Identifying High-Quality Leads among Screened Anticancerous Compounds Using SMILES Representations.

Cancer is a lethal disease that affects numerous people worldwide. Chemotherapy stands as one of the most effective treatment regimens to combat cancer. Nevertheless, anticancer drugs face a high failure rate due to safety and efficacy issues. Drug failure could be subdued by instigating drug leads with reduced toxicity and enhanced efficacy. Computer-aided drug discovery endorses drug leads in manoeuvring protein and ligand structures or representations. Simplified molecular input line entry system (SMILES) is a linear notation representing the three-dimensional structure of a molecule using symbols and alphanumeric characters. SMILES representation hoards rings and scaffold structures in its depiction. Mining ring and scaffold patterns from molecular SMILES would assist in ascertaining biological properties based on molecular patterns. Moreover, the emergence of artificial intelligence (AI) technologies would accelerate identification of efficient anticancer drug leads. AI algorithms proclaimed for their pattern recognition ability could be employed for identifying molecular patterns from SMILES representation, thereby enabling property prediction. Consequently, we developed a multilayer perceptron (MLP) model for the prediction of anticancer activity using SMILES of NCI-60 cancer growth inhibition data. Furthermore, the top 8 frequent scaffolds were identified on preliminary analysis of cancer growth inhibition data and ChEMBL drugs. The developed MLP model classified anticancer and nonanticancer compounds with a classification accuracy of 0.92. Also, benchmarking of the developed model with machine learning algorithms exhibited better performance of the MLP model.

Modulation of DNA-protein Interactions by Proximal Genetic Elements as Uncovered by Interpretable Deep Learning.

Transcription factors (TF) recognize specific motifs in the genome that are typically 6-12 bp long to regulate various aspects of the cellular machinery. Presence of binding motifs and favorable genome accessibility are key drivers for a consistent TF-DNA interaction. Although these pre-requisites may occur thousands of times in the genome, there seems to be a high degree of selectivity for the sites that are actually bound. Here, we present a deep-learning framework that identifies and characterizes the upstream and downstream genetic elements to the binding motif, for their role in enforcing the mentioned selectivity. The proposed framework is based on an interpretable recurrent neural network architecture that enables for the relative analysis of sequence context features. We apply the framework to model twenty-six transcription factors and score the TF-DNA binding at a base-pair resolution. We find significant differences in activations of DNA context features for bound and unbound sequences. In addition to standardized evaluation protocols, we offer outstanding interpretability that enables us to identify and annotate DNA sequence with possible elements that modulate TF-DNA binding. Also, differences in data processing have a huge influence on the overall model performance. Overall, the proposed framework allows for novel insights on the non-coding genetic elements and their role in facilitating a stable TF-DNA interaction.

Isolation, characterization and expression analysis of stress responsive plant nuclear transcriptional factor subunit (NF-YB2) from commercial Saccharum hybrid and wild relative Erianthus arundinaceus.

Plant nuclear factor (NF-Y) is a transcription activating factor, consisting of three subunits, and plays a key regulatory role in many stress-responsive mechanisms including drought and salinity stresses. NF-Ys function both as complex and individual subunits. Considering the importance of sugarcane as a commercial crop with high socio-economic importance and the crop being affected mostly by water deficit stress and salinity stress causing significant yield loss, nuclear transcriptional factor NF-YB2 was focused in this study. Plant nuclear factor subunit B2 from Erianthus arundinaceus (EaNF-YB2), a wild relative of sugarcane which is known for its drought and salinity stress tolerance, and commercial Saccharum hybrid Co 86032 (ShNF-YB2) was isolated and characterized. Both EaNF-YB2 and ShNF-YB2 genes are 543 bp long that encodes for a polypeptide of 180 amino acid residues. Comparison of EaNF-YB2 and ShNF-YB2 gene sequences revealed nucleotide substitutions at nine positions corresponding to three synonymous and six nonsynonymous amino acid substitutions that resulted in variations in physiochemical properties. However, multiple sequence alignment (MSA) of NF-YB2 proteins showed conservation of functionally important amino acid residues. In silico analysis revealed NF-YB2 to be a hydrophilic and intracellular protein, and EaNF-YB2 is thermally more stable than that of ShNF-YB2. Phylogenetic analysis suggested the lower rate of evolution of NF-YB2. Subcellular localization in sugarcane callus revealed NF-YB2 localization at nucleus that further evidenced it to be a transcription activation factor. Comparative RT-qPCR experiments showed a significantly higher level of NF-YB2 expression in E. arundinaceus when compared to that in the commercial Saccharum hybrid Co 86032 under drought and salinity stresses. Hence, EaNF-YB2 could be an ideal candidate gene, and its overexpression in sugarcane through genetic engineering approach might enhance tolerance to drought and salinity stresses.

In Silico Approach to Identify Potential Inhibitors for Axl-Gas6 Signaling.

Axl-Gas6 signaling plays an important role in numerous cancers. Axl kinase, a member of receptor tyrosine kinase family is activated by different mechanisms with Gas6 as its major activator. Targeting the Axl with inhibitors may block the binding of Gas6 and further hinders the activation of Axl. This in turn inhibits the Axl-Gas6 signaling. Thus, inhibitors of the Axl kinase may serve as ideal drug candidates for treating many human cancers. In this study we carried out virtual screening of drug-like molecules from ZINC database to identify potential inhibitors for Axl kinase. Our virtual screening study showed that ZINC83758120, ZINC34079369, and ZINC83758121 are potential drug-like lead molecules to inhibit Axl kinase.