Dataset Information

A Bioelectronic Platform Modulates pH in Biologically Relevant Conditions.

ABSTRACT: Bioelectronic devices that modulate pH can affect critical biological processes including enzymatic activity, oxidative phosphorylation, and neuronal excitability. A major challenge in controlling pH is the high buffering capacity of many biological media. To overcome this challenge, devices need to be able to store and deliver a large number of protons on demand. Here, a bioelectronic modulator that controls pH using palladium nanoparticles contacts with high surface area as a proton storage medium is developed. Reversible electronically triggered acidosis (low pH) and alkalosis (high pH) in physiologically relevant buffer conditions are achieved. As a proof of principle, this new platform is used to control the degradation and fluorescence of acid sensitive polymeric microparticles loaded with a pH sensitive fluorescent dye.

SUBMITTER: Strakosas X

PROVIDER: S-EPMC6446605 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Bioelectronic Platform Modulates pH in Biologically Relevant Conditions.

Strakosas Xenofon X Selberg John J Zhang Xiaolin X Christie Noah N Hsu Peng-Hao PH Almutairi Adah A Rolandi Marco M

Advanced science (Weinheim, Baden-Wurttemberg, Germany) 20190128 7

Bioelectronic devices that modulate pH can affect critical biological processes including enzymatic activity, oxidative phosphorylation, and neuronal excitability. A major challenge in controlling pH is the high buffering capacity of many biological media. To overcome this challenge, devices need to be able to store and deliver a large number of protons on demand. Here, a bioelectronic modulator that controls pH using palladium nanoparticles contacts with high surface area as a proton storage me ...[more]

PMID: 30989015

Similar Datasets

Project description:BackgroundThe use of 3-D similarity techniques in the analysis of biological data and virtual screening is pervasive, but what is a biologically meaningful 3-D similarity value? Can one find statistically significant separation between "active/active" and "active/inactive" spaces? These questions are explored using 734,486 biologically tested chemical structures, 1,389 biological assay data sets, and six different 3-D similarity types utilized by PubChem analysis tools.ResultsThe similarity value distributions of 269.7 billion unique conformer pairs from 734,486 biologically tested compounds (all-against-all) from PubChem were utilized to help work towards an answer to the question: what is a biologically meaningful 3-D similarity score? The average and standard deviation for the six similarity measures STST-opt, CTST-opt, ComboTST-opt, STCT-opt, CTCT-opt, and ComboTCT-opt were 0.54 ± 0.10, 0.07 ± 0.05, 0.62 ± 0.13, 0.41 ± 0.11, 0.18 ± 0.06, and 0.59 ± 0.14, respectively. Considering that this random distribution of biologically tested compounds was constructed using a single theoretical conformer per compound (the "default" conformer provided by PubChem), further study may be necessary using multiple diverse conformers per compound; however, given the breadth of the compound set, the single conformer per compound results may still apply to the case of multi-conformer per compound 3-D similarity value distributions. As such, this work is a critical step, covering a very wide corpus of chemical structures and biological assays, creating a statistical framework to build upon.The second part of this study explored the question of whether it was possible to realize a statistically meaningful 3-D similarity value separation between reputed biological assay "inactives" and "actives". Using the terminology of noninactive-noninactive (NN) pairs and the noninactive-inactive (NI) pairs to represent comparison of the "active/active" and "active/inactive" spaces, respectively, each of the 1,389 biological assays was examined by their 3-D similarity score differences between the NN and NI pairs and analyzed across all assays and by assay category types. While a consistent trend of separation was observed, this result was not statistically unambiguous after considering the respective standard deviations. While not all "actives" in a biological assay are amenable to this type of analysis, e.g., due to different mechanisms of action or binding configurations, the ambiguous separation may also be due to employing a single conformer per compound in this study. With that said, there were a subset of biological assays where a clear separation between the NN and NI pairs found. In addition, use of combo Tanimoto (ComboT) alone, independent of superposition optimization type, appears to be the most efficient 3-D score type in identifying these cases.ConclusionThis study provides a statistical guideline for analyzing biological assay data in terms of 3-D similarity and PubChem structure-activity analysis tools. When using a single conformer per compound, a relatively small number of assays appear to be able to separate "active/active" space from "active/inactive" space.

Dataset Information

A Bioelectronic Platform Modulates pH in Biologically Relevant Conditions.

Publications

A Bioelectronic Platform Modulates pH in Biologically Relevant Conditions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure