Molecular simulation approach integrated with deep learning model for the discovery of Tankyrase inhibitors

Tankyrases belong to the poly (ADP-ribose) polymerase family epitomized as a novel group of medicinal targets with various prospective diseased conditions and it is appraised to be a challenging drug target for the intervention of multiple cancers. Thus, the principal objective of our study is to explore the dual-site selective tankyrase 1 inhibitor by employing the pharmacophore strategy. Initially, the ligand-based pharmacophore study generated five featured pharmacophore hypothesis, which was then employed for the database screening. The screened molecules were scrutinized through docking, MM/GBSA calculations and molecular simulation alongside pharmacokinetics properties. The analysis yielded potent dual-site tankyrase 1 inhibitors such as nebivolol and ondansetron from the DrugBank repository. Notably, the recognized lead molecules were perceived to have higher XP GScore and binding energy scores. Subsequently, simulation studies were executed to validate the structural stability of the lead molecules. It is worth mentioning that the existence of benzopyran and carbazole scaffolds in the lead molecules displayed anti-neoplastic activity and also facilitate the effective binding with tankyrase 1 protein. Ultimately, the IC50 values of the lead molecules were examined against the NCI-H596 cell line using a deep learning model. Indeed, these results are of immense importance and provide a clue to the experimental biologist in developing a potent tankyrase 1 dual-site inhibitor.

EasyModel: user-friendly tool for building and analysis of simple mathematical models in systems biology

Summary EasyModel is a new user-friendly web application that contains ready-for-simulation versions of the BioModels Database, and allows for the intuitive creation of new models. Its main target audience is the experimental biologist and students of bioinformatics or systems biology without programming skills. Expert users can also benefit from it by implementing basic models quickly and downloading the code for further tailoring. Availability and implementation Freely available on the web at https://easymodel.udl.cat. Implementation is described in its own section.

Prediction of lysine post-translational modifications using bioinformatic tools

Our understanding of the importance of lysine post-translational modifications in mediating protein function has led to a significant improvement in the experimental tools aimed at characterizing their existence. Nevertheless, it remains likely that at present we have only experimentally detected a small fraction of all lysine modification sites across the commonly studied proteomes. As a result, online computational tools aimed at predicting lysine modification sites have the potential to provide valuable insight to researchers developing hypotheses regarding these modifications. This chapter discusses the metrics and procedures used to assess predictive tools and surveys 11 online computational tools aimed at the prediction of the four most widely studied lysine post-translational modifications (acetylation, methylation, SUMOylation and ubiquitination). Analyses using unbiased testing data sets suggest that nine of the 11 lysine post-translational modification tools perform no better than random, or have false-positive rates which make them unusable by the experimental biologist, despite self-reported sensitivity and specificity values to the contrary. The implications of these findings for those using and creating lysine post-translational modification software are discussed.

THE PREDICTION OF BACTERIAL TRANSCRIPTION START SITES USING SVMS

Identifying promoters is the key to understanding gene expression in bacteria. Promoters lie in tightly constrained positions relative to the transcription start site (TSS). In this paper, we address the problem of predicting transcription start sites in Escherichia coli. Knowing the TSS position, one can then predict the promoter position to within a few base pairs, and vice versa. The accepted method for promoter prediction is to use a pair of position weight matrices (PWMs), which define conserved motifs at the sigma-factor binding site. However this method is known to result in a large number of false positive predictions, thereby limiting its usefulness to the experimental biologist. We adopt an alternative approach based on the Support Vector Machine (SVM) using a modified mismatch spectrum kernel. Our modifications involve tagging the motifs with their location, and selectively pruning the feature set. We quantify the performance of several SVM models and a PWM model using a performance metric of area under the detection-error tradeoff (DET) curve. SVM models are shown to outperform the PWM on a biologically realistic TSS prediction task. We also describe a more broadly applicable peak scoring technique which reduces the number of false positive predictions, greatly enhancing the utility of our results.