THz frequency spectrum of protein–solvent interaction energy using a recurrence plot‐based Wiener–Khinchin method

Karain, Wael I.

doi:10.1002/prot.25097

Cited by 3 publications

(2 citation statements)

References 71 publications

(128 reference statements)

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“…The Fourier spectrum of a time series can be estimated from the RQA measure τ-Recurrence Rate (RR τ ) and is able to identify higher periodic orbits, which a classical spectrum estimation fails to achieve [15]. This technique has already been successfully applied to reveal unstable and stable periodic orbits of various continuous and discrete theoretical dynamical systems [16,17]; to characterize hyperspectral geological data [18], proteinsolvent interaction energy [19], and a phasor measurement unit in electrical power systems [20].…”

Section: Introductionmentioning

confidence: 99%

Recurrence Rate spectrograms for the classification of nonlinear and noisy signals

Hertrampf,

Oberst

2024

Phys. Scr.

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Time series analysis of real-world measurements is fundamental in naturalsciences and engineering, and machine learning has been recently of great assistance especiallyfor classification of signals and their understanding. Yet, the underlying system’s nonlinearresponse behaviour is often neglected. Recurrence Plot (RP) based Fourier-spectra constructedthrough τ-Recurrence Rate (RRτ) have shown the potential to reveal nonlinear traits otherwisehidden from conventional data processing. We report a so far disregarded eligibility forsignal classification of nonlinear time series by training RESnet-50 on spectrogram images,which allows recurrence-spectra to outcompete conventional Fourier analysis. To exemplify itsfunctioning, we employ a simple nonlinear physical flow of a continuous stirred tank reactor,able to exhibit exothermic, first order, irreversible, cubic autocatalytic chemical reactions, anda plethora of fast-slow dynamics. For dynamics with noise being ten times stronger than thesignal, the classification accuracy was up to ≈75 % compared to ≈17 % for the periodogram.We show that an increase in entropy only detected by the RRτ allows differentiation. Thisshows that RP power spectra, combined with off-the-shelf machine learning techniques, havethe potential to significantly improve the detection of nonlinear and noise contaminated signals.

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Section: Introductionmentioning

confidence: 99%

Recurrence Rate spectrograms for the classification of nonlinear and noisy signals

Hertrampf,

Oberst

2024

Phys. Scr.

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“…Ge et al (2015) used terahertz absorption spectra to distinguish eight wheat varieties and established a regression model for wheat variety discrimination using the partial least squares method, with a cross-validated correlation coefficient (R 2 ) and cross-validated root mean square error (RMSECV) of 0.985 and 1.162, respectively. Karain (2016) performed terahertz spectroscopy of protein-solvent interaction energy based on the Wiener-Khinchin method and discovered that the spectroscopy spectrum could characterise the spectral range of macromolecular protein mode shapes. Li et al (2017) showed that THz-TDS could be used in qualitative and quantitative research of d-anhydrous glucose, and they provided a reference for rapid glucose detection in fruits, vegetables, foods, and pharmaceuticals using terahertz spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

Detection of the Nutritional Status of Phosphorus in Lettuce Using THZ Time-Domain Spectroscopy

et al. 2021

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Phosphorus deficiency can lead to serious reductions in crop yield and quality. Rapid and accurate determination of phosphorus stress status in vegetables is essential for ensuring the appropriate application of water and fertiliser, improving the yield and quality of vegetables, avoiding the waste of resources and serious non-point pollution source caused by the abuse of chemical fertilisers, and promoting green production and sustainability of facility agriculture. Chemical analysis and spectral or visual imaging methods for detection of phosphorus nutritional status are not conducive in facility production owing to their low accuracy and damage to plants. Owing to its penetration and fingerprint characteristics, terahertz time-domain spectroscopy (THz-TDS) can distinguish differences in internal components caused by excess phosphorus nutrients and changes in nucleic acids, nuclear proteins, phospholipids, and other macromolecules; thus, it could potentially be used to evaluate the nutritional status of crops. In this study, an innovative THz-TDS-based method was used to detect the nutritional status of phosphorus in lettuce. Lettuce was grown with different phosphorus levels using soilless cultivation and different nutrient solutions. Based on the standard formula of Yamasaki nutrient solution, the phosphorus content in the nutrient solutions was reduced or increased by 20%, 60%, 100%, and 150%, and lettuce samples exposed to each of these phosphorus concentrations were collected. Terahertz spectra are highly sensitive to water; thus, the lettuce samples were freeze-dried to minimise the effect of water and maintain their original quality and bioactivity. The spectra of lettuce were recorded using a TS7400 THz-TDS system; noise and interference were eliminated via normalisation based on Savitzky-Golay smoothing. The correction and validation sets were divided using sample set partitioning based on the joint x-y distance (SPXY). The stability competitive adaptive reweighted algorithm, iterative retention information variable algorithm, and interval combination optimisation algorithm were used to select the terahertz characteristic wavelength, and the successive projection algorithm was then used for secondary optimisation. Finally, a THz-TDS model of lettuce phosphorus was established using the partial least squares method with five principal component variables. The coefficient of determination of the model reached 0.7005, and the root-mean-square error of the predictions was 0.003273, indicating that this method has a high prediction accuracy.

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Detecting transitions in protein dynamics using a recurrence quantification analysis based bootstrap method

Karain

2017

BMC Bioinformatics

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BackgroundProteins undergo conformational transitions over different time scales. These transitions are closely intertwined with the protein’s function. Numerous standard techniques such as principal component analysis are used to detect these transitions in molecular dynamics simulations. In this work, we add a new method that has the ability to detect transitions in dynamics based on the recurrences in the dynamical system. It combines bootstrapping and recurrence quantification analysis. We start from the assumption that a protein has a “baseline” recurrence structure over a given period of time. Any statistically significant deviation from this recurrence structure, as inferred from complexity measures provided by recurrence quantification analysis, is considered a transition in the dynamics of the protein.ResultsWe apply this technique to a 132 ns long molecular dynamics simulation of the β-Lactamase Inhibitory Protein BLIP. We are able to detect conformational transitions in the nanosecond range in the recurrence dynamics of the BLIP protein during the simulation. The results compare favorably to those extracted using the principal component analysis technique.ConclusionsThe recurrence quantification analysis based bootstrap technique is able to detect transitions between different dynamics states for a protein over different time scales. It is not limited to linear dynamics regimes, and can be generalized to any time scale. It also has the potential to be used to cluster frames in molecular dynamics trajectories according to the nature of their recurrence dynamics. One shortcoming for this method is the need to have large enough time windows to insure good statistical quality for the recurrence complexity measures needed to detect the transitions.

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THz frequency spectrum of protein–solvent interaction energy using a recurrence plot‐based Wiener–Khinchin method

Cited by 3 publications

References 71 publications

Recurrence Rate spectrograms for the classification of nonlinear and noisy signals

Recurrence Rate spectrograms for the classification of nonlinear and noisy signals

Detection of the Nutritional Status of Phosphorus in Lettuce Using THZ Time-Domain Spectroscopy

Detecting transitions in protein dynamics using a recurrence quantification analysis based bootstrap method

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