Backbone entropy of loops as a measure of their flexibility: Application to a Ras protein simulated by molecular dynamics

Meirovitch, Hagai; Hendrickson, Thomas F.

doi:10.1002/(sici)1097-0134(199710)29:2<127::aid-prot1>3.0.co;2-a

Cited by 17 publications

(7 citation statements)

References 72 publications

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“…In order to correlate the findings form the MD simulation about the motions of the peptide's backbone with the experimental X-ray structure of the bound peptide, the quasi harmonic estimated entropy of the backbone atoms has been calculated and compared with the B factor values [58,59] of the C a atoms of the peptide. The results are illustrated in Fig.…”

Section: Backbone Dynamics Of the Peptidementioning

confidence: 99%

Insights into the structure of the LC13 TCR/HLA-B8-EBV peptide complex with molecular dynamics simulations

Stavrakoudis

2011

Cell Biochem Biophys

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One key step in the immune response against infected or tumor cells is the recognition of the T-cell receptor (TCR) by class I major histocompatibility complexes. The complex between the HLA-B8 molecule and the immunodominant peptide with sequence FLRGRAYGL, derived from the Epstein-Barr virus, with the LC13 TCR has been determined by X-ray diffraction. The complex has been used as a starting point in a molecular dynamics study in order to investigate the dynamics of the complex association and to explore the specific interactions of the complex formation. The analyzed structures provided evidence that the peptide adopts an open type b-turn conformation close to C-terminal part, which dominates peptide/TCR interactions. Conformational energy landscape analysis indicated the presence of two conformational clusters in the peptide's structure, underlying the backbone flexibility of the peptide despite being surrounded by two receptors. The peptide/MHC/TCR interface was found to hold significant number of solvent molecules, more specifically the peptide has been found to have approximately seventeen hydrogen bonds with water molecules. The molecular dynamics simulation indicated the disruption of some MHC/TCR contacts, mainly with the CDR1a loop. However, several other interactions emerged that resulted in a stable association during the 20 ns trajectory, as revealed by the buried surface area analysis.

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Section: Backbone Dynamics Of the Peptidementioning

confidence: 99%

Insights into the structure of the LC13 TCR/HLA-B8-EBV peptide complex with molecular dynamics simulations

Stavrakoudis

2011

Cell Biochem Biophys

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“…Thus far we have dealt with microstates (and their populations) without providing a practical definition for them This, however, is not a straightforward task which has been ignored to a large extent in the literature but has been given considerable thought by us in the course of the years ,,,– (see also paper I). To illustrate the problem assume a peptide model based on constant bond lengths and bond angles in a helical microstate Ω h , i.e., the dihedral angles φ i and Ψ i are expected to vary within relatively small ranges Δφ i and ΔΨ i around φ i = −60 ° and Ψ i = −50 ° (we ignore for a moment the side chains).…”

Section: Introductionmentioning

confidence: 99%

Entropy and Free Energy of a Mobile Protein Loop in Explicit Water

2008

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Estimation of the energy from a given Boltzmann sample is straightforward since one just has to average the contribution of the individual configurations. On the other hand, calculation of the absolute entropy, S (hence the absolute free energy F) is difficult because it depends on the entire (unknown) ensemble. We have developed a new method called, "the hypothetical scanning molecular dynamics" (HSMD) for calculating the absolute S from a given sample (generated by any simulation technique). In other words, S (like the energy) is "written" on the sample configurations, where HSMD provides a prescription of how to "read" it. In practice, each sample conformation, i is reconstructed with transition probabilities and their product leads to the probability of i, hence to the entropy. HSMD is an exact method where all interactions are considered and the only approximation is due to insufficient sampling. In previous studies HSMD (and HS Monte Carlo -HSMC) has been extended systematically to systems of increasing complexity, where the most recent is the 7-residue mobile loop, 304-310 (Gly-His-Gly-Ala-Gly-Gly-Ser) of the enzyme porcine pancreatic α-amylase modeled by the AMBER force field and AMBER with the implicit solvation GB/SA (paper I). In the present paper we make a step further and extend HSMD to the same loop capped with TIP3P explicit water at 300 K. As in paper I, we are mainly interested in entropy and free energy differences between the free and bound microstates of the loop, which are obtained from two separate MD samples of these microstates. The contribution of the loop to S and F is calculated by HSMD and that of water by a particular thermodynamic integration procedure. As expected, the free microstate is more stable than the bound microstate by a total free energy difference, F free − F bound = −4.8 ± 1, as compared to −25.5 kcal/mol obtained with GB/SA. We find that relatively large systematic errors in the loop entropies, S free (loop) and S bound (loop) are cancelled in their difference which is thus obtained efficiently and with high accuracy, i.e. with a statistical error of 0.1 kcal/mol. This cancellation, which has been observed in previous HSMD studies, is in accord with theoretical arguments given in paper I.

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“…One of the first was the stochastic boundary model of Karplus' group, where the region of interest (including the protein and the solvent) is divided into subregions of decreasing importance; 52 we have used this model for calculating the backbone entropy of loops in the protein ras. 53 In many other studies of ligands in active sites, caps of water molecules were built around these sites, with the number of water molecules typically increasing as computers have become more powerful. For example, in 1986, Bash et al 54 used only 168 waters to cover the active site of thermolysine in their calculations of the relative free energy of binding of two inhibitors, whereas in 1991, Merz used 300 waters for calculating the binding of CO 2 to human carbonic anyhdrase II.…”

Section: Introductionmentioning

confidence: 99%

“…It should be pointed out that approximate explicit solvation models, where only part of the protein (around the active site) is considered (and solvated), have been suggested before. One of the first was the stochastic boundary model of Karplus' group, where the region of interest (including the protein and the solvent) is divided into subregions of decreasing importance; we have used this model for calculating the backbone entropy of loops in the protein ras …”

Section: Introductionmentioning

confidence: 99%

Minimalist Explicit Solvation Models for Surface Loops in Proteins

White

Meirovitch

2006

J. Chem. Theory Comput.

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We have performed molecular dynamics simulations of protein surface loops solvated by explicit water, where a prime focus of the study is the small numbers (e.g., ~100) of explicit water molecules employed. The models include only part of the protein (typically 500 -1000 atoms), and the water molecules are restricted to a region surrounding the loop. In this study, the number of water molecules (N w ) is systematically varied, and convergence with large N w is monitored to reveal N w (min), the minimum number required for the loop to exhibit realistic (fully hydrated) behavior. We have also studied protein surface coverage, as well as diffusion and residence times for water molecules as a function of N w . A number of other modeling parameters are also tested. These include the number of environmental protein atoms explicitly considered in the model, as well as two ways to constrain the water molecules to the vicinity of the loop (where we find one of these methods to perform better when N w is small). The results (for RMSD and its fluctuations for four loops) are further compared to much larger, fully solvated systems (using ~10,000 water molecules under periodic boundary conditions and Ewald electrostatics), and to results for the GBSA implicit solvation model. We find that the loop backbone can stabilize with a surprisingly small number of water molecules (as low as 5 molecules per amino acid residue). The side chains of the loop require somewhat larger N w , where the atomic fluctuations become too small if N w is further reduced. Thus, in general, we find adequate hydration to occur at roughly 12 water molecules per residue. This is an important result, because at this hydration level, computational times are comparable to those required for GBSA. Therefore these "minimalist explicit models" can provide a viable and potentially more accurate alternative. The importance of protein loop modeling is discussed in the context of these, and other, loop models, along with other challenges including the relevance of appropriate free energy simulation methodology for assessment of conformational stability.

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Backbone entropy of loops as a measure of their flexibility: Application to a Ras protein simulated by molecular dynamics

Cited by 17 publications

References 72 publications

Insights into the structure of the LC13 TCR/HLA-B8-EBV peptide complex with molecular dynamics simulations

Insights into the structure of the LC13 TCR/HLA-B8-EBV peptide complex with molecular dynamics simulations

Entropy and Free Energy of a Mobile Protein Loop in Explicit Water

Minimalist Explicit Solvation Models for Surface Loops in Proteins

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