Simple MD-based model for oxidative folding of peptides and proteins

Izmailov, Sergei A.; Podkorytov, Ivan S.; Skrynnikov, Nikolai R.

doi:10.1038/s41598-017-09229-7

Cited by 10 publications

(8 citation statements)

References 90 publications

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“…The energies of the resulting conformers were evaluated by means of the sander program from AmberTools using the Amber ff14SB force field and implicit solvent (option igb ¼ 8) (61). The obtained data were then used to construct the energy histogram, resulting in a bell-shaped distribution (62). The initial pep N H4 structure was chosen in a random manner from among 215 structures in the central part of this histogram.…”

Section: Simulationsmentioning

confidence: 99%

What Drives 15N Spin Relaxation in Disordered Proteins? Combined NMR/MD Study of the H4 Histone Tail

Kämpf

Izmailov

Rabdano

et al. 2018

Biophysical Journal

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Backbone ( 15 N) NMR relaxation is one of the main sources of information on dynamics of disordered proteins. Yet, we do not know very well what drives 15 N relaxation in such systems, i.e., how different forms of motion contribute to the measurable relaxation rates. To address this problem, we have investigated, both experimentally and via molecular dynamics simulations, the dynamics of a 26-residue peptide imitating the N-terminal portion of the histone protein H4. One part of the peptide was found to be fully flexible, whereas the other part features some transient structure (a hairpin stabilized by hydrogen bonds). The following motional modes proved relevant for 15 N relaxation. 1) Sub-picosecond librations attenuate relaxation rates according to S 2 $0.85-0.90. 2) Axial peptide-plane fluctuations along a stretch of the peptide chain contribute to relaxation-active dynamics on a fast timescale (from tens to hundreds of picoseconds). 3) 4/j backbone jumps contribute to relaxation-active dynamics on both fast (from tens to hundreds of picoseconds) and slow (from hundreds of picoseconds to a nanosecond) timescales. The major contribution is from polyproline II (PPII) 4 b transitions in the Ramachandran space; in the case of glycine residues, the major contribution is from PPII 4 (b) 4 rPPII transitions, in which rPPII is the mirror-image (right-handed) version of the PPII geometry, whereas b geometry plays the role of an intermediate state. 4) Reorientational motion of certain (sufficiently long-lived) elements of transient structure, i.e., rotational tumbling, contributes to slow relaxation-active dynamics on $1-ns timescale (however, it is difficult to isolate this contribution). In conclusion, recent advances in the area of force-field development have made it possible to obtain viable Molecular Dynamics models of protein disorder. After careful validation against the experimental relaxation data, these models can provide a valuable insight into mechanistic origins of spin relaxation in disordered peptides and proteins.

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Section: Simulationsmentioning

confidence: 99%

What Drives 15N Spin Relaxation in Disordered Proteins? Combined NMR/MD Study of the H4 Histone Tail

Kämpf

Izmailov

Rabdano

et al. 2018

Biophysical Journal

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“…This decision is made using the rejection sampling strategy with p 0 set to 0.1 (i.e. the decision is random with the probability of positive outcome 0.1) 92 . If the outcome is negative, the scanning of the frames continues.…”

Section: Methodsmentioning

confidence: 99%

Molecular Dynamics model of peptide-protein conjugation: case study of covalent complex between Sos1 peptide and N-terminal SH3 domain from Grb2

Luzik

Rogacheva

Izmailov

et al. 2019

Sci Rep

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We have investigated covalent conjugation of VPPPVPPRRRX′ peptide (where X′ denotes Nε-chloroacetyl lysine) to N-terminal SH3 domain from adapter protein Grb2. Our experimental results confirmed that the peptide first binds to the SH3 domain noncovalently before establishing a covalent linkage through reaction of X′ with the target cysteine residue C32. We have also confirmed that this reaction involves a thiolate-anion form of C32 and follows the SN2 mechanism. For this system, we have developed a new MD-based protocol to model the formation of covalent conjugate. The simulation starts with the known coordinates of the noncovalent complex. When two reactive groups come into contact during the course of the simulation, the reaction is initiated. The reaction is modeled via gradual interpolation between the two sets of force field parameters that are representative of the noncovalent and covalent complexes. The simulation proceeds smoothly, with no appreciable perturbations to temperature, pressure or volume, and results in a high-quality MD model of the covalent complex. The validity of this model is confirmed using the experimental chemical shift data. The new MD-based approach offers a valuable tool to explore the mechanics of protein-peptide conjugation and build accurate models of covalent complexes.

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“…Very recently, it was shown that the cis / trans isomerization of N-methylated cyclic hexapeptides cannot be reliably predicted [109]. Recently, simulation of guanylin (a 15-residue peptide with four cysteines) showed that the distribution of three disulfide-bond isomers is in qualitative agreement with experiment, but the most stable conformation of the isomer 2 is significantly different from the poorly ordered structure of the truncated peptide [110]. In another study, BE-META simulations were performed on five cyclic isoDGR-containing α/β-peptides using eight widely used force fields with explicit water to reproduce 79 NMR observables [111].…”

Section: Peptide Structure Predictionmentioning

confidence: 99%

Applications of Molecular Dynamics Simulation in Structure Prediction of Peptides and Proteins

Geng

Chen

et al. 2019

Computational and Structural Biotechnology Journal

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Compared with rapid accumulation of protein sequences from high-throughput DNA sequencing, obtaining experimental 3D structures of proteins is still much more difficult, making protein structure prediction (PSP) potentially very useful. Currently, a vast majority of PSP efforts are based on data mining of known sequences, structures and their relationships (informatics-based). However, if closely related template is not available, these methods are usually much less reliable than experiments. They may also be problematic in predicting the structures of naturally occurring or designed peptides. On the other hand, physics-based methods including molecular dynamics (MD) can utilize our understanding of detailed atomic interactions determining biomolecular structures. In this mini-review, we show that all-atom MD can predict structures of cyclic peptides and other peptide foldamers with accuracy similar to experiments. Then, some notable successes in reproducing experimental 3D structures of small proteins through MD simulations (some with replica-exchange) of the folding were summarized. We also describe advancements of MD-based refinement of structure models, and the integration of limited experimental or bioinformatics data into MD-based structure modeling.

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Simple MD-based model for oxidative folding of peptides and proteins

Cited by 10 publications

References 90 publications

What Drives 15N Spin Relaxation in Disordered Proteins? Combined NMR/MD Study of the H4 Histone Tail

What Drives 15N Spin Relaxation in Disordered Proteins? Combined NMR/MD Study of the H4 Histone Tail

Molecular Dynamics model of peptide-protein conjugation: case study of covalent complex between Sos1 peptide and N-terminal SH3 domain from Grb2

Applications of Molecular Dynamics Simulation in Structure Prediction of Peptides and Proteins

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