Savas, B. | bioRxiv Science

bioRxiv2022

Truncated KDM6A exhibits differential chromatin occupancy and regulatory interactions

Epigenetic deregulation is a critical theme which needs further investigation for bladder cancer research. One of the highly mutated genes in bladder cancer is KDM6A, functioning as a H3K27 demethylase and is part of the MLL3/4 complexes. To decipher the role of KDM6A in normal versus tumor setting, we identified the genomic landscape of KDM6A in normal, immortalized and cancer bladder cells. Our results showed differential KDM6A occupancy at the genes involved in cell differentiation, chromatin organization and Notch signaling depending on the cell type and the mutation status of KDM6A. Transcription factor motif analysis revealed HES1 to be enriched at KDM6A peaks identified for T24 bladder cancer cell line, which has a truncating mutation in KDM6A, lacking demethylase domain. Our co-immunoprecipitation experiments reveal TLE co-repressors and HES1 as potential truncated and wild type KDM6A interactors. With the aid of structural modeling, we explored how the truncated KDM6A could interact with TLE, HES1, as well RUNX, HHEX transcription factors. These structures provide a solid mean to study the functions of KDM6A independent of its demethylase activity. Collectively, our work provides important contributions to the understanding of KDM6A malfunction in bladder cancer.

cancer biology↗

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↗