Alteration in the expression of the chemotherapy resistance‑related genes in response to chronic and acute hypoxia in pancreatic cancer

Pancreatic cancer is currently one of the least curable types of human cancer and remains a key health problem. One of the most important characteristics of pancreatic cancer is its ability to grow under hypoxic conditions. Hypoxia is associated with resistance of cancer cells to radiotherapy and chemotherapy. It is a major contributor to pancreatic cancer genetic instability, which local and systemic resistance that may result in poor clinical outcome. Accordingly, identifying gene expression changes in cancer resistance genes that occur under hypoxic conditions may identify a new therapeutic target. The aim of the present study was to explore the association between hypoxia and resistance to chemotherapy and determine the alteration in the expression of cancer resistance-related genes in the presence of hypoxia. Pancreatic cancer cells (PANC-1) were exposed to 8 h hypoxic episodes (<1% oxygen) three times/week for a total of 20 episodes (chronic hypoxia) or 72 h hypoxic episodes twice/week for a total of 10 episodes (acute hypoxia). The alterations in gene expression were examined using reverse transcription-quantitative PCR array compared with normoxic cells. Chemoresistance of hypoxic cells toward doxorubicin was assessed using MTT cell proliferation assay. Both chronic and acute hypoxia induced chemoresistance toward doxorubicin in PANC-1 pancreatic cancer cell line. The greatest changes occurred in estrogen Receptor Alpha Gene (ESR1) and ETS Like-1 protein (ELK1) pathways, in nucleic transcription factor Peroxisome proliferator-activated receptors (PPARs) and in a cell cycle inhibitor cyclin dependent kinase inhibitor 1A (CDKN1A). The present study demonstrated that exposing cells to prolonged hypoxia results in different gene expression changes involving pleotropic pathways that serve a role in inducing resistance in pancreatic cancer.

Solvent Accessibility Promotes Rotamer Errors during Protein Modeling with Major Side-Chain Prediction Programs

Side-chain rotamer prediction is one of the most critical late stages in protein 3D structure building. Highly advanced and specialized algorithms (e.g., FASPR, RASP, SCWRL4, and SCWRL4v) optimize this process by use of rotamer libraries, combinatorial searches, and scoring functions. We seek to identify the sources of key rotamer errors as a basis for correcting and improving the accuracy of protein modeling going forward. In order to evaluate the aforementioned programs, we process 2496 high-quality single-chained all-atom filtered 30% homology protein 3D structures and use discretized rotamer analysis to compare original with calculated structures. Among 513,024 filtered residue records, increased amino acid residue-dependent rotamer errors─associated in particular with polar and charged amino acid residues (ARG, LYS, and GLN)─clearly correlate with increased amino acid residue solvent accessibility and an increased residue tendency toward the adoption of non-canonical off rotamers which modeling programs struggle to predict accurately. Understanding the impact of solvent accessibility now appears key to improved side-chain prediction accuracies.

Anti-Proliferative Effect of Potential LSD1/CoREST Inhibitors Based on Molecular Dynamics Model for Treatment of SH-SY5Y Neuroblastoma Cancer Cell Line

BACKGROUND

Lysine-specific demethylase is a demethylase enzyme that can remove methyl groups from histones H3K4me1/2 and H3K9me1/2. It is expressed in many cancers, where it impedes differentiation and contributes to cancer cell proliferation, cell metastasis and invasiveness, and is associated with inferior prognosis. LSD1 is associated with its corepressor protein CoREST, and utilizes tetrahydrofolate as a cofactor to accept CH2 from the demethylation process. The fact that the cofactor is best bound to the active site inspired us to explore its interactions to LSD1/CoREST enzyme complex utilizing molecular dynamics simulation, which aids designing novel and potent inhibitors.

OBJECTIVE

In this study we minted to identify a new potential LSD1/CoREST inhibitors and test the potency and the safety of such inhibitors against human neuroblastoma and fibroblast cells lines.

METHODS

We have implemented a previously derived model from the molecular dynamics simulation study and the key contacts to the active site in a subsequent structure based drug design and in-silico screening, which revealed a number of potential inhibitors toward LSD1/CoREST complex. The anti-proliferative activities of the identified compounds will be tested against neuroblastoma SH-SY5Y cancer cell line which known to highly express LSD1/CoREST complex.

RESULTS

In-silico mining on National Cancer Institute (NCI) database identified 55 promising and structurally diverse inhibitors. Applying the abovementioned molecular modeling procedure yielded four compounds of LSD1/CoREST inhibiters with IC50 < 2µM. The four lead compounds were tested against SH-SY5Y neuroblastoma cell line that known to express high level of LSD1 and illustrated a potent activity with an IC50 ranging from 0.195 to 1.52µM. To estimate the toxicity of the selective leads, they were tested against normal fibroblast cells and scored a relatively high IC50 ranging from 0.303 to ≥ 100µM.

CONCLUSION

Our model revealed promising inhibitors that can be used in treating cancers that overexpress the LSD1 enzyme such as the SH-SY5Y neuroblastoma.

A rotamer relay information system in the epidermal growth factor receptor-drug complexes reveals clues to new paradigm in protein conformational change

Cancer cells can escape the effects of chemotherapy through mutations and upregulation of a tyrosine kinase protein called the epidermal growth factor receptor (EGFR). In the past two decades, four generations of tyrosine kinase inhibitors targeting EGFR have been developed. Using comparative structure analysis of 116 EGFR-drug complex crystal structures, cluster analysis produces two clans of 73 and 43 structures, respectively. The first clan of 73 structures is larger and is comprised mostly of the C-helix-IN conformation while the second clan of 43 structures correlates with the C-helix-OUT conformation. A deep rotamer analysis identifies 43 residues (18%) of the total of 237 residues spanning the kinase structures under investigation with significant rotamer variations between the C-helix-IN and C-helix-OUT clans. The locations of these rotamer variations take on the appearance of side chain conformational relays extending out from points of EGFR mutation to different regions of the EGFR kinase. Accordingly, we propose that key EGFR mutations act singly or together to induce drug resistant conformational changes in EGFR that are communicated via these side chain conformational relays. Accordingly, these side chain conformational relays appear to play a significant role in the development of tumour resistance. This phenomenon also suggests a new paradigm in protein conformational change that is mediated by supportive relays of rotamers on the protein surface, rather than through conventional backbone movements.

Homology modeling in the time of collective and artificial intelligence

Homology modeling is a method for building protein 3D structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and ab initio modeling. Critical Assessment of protein Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3D structure. In the past few years, contact maps deep machine learning was introduced to the 3D structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field.

Comparative anti-proliferative effects of potential HER2 inhibitors on a panel of breast cancer cell lines

BACKGROUND

Breast cancer is one of the most lethal types of cancer in women worldwide. The human epidermal growth factor receptor 2 (HER2) is considered as a validated target in breast cancer therapy. Previously, we have used quantitative structure activity relationship QSAR equations and their associated pharmacophore models to screen for new promising HER2 structurally diverse inhibitory leads which were tested against HER2-overexpressing SKOV3 ovarian cancer cell line.

OBJECTIVE

In this study, we sought to explore the effect of most active ligands against different normal and breast cancer cell lines that represent different breast cancer subtypes with distinguished expression levels in HER2 and HER1.

METHODS

We have tested the promising compounds against SKBR3, MDA-MB-231, MCF7, human fibroblast, and MCF10 cell lines. To understand the inhibitory effects of the active ligands against HER2 over expressed breast cancer cell lines, all inhibitors and the control compound, lapatinib, were docked into the active site of HER2 enzyme performed using Ligand Fit docking engine and PMF scoring function.

RESULTS

Five ligands exhibited promising results with relatively low IC₅₀ values on cells that amplify HER2 and high IC₅₀ on those that do not express such a receptor. The most potent compound (compound 13) showed an IC₅₀ of 0.046 µM. To test their toxicity against normal cells, the active compounds were tested against both normal fibroblast and normal breast cancer cell MCF-10 and relatively high IC₅₀ values were scored. The IC₅₀ values on HER2 over-expressed breast cancer and normal fibroblast cells provided a promising safety index. Docking results showed the highest similarity in the binding site between the most active ligand and the lapatinib.

CONCLUSION

Our pharmacophore model resulted in a high potent ligand that shows high potency against HER2 positive breast cancer and relatively low toxicity towards the normal human cells.