Bioenergetics: Open Access

Objective: To evaluate the dose-response effect of aerobic exercise on sleep duration, sleep efficiency and sleep quality in previously sedentary, moderately overweight men. Methods: In a randomized, controlled trial, 53 sedentary Caucasian men aged between 20 and 40 years (VO2max<45 mL O2 kg -1 min-1, BMI: 25-30 kg/m2, body fat>25%) completed a 13-week aerobic exercise intervention consisting of either a physical activity energy deficit of 600 kcal day-1 (HIGH: n=18), 300 kcal day-1 (MOD: n=18), or being sedentary (CON: n=17). The endpoints were sleep duration (objectively measured by actigraphy over 3 days), sleep efficiency (3-day actigraphy), and subjectively rated sleep quality (Pittsburgh Sleep Quality Index). Results: Because of missing sleep data, a total of 32 subjects were included in the present analysis (CON: n=12, MOD: n=12, HIGH: n=8). A significant increase in sleep duration was observed in HIGH (80 ± 30 min, p=0.03). However, the change was not significantly different from the change in CON. Sleep efficiency tended to decrease in HIGH (p=0.05), and there was a tendency towards an improved sleep quality within MOD and HIGH (p=0.08 in both). Conclusion: Our study suggests that a high daily dose of aerobic exercise for 13 weeks increases sleep duration, tends to decrease sleep efficiency, and tends to improve subjective sleep quality in sedentary, moderately overweight men. Because our sample included relatively young and sleep-efficient individuals, future studies should examine the dose-response effects of aerobic exercise on sleep parameters in older adults with sleeping problems. Citation: Kjeldsen JS, Rosenkilde M, Nielsen SW, Reichkendler M, Auerbach P, et al. (2012) Effect of Different Doses of Exercise on Sleep Duration, Sleep Efficiency and Sleep Quality in Sedentary, Overweight Men. Bioenergetics S1:002. doi:10.4172/2167-7662.S1-002 Kjeldsen et al., Bioenergetics 2012, S:1 http://dx.doi.org/10.4172/2167-7662.S1-002

A part of today's routine in computer-aided drug design, particularly in the drug lead identification phase, is protein-ligand binding free energy calculation [1].The binding free energy calculation methods that combine molecular mechanical force fields with continuum solvent models have gained popularity as they can achieve a good balance between efficiency and accuracy [2][3][4].Good examples include MM-PBSA (Molecular Mechanics-Poisson Boltzmann Surface Area) and MM-GBSA (Molecular Mechanics-Generalized Born Surface Area) [5].Although MM-PB/GBSA is theoretically not as rigorous as free energy perturbation or thermodynamic integration, it is much less computer-resource demanding.In addition, MM-PB/GBSA frequently achieves a much better performance than docking scoring functions, a rationale for using it to re-rank docking poses in molecular docking studies.
In the MM-PB/GBSA theory, the free energy of a molecule is composed of three terms (Eq.1): the gas phase molecular mechanical energy, the solvation free energy and the configurational entropy term.The gas phase molecular mechanical energy consists of three terms: the bonded, van der Waals, and electrostatic (Eq.2).The solvation free energy is further decomposed into the polar and nonpolar parts (Eq.3).The polar part is calculated by solving either the Poisson-Boltzmann equation (Eq.4) for PBSA or the generalized Born equation (Eq.5) for GBSA.The nonpolar part in Eq. 3 is typically estimated using solvent accessible surface area (SASA) as the energy for creating a cavity in solvent is proportional to SASA [2]. (1) (2) (3) There are a lot of successful stories of using this technique to model protein complexes and predict binding affinities [6][7][8].It is widely accepted that MM-PB/GBSA can more reliably predict the relative binding free energies of a series of compounds binding to the same target, while the performance of the absolute binding free energy prediction strongly depends on systems [9][10][11].
As a relatively new technology, MM-PB/GBSA needs to overcome two challenges.The first hurdle is how to wisely set the intrinsic dielectric constant ε int .It has been known that the calculated electrostatic energy (E elec + pol solv G ) strongly depends on the choice of intrinsic dielectric constant ε int .As illustrated in Figure 1, seven biotin/avidin-protein complexes have wide spread distributions of absolute binding free energies predicted by six MM-PB/GBSA models [10].The performance of reproducing the experimental relative binding free energy also varies from model to model and the correlation coefficient squares R 2 are 0.87, 0.34, 0.12, 0.86, 0.50 and 0.20 for PBSA (ε int =1), PBSA (ε int =2), PBSA (ε int =4), GBSA (ε int =1), GBSA (ε int =2), and GBSA (ε int =4), respectively.The same system was also studied by Genheden et al. [12] using eight different solvation models (two PBSA, four GBSA and two MM/3D-RISM) and similar results were observed.
One possible solution to this challenge is to use variable dielectric constants for very heterogeneous environments of a protein.Nevertheless, the use of multiple dielectric constants that depend on subtle chemical environments or functional groups would over kill such a physically simplistic approach.On the other hand, Schutz and Warshel [13] argued that ε int should be model-dependent and the more implicit the model is the larger optimal ε int is needed.If it is the case, more physical model, such as a PBSA model that explicitly considers the induced dipole interaction, could lead to better results.
The second challenge of the MM-PB/GBSA is how to calculate the entropic term efficiently and accurately.Although for large molecules, solving the Poisson-Boltzmann equation does take time, the bottleneck of this technique is to calculate the conformational entropy by normal mode analysis (NMA) or quasiharmonic [14].For NMA, the structures must be fully minimized in order to make the harmonic assumption valid.Otherwise, the calculation result is meaningless or has a large computational error.A mass-weighted Hessian matrix is generated based on the minimized structure and diagonalized to get vibrational modes.Both the geometrical optimization and the following normal mode analysis are time-consuming and computer memory demanding Experiment Binding Free Energy (kcal/mol) Volume 1 for large biological molecules [15].The quasi-harmonic approach is even more challenging than NMA because a long molecular dynamics or Monte Carlo simulation is needed to make the fluctuation matrix converge [16].How to overcome this challenge?It is probably a sound idea to weigh solvent accessible surface areas classified by atom types to estimate the entropy term as researchers do for other molecular properties including solvation free energy, aqueous solubility, etc. Certainly, approaches that can fasten minimization procedure and simplify the diagonalization of Hessian matrix can also break the bottleneck.
In summary, MM-PB/GBSA is a promising technique in calculating the binding affinities in a larger scale.If the two challenges can be overcome, the MM-PB/GBSA technique will be more accurate and efficient, and thus will have more applications in computer-aided drug design.

*Figure 1 :
Figure 1: Prediction of the absolute binding free energies of seven biotin/ avidin complexes using six MM-PB/GBSA models.