Protein domain-based prediction of drug/compound–target interactions and experimental validation on LIM kinases

Predictive approaches such as virtual screening have been used in drug discovery with the objective of reducing developmental time and costs. Current machine learning and network-based approaches have issues related to generalization, usability, or model interpretability, especially due to the complexity of target proteins’ structure/function, and bias in system training datasets. Here, we propose a new method “DRUIDom” (DRUg Interacting Domain prediction) to identify bio-interactions between drug candidate compounds and targets by utilizing the domain modularity of proteins, to overcome problems associated with current approaches. DRUIDom is composed of two methodological steps. First, ligands/compounds are statistically mapped to structural domains of their target proteins, with the aim of identifying their interactions. As such, other proteins containing the same mapped domain or domain pair become new candidate targets for the corresponding compounds. Next, a million-scale dataset of small molecule compounds, including those mapped to domains in the previous step, are clustered based on their molecular similarities, and their domain associations are propagated to other compounds within the same clusters. Experimentally verified bioactivity data points, obtained from public databases, are meticulously filtered to construct datasets of active/interacting and inactive/non-interacting drug/compound–target pairs (~2.9M data points), and used as training data for calculating parameters of compound–domain mappings, which led to 27,032 high-confidence associations between 250 domains and 8,165 compounds, and a finalized output of ~5 million new compound–protein interactions. DRUIDom is experimentally validated by syntheses and bioactivity analyses of compounds predicted to target LIM-kinase proteins, which play critical roles in the regulation of cell motility, cell cycle progression, and differentiation through actin filament dynamics. We showed that LIMK-inhibitor-2 and its derivatives significantly block the cancer cell migration through inhibition of LIMK phosphorylation and the downstream protein cofilin. One of the derivative compounds (LIMKi-2d) was identified as a promising candidate due to its action on resistant Mahlavu liver cancer cells. The results demonstrated that DRUIDom can be exploited to identify drug candidate compounds for intended targets and to predict new target proteins based on the defined compound–domain relationships. Datasets, results, and the source code of DRUIDom are fully-available at: https://github.com/cansyl/DRUIDom

Hücre takibinde nanoparçacık işaretlenmesinin kullanımı

Cataloged from PDF version of thesis.Thesis (Ph.D.): Bilkent University, The Department of Molecular Biology and Genetics and the Graduate School of Engineering and Science of İhsan Doğramacı Bilkent University, 2015.Includes bibliographical references (leaves 101-123).Adult stem cells (ASCs) are a population of multipotent cells which have ability of self-renewal and tissue regeneration. Due to their protective and restorative roles, ASCs become candidate for cellular therapies. Some cellular imaging methods have been developed to monitor stem cell differentiation and migration. Regrettably, none of these techniques possess the properties of an ideal imaging methods such as photostability and non-toxicity. A new type of probe, conjugated polymer based water-dispersible nanoparticles (CPN) that possess strong fluorescence light emission, non-toxicity, photosability and high brightness, has been developed to fulfill the needs of cellular tracking. The aim of this study is to show the utilization of CPN labeling in in vitro and in vivo cellular tracking. We initially focused on the monitoring of the differentiation and migration of MSCs which have been proved to be a promising therapeutic tool. First we showed 24h CPN labeling did not cause severe decrease in the cellular activity of MSCs and had no effect on their marker expression and differentiation capacity in vitro. In addition, 24h CPN labeled MSCs showed very intense green fluorescence emission which was still bright after 3 weeks of MSC differentiation. We also showed CPN labeled MSCs were able to migrate to the damaged site and retained their labels in vivo. Similar to MSCs, DPSCs were labeled intensely and with negligible decrease in their cellular activity within 24h CPN incubation and it had no effect on their differentiation capacity. iv In addition, cancer cell tracking is important for the understanding of steps of metastasis and chemotherapeutic drug’s mode of action. Therefore, we tested CPN labeling in Huh7 cells, and we showed that labeled cells had very intense fluorescence emission without any change in their cellular activity. Moreover, tumor xenograft model that were generated with either 24h or 72h CPN labeled Huh7 cells showed that CPN labeling retained for about 2-3 months in vivo and did not lose their brightness. To conclude, we aim to propose a new approach for in vivo cellular tracking in order to obtain unattainable information of the migration and homing behaviors of the stem cells. In addition, our approach can also be used for evaluation of cancer cell metastasis as well as the success of stem cell or anti-cancer therapy.by Ece Akhan Güzelcan.Ph.D

Nanoparticle Labeling of Bone Marrow-Derived Rat Mesenchymal Stem Cells: Their Use in Differentiation and Tracking

Mesenchymal stem cells (MSCs) are promising candidates for cellular therapies due to their ability to migrate to damaged tissue without inducing immune reaction. Many techniques have been developed to trace MSCs and their differentiation efficacy; however, all of these methods have limitations. Conjugated polymer based water-dispersible nanoparticles (CPN) represent a new class of probes because they offer high brightness, improved photostability, high fluorescent quantum yield, and noncytotoxicity comparing to conventional dyes and quantum dots. We aimed to use this tool for tracing MSCs’ fate in vitro and in vivo. MSC marker expression, survival, and differentiation capacity were assessed upon CPN treatment. Our results showed that after CPN labeling, MSC markers did not change and significant number of cells were found to be viable as revealed by MTT. Fluorescent signals were retained for 3 weeks after they were differentiated into osteocytes, adipocytes, and chondrocytes in vitro. We also showed that the labeled MSCs migrated to the site of injury and retained their labels in an in vivo liver regeneration model. The utilization of nanoparticle could be a promising tool for the tracking of MSCs in vivo and in vitro and therefore can be a useful tool to understand differentiation and homing mechanisms of MSCs

Benchmarking of Regression Algorithms and Time Series Analysis Techniques for Sales Forecasting

Predicting the sales amount as close as to the actual sales amount can provide many benefits to companies. Since the fashion industry is not easily predictable, it is not straightforward to make an accurate prediction of sales. In this study, we applied not only regression methods in machine learning, but also time series analysis techniques to forecast the sales amount based on several features. We applied our models on Walmart sales data in Microsoft Azure Machine Learning Studio platform. The following regression techniques were applied: Linear Regression, Bayesian Regression, Neural Network Regression, Decision Forest Regression and Boosted Decision Tree Regression. In addition to these regression techniques, the following time series analysis methods were implemented: Seasonal ARIMA, Non-Seasonal ARIMA, Seasonal ETS, Non -Seasonal ETS, Naive Method, Average Method and Drift Method. It was shown that Boosted Decision Tree Regression provides the best performance on this sales data. This project is a part of the development of a new decision support system for the retail industry

Identification of gene mutations ınvolved in drug resistance in liver cancer using rna-seq data analysis

Cancer is the leading cause of death worldwide and the risk increases with aging. A significant concern in cancer research is the detection of cancer drug resistance associated somatic mutations. According to Global Cancer Statistics (GCS) liver cancer is the 5th most common and 2nd deadliest cancer in the world. Over the past two decades, the death rate for liver cancer increased by 2.5% per year and incidents were 3 times higher in men than in women (American Cancer Society). Appropriate treatment of hepatocellular carcinoma (HCC, primary liver cancer) depends on the disease stage, patient’s age and overall health and individual priorities. Targeted therapies are known to block cancer-associated proteins or prevent cell proliferation and invasion. Unlike conventional chemotherapy, which affects the whole normal and cancerous fast-growing cells, targeted drugs attack specific molecules in cancer cells and have much less impact on healthy tissues. With targeted therapy in mind, in this study, the relationship between mutation status and drug treatment response of well-differentiated Huh7 and poorly-differentiated Mahlavu liver cancer cells were analyzed. The RNA-Seq data of each cancer cell line (as control) was compared to “sorafenib” and “PI3K/Akt Pathway inhibitors” treated samples. Somatic mutations associated with drug resistance were comparatively identified with MuTect tool (Cibulskis, 2013). The results were then filtered to distinguish the missense mutations. The common genes among drugresistant sets were found to be associated with liver cancer perseverance and aggressiveness. SLC39A5, FRG1, PPHLN1 and SRP9 gene mutations were found to be the most significant, shared among three drug treated sets. The sets were further investigated in detail to discover the liver cancer associated survival genes. Using our results, appropriate targets can be defined that play critical roles in cancerous cell growth for drug development purposes. Drugs with specific targets can find the appointed place and turn the target gene off to disturb the cancer cells’ proliferation. Accordingly, the mutated genes activities are stopped and the disease progression is prevented. The mutated genes that we identified during the chemical knockdown studies can be further studied in gene expression vs. patient survival data. These genes are being analyzed in laboratory to test whether their silence (knocking-down founded gene mutations which are correlated with liver cancer under drug treatments) decreases cancer growth or not. In our success we can target these genes in future cancer treatments. Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnology (2013).doi:10.1038/nbt.251

Synthesis of new derivatives of boehmeriasin A and their biological evaluation in liver cancer

Two series of boehmeriasin A analogs have been synthesized in short and high yielding processes providing derivatives differing either in the alkaloid's pentacyclic scaffold or its peripheral substitution pattern. These series have enabled, for the first time, comparative studies into key biological properties revealing a new lead compound with exceptionally high activity against liver cancer cell lines in the picomolar range for both well (Huh7, Hep3B and HepG2) and poorly (Mahlavu, FOCUS and SNU475) differentiated cells. The cell death was characterized as apoptosis by cytochrome-C release, PARP protein cleavage and SubG1 cell cycle arrest. Subsequent testing associated apoptosis via oxidative stress with in situ formation of reactive oxygen species (ROS) and altered phospho-protein levels. Compound 19 decreased Akt protein phosphorylation which is crucially involved in liver cancer tumorigenesis. Given its simple synthetic accessibility and intriguing biological properties this new lead compound could address unmet challenges within liver cancer therapy

Iterative H-Minima-Based Marker-Controlled Watershed for Cell Nucleus Segmentation

Automated microscopy imaging systems facilitate high-throughput screening in molecular cellular biology research. The first step of these systems is cell nucleus segmentation, which has a great impact on the success of the overall system. The marker-controlled watershed is a technique commonly used by the previous studies for nucleus segmentation. These studies define their markers finding regional minima on the intensity/gradient and/or distance transform maps. They typically use the h-minima transform beforehand to suppress noise on these maps. The selection of the h value is critical; unnecessarily small values do not sufficiently suppress the noise, resulting in false and oversegmented markers, and unnecessarily large ones suppress too many pixels, causing missing and undersegmented markers. Because cell nuclei show different characteristics within an image, the same h value may not work to define correct markers for all the nuclei. To address this issue, in this work, we propose a new watershed algorithm that iteratively identifies its markers, considering a set of different h values. In each iteration, the proposed algorithm defines a set of candidates using a particular h value and selects the markers from those candidates provided that they fulfill the size requirement. Working with widefield fluorescence microscopy images, our experiments reveal that the use of multiple h values in our iterative algorithm leads to better segmentation results, compared to its counterparts. (C) 2016 International Society for Advancement of Cytometr