Deciphering LncRNA-protein interactions using docking complexes

Epigenetics, genomics imprinting and non-coding RNAs

Epigenetic traits are heritable phenotypes caused by alterations in chromosomes rather than DNA sequences. The actual epigenetic expression of the somatic cells of a species is identical, however, they may show distinct subtleties in various cell types in which they may be affected. Several recent studies demonstrated that the epigenetic system plays a very important role in regulating all biological natural processes in the body from birth to death. We outline the essential elements of epigenetics, genomic imprinting, and non-coding RNAs in this mini-review.

LPI-HyADBS: a hybrid framework for lncRNA-protein interaction prediction integrating feature selection and classification

Background

Long noncoding RNAs (lncRNAs) have dense linkages with a plethora of important cellular activities. lncRNAs exert functions by linking with corresponding RNA-binding proteins. Since experimental techniques to detect lncRNA-protein interactions (LPIs) are laborious and time-consuming, a few computational methods have been reported for LPI prediction. However, computation-based LPI identification methods have the following limitations: (1) Most methods were evaluated on a single dataset, and researchers may thus fail to measure their generalization ability. (2) The majority of methods were validated under cross validation on lncRNA-protein pairs, did not investigate the performance under other cross validations, especially for cross validation on independent lncRNAs and independent proteins. (3) lncRNAs and proteins have abundant biological information, how to select informative features need to further investigate.

Results

Under a hybrid framework (LPI-HyADBS) integrating feature selection based on AdaBoost, and classification models including deep neural network (DNN), extreme gradient Boost (XGBoost), and SVM with a penalty Coefficient of misclassification (C-SVM), this work focuses on finding new LPIs. First, five datasets are arranged. Each dataset contains lncRNA sequences, protein sequences, and an LPI network. Second, biological features of lncRNAs and proteins are acquired based on Pyfeat. Third, the obtained features of lncRNAs and proteins are selected based on AdaBoost and concatenated to depict each LPI sample. Fourth, DNN, XGBoost, and C-SVM are used to classify lncRNA-protein pairs based on the concatenated features. Finally, a hybrid framework is developed to integrate the classification results from the above three classifiers. LPI-HyADBS is compared to six classical LPI prediction approaches (LPI-SKF, LPI-NRLMF, Capsule-LPI, LPI-CNNCP, LPLNP, and LPBNI) on five datasets under 5-fold cross validations on lncRNAs, proteins, lncRNA-protein pairs, and independent lncRNAs and independent proteins. The results show LPI-HyADBS has the best LPI prediction performance under four different cross validations. In particular, LPI-HyADBS obtains better classification ability than other six approaches under the constructed independent dataset. Case analyses suggest that there is relevance between ZNF667-AS1 and Q15717.

Conclusions

Integrating feature selection approach based on AdaBoost, three classification techniques including DNN, XGBoost, and C-SVM, this work develops a hybrid framework to identify new linkages between lncRNAs and proteins.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04485-x.

Nutraceuticals and Herbs in Reducing the Risk and Improving the Treatment of COVID-19 by Targeting SARS-CoV-2

The worldwide transmission of acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a deadly or devastating disease is known to affect thousands of people every day, many of them dying all over the planet. The main reason for the massive effect of COVID-19 on society is its unpredictable spread, which does not allow for proper planning or management of this disease. Antibiotics, antivirals, and other prescription drugs, necessary and used in therapy, obviously have side effects (minor or significant) on the affected person, there are still not clear enough studies to elucidate their combined effect in this specific treatment, and existing protocols are sometimes unclear and uncertain. In contrast, it has been found that nutraceuticals, supplements, and various herbs can be effective in reducing the chances of SARS-CoV-2 infection, but also in alleviating COVID-19 symptoms. However, not enough specific details are yet available, and precise scientific studies to validate the approved benefits of natural food additives, probiotics, herbs, and nutraceuticals will need to be standardized according to current regulations. These alternative treatments may not have a direct effect on the virus or reduce the risk of infection with it, but these products certainly stimulate the human immune system so that the body is better prepared to fight the disease. This paper aims at a specialized literary foray precisely in the field of these “cures” that can provide real revelations in the therapy of coronavirus infection

A Survey of Current Resources to Study lncRNA-Protein Interactions

Phenotypes are driven by regulated gene expression, which in turn are mediated by complex interactions between diverse biological molecules. Protein-DNA interactions such as histone and transcription factor binding are well studied, along with RNA-RNA interactions in short RNA silencing of genes. In contrast, lncRNA-protein interaction (LPI) mechanisms are comparatively unknown, likely directed by the difficulties in studying LPI. However, LPI are emerging as key interactions in epigenetic mechanisms, playing a role in development and disease. Their importance is further highlighted by their conservation across kingdoms. Hence, interest in LPI research is increasing. We therefore review the current state of the art in lncRNA-protein interactions. We specifically surveyed recent computational methods and databases which researchers can exploit for LPI investigation. We discovered that algorithm development is heavily reliant on a few generic databases containing curated LPI information. Additionally, these databases house information at gene-level as opposed to transcript-level annotations. We show that early methods predict LPI using molecular docking, have limited scope and are slow, creating a data processing bottleneck. Recently, machine learning has become the strategy of choice in LPI prediction, likely due to the rapid growth in machine learning infrastructure and expertise. While many of these methods have notable limitations, machine learning is expected to be the basis of modern LPI prediction algorithms.