scholarly journals Computational Screening of Natural Compounds for Identification of Potential Anti-Cancer Agents Targeting MCM7 Protein

Molecules ◽  
2021 ◽  
Vol 26 (19) ◽  
pp. 5878
Author(s):  
Mohammad Y. Alshahrani ◽  
Kholoud M. Alshahrani ◽  
Munazzah Tasleem ◽  
Arshiya Akeel ◽  
Tahani M. Almeleebia ◽  
...  
Keyword(s):  
Binding Energy ◽  
Computational Prediction ◽  
Cellular Growth ◽  
Minichromosome Maintenance ◽  
Computational Screening ◽  
Property Evaluation ◽  
Anti Cancer ◽  
Cancer Types ◽  
Biogenic Compounds ◽  
Control Compound

Minichromosome maintenance complex component 7 (MCM7) is involved in replicative licensing and the synthesis of DNA, and its overexpression is a fascinating biomarker for various cancer types. There is currently no effective agent that can prevent the development of cancer caused by the MCM7 protein. However, on the molecular level, inhibiting MCM7 lowers cancer-related cellular growth. With this purpose, this study screened 452 biogenic compounds extracted from the UEFS Natural Products dataset against MCM protein by using the in silico art of technique. The hit compounds UEFS99, UEFS137, and UEFS428 showed good binding with the MCM7 protein with binding energy values of −9.95, −8.92, and −8.71 kcal/mol, which was comparatively higher than that of the control compound ciprofloxacin (−6.50). The hit (UEFS99) with the minimum binding energy was picked for molecular dynamics (MD) simulation investigation, and it demonstrated stability at 30 ns. Computational prediction of physicochemical property evaluation revealed that these hits are non-toxic and have good drug-likeness features. It is suggested that hit compounds UEFS99, UEFS137, and UEFS428 pave the way for further bench work validation in novel inhibitor development against MCM7 to fight the cancers.

Immune Checkpoint Inhibitors: Basics and Challenges

2019 ◽  
Vol 26 (17) ◽  
pp. 3009-3025 ◽  
Author(s):  
Bin Li ◽  
Ho Lam Chan ◽  
Pingping Chen
Keyword(s):  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Complete Response ◽  
Modern World ◽  
Basic Principles ◽  
Mutation Status ◽  
Anti Cancer ◽  
Cancer Types ◽  
Shed Light

Cancer is one of the most deadly diseases in the modern world. The last decade has witnessed dramatic advances in cancer treatment through immunotherapy. One extremely promising means to achieve anti-cancer immunity is to block the immune checkpoint pathways – mechanisms adopted by cancer cells to disguise themselves as regular components of the human body. Many review articles have described a variety of agents that are currently under extensive clinical evaluation. However, while checkpoint blockade is universally effective against a broad spectrum of cancer types and is mostly unrestricted by the mutation status of certain genes, only a minority of patients achieve a complete response. In this review, we summarize the basic principles of immune checkpoint inhibitors in both antibody and smallmolecule forms and also discuss potential mechanisms of resistance, which may shed light on further investigation to achieve higher clinical efficacy for these inhibitors.

scholarly journals Large-scale literature mining to assess the relation between anti-cancer drugs and cancer types

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Chris Bauer ◽  
Ralf Herwig ◽  
Matthias Lienhard ◽  
Paul Prasse ◽  
Tobias Scheffer ◽  
...  
Keyword(s):  
Text Mining ◽  
Knowledge Base ◽  
Survival Data ◽  
Scientific Literature ◽  
Entity Recognition ◽  
Literature Mining ◽  
Cancer Drugs ◽  
Classical Text ◽  
Anti Cancer ◽  
Cancer Types

Abstract Background There is a huge body of scientific literature describing the relation between tumor types and anti-cancer drugs. The vast amount of scientific literature makes it impossible for researchers and physicians to extract all relevant information manually. Methods In order to cope with the large amount of literature we applied an automated text mining approach to assess the relations between 30 most frequent cancer types and 270 anti-cancer drugs. We applied two different approaches, a classical text mining based on named entity recognition and an AI-based approach employing word embeddings. The consistency of literature mining results was validated with 3 independent methods: first, using data from FDA approvals, second, using experimentally measured IC-50 cell line data and third, using clinical patient survival data. Results We demonstrated that the automated text mining was able to successfully assess the relation between cancer types and anti-cancer drugs. All validation methods showed a good correspondence between the results from literature mining and independent confirmatory approaches. The relation between most frequent cancer types and drugs employed for their treatment were visualized in a large heatmap. All results are accessible in an interactive web-based knowledge base using the following link: https://knowledgebase.microdiscovery.de/heatmap. Conclusions Our approach is able to assess the relations between compounds and cancer types in an automated manner. Both, cancer types and compounds could be grouped into different clusters. Researchers can use the interactive knowledge base to inspect the presented results and follow their own research questions, for example the identification of novel indication areas for known drugs.

scholarly journals BART Cancer: a web resource for transcriptional regulators in cancer genomes

NAR Cancer ◽  
2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Zachary V Thomas ◽  
Zhenjia Wang ◽  
Chongzhi Zang
Keyword(s):  
Gene Expression ◽  
Cancer Research ◽  
Computational Prediction ◽  
Transcriptional Regulators ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Regulation Of Transcription ◽  
Data Resource ◽  
Web Resource ◽  
Cancer Types

Abstract Dysregulation of gene expression plays an important role in cancer development. Identifying transcriptional regulators, including transcription factors and chromatin regulators, that drive the oncogenic gene expression program is a critical task in cancer research. Genomic profiles of active transcriptional regulators from primary cancer samples are limited in the public domain. Here we present BART Cancer (bartcancer.org), an interactive web resource database to display the putative transcriptional regulators that are responsible for differentially regulated genes in 15 different cancer types in The Cancer Genome Atlas (TCGA). BART Cancer integrates over 10000 gene expression profiling RNA-seq datasets from TCGA with over 7000 ChIP-seq datasets from the Cistrome Data Browser database and the Gene Expression Omnibus (GEO). BART Cancer uses Binding Analysis for Regulation of Transcription (BART) for predicting the transcriptional regulators from the differentially expressed genes in cancer samples compared to normal samples. BART Cancer also displays the activities of over 900 transcriptional regulators across cancer types, by integrating computational prediction results from BART and the Cistrome Cancer database. Focusing on transcriptional regulator activities in human cancers, BART Cancer can provide unique insights into epigenetics and transcriptional regulation in cancer, and is a useful data resource for genomics and cancer research communities.

Synthesis, Electronic Structure and Anti-Cancer Activity of the Phenyl Substituted Pyrazolo[1,5-a][1,3,5]triazines

2021 ◽  
Vol 25 ◽  
Author(s):  
Evgenia S. Veligina ◽  
Nataliya V. Obernikhina ◽  
Stepan G. Pilyo ◽  
Oleksiy D. Kachkovsky ◽  
Volodymyr S. Brovarets
Keyword(s):  
Electronic Transition ◽  
Lone Electron Pair ◽  
Electron Pair ◽  
Anti Cancer ◽  
Protein Molecules ◽  
Biological Affinity ◽  
Cancer Types ◽  
Derivatives Of ◽  
Cancer Activity

: Background: Synthesis of a series of 2-(dichloromethyl)pyrazolo[1,5- a][1,3,5]triazines was carried out and evaluated in vitro for their anticancer activity against a panel of 60 cell lines derived from nine cancer types. The joint quantum-chemical and experimental study of the influence of the extended πconjugated phenyl substituents on the electron structure of the pyrazolo[1,5-a][1,3,5]triazines as Pharmacophores were performed. It is shown that the decrease in the barriers to the rotation of phenyl substituents in compounds 1-7 possibly leads to an increase in the anti-cancer activity, which is in agreement with the change in the parameter biological affinity ϕ0. Analysis of the S0 → S1 electronic transitions (π→π*) of the pyrazolo[1,5-a][1,3,5]triazines shows that an increase in their intensity correlates with anti-cancer activity. Thus, the introduction of phenyl substituents increases the likelihood of investigated pyrazolo[1,5-a][1,3,5]triazines interacting with protein molecules (Biomolecule) by the π stacking mechanism. In both methyl and phenyl derivatives of pyrazolo[1,5-a][1,3,5]triazines, the second electronic transition includes the n-MO (the level of the lone electron pair in two-coordinated nitrogen atoms). The highest intensity of the η→π* electronic transition is observed in pyrazolo[1,5-a][1,3,5]triazine with pyridine residue, which does not exhibit anti-cancer activity, but exhibits antiviral activity [13]. It can be assumed that the possibility of the formation of [Pharmacophore-Biomolecule] complex by hydrogen bonding ([H-B]) mechanism with protein molecules increases.

scholarly journals Materials Precursor Score: Modelling Chemists' Intuition for the Synthetic Accessibility of Porous Organic Cages

2021 ◽  
Author(s):  
Steven Bennett ◽  
Filip Szczypiński ◽  
Lukas Turcani ◽  
Michael Briggs ◽  
Rebecca L. Greenaway ◽  
...  
Keyword(s):  
Organic Molecules ◽  
Class Imbalance ◽  
Computational Prediction ◽  
Material Development ◽  
Computational Screening ◽  
Machine Learning Model ◽  
Synthetic Accessibility ◽  
Internal Cavities ◽  
Computational Discovery ◽  
Novel Structures

<div>Computation is increasingly being used to try to accelerate the discovery of new materials. One specific example of this is porous molecular materials, specifically porous organic cages, where the porosity of the materials predominantly comes from the internal cavities of the molecules themselves. The computational discovery of novel structures with useful properties is currently hindered by the difficulty in transitioning from a computational prediction to synthetic realisation. Attempts at experimental validation are often time-consuming, expensive and, frequently, the key bottleneck of material discovery. In this work, we developed a computational screening workflow for porous molecules that includes consideration of the synthetic difficulty of material precursors, aimed at easing the transition between computational prediction and experimental realisation. We trained a machine learning model by first collecting data on 12,553 molecules categorised either as `easy-to-synthesise' or `difficult-to-synthesise' by expert chemists with years of experience in organic synthesis. We used an approach to address the class imbalance present in our dataset, producing a binary classifier able to categorise easy-to-synthesise molecules with few false positives. We then used our model during computational screening for porous organic molecules to bias towards precursors whose easier synthesis requirements would make them promising candidates for experimental realisation and material development. We found that even by limiting precursors to those that are easier-to-synthesise, we are still able to identify cages with favourable, and even some rare, properties. </div>

scholarly journals Potential role of acetylsalicilic acid and other non-steroidal anti-inflammatory drugs in cancer risk reduction (literature review)

2021 ◽  
Vol 23 (3) ◽  
Author(s):  
V. V. Buheruk ◽  
O. B. Voloshyna ◽  
L. I. Kovalchuk ◽  
I. V. Balashova ◽  
O. V. Naidionova
Keyword(s):  
Cancer Risk ◽  
Acetylsalicylic Acid ◽  
Potential Role ◽  
Task Force ◽  
Current Systematic Review ◽  
Anti Cancer ◽  
Cancer Types ◽  
Inflammatory Drugs ◽  
Anti Inflammatory

The aim of this review is to analyze and summarize the existing evidence regarding the possibilities of using acetylsalicylic acid (ASA) and other non-steroidal anti-inflammatory drugs (NSAIDs) to reduce cancer risk. Conclusions. Chronic inflammation facilitates the onset and progress of tumour growth. Anti-cancer properties of acetylsalicylic acid and other non-steroidal anti-inflammatory drugs are mediated via cyclooxygenase COX-dependent mechanisms, as well as other tumorigenic pathways. Current systematic review addresses potential role of ASA and other NSAIDs in reduction of cancer risk for the following localizations: head and neck, lungs, gastrointestinal tract, breast, ovaries, prostate, and skin. The role of ASA in primary prevention of colorectal cancer in specific populations is presented in 2016 U. S. Preventive Services Task Force guidelines. Studies indicate heterogeneous protective potential of ASA against different cancer types, depending on studied population, duration of intake and dose. Influence of non-aspirin NSAIDs on cancer morbidity and mortality is more controversial.

Identification of Natural Compounds to Inhibit Sonic Hedgehog Pathway in Oral Cancer

2021 ◽  
Vol 21 ◽  
Author(s):  
Hitarth Patel ◽  
Jigna Joshi ◽  
Apexa Raval ◽  
Franky Shah
Keyword(s):  
Stem Cell ◽  
Binding Energy ◽  
Oral Cancer ◽  
Sonic Hedgehog ◽  
Hedgehog Signaling ◽  
Treatment Resistance ◽  
Natural Compounds ◽  
Anti Cancer ◽  
Cancer Agent ◽  
Hedgehog Inhibitor

Background: Conventional treatment resistance remains a significant problem in cancer care. Cancer stem cells might play a major role in treatment resistance, and as a result, basic stem cell pathways are instrumental in cancer. Sonic Hedgehog signaling has not been widely studied in oral cancer, and being one of the major cancer stem cell pathways, targeting it with natural compounds could open many opportunities in the treatment scenario. Objective: The objective of the study was to identify the role of various natural compounds as an anti-cancer agent for oral cancer by targeting the Hedgehog signaling pathway. Methods: The selection of natural compounds were identified through literature review and NPACT database. The protein (3M1N and 3MXW) and ligand molecules were retrieved through the PDB and PubChem database. To carry out docking experiments, the AutoDock 4.2 program was used to study the interaction between the identified protein and ligand. Results: Among the 13 identified natural compounds, the top three were selected based on their binding energy. The higher the binding energy on the negative side, the better the interaction formed between protein and ligand. The natural compound showing best results with 3M1N protein were Butein, Biochanin-A, and Curcumin, whereas, with 3MXW, Zerumbone, Curcumin, and Butein were identified. Conclusion: The identified natural compounds have shown better binding energy to bind the Hh ligands in the absence/presence of a known Sonic Hedgehog inhibitor. Based on the results, natural compounds can be utilized in the current treatment modality for oral cancer either as an individual anti-cancer agent or in combination with the known Sonic Hedgehog inhibitor to curb the increasing incidence rate. Yet, in-vitro evidence in lab setup is required.

Metformin Treatment Sensitizes Human Laryngeal Cancer Cell Line Hep- 2 to 5-Fluorouracil

2020 ◽  
Vol 7 (1) ◽  
pp. 16-24
Author(s):  
Neslisah Barlak ◽  
Fatma Sanli ◽  
Ozel Capik ◽  
Elanur Tuysuz ◽  
Elanur Aydın Karatas ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Cancer Cell Line ◽  
Inhibitory Effect ◽  
Soft Agar ◽  
Invasive Potential ◽  
Matrigel Invasion ◽  
Soft Agar Assay ◽  
Anti Cancer ◽  
Apoptosis Assay ◽  
Cancer Types

Background: Larynx cancer (LCa) is the most common head and neck cancer and accounts for 1-2.5% of all human cancers worldwide. Metformin, an oral anti-diabetic drug, has been recently shown to have anti-cancer activity in various cancer types, and there are several studies in the literature pointing to its potential to sensitize cancer cells to chemotherapeutic drugs. Objective: This study was aimed at exploring the anti-cancer effects of metformin alone or in combination with 5-fluorouracil (5-FU) on Hep-2 cells. Methods: The effects of metformin and/or 5-FU on the proliferative, clonogenic, and apoptotic potential of Hep-2 cells were evaluated with Cell Viability Detection Kit-8, soft agar assay and Annexin VFITC Apoptosis assay, respectively. Migratory and invasive potential of cells was tested using scratch, transwell migration and Matrigel invasion assays. Gene expression of cells exposed to metformin and/or 5-FU was profiled using RT2 mRNA PCR Array plates. Results: Treatment of Hep-2 cells with metformin inhibited cell proliferation by inducing apoptosis, and suppressed cell migration. Besides, treatment of metformin along with 5-FU improved the antiproliferative and anti-migratory effects of 5-FU. However, unexpectedly, metformin was found to enhance cellular invasion and reverse the inhibitory effect of 5-FU on the invasive potential of Hep-2 cells. Conclusion: Our findings suggest that metformin might be used as an adjuvant agent in the treatment of LCa. However, the potential of metformin to promote the invasion of cancer cells should not be neglected.

scholarly journals Computational prediction and experimental confirmation of B-site doping in YBa2Fe3O8

2014 ◽  
Vol 5 (4) ◽  
pp. 1493-1505 ◽  
Author(s):  
Christopher Collins ◽  
Matthew S. Dyer ◽  
Antoine Demont ◽  
Philip A. Chater ◽  
Michael F. Thomas ◽  
...  
Keyword(s):  
Computational Prediction ◽  
Experimental Confirmation ◽  
Synthesis And Characterization ◽  
Computational Screening ◽  
Mn Substitution

Computational screening of potential substitution species and sites in YBa2Fe3−xMxO8 predicted that Mn substitution at x = 1 should be possible. Experimental synthesis and characterization of Y1.175Ba1.825Fe2MnO8 confirms this prediction.

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