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

Comparison of TNM 1987 and TNM 1997 classifýcations in staging of renal adenocarcinoma

Amaç: Böbrek adenokarsinomunun klinik olarak doğru evrelenmesi prognoza yönelik bilgiler ve doğru tedavi seçimi sağlama açısından önem taþımaktadır. Bu çalıþmada bilgisayarlı tomografi (BT) ile klinik evrelemede, hemTNM87 hem de 97 sınıflamasının patolojik evreleme ile korelasyonunu incelemeyi amaçladık. Yöntem: Ocak 1995-Kasım 2000 tarihleri arasında böbrek adenokarsinomu tanısı ile radikal nefrektomi uygulanan 66 olgununBTve patoloji bulgularına göre yeniden evrelemesi yapıldı. Bulgular: TNM 97 evreleme sistemi uygulandığında, TNM 87'ye göre T2 evre olan 23 olgunun T1 evreye geçtiği belirlendi. T3 olgularında ise evrelemede bir değiþim olmadı. BT ile klinik evrelemenin patolojik evrelemeye korelasyonu, hem TNM 87 hem TNM 97 sınıflamasına göre istatiksel olarak anlamlı bulundu (p<0.001). Sonuç: Böbrek adenokarsinomlarının klinik evrelemesinde BT etkin ve güvenilir bir tanı yöntemidir. TNM 97 evreleme sisteminin patolojik evre ile olan uyumu, TNM 87 sistemine göre istatistiksel olarak daha yüksek bulunmuþtur (p<0.01). Yeni sınıflama ile saptanan evre II'den evre I'e belirgin kaymanın olgulardaki takip maliyetlerini azaltabileceği sonucuna vardık.Objective: Accurate clinical staging of renal adenocarcinomas is important in determining prognosis and correct mode of therapy. In the present study, we aimed to examine the correlation of clinical staging with computerized tomography (CT)accordingtobothTNM 1987 and 1997 classifications withpathological staging. Material and Methods: Sixty-six patients with a diagnosis of renal adenocarcinoma who underwent radical nephrectomy between January 1995-November 2000 were re-staged according to CT and histopathological findings. Results: Using the TNM 1997 classification resulted in a redistribution of 23 patients from stage pT2 to stage pT1. There was no change in classification of patients with pT3 disease. Clinical staging with CT and pathological correlation was found to be statistically significant in both TNM 1987 and TNM 1997 staging classifications (p<0.001). Conclusion: Clinical staging of renal adenocarcinomas with CT is an effective and reliable method. TNM 1997 staging has a statistically higher correlation with pathological staging compared to TNM 1987. It is concluded that the apparent shift of cases from stage II to stage I with the new classification will help decrease of follow-up costs

GOPred: GO Molecular Function Prediction by Combined Classifiers

Functional protein annotation is an important matter for in vivo and in silico biology. Several computational methods have been proposed that make use of a wide range of features such as motifs, domains, homology, structure and physicochemical properties. There is no single method that performs best in all functional classification problems because information obtained using any of these features depends on the function to be assigned to the protein. In this study, we portray a novel approach that combines different methods to better represent protein function. First, we formulated the function annotation problem as a classification problem defined on 300 different Gene Ontology (GO) terms from molecular function aspect. We presented a method to form positive and negative training examples while taking into account the directed acyclic graph (DAG) structure and evidence codes of GO. We applied three different methods and their combinations. Results show that combining different methods improves prediction accuracy in most cases. The proposed method, GOPred, is available as an online computational annotation tool (http://kinaz.fen.bilkent.edu.tr/gopred)