Discovering protein-binding RNA motifs with a generative model of RNA sequences

DAPTEV: Deep aptamer evolutionary modelling for COVID-19 drug design

Typical drug discovery and development processes are costly, time consuming and often biased by expert opinion. Aptamers are short, single-stranded oligonucleotides (RNA/DNA) that bind to target proteins and other types of biomolecules. Compared with small-molecule drugs, aptamers can bind to their targets with high affinity (binding strength) and specificity (uniquely interacting with the target only). The conventional development process for aptamers utilizes a manual process known as Systematic Evolution of Ligands by Exponential Enrichment (SELEX), which is costly, slow, dependent on library choice and often produces aptamers that are not optimized. To address these challenges, in this research, we create an intelligent approach, named DAPTEV, for generating and evolving aptamer sequences to support aptamer-based drug discovery and development. Using the COVID-19 spike protein as a target, our computational results suggest that DAPTEV is able to produce structurally complex aptamers with strong binding affinities.

Roles of RNA-binding proteins in neurological disorders, COVID-19, and cancer

RNA-binding proteins (RBPs) have emerged as important players in multiple biological processes including transcription regulation, splicing, R-loop homeostasis, DNA rearrangement, miRNA function, biogenesis, and ribosome biogenesis. A large number of RBPs had already been identified by different approaches in various organisms and exhibited regulatory functions on RNAs' fate. RBPs can either directly or indirectly interact with their target RNAs or mRNAs to assume a key biological function whose outcome may trigger disease or normal biological events. They also exert distinct functions related to their canonical and non-canonical forms. This review summarizes the current understanding of a wide range of RBPs' functions and highlights their emerging roles in the regulation of diverse pathways, different physiological processes, and their molecular links with diseases. Various types of diseases, encompassing colorectal carcinoma, non-small cell lung carcinoma, amyotrophic lateral sclerosis, and Severe acute respiratory syndrome coronavirus 2, aberrantly express RBPs. We also highlight some recent advances in the field that could prompt the development of RBPs-based therapeutic interventions.

ENNGene: an Easy Neural Network model building tool for Genomics

Background

The recent big data revolution in Genomics, coupled with the emergence of Deep Learning as a set of powerful machine learning methods, has shifted the standard practices of machine learning for Genomics. Even though Deep Learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are becoming widespread in Genomics, developing and training such models is outside the ability of most researchers in the field.

Results

Here we present ENNGene—Easy Neural Network model building tool for Genomics. This tool simplifies training of custom CNN or hybrid CNN-RNN models on genomic data via an easy-to-use Graphical User Interface. ENNGene allows multiple input branches, including sequence, evolutionary conservation, and secondary structure, and performs all the necessary preprocessing steps, allowing simple input such as genomic coordinates. The network architecture is selected and fully customized by the user, from the number and types of the layers to each layer's precise set-up. ENNGene then deals with all steps of training and evaluation of the model, exporting valuable metrics such as multi-class ROC and precision-recall curve plots or TensorBoard log files. To facilitate interpretation of the predicted results, we deploy Integrated Gradients, providing the user with a graphical representation of an attribution level of each input position. To showcase the usage of ENNGene, we train multiple models on the RBP24 dataset, quickly reaching the state of the art while improving the performance on more than half of the proteins by including the evolutionary conservation score and tuning the network per protein.

Conclusions

As the role of DL in big data analysis in the near future is indisputable, it is important to make it available for a broader range of researchers. We believe that an easy-to-use tool such as ENNGene can allow Genomics researchers without a background in Computational Sciences to harness the power of DL to gain better insights into and extract important information from the large amounts of data available in the field.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08414-x.