Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387492017> ?p ?o ?g. }
- W4387492017 abstract "Abstract Here, a machine learning method based on a kinetically informed neural network (NN) is introduced. The proposed method is designed to analyze a time series of difference electron density (DED) maps from a time-resolved X-ray crystallographic experiment. The method is named KINNTREX (Kinetics Inspired NN for Time-Resolved X-ray Crystallography). To validate KINNTREX, multiple realistic scenarios were simulated with increasing level of complexity. For the simulations, time-resolved X-ray data was generated that mimic data collected from the photocycle of the photoactive yellow protein (PYP). KINNTREX only requires the number of intermediates and approximate relaxation times (both obtained from a singular valued decomposition) and does not require an assumption of a candidate mechanism. It successfully predicts a consistent chemical kinetic mechanism, together with difference electron density maps of the intermediates that appear during the reaction. These features make KINNTREX attractive for tackling a wide range of biomolecular questions. In addition, the versatility of KINNTREX can inspire more NN-based applications to time-resolved data from biological macromolecules obtained by other methods." @default.
- W4387492017 created "2023-10-11" @default.
- W4387492017 creator A5003356590 @default.
- W4387492017 creator A5010276043 @default.
- W4387492017 creator A5019504319 @default.
- W4387492017 creator A5079741689 @default.
- W4387492017 date "2023-10-10" @default.
- W4387492017 modified "2023-10-15" @default.
- W4387492017 title "KINNTREX: A Neural Network Unveils Protein Mechanism from Time Resolved X-ray Crystallography" @default.
- W4387492017 cites W1554093359 @default.
- W4387492017 cites W1806891645 @default.
- W4387492017 cites W1972563656 @default.
- W4387492017 cites W1993845689 @default.
- W4387492017 cites W1996112440 @default.
- W4387492017 cites W2018883772 @default.
- W4387492017 cites W2028525721 @default.
- W4387492017 cites W2040116801 @default.
- W4387492017 cites W2040870580 @default.
- W4387492017 cites W2048944220 @default.
- W4387492017 cites W2049740050 @default.
- W4387492017 cites W204991446 @default.
- W4387492017 cites W2056003475 @default.
- W4387492017 cites W2063683697 @default.
- W4387492017 cites W2085955178 @default.
- W4387492017 cites W2087070363 @default.
- W4387492017 cites W2098666958 @default.
- W4387492017 cites W2101926813 @default.
- W4387492017 cites W2105059492 @default.
- W4387492017 cites W2108921801 @default.
- W4387492017 cites W2115155685 @default.
- W4387492017 cites W2125599365 @default.
- W4387492017 cites W2136600643 @default.
- W4387492017 cites W2140031846 @default.
- W4387492017 cites W2140951337 @default.
- W4387492017 cites W2142654956 @default.
- W4387492017 cites W2143545157 @default.
- W4387492017 cites W2151082578 @default.
- W4387492017 cites W2151971205 @default.
- W4387492017 cites W2165385023 @default.
- W4387492017 cites W2168435571 @default.
- W4387492017 cites W2169035712 @default.
- W4387492017 cites W2170408275 @default.
- W4387492017 cites W2180262932 @default.
- W4387492017 cites W2256578114 @default.
- W4387492017 cites W2724538250 @default.
- W4387492017 cites W2805692389 @default.
- W4387492017 cites W2899283552 @default.
- W4387492017 cites W2952053429 @default.
- W4387492017 cites W2962949934 @default.
- W4387492017 cites W3007609684 @default.
- W4387492017 cites W3043300003 @default.
- W4387492017 cites W3094051077 @default.
- W4387492017 cites W3101706855 @default.
- W4387492017 cites W3163993681 @default.
- W4387492017 cites W3173107846 @default.
- W4387492017 cites W3177828909 @default.
- W4387492017 cites W3196455401 @default.
- W4387492017 cites W3216454937 @default.
- W4387492017 cites W4238104585 @default.
- W4387492017 cites W4385858818 @default.
- W4387492017 doi "https://doi.org/10.1101/2023.10.06.561268" @default.
- W4387492017 hasPublicationYear "2023" @default.
- W4387492017 type Work @default.
- W4387492017 citedByCount "0" @default.
- W4387492017 crossrefType "posted-content" @default.
- W4387492017 hasAuthorship W4387492017A5003356590 @default.
- W4387492017 hasAuthorship W4387492017A5010276043 @default.
- W4387492017 hasAuthorship W4387492017A5019504319 @default.
- W4387492017 hasAuthorship W4387492017A5079741689 @default.
- W4387492017 hasBestOaLocation W43874920171 @default.
- W4387492017 hasConcept C11413529 @default.
- W4387492017 hasConcept C120665830 @default.
- W4387492017 hasConcept C121332964 @default.
- W4387492017 hasConcept C121864883 @default.
- W4387492017 hasConcept C124681953 @default.
- W4387492017 hasConcept C125485243 @default.
- W4387492017 hasConcept C143724316 @default.
- W4387492017 hasConcept C147120987 @default.
- W4387492017 hasConcept C148898269 @default.
- W4387492017 hasConcept C151730666 @default.
- W4387492017 hasConcept C154945302 @default.
- W4387492017 hasConcept C15744967 @default.
- W4387492017 hasConcept C159467904 @default.
- W4387492017 hasConcept C159985019 @default.
- W4387492017 hasConcept C178790620 @default.
- W4387492017 hasConcept C185592680 @default.
- W4387492017 hasConcept C186060115 @default.
- W4387492017 hasConcept C192562407 @default.
- W4387492017 hasConcept C204323151 @default.
- W4387492017 hasConcept C2776029896 @default.
- W4387492017 hasConcept C2779328170 @default.
- W4387492017 hasConcept C41008148 @default.
- W4387492017 hasConcept C50644808 @default.
- W4387492017 hasConcept C62520636 @default.
- W4387492017 hasConcept C77805123 @default.
- W4387492017 hasConcept C86803240 @default.
- W4387492017 hasConcept C89611455 @default.
- W4387492017 hasConceptScore W4387492017C11413529 @default.
- W4387492017 hasConceptScore W4387492017C120665830 @default.
- W4387492017 hasConceptScore W4387492017C121332964 @default.