Conformational Propensities of Intrinsically Disordered Proteins from NMR Chemical Shifts

Intrinsically disordered proteins (IDPs) are abundant in eukaryotic proteomes, play a major role in cell signaling, and are associated with human diseases. To understand IDP function it is critical to determine their configurational ensemble, i.e., the collection of 3-dimensional structures they adopt, and this remains an immense challenge in structural biology. Attempts to determine this ensemble computationally have been hitherto hampered by the necessity of reweighting molecular dynamics (MD) results or biasing simulation in order to match ensemble-averaged experimental observables, operations that reduce the precision of the generated model because different structural ensembles may yield the same experimental observable. Here, by employing enhanced sampling MD we reproduce the experimental small-angle neutron and X-ray scattering profiles and the NMR chemical shifts of the disordered N terminal (SH4UD) of c-Src kinase without reweighting or constraining the simulations. The unbiased simulation results reveal a weakly funneled and rugged free energy landscape of SH4UD, which gives rise to a heterogeneous ensemble of structures that cannot be described by simple polymer theory. SH4UD adopts transient helices, which are found away from known phosphorylation sites and could play a key role in the stabilization of structural regions necessary for phosphorylation. Our findings indicate that adequately sampled molecular simulations can be performed to provide accurate physical models of flexible biosystems, thus rationalizing their biological function.

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

Journal of Biomolecular NMR ◽

10.1007/s10858-019-00283-z ◽

2019 ◽

Vol 73 (12) ◽

pp. 713-725 ◽

Cited By ~ 4

Author(s):

Ruth Hendus-Altenburger ◽

Catarina B. Fernandes ◽

Katrine Bugge ◽

Micha B. A. Kunze ◽

Wouter Boomsma ◽

...

Keyword(s):

Chemical Shift ◽

Secondary Structure ◽

Intrinsically Disordered Proteins ◽

Chemical Shifts ◽

Random Coil ◽

Disordered Proteins ◽

Phosphoryl Group ◽

Intrinsically Disordered ◽

Intrinsically Disordered Regions ◽

Secondary Chemical

Abstract 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.

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Using Chemical Shifts to Generate Structural Ensembles for Intrinsically Disordered Proteins with Converged Distributions of Secondary Structure

Biophysical Journal ◽

10.1016/j.bpj.2014.11.1256 ◽

2015 ◽

Vol 108 (2) ◽

pp. 227a-228a

Author(s):

F. Marty Ytreberg ◽

Wade Borcherds ◽

Hongwei Wu ◽

Gary W. Daughdrill

Keyword(s):

Secondary Structure ◽

Intrinsically Disordered Proteins ◽

Chemical Shifts ◽

Disordered Proteins ◽

Intrinsically Disordered ◽

Structural Ensembles

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CheSPI: Chemical shift Secondary structure Population Inference

10.1101/2021.02.20.432095 ◽

2021 ◽

Author(s):

Jakob Toudahl Nielsen ◽

Frans A.A. Mulder

Keyword(s):

Intrinsically Disordered Proteins ◽

Chemical Shifts ◽

Protein Structures ◽

Random Coil ◽

Disordered Proteins ◽

Residual Structure ◽

Intrinsically Disordered ◽

Population Inference ◽

Local Protein Structure ◽

Structural Classes

AbstractNMR chemical shifts (CSs) are delicate reporters of local protein structure, and recent advances in random coil CS (RCCS) prediction and interpretation now offer the compelling prospect of inferring small populations of structure from small deviations from RCCSs. Here, we present CheSPI, a simple and efficient method that provides unbiased and sensitive aggregate measures of local structure and disorder. It is demonstrated that CheSPI can predict even very small amounts of residual structure and robustly delineate subtle differences into four structural classes for intrinsically disordered proteins. For structured regions and proteins, CheSPI can assign up to eight structural classes, which coincide with the well-known DSSP classification. The program is freely available, and can either be invoked from URL www.protein-nmr.org as a web implementation, or run locally from command line as a python program. CheSPI generates comprehensive numeric and graphical output for intuitive annotation and visualization of protein structures. A number of examples are provided.

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The accuracy of force fields on the simulation of intrinsically disordered proteins: A benchmark test on the human p53 tumor suppressor

Journal of Theoretical and Computational Chemistry ◽

10.1142/s021963362050011x ◽

2020 ◽

Vol 19 (04) ◽

pp. 2050011

Author(s):

Shangbo Ning ◽

Jun Liu ◽

Na Liu ◽

Dazhong Yan

Keyword(s):

Md Simulation ◽

Intrinsically Disordered Proteins ◽

Force Fields ◽

Chemical Shifts ◽

Disordered Proteins ◽

Experimental Measurements ◽

Intrinsically Disordered ◽

Structural Ensembles ◽

Water Models ◽

Structural Ensemble

Intrinsically disordered proteins (IDPs) are a class of proteins without stable three-dimensional structures under physiological conditions. IDPs exhibit high dynamic nature and could be described by structural ensembles. As one of the most widely used tools, molecular dynamics (MD) simulation could provide the atomic descriptions of the structural ensemble of IDPs. However, the accuracy of the MD simulation largely depends on the accuracy of the force field. In this paper, we compared the structural ensembles of the activation domain 1 (AD1) in p53 tumor suppressor obtained from the widely used force fields, AMBER99SB-ILDN, CHARMM27, CHARMM36m with different water models. The results show that CHARMM36m generates more extended conformations than other force fields, while CHARMM27 prefers to sample the [Formula: see text]-helical structure. Moreover, the chemical shifts obtained by CHARMM36m are the closest to the experimental measurements. These results indicate that the CHARMM36m force field performs best in characterizing the structure properties of p53 AD1. Water models are also critical to describe the structural ensemble of IDPs. TIP4P water model can obtain more extended conformations and produce more local helical conformations than the TIP3P model in our simulation. In addition, we also compare the chemical shifts predicted by different chemical shift predicting programs with experimental measurements, the results show that SHIFTX2 obtains the best performance in the chemical shifts prediction.

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Full structural ensembles of intrinsically disordered proteins from unbiased molecular dynamics simulations

Communications Biology ◽

10.1038/s42003-021-01759-1 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Utsab R. Shrestha ◽

Jeremy C. Smith ◽

Loukas Petridis

Keyword(s):

Molecular Dynamics ◽

Small Angle ◽

Intrinsically Disordered Proteins ◽

Chemical Shifts ◽

Small Angle Scattering ◽

Disordered Proteins ◽

Scattering Data ◽

General Applicability ◽

Intrinsically Disordered ◽

Angle Scattering

AbstractMolecular dynamics (MD) simulation is widely used to complement ensemble-averaged experiments of intrinsically disordered proteins (IDPs). However, MD often suffers from limitations of inaccuracy. Here, we show that enhancing the sampling using Hamiltonian replica-exchange MD (HREMD) led to unbiased and accurate ensembles, reproducing small-angle scattering and NMR chemical shift experiments, for three IDPs of varying sequence properties using two recently optimized force fields, indicating the general applicability of HREMD for IDPs. We further demonstrate that, unlike HREMD, standard MD can reproduce experimental NMR chemical shifts, but not small-angle scattering data, suggesting chemical shifts are insufficient for testing the validity of IDP ensembles. Surprisingly, we reveal that despite differences in their sequence, the inter-chain statistics of all three IDPs are similar for short contour lengths (< 10 residues). The results suggest that the major hurdle of generating an accurate unbiased ensemble for IDPs has now been largely overcome.

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Artificial Intelligence Guided Conformational Mining of Intrinsically Disordered Proteins

10.1101/2021.11.21.469457 ◽

2021 ◽

Author(s):

Aayush Gupta ◽

Souvik Dey ◽

Huan-Xiang Zhou

Keyword(s):

Artificial Intelligence ◽

Intrinsically Disordered Proteins ◽

Chemical Shifts ◽

Computational Cost ◽

Md Simulations ◽

Conformational Space ◽

Disordered Proteins ◽

Training Dataset ◽

Multivariate Gaussian Distribution ◽

Intrinsically Disordered

Artificial intelligence recently achieved the breakthrough of predicting the three-dimensional structures of proteins. The next frontier is presented by intrinsically disordered proteins (IDPs), which, representing 30% to 50% of proteomes, readily access vast conformational space. Molecular dynamics (MD) simulations are promising in sampling IDP conformations, but only at extremely high computational cost. Here, we developed generative autoencoders that learn from short MD simulations and generate full conformational ensembles. An encoder represents IDP conformations as vectors in a reduced-dimensional latent space. The mean vector and covariance matrix of the training dataset are calculated to define a multivariate Gaussian distribution, from which vectors are sampled and fed to a decoder to generate new conformations. The ensembles of generated conformations cover those sampled by long MD simulations and are validated by small-angle X-ray scattering profile and NMR chemical shifts. This work illustrates the vast potential of artificial intelligence in conformational mining of IDPs.

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NMR Line Shapes in Molecular Mechanisms with Ligand Binding and Multiple Conformers

10.1101/2020.01.03.894485 ◽

2020 ◽

Author(s):

Evgenii L Kovrigin

Keyword(s):

Ligand Binding ◽

Molecular Mechanisms ◽

Intrinsically Disordered Proteins ◽

Chemical Shifts ◽

Disordered Proteins ◽

Biological Macromolecules ◽

Induced Fit ◽

Line Shapes ◽

Intrinsically Disordered ◽

Specific Effects

ABSTRACTInteractions of ligands with biological macromolecules are sensitively detected through changes of chemical shifts and line shapes of the NMR signals. This paper reports a mathematical analysis and simulations of NMR line shapes expected in titrations when ligand binding is coupled to multiple isomerization transitions. Such molecular mechanisms may correspond to ligand binding by intrinsically disordered proteins or by autoinhibited enzymes. Based on the simulation results, we anticipate several specific effects that may be observed in practice. First, the presence of non-binding conformers of the receptor molecule leads to a remarkable broadening in the binding transition even if the exchange between binding and non-binding conformers is very slow. Second, the ligand-binding mechanisms involving induced fit are expected to demonstrate deceptively decelerated exchange regimes even when the underlying kinetics are very fast. Conversely, the observation of fast-exchange shifting resonances with modest line-broadening (“marching peaks”) in practical NMR titrations may involve conformational selection transitions but less likely to be observed for the induced fit. Finally, in auto-inhibited molecules that open to form multiple binding-competent conformers, the fast dynamics of opening/closing transition are capable of masking the true kinetics of interconversion among transiently open forms of the receptor.

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