Biology subjects

Eicher, T.

Publications and source records attributed to Eicher, T..

3 recordsLinked to original sources

bioRxiv2024

Conformational plasticity across phylogenetic clusters of RND multidrug efflux pumps and its impact on substrate specificity

Antibiotic efflux plays a key role for the multidrug resistance in Gram-negative bacteria 1-3. Multidrug efflux pumps of the resistance nodulation and cell division (RND) superfamily function as part of cell envelope spanning systems and provide resistance to diverse antibiotics 4,5. Here, we identify two phylogenetic clusters of RND proteins with conserved binding pocket residues. Based on the characterisation of one representative of each cluster, K. pneumoniae OqxB and E. coli AcrB, we show that the transfer of a single conserved residue between both clusters alters the resistance against a panel of structurally unrelated drugs. The substitution is not only associated with changes in the binding pocket architecture, but also alters the equilibrium between the conformational states of the transport cycle. We show that AcrB and OqxB adopt fundamentally different apo states that suggest different mechanisms of initial substrate binding and might determine the differences between the substrate preferences of both pumps. The observed conformational heterogeneity between different RND clusters is suggested to be phylogenetically conserved and might play a role for the diversification of the resistance phenotype between homologous RND multidrug efflux pumps.

biochemistry↗

bioRxiv2022

RaMP-DB 2.0: a renovated knowledgebase for deriving biological and chemical insight from genes, proteins, and metabolites

RaMP-DB 2.0 is a web interface, API, relational database and R package designed for straightforward and comprehensive functional interpretation of metabolomic and multi-omic data. Since its first release in 2018, RaMP-DB 2.0 has been upgraded with an expanded breadth and depth of functional and chemical annotation. Content from the source databases (Reactome, HMDB, and Wikipathways) has been updated, and new data types related to metabolite annotations have been incorporated. Structural information incorporated in RaMP-DB 2.0 includes SMILES strings, InChIs, InChIKeys. Chemical classes have been sourced from ClassyFire and LIPID MAPS. Accordingly, the RaMP-DB 2.0 R package has been updated and supports queries on pathways, common reactions, ontologies, chemical classes, and chemical structures. Additionally, RaMP-DB 2.0 now supports enrichment analyses on pathways and chemical classes. Our process for integrating annotations across resources has also been upgraded to lessen the burden of harmonization, thereby supporting more frequent updates. The code used to build all components of RaMP-DB 2.0 is freely available on GitHub at https://github.com/ncats/ramp-db and https://github.com/ncats/RaMP-Backend.

bioinformatics↗

bioRxiv2020

An OpenData portal to share COVID-19 drug repurposing data in real time

The National Center for Advancing Translational Sciences (NCATS) has developed an online open science platform - named the NCATS OpenData Portal (ODP) - for quickly and freely sharing complete NCATS translational datasets via an open-access, user-friendly interface. This paper describes the establishment of the ODP, initially deployed during the COVID-19 crisis, and provides a detailed analysis of COVID-19 drug repurposing screening datasets that served as the first large-scale use case for the platform. Over 10,000 compounds were tested across 17 quantitative high-throughput assays, covering a wide spectrum of the SARS-CoV-2 life cycle. In total, over 87,000 concentration-response curves and 426,000 data points were made publicly available on ODP in near real-time, enabling immediate access to complete datasets. The resource is flexible in accommodating various types and structures of data, and it has already expanded since its launch to host additional datasets for COVID-19, other viruses of pandemic potential, and beyond. The OpenData Portal has been designed as a scalable platform for real-time data sharing across drug discovery campaigns, regardless of disease area, with the overarching goal of accelerating discovery at NCATS, the NIH, and the greater scientific community.

microbiology↗