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In silico insights into prediction and analysis of potential novel pyrrolopyridine analogs against human MAPKAPK-2: a new SAR-based hierarchical clustering approach.

ABSTRACT: In the present study, we have focused on to elucidate potential bioactive pyrrolopyridine (PYP23) analogs against human mitogen-activated protein kinase-activated protein kinase-2 (MK-2). Here, in silico methods and computational systems biology tools were used as rational strategies to predict novel PYP23 analogs against the MK-2. Initially, crystal structure (PDB-ID: 2P3G) consists steriochemical conflicts were rectified by structure-optimization approaches using the Modeller program, and a new optimized-high resolution model was generated. The stereochemical qualities of the predicted MK-2 model were judged; these showed that the model was reliable for docking assessments. SAR-based bioactivity analysis showed that among the 197 datasets only 15 candidates contained bioactivity data and were accepted as probable MK-2 inhibitors. Virtual screening and docking strategies of dataset compounds against the ligand-binding domain of MK-2 recognized 13 composites containing high binding affinity than known compounds. Furthermore, the comparative structure clustering, in silico toxicogenomics and QSAR-based anticancer properties prediction approaches were successful in the recognition of five best potential compounds such as 60118340, 60118338, 60117736, 60118473 and 60118322, which have great anticancer and drug-likeness with non-toxicity class indices. Leu70, Glu139, Leu141, Glu145, Glu190, Thr206 and Asp207 were found to be novel hotspot residues prominently involved in H-bonds framing with ligands. Interestingly, they have shown better molecular similarity with known bioactive PYP inhibitors. Thus, predicted five compounds can useful as possible chemotherapeutic agents for MK-2 and show similar molecular actions like known PYP inhibitors. Overall, these streamlined new methods may have great potential to reveal possible ligands toward other molecular targets and biomarkers.

SUBMITTER: Konidala KK

PROVIDER: S-EPMC6107479 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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In silico insights into prediction and analysis of potential novel pyrrolopyridine analogs against human MAPKAPK-2: a new SAR-based hierarchical clustering approach.

Konidala Kranthi Kumar KK Bommu Uma Devi UD Yeguvapalli Suneetha S Pabbaraju Neeraja N

3 Biotech 20180823 9

In the present study, we have focused on to elucidate potential bioactive pyrrolopyridine (PYP23) analogs against human mitogen-activated protein kinase-activated protein kinase-2 (MK-2). Here, in silico methods and computational systems biology tools were used as rational strategies to predict novel PYP23 analogs against the MK-2. Initially, crystal structure (PDB-ID: 2P3G) consists steriochemical conflicts were rectified by structure-optimization approaches using the Modeller program, and a ne ...[more]

PMID: 30148035

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Project description:BACKGROUND:The study of functional associations between ncRNAs and human diseases is a pivotal task of modern research to develop new and more effective therapeutic approaches. Nevertheless, it is not a trivial task since it involves entities of different types, such as microRNAs, lncRNAs or target genes whose expression also depends on endogenous or exogenous factors. Such a complexity can be faced by representing the involved biological entities and their relationships as a network and by exploiting network-based computational approaches able to identify new associations. However, existing methods are limited to homogeneous networks (i.e., consisting of only one type of objects and relationships) or can exploit only a small subset of the features of biological entities, such as the presence of a particular binding domain, enzymatic properties or their involvement in specific diseases. RESULTS:To overcome the limitations of existing approaches, we propose the system LP-HCLUS, which exploits a multi-type hierarchical clustering method to predict possibly unknown ncRNA-disease relationships. In particular, LP-HCLUS analyzes heterogeneous networks consisting of several types of objects and relationships, each possibly described by a set of features, and extracts multi-type clusters that are subsequently exploited to predict new ncRNA-disease associations. The extracted clusters are overlapping, hierarchically organized, involve entities of different types, and allow LP-HCLUS to catch multiple roles of ncRNAs in diseases at different levels of granularity. Our experimental evaluation, performed on heterogeneous attributed networks consisting of microRNAs, lncRNAs, diseases, genes and their known relationships, shows that LP-HCLUS is able to obtain better results with respect to existing approaches. The biological relevance of the obtained results was evaluated according to both quantitative (i.e., TPR@k, Areas Under the TPR@k, ROC and Precision-Recall curves) and qualitative (i.e., according to the consultation of the existing literature) criteria. CONCLUSIONS:The obtained results prove the utility of LP-HCLUS to conduct robust predictive studies on the biological role of ncRNAs in human diseases. The produced predictions can therefore be reliably considered as new, previously unknown, relationships among ncRNAs and diseases.

Dataset Information

In silico insights into prediction and analysis of potential novel pyrrolopyridine analogs against human MAPKAPK-2: a new SAR-based hierarchical clustering approach.

Publications

In silico insights into prediction and analysis of potential novel pyrrolopyridine analogs against human MAPKAPK-2: a new SAR-based hierarchical clustering approach.

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