Dataset Information

PPAI: a web server for predicting protein-aptamer interactions.

ABSTRACT:

Background

The interactions between proteins and aptamers are prevalent in organisms and play an important role in various life activities. Thanks to the rapid accumulation of protein-aptamer interaction data, it is necessary and feasible to construct an accurate and effective computational model to predict aptamers binding to certain interested proteins and protein-aptamer interactions, which is beneficial for understanding mechanisms of protein-aptamer interactions and improving aptamer-based therapies.

Results

In this study, a novel web server named PPAI is developed to predict aptamers and protein-aptamer interactions with key sequence features of proteins/aptamers and a machine learning framework integrated adaboost and random forest. A new method for extracting several key sequence features of both proteins and aptamers is presented, where the features for proteins are extracted from amino acid composition, pseudo-amino acid composition, grouped amino acid composition, C/T/D composition and sequence-order-coupling number, while the features for aptamers are extracted from nucleotide composition, pseudo-nucleotide composition (PseKNC) and normalized Moreau-Broto autocorrelation coefficient. On the basis of these feature sets and balanced the samples with SMOTE algorithm, we validate the performance of PPAI by the independent test set. The results demonstrate that the Area Under Curve (AUC) is 0.907 for prediction of aptamer, while the AUC reaches 0.871 for prediction of protein-aptamer interactions.

Conclusion

These results indicate that PPAI can query aptamers and proteins, predict aptamers and predict protein-aptamer interactions in batch mode precisely and efficiently, which would be a novel bioinformatics tool for the research of protein-aptamer interactions. PPAI web-server is freely available at http://39.96.85.9/PPAI.

SUBMITTER: Li J

PROVIDER: S-EPMC7285591 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Publications

PPAI: a web server for predicting protein-aptamer interactions.

Li Jianwei J Ma Xiaoyu X Li Xichuan X Gu Junhua J

BMC bioinformatics 20200609 1

<h4>Background</h4>The interactions between proteins and aptamers are prevalent in organisms and play an important role in various life activities. Thanks to the rapid accumulation of protein-aptamer interaction data, it is necessary and feasible to construct an accurate and effective computational model to predict aptamers binding to certain interested proteins and protein-aptamer interactions, which is beneficial for understanding mechanisms of protein-aptamer interactions and improving aptame ...[more]

PMID: 32517696

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Project description:BACKGROUND: The emergence of drug resistant tuberculosis poses a serious concern globally and researchers are in rigorous search for new drugs to fight against these dreadful bacteria. Recently, the bacterial GlmU protein, involved in peptidoglycan, lipopolysaccharide and techoic acid synthesis, has been identified as an important drug target. A unique C-terminal disordered tail, essential for survival and the absence of gene in host makes GlmU a suitable target for inhibitor design. RESULTS: This study describes the models developed for predicting inhibitory activity (IC50) of chemical compounds against GlmU protein using QSAR and docking techniques. These models were trained on 84 diverse compounds (GlmU inhibitors) taken from PubChem BioAssay (AID 1376). These inhibitors were docked in the active site of the C-terminal domain of GlmU protein (2OI6) using the AutoDock. A QSAR model was developed using docking energies as descriptors and achieved maximum correlation of 0.35/0.12 (r/r2) between actual and predicted pIC50. Secondly, QSAR models were developed using molecular descriptors calculated using various software packages and achieved maximum correlation of 0.77/0.60 (r/r2). Finally, hybrid models were developed using various types of descriptors and achieved high correlation of 0.83/0.70 (r/r2) between predicted and actual pIC50. It was observed that some molecular descriptors used in this study had high correlation with pIC50. We screened chemical libraries using models developed in this study and predicted 40 potential GlmU inhibitors. These inhibitors could be used to develop drugs against Mycobacterium tuberculosis. CONCLUSION: These results demonstrate that docking energies can be used as descriptors for developing QSAR models. The current work suggests that docking energies based descriptors could be used along with commonly used molecular descriptors for predicting inhibitory activity (IC50) of molecules against GlmU. Based on this study an open source platform, http://crdd.osdd.net/raghava/gdoq, has been developed for predicting inhibitors GlmU.

Dataset Information

PPAI: a web server for predicting protein-aptamer interactions.

Background

Results

Conclusion

Publications

PPAI: a web server for predicting protein-aptamer interactions.

Similar Datasets

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