Prediction of nearest neighbor effects on backbone torsion angles and NMR scalar coupling constants in disordered proteins

Shen, Yang; Roche, Julien; Grishaev, Alexander; Bax, Ad

doi:10.1002/pro.3292

Cited by 28 publications

(31 citation statements)

References 55 publications

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“…However, measurements of the 3 J HNHA coupling constants over a pH range from 4.0 to 8.0 showed that those of the phosphorylated serine and threonine, but not those of the tyrosine or the glutamines, were pH dependent. At low pH, the 3 J HNHA coupling constants had random coil values (Shen et al 2018), but decreased and reached 4.7 Hz (pSer) and 4.0 Hz (pThr), respectively, at pH 8.0 (Fig. 2c).…”

Section: Resultsmentioning

confidence: 87%

Random coil chemical shifts for serine, threonine and tyrosine phosphorylation over a broad pH range

et al. 2019

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Phosphorylation is one of the main regulators of cellular signaling typically occurring in flexible parts of folded proteins and in intrinsically disordered regions. It can have distinct effects on the chemical environment as well as on the structural properties near the modification site. Secondary chemical shift analysis is the main NMR method for detection of transiently formed secondary structure in intrinsically disordered proteins (IDPs) and the reliability of the analysis depends on an appropriate choice of random coil model. Random coil chemical shifts and sequence correction factors were previously determined for an Ac-QQXQQ-NH2-peptide series with X being any of the 20 common amino acids. However, a matching dataset on the phosphorylated states has so far only been incompletely determined or determined only at a single pH value. Here we extend the database by the addition of the random coil chemical shifts of the phosphorylated states of serine, threonine and tyrosine measured over a range of pH values covering the pKas of the phosphates and at several temperatures (www.bio.ku.dk/sbinlab/randomcoil). The combined results allow for accurate random coil chemical shift determination of phosphorylated regions at any pH and temperature, minimizing systematic biases of the secondary chemical shifts. Comparison of chemical shifts using random coil sets with and without inclusion of the phosphoryl group, revealed under/over estimations of helicity of up to 33%. The expanded set of random coil values will improve the reliability in detection and quantification of transient secondary structure in phosphorylation-modified IDPs.Electronic supplementary materialThe online version of this article (10.1007/s10858-019-00283-z) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 87%

Random coil chemical shifts for serine, threonine and tyrosine phosphorylation over a broad pH range

et al. 2019

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“…fig.1); this led us to adopt a standard deviaVon normalisaVon for the observed torsional angles. While several different normalisaVon approaches exist for dihedral angles [30], [31], the mathemaVcal techniques directly depend on the parameter incorporaVon into further equaVons which, in our case, needed to be formulated in a way to preserve Ramachandran plot direcVonality based on posiVve and negaVve value so that mulVplicaVon operaVon allowed to predict either β-sheet/strand type of conformaVon (negaVve) or α-helix (posiVve) for the most predominant secondary structure elements (supl. fig.1).…”

Section: Discussion and Resultsmentioning

confidence: 99%

Fi-score: a novel approach to characterise protein topology and aid in drug discovery studies

Kanapeckaitė

Beaurivage

Hancock

et al. 2020

Preprint

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ACKNOLEGEMENTSThe authors would like to thank Dr. Farhad Forouhar, Columbia University, for kindly offering technical advice on structure visualisaVon AUTHORS' CONTRIBUTIONS AK devised the methodology, performed the analysis and wrote the manuscript. CB and EV criVcally reviewed the manuscript and provided suggesVons, MH criVcally reviewed and edited the manuscript. All authors read and approved the final manuscript. ABBREVIATIONS: HTS high-throughput screening PLI target protein-ligand interacVons AIC Akaike informaVon criterion BIC Bayesian informaVon criterion KEYWORDS: Drug discovery Dihedral angles B-factor Machine learning Gaussian mixture models ConformaVon distal informaVon Protein site characterisaVon AbstractTarget evaluaVon is at the centre of raVonal drug design and biologics development. In order to successfully engineer anVbodies, T-cell receptors or small molecules it is necessary to idenVfy and characterise potenVal binding or contact sites on therapeuVcally relevant target proteins. Currently, there are numerous challenges in achieving a be;er docking precision as well as characterising relevant sites. We devised a first-of-its-kind in silico protein fingerprinVng approach based on dihedral angle and Bfactor distribuVon to probe binding sites and sites of structural importance. In addiVon, we showed that the enVre protein regions or individual structural subsets can be profiled using our derived fi-score based on amino acid dihedral angle and B-factor distribuVon. We further described a method to assess the structural profile and extract informaVon on sites of importance using machine learning Gaussian mixture models. In combinaVon, these biophysical analyVcal methods could potenVally help to classify and systemaVcally analyse not only targets but also drug candidates that bind to specific sites which would greatly improve pre-screening stage, target selecVon and drug repurposing efforts in finding other matching targets.

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“…The increasing frequency of information exchange has been prompted to transfer from old information transmission mod to more convenient transmission mod. With the rapid development of computer and network, network transmission has been highly paid attention [1]. The text, image, video data and other information involved in transmission are insignificant, and some are related to state secrets, trade secrets or personal privacy.…”

Section: Introductionmentioning

confidence: 99%

Image encryption algorithm for torsional components of generator based on compound chaotic model

Shao

2020

THERM SCI

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Original scientific paper https://doi.org/10.2298/TSCI190717078SIn order to solve the problem that the image encryption algorithm cannot eliminate the strong correlation between adjacent pixels in the image, with poor ability to resist attack and low efficiency, an Image encryption algorithm for torsional components of generators based on complex chaotic model is proposed. Extracting the RGB torsional vibration component of the image for discrete cosine transform transformation, it is then rotated and fused to complete the initial encryption of the image information. In order to further enhance the security of image information, 2-D discrete cosine transform and 2-D compressed sensing measurement are applied to the initial encrypted image information. The real number matrix of measurement is merged into the complex value matrix, and the adaptive random phase coding is applied. The image information complex chaotic encryption model is constructed according to the coding result, so as to eliminate the strong correlation between the adjacent pixels. Particle swarm optimization is used to co-ordinate and optimize the parameters of the compound chaotic encryption model to improve the encryption performance of the model. Experimental results show that the pixel has low correlation, high security and strong ability to resist attacks after encrypting with this algorithm.

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Prediction of nearest neighbor effects on backbone torsion angles and NMR scalar coupling constants in disordered proteins

Cited by 28 publications

References 55 publications

Random coil chemical shifts for serine, threonine and tyrosine phosphorylation over a broad pH range

Random coil chemical shifts for serine, threonine and tyrosine phosphorylation over a broad pH range

Fi-score: a novel approach to characterise protein topology and aid in drug discovery studies

Image encryption algorithm for torsional components of generator based on compound chaotic model

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