pepKalc: scalable and comprehensive calculation of electrostatic interactions in random coil polypeptides

Tamiola, Kamil; Scheek, Ruud M.; Meulen, Pieter S. van der; Mulder, Frans A. A.

doi:10.1093/bioinformatics/bty033

Cited by 12 publications

(15 citation statements)

References 30 publications

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“…Thus, these effects may falsely be interpreted as such, unless the random coil CSs used in the calculations are matched to the pH of the NMR sample. However, the pKa value of individual phosphorylation sites in the protein context will depend on several other factors, including the presence of charges or polar residues in the neighboring sequence, additional phosphorylation sites (Tamiola et al 2018), as well as local or, in the case of a folded protein, the global protein conformation (Buckingham 1960; Kukic et al 2013; Wishart 2011; Tomlinson et al 2010). In these cases, care should be taken in interpreting effects of phosphorylation, and a pKa determination of the different sites in urea, which may also provide the intrinsic random coils shifts, can be a solution.…”

Section: Resultsmentioning

confidence: 99%

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: 99%

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

et al. 2019

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“…The electrostatic potential was calculated by adding a dummy charge at each position in the sequence one-by-one and evaluating the electrostatic energy following a statistical mechanics algorithm implemented in pepKalc 125 using default values for the physical parameters.…”

Section: Appendix: Online Methodsmentioning

confidence: 99%

ODiNPred: comprehensive prediction of protein order and disorder

Dass

Mulder

Nielsen

2020

Sci Rep

162

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Structural disorder is widespread in eukaryotic proteins and is vital for their function in diverse biological processes. It is therefore highly desirable to be able to predict the degree of order and disorder from amino acid sequence. It is, however, notoriously difficult to predict the degree of local flexibility within structured domains and the presence and nuances of localized rigidity within intrinsically disordered regions. To identify such instances, we used the CheZOD database, which encompasses accurate, balanced, and continuous-valued quantification of protein (dis)order at amino acid resolution based on NMR chemical shifts. To computationally forecast the spectrum of protein disorder in the most comprehensive manner possible, we constructed the sequence-based protein order/disorder predictor ODiNPred, trained on an expanded version of CheZOD. ODiNPred applies a deep neural network comprising 157 unique sequence features to 1325 protein sequences together with the experimental NMR chemical shift data. Cross-validation for 117 protein sequences shows that ODiNPred better predicts the continuous variation in order along the protein sequence, suggesting that contemporary predictors are limited by the quality of training data. The inclusion of evolutionary features reduces the performance gap between ODiNPred and its peers, but analysis shows that it retains greater accuracy for the more challenging prediction of intermediate disorder.

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“…Considerable efforts have been invested in developing methods to predict the strengths of intramolecular charge interactions and pK a shifts in both folded proteins and IDPs . A simple and effective approach involves modeling IDP dynamics using the Gaussian coil (GC) approximation, where the probability distribution for the distance between any two side chains, p ( r ) is identical to that of a freely jointed non‐self‐avoiding chain

p ((), r) = 4 π r^{2} {(\frac{3}{2 π (〈〉, r^{2})})}^{\frac{3}{2}} e^{- \frac{3 r^{2}}{2 (〈〉, r^{2})}},

where < r 2 > is the mean squared distance, which scales linearly with the separation in primary amino acid sequence.…”

Section: Resultsmentioning

confidence: 99%

“…However, it becomes intractable for larger IDPs, as the total number of distinct ionization configurations grows as 2 N , where N is the number of ionizable groups. A recent refinement involves averaging over the ionization configurations using a hybrid mean‐field approach that permits this method to be used for IDPs of arbitrary size. We applied this modified GC calculation to the γ‐CT and obtained pK a values in relatively good agreement with those that we measured (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Differences between experimental side chain pK a s in the γ‐CT and those of the same residue types in the context of model tripeptides (ΔpK a = pK a,NMR – pK a,mod ) (a). Differences between pK a values predicted for the γ‐CT using pepKalc software, and those of the same residue types in the context of model tripeptides (ΔpK a = pK a,pepKalc – pK a,mod ) (b).…”

Section: Resultsmentioning

confidence: 99%

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Side chain electrostatic interactions and pH‐dependent expansion of the intrinsically disordered, highly acidic carboxyl‐terminus of γ‐tubulin

2019

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Intramolecular electrostatic attraction and repulsion strongly influence the conformational sampling of intrinsically disordered proteins and domains (IDPs). In order to better understand this complex relationship, we have used nuclear magnetic resonance to measure side chain pKa values and pH‐dependent translational diffusion coefficients for the unstructured and highly acidic carboxyl‐terminus of γ‐tubulin (γ‐CT), providing insight into how the net charge of an IDP relates to overall expansion or collapse of the conformational ensemble. Many of the pKa values in the γ‐CT are shifted upward by 0.3–0.4 units and exhibit negatively cooperative ionization pH profiles, likely due to the large net negative charge that accumulates on the molecule as the pH is raised. pKa shifts of this magnitude correspond to electrostatic interaction energies between the affected residues and the rest of the charged molecule that are each on the order of 1 kcal mol−1. Diffusion of the γ‐CT slowed with increasing net charge, indicative of an expanding hydrodynamic radius (rH). The degree of expansion agreed quantitatively with what has been seen from comparisons of IDPs with different charge content, yielding the general trend that every 0.1 increase in relative charge (|Q|/res) produces a roughly 5% increase in rH. While γ‐CT pH titration data followed this trend nearly perfectly, there were substantially larger deviations for the database of different IDP sequences. This suggests that other aspects of an IDP's primary amino acid sequence beyond net charge influence the sensitivity of rH to electrostatic interactions.

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pepKalc: scalable and comprehensive calculation of electrostatic interactions in random coil polypeptides

Abstract: http://protein-nmr.org and https://github.com/PeptoneInc/pepkalc.

Cited by 12 publications

References 30 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

ODiNPred: comprehensive prediction of protein order and disorder

Side chain electrostatic interactions and pH‐dependent expansion of the intrinsically disordered, highly acidic carboxyl‐terminus of γ‐tubulin

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