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

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

Kanserli dokuların mikroskop görüntülerinde kanser kök hücre oranın otomatik olarak belirlenmesi ve klinikte kullanılacak yazılım geliştirilmesi

TÜBİTAK EEEAG01.12.2016Kanser anormal hücrelerin kontrolsüz çoğalması ve yayılması olarak tanımlanan karmaşık bir hastalıktır. Bu nedenle kanser dokusu farklı özellikleri olan-ek popülasyon denilen-hücre gruplarını içerir. Bu hücrelerden olan kanser kök hücreleri normal kök hücreleri gibi kendini yenileme ve farklılaşma özelliklerini taşırken normal kök hücrelerinin aksine homeostatik kontrolleri olmayan, yani farklılaşma özelliklerini dengeleyip, çevresel sinyallere göre programlama özellikleri olmayan hücrelerdir. Bu özellikleri dolayısıyla da son yıllarda kanser kök hücre popülasyonu kanserin kötü prognozundan sorumlu hücreler olarak tanımlanmaktadırlar. Bu bağlamda kanser tedavisinde kanser kök hücre oran bilgisi, hastaya verilecek tedavinin planlanması amacıyla kullanılabilir. Hekimler kanserli dokunun kanser kök hücre popülasyon onarına göre henüz kanser metastaz yapmamış bile olsa bireye özgü tedavi yaklaşımı izlenebilir. Gerçekleştirilen bu proje kapsamında, kanserli parafin bloklara gömülü doku biyopsi örneklerinde, kanser kök hücre tanımlama öngörü aracını geliştirdik. Kovaryans matrisine dayalı sınıflandırıcılara ve Ana Bileşenler Analizi (PCA) algoritması sonucunda üç sınıf H&E boyanmış karaciğer kanseri dokularının sınıflandırma probleminde 76.0% imge sınıflandırma başarısı elde edilmiştir. Ana Bileşenler Analizi kanser hücresi belirleme probleminde %90’nın üstünde başarı ve kovaryans matrisine dayalı sınıflandırıcılar ile üç sınıf ayrıştırma probleminde %90’a yakın başarı sağlanmıştır. Kanserli hastalarda bireye ve hedefe yönelik tedavi yol haritasının belirlenmesine destek olabilecek ve hastalığın tedavisinde ve prognozunda iyileştirme sağlayabilecek yazılımı hekimlerin kullanıma sunduk. CANSTEM yazılıma http://users.metu.edu.tr/rengul/canstem.html adresinden ulaşılabilir.Cancer is a complex disease characterized by uncontrolled cell proliferation and metastasis. For this reason, cancer tissue includes cell groups called side-population, which have different properties. Similar to the normal stem cells, cancer stem cells possess self- renewing and differentiation capacities but lack homeostatic controls. Cancer stem cells don’t respond to the external which control growth regulation and differentiation. Because of these features, cancer stem cells in recent years have been described as cell populations responsible for the poor prognosis of cancer. In this context, cancer stem cell ratio information can be used to plan the treatment. Physicians can monitor the individualized treatment approach even if the cancer has not yet metastasized at early stage. Within the scope of this project, we developed a cancer stem cell prediction tool in cancerous tissue biopsy specimens embedded in paraffin blocks. Classification based on the covariance matrix and PCA algorithm resulted in 76.0% image classification success for the classification H&E stained liver cancer tissues. The Principal Component Analysis achieved over 90% success and the covariance matrix in the classification problem of three classes had a success nearly 90% in cancer detection. The newly developed CANSTEM software allows the identification cancer stem cell ratio in cancer tissues for individualized and targeted treatment and prognosis of the disease. CANSTEM software is available at http://users.metu.edu.tr/rengul/canstem.html