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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Sequential data assimilation for single-molecule FRET photon-counting data

Data assimilation is a statistical method designed to improve the quality of numerical simulations in combination with real observations. Here, we develop a sequential data assimilation method that incorporates one-dimensional time-series data of smFRET (single-molecule Förster resonance energy transfer) photon-counting into conformational ensembles of biomolecules derived from “replicated” molecular dynamics (MD) simulations. A particle filter using a large number of “replicated” MD simulations with a likelihood function for smFRET photon-counting data is employed to screen the conformational ensembles that match the experimental data. We examine the performance of the method using emulated smFRET data and coarse-grained (CG) MD simulations of a dye-labeled polyproline-20. The method estimates the dynamics of the end-to-end distance from smFRET data as well as revealing that of latent conformational variables. The particle filter is also able to correct model parameter dependence in CG MD simulations. We discuss the applicability of the method to real experimental data for conformational dynamics of biomolecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

FRET Based Biosensors for CBRN Threat Detection

Biosensors are devices used to detect the presence/concentration of a biological analyte. Biosensors consist of three parts: 1) a component that recognizes the analyte and produces a signal, 2) a signal transducer, and 3) a reader device. A multiplexed biosensor combines different biosensors for increased detection capabilities.

Demers, Steven M.↗
DOE OSTI2017

Single-molecule FRET unveils induced-fit mechanism for substrate selectivity in flap endonuclease 1

Human flap endonuclease 1 (FEN1) and related structure-specific 5’nucleases precisely identify and incise aberrant DNA structures during replication, repair and recombination to avoid genomic instability. Yet, it is unclear how the 5’nuclease mechanisms of DNA distortion and protein ordering robustly mediate efficient and accurate substrate recognition and catalytic selectivity. Here, single-molecule sub-millisecond and millisecond analyses of FEN1 reveal a protein-DNA induced-fit mechanism that efficiently verifies substrate and suppresses off-target cleavage. FEN1 sculpts DNA with diffusion-limited kinetics to test DNA substrate. This DNA distortion mutually ‘locks’ protein and DNA conformation and enables substrate verification with extreme precision. Strikingly, FEN1 never misses cleavage of its cognate substrate while blocking probable formation of catalytically competent interactions with noncognate substrates and fostering their pre-incision dissociation. These findings establish FEN1 has practically perfect precision and that separate control of induced-fit substrate recognition sets up the catalytic selectivity of the nuclease active site for genome stability.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2019

Formalizing and Analyzing Requirements with FRET

Formal verification and simulation are powerful tools to validate requirements against complex systems. Requirements are developed in early stages of the software lifecycle and are typically written in ambiguous natural language. There is a gap between such requirements and formal notations that can be used by verification tools, and lack of support for proper association of requirements with software artifacts for verification. We propose to write requirements in an intuitive, structured natural language with formal semantics, and to support formalization and model/code verification as a smooth, well-integrated process. To this end, we have developed an end-to-end, open source requirements analysis framework that checks Simulink models against requirements written structured natural language.

Mavridou, Anastasia↗