Michaleas, A. M. | Search bioRxiv

bioRxiv2024

Comprehensive Guide of Epigenetics and Transcriptomics Data Quality Control

Host response to environmental exposures such as pathogens and chemicals can cause modifications to the epigenome and transcriptome. Analysis of these modifications can reveal signatures with regards to the agent and timing of exposure. Exhaustive interrogation of the cascade of the epigenome and transcriptome requires analysis of disparate datasets from multiple assay types, often at single cell resolution, from the same biospecimen. Improved signature discovery has been enabled by advancements in assaying techniques to detect RNA expression, DNA base modifications, histone modifications, and chromatin accessibility. However, there remains a paucity of rigorous quality control standards of those datasets that reflect quality assurance of the underlying assay. This guide outlines a comprehensive suite of metrics that can be used to ensure quality from 11 different epigenetics and transcriptomics assays. Recommendations on mitigation approaches to address failed metrics and poor quality data are provided. The workflow consists of assessing dataset quality and reiterating benchwork protocols for improved results to generate accurate exposure signatures.

bioinformatics↗

bioRxiv2023

Human Cytokine and Coronavirus Nucleocapsid Protein Interactivity Using Large-Scale Virtual Screens

Understanding the interactions between SARS-CoV-2 and the human immune system is paramount to the characterization of novel variants as the virus co-evolves with the human host. In this study, we employed state-of-the-art molecular docking tools to conduct large-scale virtual screens, predicting the binding affinities between 64 human cytokines against 17 nucleocapsid proteins from six betacoronaviruses. Our comprehensive in silico analyses reveal specific changes in cytokine-nucleocapsid protein interactions, shedding light on potential modulators of the host immune response during infection. These findings offer valuable insights into the molecular mechanisms underlying viral pathogenesis and may guide the future development of targeted interventions. This manuscript serves as insight into the comparison of deep learning based AlphaFold2-Multimer and the semi-physicochemical based HADDOCK for protein-protein docking. We show the two methods are complementary in their predictive capabilities. We also introduce a novel algorithm for rapidly assessing the binding interface of protein-protein docks using graph edit distance: graph-based interface residue assessment function (GIRAF). The high-performance computational framework presented here will not only aid in accelerating the discovery of effective interventions against emerging viral threats, but extend to other applications of high throughput protein-protein screens.

bioinformatics↗