 cell line. Conclusion: Calprotectin has an anti-tumor effect on the Nalm6 cell line by increasing apoptosis.

Synthesis, Characterization, Biological Evaluation and Molecular Docking Studies of New Oxoacrylate and Acetamide on heLa Cancer Cell Lines

2020 ◽  
Vol 16 ◽  
Author(s):  
Nevin Çankaya ◽  
Mehmetcan İzdal ◽  
Serap Yalçin Azarkan
Keyword(s):  
Molecular Docking ◽  
Hela Cell ◽  
Anticancer Activity ◽  
Cell Line ◽  
Critical Role ◽  
Biological Evaluation ◽  
New Drugs ◽  
Hela Cell Line ◽  
Molecular Docking Study

Background: In recent years, discovery and development of new drugs play a critical role in cancer therapy. Objective: In this study, the effect of MPAEA and p-acetamide on cellular toxicity and on silico in HeLa cancer cells have been investigated. Methods: In this study, 2-choloro-N-(4-methoxyphenyl)acetamide (p-acetamide) and 2-(4-methoxyphenylamino)-2- oxoethyl acrylate (MPAEA) have been synthesized and characterized by FTIR, 1H, and 13C-NMR. Cytotoxicity of pacetamide and MPAEA have been investigated by XTT cell proliferation assay on the HeLa cell line. IC50 values of pacetamide and MPAEA have been identified on the HeLa cell line. Further, molecular docking study was carried out by Autodock Vina using BCL-2 (PDB ID: 4MAN), BCL-W (PDB ID: 2Y6W), MCl-1 (PDB ID: 5FDO) AKT (PDB ID: 4GV1) and BRAF (PDB ID: 5VAM) as a possible apoptotic target for anticancer activity. Results: According to the obtained results, MPAEA and p-acetamide were successfully synthesized and characterized. The interactions between ligands and anti-apoptotic proteins were evaluated by molecular docking and their free energy of binding were calculated and used as descriptor. Conclusion: In vitro and in silico the results demonstrated that MPAEA had potent anticancer activity on HeLa cell line.

scholarly journals Learning from the ligand: using ligand-based features to improve binding affinity prediction

2019 ◽  
Author(s):  
Fergus Boyles ◽  
Charlotte M Deane ◽  
Garrett M Morris
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Pearson Correlation ◽  
Scoring Function ◽  
Supplementary Information ◽  
Scoring Functions ◽  
Limited Information ◽  
Ligand Complex ◽  
Binding Affinity Prediction ◽  
Affinity Prediction

Abstract Motivation Machine learning scoring functions for protein–ligand binding affinity prediction have been found to consistently outperform classical scoring functions. Structure-based scoring functions for universal affinity prediction typically use features describing interactions derived from the protein–ligand complex, with limited information about the chemical or topological properties of the ligand itself. Results We demonstrate that the performance of machine learning scoring functions are consistently improved by the inclusion of diverse ligand-based features. For example, a Random Forest (RF) combining the features of RF-Score v3 with RDKit molecular descriptors achieved Pearson correlation coefficients of up to 0.836, 0.780 and 0.821 on the PDBbind 2007, 2013 and 2016 core sets, respectively, compared to 0.790, 0.746 and 0.814 when using the features of RF-Score v3 alone. Excluding proteins and/or ligands that are similar to those in the test sets from the training set has a significant effect on scoring function performance, but does not remove the predictive power of ligand-based features. Furthermore a RF using only ligand-based features is predictive at a level similar to classical scoring functions and it appears to be predicting the mean binding affinity of a ligand for its protein targets. Availability and implementation Data and code to reproduce all the results are freely available at http://opig.stats.ox.ac.uk/resources. Supplementary information Supplementary data are available at Bioinformatics online.

Extended connectivity interaction features: improving binding affinity prediction through chemical description

2020 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
José L Medina-Franco ◽  
Jordi Mestres ◽  
Xavier Barril
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Supplementary Information ◽  
Scoring Functions ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Extended Connectivity ◽  
Chemical Description

Abstract Motivation Machine-learning scoring functions (SFs) have been found to outperform standard SFs for binding affinity prediction of protein–ligand complexes. A plethora of reports focus on the implementation of increasingly complex algorithms, while the chemical description of the system has not been fully exploited. Results Herein, we introduce Extended Connectivity Interaction Features (ECIF) to describe protein–ligand complexes and build machine-learning SFs with improved predictions of binding affinity. ECIF are a set of protein−ligand atom-type pair counts that take into account each atom’s connectivity to describe it and thus define the pair types. ECIF were used to build different machine-learning models to predict protein–ligand affinities (pKd/pKi). The models were evaluated in terms of ‘scoring power’ on the Comparative Assessment of Scoring Functions 2016. The best models built on ECIF achieved Pearson correlation coefficients of 0.857 when used on its own, and 0.866 when used in combination with ligand descriptors, demonstrating ECIF descriptive power. Availability and implementation Data and code to reproduce all the results are freely available at https://github.com/DIFACQUIM/ECIF. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Repurposing the Antiemetic Metoclopramide as an Antiviral Against Dengue Virus Infection in Neuronal Cells

2021 ◽  
Vol 10 ◽  
Author(s):  
Ting-Jing Shen ◽  
Vu Thi Hanh ◽  
Thai Quoc Nguyen ◽  
Ming-Kai Jhan ◽  
Min-Ru Ho ◽  
...  
Keyword(s):  
Dengue Virus ◽  
Cell Line ◽  
Plaque Assay ◽  
Neuronal Cell ◽  
Antiviral Effect ◽  
Denv Infection ◽  
New Drugs ◽  
Dopamine 2 Receptor

Dengue virus (DENV) is transmitted by Aedes mosquitoes to humans and is a threat worldwide. No effective new drugs have been used for anti-dengue treatment, and repurposing drugs is an alternative approach to treat this condition. Dopamine 2 receptor (D2R) is a host receptor positively associated with DENV infection. Metoclopramide (MCP), a D2R antagonist clinically used to control vomiting and nausea in patients with DENV infection, was putatively examined for inhibition of DENV infection by targeting D2R. In the mouse neural cell line Neuro-2a with D2R expression, a plaque assay demonstrated the antiviral efficacy of MCP treatment. However, in the cell line BHK-21, which did not express D2R, MCP treatment caused no further inhibition of DENV infection. Either MCP treatment or exogenous administration of a neutralizing D2R antibody blocked DENV binding. Treatment with MCP also reduced DENV dsRNA replication and DENV-induced neuronal cell cytotoxicity in vitro. An in vivo study demonstrated the antiviral effect of MCP against DENV-induced CNS neuropathy and mortality. These results showed that repurposing the D2R-targeting antiemetic MCP is a potential therapeutic strategy against DENV infection.

Ultrastructure of Choriocarcinoma in Vitro

1969 ◽  
Vol 27 ◽  
pp. 362-363
Author(s):  
John C. Garancis ◽  
R. A. Pattillo
Keyword(s):  
Cell Line ◽  
Physiological Function ◽  
Cell System ◽  
Bewo Cell ◽  
Bewo Cells ◽  
Gestational Choriocarcinoma ◽  
Bewo Cell Line

Growth of cell system (BeWo-cell line) derived from human gestational choriocarcinoma has been established and continuously maintained in-vitro. Furthermore, it is evident from the previous studies that this cell line has retained the physiological function of the placental trophoblasts, namely the synthesis of human chorionic gonadotrophil(HCG).The BeWo cells were relatively small and possessed single nuclei, thus indicating that this cell line consists exclusively of cytotrophoblasts. In some instances cells appeared widely separated and their lateral surfaces were provided with numerous microvilli (Fig.1).

ANTITUMOR EFFECT OF SUNITINIB AND BORTEZOMIB ON BREAST CANCER CELL LINE IN VITRO

2017 ◽  
Vol 63 (1) ◽  
pp. 141-145
Author(s):  
Yuliya Khochenkova ◽  
Eliso Solomko ◽  
Oksana Ryabaya ◽  
Yevgeniya Stepanova ◽  
Dmitriy Khochenkov
Keyword(s):  
Breast Cancer ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Antitumor Effect ◽  
Cancer Cell Lines ◽  
Breast Cancer Cell Lines ◽  
Actual Problem

The discovery for effective combinations of anticancer drugs for treatment for breast cancer is the actual problem in the experimental chemotherapy. In this paper we conducted a study of antitumor effect of the combination of sunitinib and bortezomib against MDA-MB-231 and SKBR-3 breast cancer cell lines in vitro. We found that bortezomib in non-toxic concentrations can potentiate the antitumor activity of sunitinib. MDA-MB-231 cell line has showed great sensitivity to the combination of bortezomib and sunitinib in vitro. Bortezomib and sunitinib caused reduced expression of receptor tyrosine kinases VEGFR1, VEGFR2, PDGFRa, PDGFRß and c-Kit on HER2- and HER2+ breast cancer cell lines

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