Arslan, N. | bioRxiv Science

bioRxiv2024

Assessment of the transcriptional regulation of EMT and MET through accessible transposable elements

Gene expression is regulated at the transcriptional and translational levels and a plethora of epigenetic mechanisms. Regulation of gene expression by transposable elements is well documented. However, a comprehensive analysis of their regulatory roles is challenging due to the lack of dedicated approaches to define their contribution. Here, we present regulaTER, a new R library dedicated to deciphering the regulatory potential of transposable elements in given phenotype. regulaTER utilizes a variety of genomics data of any origin and combines gene expression level information to predict the regulatory roles of transposable elements. We further validated its capabilities using data generated from an epithelial-mesenchymal and mesenchymal-epithelial transition cellular model. regulaTER stands out as an essential asset for uncovering the impact of transposable elements on the regulation of gene expression, with high flexibility to perform a range of transposable element-focused analyses. Our results also provided insights on the contribution of the MIR and B element subfamilies in regulating EMT and MET through the FoxA transcription factor family. regulaTER is publicly available and can be downloaded from https://github.com/karakulahg/regulaTER.

genomics↗

bioRxiv2022

Benchmarking the Widely Used Structure-based Binding Affinity Predictors on the Spike-ACE2 Deep Mutational Interaction Set

Since the start of COVID-19 pandemic, a huge effort has been devoted to understanding the Spike (SARS-CoV-2)-ACE2 recognition mechanism. To this end, two deep mutational scanning studies traced the impact of all possible mutations across Receptor Binding Domain (RBD) of Spike and catalytic domain of human ACE2. By concentrating on the interface mutations of these experimental data, we benchmarked six commonly used structure-based binding affinity predictors (FoldX, EvoEF1, MutaBind2, SSIPe, HADDOCK, and UEP). These predictors were selected based on their user-friendliness, accessibility, and speed. As a result of our benchmarking efforts, we observed that none of the methods could generate a meaningful correlation with the experimental binding data. The best correlation is achieved by FoldX (R = -0.51). Also, when we simplified the prediction problem to a binary classification, i.e., whether a mutation is enriching or depleting the binding, we showed that the highest accuracy is achieved by FoldX with 64% success rate. Surprisingly, on this set, simple energetic scoring functions performed significantly better than the ones using extra evolutionary-based terms, as in Mutabind and SSIPe. Furthermore, we also demonstrated that recent AI approaches, mmCSM-PPI and TopNetTree, yielded comparable performances to the force field-based techniques. These observations suggest plenty of room to improve the binding affinity predictors in guessing the variant-induced binding profile changes of a host-pathogen system, such as Spike-ACE2. To aid such improvements we provide our benchmarking data at https://github.com/CSB-KaracaLab/RBD-ACE2-MutBench with the option to visualize our mutant models at https://rbd-ace2-mutbench.github.io/

bioinformatics↗