**1. Introduction**

Intrinsically disordered proteins (IDPs) are characterized by a lack of a tertiary structure under physiological conditions [1,2], which means that they are better described by an ensemble of different conformations than a single structure. This is reflected in their free energy landscapes, which normally are rather flat without a deep energy minimum as for globular proteins [3]. The flattened energy landscape makes IDPs very sensitive to changes in the environment and post-translational modifications (PTMs) of the sequence. A common type of reversible PTM is phosphorylation, which introduces extra negative charges and the possibility of forming hydrogen bonds and salt bridges [4]. Phosphorylation is commonly employed by cells as a regulatory mechanism, as it can change both the conformational ensemble and the dynamics, as well as the interaction with a binding partner, and therefore affect function. The functional implications of phosphorylation can be drastic, such as for the disordered neuroprotein tau, for which hyperphosphorylation has been related to amyloid fibril formation in Alzheimer's disease [5]. In proteins such as statherin and caseins, the phosphorylated residues are essential for their ability to bind to the tooth surface [6,7] or sequester calcium [8].

Experimental techniques such as small-angle X-ray scattering (SAXS) and fluorescence resonance energy transfer (FRET) have been used to provide information on global conformational changes upon phosphorylation of intrinsically disordered proteins or regions, while circular dichroism spectroscopy and nuclear magnetic resonance (NMR) have detected changes in secondary structure or other local arrangements such as salt

**Citation:** Rieloff, E.; Skepö, M. Molecular Dynamics Simulations of Phosphorylated Intrinsically Disordered Proteins: A Force Field Comparison. *Int. J. Mol. Sci.* **2021**, *22*, 10174. https://doi.org/10.3390/ ijms221810174

Academic Editors: Istvan Simon and Csaba Magyar

Received: 1 September 2021 Accepted: 16 September 2021 Published: 21 September 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

bridges [9–14]. However, due to the vast conformational ensembles possessed by IDPs, computer simulations are often a useful complement to obtain more detailed information, though this requires accurate models and force fields. We have previously shown that a coarse-grained "one bead per residue model" has proven to accurately predict average radius of gyration (Rg) and scattering curves for various IDPs, including statherin, although producing overly compact conformations of other more phosphorylated IDPs [15]. The two-site UNRES model has recently been extended with parameters for phosphorylated residues [16] and applied to study phosphorylation-induced folding of an IDP [17]. Although coarse-grained models are more computationally efficient and generally easier to interpret than atomistic models, they can lack in detail. In atomistic modelling, there is continuous development of force fields and water models towards more accurately describing IDPs, and some important adjustment have been the refinement of the backbone dihedral angles and balancing the water–protein and protein–protein interactions; see for example the following reviews and references within [18,19]. However, we recently showed that while the commonly used force fields CHARMM36m and Amber ff99SB-ILDN+TIP4P-D accurately captured the global dimensions of the 15-residue-long N-terminal fragment of Statherin in the nonphosphorylated state, it overestimated the compactness in the phosphorylated state [20]. More recently, overcompaction was also observed for two approximately 80-residue-long phosphorylated IDPs in several force fields, where it was suggested to depend on an overestimation of charge–charge interactions [21], in line with an overstabilization of salt bridges in standard force fields [22]. In this study, we made a further comparison of the two aforementioned force fields, by applying them to four phosphorylated peptides, namely two different fragments from tau, specifically residues 173-183 (Tau1) and 225-246 (Tau2), the first 25 amino acids in the milk protein β-casein (bCPP) and the saliva protein statherin (Stath). For all peptides, CHARMM36m was shown to sample more compact conformations than Amber ff99SB-ILDN+TIP4P-D, associated with a much higher probability for salt bridges. The effect was more pronounced in sequences with large separation between phosphorylated residues and positively charged residues, showing the importance of charge distribution. In bCPP, which showed the largest differences between the force fields, the addition of 150 mM NaCl did not change the average size estimates and shape significantly, despite a significant reduction of salt bridge occurrence in CHARMM36m. This implies that salt bridges are still of importance at 150 mM salt and that we can ignore the effects of salt concentration in this study.

## **2. Results and Discussion**

Four phosphorylated peptides, shown in Table 1, were simulated at physiological pH using two different force fields: Amber ff99SB-ILDN [23] with the TIP4P-D [24] water model and parameters for the phosphorylated residues from Homeyer et al. [25] and Steinbrecher et al. [26] (A99) and CHARMM36m [27] with the CHARMM-modified TIP3P water model [28] (C36). The peptides were chosen based on availability of experimental data to compare with and size considering the computational expense.

**Table 1.** Full name and sequence of the peptides included in this study. Positively charged residues are marked in blue, negatively charged in red, and phosphorylated residues highlighted with yellow. Note that Tau1 includes three additional residues in accordance with [11], to allow for experimental comparison.

#### *2.1. Size and Shape*

For all four peptides, the two force fields produced different conformational ensembles, as seen by the distributions of the Rg and the end-to-end distance (Ree) in Figure 1. The C36 distributions were narrower and centered on values lower than the A99 distributions. For Tau2 and bCPP, the Rg distribution had a sharp peak at low values. From the average Rg and Ree presented in Table 2, it is clear that Tau1 showed the smallest differences between the force fields, while bCPP showed the largest differences. The discrepancy was larger for Ree than Rg. For Tau1, Chin et al. [11] determined the average Ree to be ∼3.17 nm, based on FRET. To obtain an Ree distance distribution from the FRET data they assumed a semi-flexible polymer model, and the resulting distribution was skewed towards longer distances, with the peak value located at 3.64 nm (Figure 4A in ref. [11]). Comparing A99 and C36 to the experimental average, A99 overestimated it approximately as much as C36 underestimated it. However, the skewed shape and peak position at 3.64 nm produced in A99 was in better experimental agreement than C36, since the distribution in C36 was more symmetrical with multiple peaks and had the main peak located at 3.03 nm.

**Figure 1.** Distribution of the radius of gyration (**top row**) and the end-to-end distance (**bottom row**) of Tau1, Tau2, bCPP, and Stath simulated with Amber ff99SB-ILDN (A99) and CHARMM36m (C36). The legend applies to all panels.

**Table 2.** Average radius of gyration and end-to-end distance of the peptides simulated with Amber ff99-SB-ILDN (A99) and CHARMM36m (C36). The difference between the force fields is expressed in relation to A99.

For Stath, earlier published SAXS data [15] provided an Rg of 1.93 ± 0.2 nm; hence, Rg was 10% smaller in A99 and 27% smaller in C36. Since Rg determined from SAXS includes a hydration shell, it was expected that Rg calculated from simulations would be slightly smaller, although not to that extent. Since it is not straightforward which contrast to use for the hydration shell in the calculations of scattering curves for IDPs [29], in Supplementary Figure S1 and Table S2, we compared the curves calculated using different contrasts of the hydration shell to the experimental curve for Stath. While the highest contrast used (0.03 *e*/Å3) yielded the best agreement with the scattering curve, it provided the worst agreement with the Kratky plot. Henriques et al. [29] showed that the optimal contrast for IDPs was often between 0.01 *e*/Å3 and 0.02 *e*/Å3, although varying with both force field and protein. The optimal values for A99 and C36 were suggested to be around 0.0075 *e*/Å3 and 0.02 *e*/Å3, respectively. While the suggested optimal value gave reasonable agreement with the experimental form factor for A99, this was not the case for C36. For C36, all contrasts > 0 clearly showed larger compaction than the experimental Kratky plot.

Even without experimental scattering curves to compare to, the dimensionless Kratky plot, presented in Figure 2, is a good way of comparing the average shape of the peptides in the two different force fields. The short peptide Tau1 exhibited a more extended shape than the other three peptides, which in A99 were shown to have more of the typical IDP behavior, resembling a Gaussian chain. For all four peptides, the Kratky plot produced in C36 had a lower slope, and for the three longest peptides, the curve started to move towards the bell-shaped curve typical of globular proteins. Hence, this implies that C36 sampled more compact or well-defined conformations than A99, in accordance with the Rg and Ree distributions. Notice also that the Kratky plot of Stath in A99 was in excellent agreement with the experimental data, while the curve corresponding to C36 fell below, as shown in Figure 2d.

**Figure 2.** Dimensionless Kratky plot from simulations with Amber ff99SB-ILDN and CHARMM36m for (**a**) Tau1, (**b**) Tau2, (**c**) bCPP, and (**d**) Stath. In Panel (d), experimental data from Cragnell et al. [15] are included for comparison. The legend in Panel (a) is applicable to all panels.

#### *2.2. Salt Bridges and Secondary Structure*

Since our previous study [20] suggested that overstabilized salt bridges are the reason why C36 produces more compact conformations than A99, we calculated the occupancy of the possible salt bridge interactions involving the phosphorylated residues. Figure 3 indeed shows that salt bridges were formed much more in C36 than A99, for all the peptides. In Tau2 and bCPP, the strong salt bridges in C36 restricted the conformational ensemble, which explains the smaller and narrower distributions of Rg and Ree. In bCPP, the saltbridging residues were well separated in the sequence, therefore having a larger effect on the Rg and Ree distributions. In Tau1, the salt bridge interactions almost exclusively appeared between the adjacent residues and between pT175 and the N-terminal.

**Figure 3.** Probability of possible salt bridge interactions for the phosphorylated residues with the N-terminus (NT) and positively charged residues in Tau1 (**first row**), Tau2 (**second row**), bCPP (**third row**), and Stath (**last row**). For Tau2, experimentally established salt bridges [12] are marked with a white star. Error bars correspond to errors calculated by block averaging.

For Tau2, there is experimental evidence of the following salt bridges, detected by NMR experiments: pT231–R230, pS237–K240, and pS238–R242 [12]. pT231–R230 and pS238–R242 are indeed two of the most often occurring salt bridges in A99, while pS237– R242 is more common than pS237–K240. Several other salt bridges are also as frequently present as pS237–K240. In C36, pT231–R230 is the most occurring salt bridge, but both pS327–R242 and pS235–K234 are more probable than pS237–K240. Hence, while both

force fields captured the experimentally established salt bridges, they also suggested other salt bridges to be present and some of them to be more common than the experimentally established ones.

Advancing to the secondary structure, Figure 4 shows that the peptides were mainly irregular, although Tau1 contained much of the polyproline type II (PPII) structure as well. In fact, all peptides contained a significant amount of PPII, as well as a significant content of bends. The content of the helical structure (α- and 310-helix) and β-strands was low in all peptides. Tau1 exhibited the largest differences between the force fields, where A99 produced 16 percentage points more of the PPII structure than C36, which instead contained a more irregular structure. For the other peptides, the differences were smaller. Overall, the peptides only had one significant difference in common, which was a higher content of bends in C36 than A99. Inspecting the content along the sequence, it was evident that it was mostly the same parts of the peptide that were enriched in a certain type of structure in both force fields (see Supplementary Figure S3). However, in C36, the helical content was completely missing from the first ten residues of Stath, which is concerning since the N-terminal region has been shown to possess helical propensity in water, although being mainly disordered [6,30]. Another striking difference between the force fields for Stath is that some residues centered on residues Y21 and Y41 occasionally formed a β-sheet or β-bridge in C36, but not in A99. Notice also that for Tau2, the bend propensity at residues V228–V229 was much higher in C36 than in A99. Since these residues were located right between K225 and pT231, which in C36 formed a stable salt bridge, this suggested that the bend was formed as a result of the salt bridge. Furthermore, for Tau2, NMR data have suggested approximately 40% α-helical propensity in region A15-R18 [12]. Both A99 and C36 sampled the helical structure in this region, however, to a lower extent than what the experimental data suggested.

**Figure 4.** Average content of different types of the secondary structure in (**a**) Tau1, (**b**) Tau2, (**c**) bCPP, and (**d**) Stath simulated with Amber ff99SB-ILDN (A99) and CHARMM36m (C36). The legend applies to all panels. The helix includes the α- 310 and a negligible content of the π-helix, while the β-strand also includes β-bridge. Error bars correspond to errors calculated by block averaging.

#### *2.3. Energy Landscapes*

The differences between the force fields in this study is well summarized by the energy landscapes in Figures 5–8. Tau2, bCPP, and Stath all showed a narrower energy landscape in C36, in line with a more restricted conformational ensemble. Tau1, which is rather short and stiff, actually gained a larger conformational landscape in C36, due to sampling more bent conformations in addition to being more stretched out as in A99; see Figure 5. Notice also that in C36, the global minimum, which was the most populated, contained conformations that were not entirely stretched out. Instead, the N-terminal end was folded over, such that a salt bridge was formed between pT175 and the positively charged N-terminus.

**Figure 5.** Energy landscapes and conformations in selected minima of Tau1. (**Left**) A99; (**right**) C36. The energy landscapes are constructed using the first two components from principal component analysis, using the same basis set for both force fields, such that they are directly comparable. Contour lines are drawn for integer energy levels in the interval 1 ≤ RT ≤ 5, and the minimum of each basin is represented by a marker: ●: energy ≤ 1*RT*, ▲: ≤ 2*RT*. In the conformations, the phosphorylated and positively charged residues are shown explicitly. Dashed black lines represent hydrogen bonds. The peptide conformations are color-coded according to the secondary structure determination in VMD, where silver is irregular (coil) and cyan is turns. The N-terminus of each conformation is the leftmost end.

Although the energy landscapes of Tau2 in A99 and C36 were located in almost the same area, the energy levels differed; see Figure 6. The most populated basin in the C36 simulation was a deep and narrow minimum, while the A99 simulation had a larger area of energy ≤1RT, containing several basins, more typical of IDPs. The salt bridges creating more compact conformations were evident in the C36 conformations, while the A99 conformations were more stretched out with fewer salt bridges. Notice that the phosphorylated residues in C36 had a tendency to interact with several positively charged residues simultaneously. In both force fields, a basin minimum with a helical region starting with pS237 and pS238 was found, in line with the secondary structure analysis.

For bCPP, there was indeed many more elongated conformations in the A99 simulation (see Figure 7), and it is clear that what caused the more compact conformations in C36 was the salt bridges between the phosphorylated serines and the arginines. In C36, all depicted conformations contained at least one salt bridge between phosphoserine and arginine, while this was much rarer in A99, explaining why the energy landscapes looked so different. Regarding Stath, comparing the conformations in Figure 8, there were two striking differences. First, there was a higher presence of salt bridges between phosphoserine and positively charged residues in C36, keeping the N-terminal end in a more bent conformation. Secondly, in C36, the β-strand and β-bridge formation between the middle region and C-terminal region detected in Supplementary Figure S3 contributed to making the conformations more compact compared to A99.

**Figure 6.** Energy landscapes and conformations in selected minima of Tau2. (**Left**) A99; (**right**) C36. The energy landscapes are constructed using the first two components from principal component analysis, using the same basis set for both force fields, such that they are directly comparable. Contour lines are drawn for integer energy levels in the interval 1 ≤ RT ≤ 5, and the minimum of each basin is represented by a marker: ●: energy ≤ 1*RT*, ▲: ≤2RT, ✖: ≤3RT. In the conformations, the phosphorylated and positively charged residues are shown explicitly. Dashed black lines represent hydrogen bonds. The peptide conformations are color-coded according to the secondary structure determination in VMD, where silver is irregular (coil), cyan is turns, magenta is the α-helix, and blue is the 310-helix. The N-terminus of each conformation is the leftmost end.

**Figure 7.** Energy landscapes and conformations in selected minima of bCPP. (**Left**) A99; (**right**) C36. The energy landscapes are constructed using the first two components from principal component analysis, using the same basis set for both force fields, such that they are directly comparable. Contour lines are drawn for integer energy levels in the interval 1 ≤ RT ≤ 5, and the minimum of each basin is represented by a marker: ●: energy ≤ 1RT, ▲: ≤2RT, ✖: ≤3RT. In the conformations, the phosphorylated and positively charged residues are shown explicitly. Dashed black lines represent hydrogen bonds. The peptide conformations are color-coded according to the secondary structure determination in VMD, where silver is irregular (coil) and cyan is turns. The N-terminus of each conformation is the leftmost end.

**Figure 8.** Energy landscapes and conformations in selected minima of Stath. (**Left**) A99; (**right**) C36. The energy landscapes are constructed using the first two components from principal component analysis, using the same basis set for both force fields, such that they are directly comparable. Contour lines are drawn for integer energy levels in the interval 1 ≤ RT ≤ 5, and the minimum of each basin is represented by a marker: ●: energy ≤ 1RT, ▲: ≤2RT, ✖: ≤3RT. In the conformations, the phosphorylated and positively charged residues are shown explicitly. Dashed black lines represent hydrogen bonds. The peptide conformations are color-coded according to the secondary structure determination in VMD, where silver is irregular (coil), cyan is turns, blue is the 310-helix, yellow is the β-sheet, and tan is the β-bridge. The N-terminus of each conformation is the leftmost/topmost end.

#### *2.4. Effect of Salt Concentration*

Since the salt bridges formed between phosphorylated and positively charged residues were shown to influence the conformational ensemble, it is of importance to also consider the effect of the screening of the electrostatic interactions. Here, we focused on bCPP, which due to showing the largest differences between force fields and having the highest fraction of charged residues in combination with the largest charge separation (see Supplementary Table S1), was expected to show the largest response to ionic strength. Figure 9 shows that in C36, four of the salt bridges were dramatically reduced upon the addition of 150 mM NaCl; however, the probability of two other salt bridges increased, whereas in A99, only one salt bridge was significantly reduced. At 150 mM salt, the salt-bridging probability was more comparable between A99 and C36, although overall still higher in C36. Supplementary

Figure S3 shows the changes in the contact map upon the addition of 150 mM NaCl for bCPP simulated in A99 and C36. For A99, we clearly saw that the preference for the N-terminal end to be in contact with the phosphorylated and negatively charged region (residues 14–21) diminished. In C36, the strongly conserved R1–pS17 and R1–pS18 contacts were greatly decreased, while the contact of R1 with surrounding residues in the negatively charged region was increased. Hence, this suggested an increased mobility, while still maintaining contact with the negatively charged region. In C36, the cross-diagonal lines also signalized a decrease of the β-sheet; however, the content was relatively low from the beginning.

**Figure 9.** Probability of possible salt bridge interactions for the phosphorylated residues with the N-terminus (NT) and positively charged residues in bCPP, simulated with the two different force fields in the presence of 0 mM or 150 mM NaCl. Error bars corresponds to errors calculated by block averaging.

By comparing the energy landscapes in Figure 10, it is clear that screening of the electrostatic interactions indeed broadened the conformational ensemble, but mainly in C36, which also showed the largest change in salt bridge probability. In C36, the addition of 150 mM NaCl led to the exploration of more stretched out conformations; however, more compact conformations still clearly dominated. A99 also showed an increased probability of visiting more stretched out conformations after the addition of 150 mM NaCl. This shift in the conformational ensemble was also observed in the distributions of Rg and Ree shown in Supplementary Figure S4. However, the changes were actually rather small, such that the average values were indistinguishable. Upon the addition of salt, the Rg changed from 1.43 ± 0.03 nm to 1.45 ± 0.03 nm for A99 and from 1.08 ± 0.02 nm to 1.08 ± 0.03 nm for C36. The changes in Ree were from 3.09 ± 0.15 nm to 3.37 ± 0.13 nm and from 1.65 ± 0.10 nm to 1.67 ± 0.10 nm, respectively. The effect of salt on the calculated scattering curves was also so small that it could be deemed negligible; see Supplementary Figure S5.

**Figure 10.** Energy landscapes of bCPP simulated with the two force fields Amber ff99SB-ILDN (A99) and CHARMM36m (C36) in the presence of 0 mM or 150 mM NaCl.

#### **3. Conclusions**

C36 produced more compact conformations of all four peptides, which indeed was expected to be caused mainly by salt bridge stability. In Tau1, the salt bridges pT175–K174 and pT181-K180 were formed without much effect on the overall conformation; however, an additional salt bridge between the N-terminus and pT175 decreased Ree and Rg in C36. In Stath, the salt bridges contributed to the discrepancy by restricting the conformation of the first 15 residues, in the same way as previously shown for that fragment studied alone [20]. However, also the β-bridge and β-strand formation between the middle and C-terminal region were shown to contribute to more compact conformations. While C36 produced good results of nonphosphorylated short IDPs, it has been shown to underestimate the size of larger IDPs (>60 residues) [31,32]. Since Stath was 43 residues long, and thus the longest peptide included in this study, it is reasonable to believe that other effects also play a role. That bCPP showed the largest difference between the force fields and Tau1 the smallest implies that the separation between the phosphorylated and positively charged residues controls how much the conformational ensemble is influenced by stable salt bridges. This is in accordance with the importance of considering the level of charge separation for predicting the conformational ensemble of IDPs with a high fraction of charges [33].

When comparing to experimental data, it is important to consider the effect of salt, since most experiments are performed in the presence of buffer and additional salt. In bCPP, the addition of 150 mM NaCl was shown to dramatically reduce the probability of some of the salt bridges in C36, whereas the probability of other salt bridges actually increased. In A99, only one salt bridge was significantly reduced, which suggests that salt bridges still are of importance at 150 mM NaCl. Considering the changes in salt bridge probability for bCPP with salt concentration, it is plausible that the discrepancies between the simulations and experimental reference for Tau2 were caused by nonmatching ionic strength, since the experiments were performed with 50 mM phosphate buffer. At the same time, it can be hard to discern the salt bridges involving close-by residues experimentally, such as for pS237, pS238, K240, and R242.

Despite significant differences in the salt-bridging probability in C36, the effect of salt concentration on the global conformational level, such as Rg and Ree, was small enough to be negligible for both force fields. In fact, the calculated form factor was indistinguishable, implying that comparing simulations performed without salt with experimental SAXS data collected at 150 mM NaCl indeed can be valid. Since bCPP is the peptide for which we expected the largest effects of salt concentration, this further strengthens the comparison with SAXS data for Stath collected at 150 mM NaCl, which showed that A99 was in good agreement, while C36 overestimated the level of compaction. Although the effects of ionic strength seem negligible in this study, this is generally not the case. For example, Jin and Gräter needed 350 mM of salt in simulations with A99 to reach experimental agreement for IDPs that are approximately 80 residues long [21], which suggested that also A99 overestimate the strength of salt bridges. Here, both Tau1 and Stath were compared to experimental size estimates, and only C36 was with certainty shown to underestimate the size. Hence, a possible overestimation of salt bridge stability in A99 is not expected to be a major issue for describing the conformational ensemble of the short IDPs studied in this work. This emphasizes the importance of benchmarking against IDPs of different length and sequence when developing and evaluating force fields. While a reduction of the strength of salt bridges appears to be a crucial step in improving the performance of C36, it appears less critical in A99. However, note that this statement is based only on the global conformational properties and that it might be different for studies of dynamics. Based on observations that many force fields have a tendency to overstabilize salt bridges, which seems to be related to side-chain partial charges [22,34–36], we suggest that readjusting the side-chains' partial charges, especially of the phosphorylated residues, is a way of improving the force fields.

Another area which has not been touched upon in this work is the influence of charge regulation and pH. The simulations have been performed with fixed charges in a state corresponding to physiological pH, where the phosphorylated residues have have a charge of −2*e*. Since the pKa of the phosphorylated residues is around six [37], in reality it can fluctuate between −1*e* and −2*e*. Recent studies have suggested the importance of the protonation state of phosphorylated residues for molecular interactions [38], hence influencing salt bridge formation and the conformational ensemble. Therefore, this is suggested to be included in future investigations.

Considering the secondary structure, the only general difference between the force fields was a higher content of bends in C36. In Tau2, it was focused on regions between salt-bridging-forming partners, suggesting that highly stable salt bridges can enforce bends depending on the separation between the salt-bridging residues. For Tau2, it was suggested that both force fields underestimated the helical propensity, and in Stath, a lack of helix propensity in the N-terminal regions was concerning for C36. However, to properly assess the performance of force fields regarding the secondary structure, detailed experimental references are important. Hence, we see that NMR experiments of phosphorylated IDPs recording coupling constants, NOEs, and chemical shifts, which capture the effects of both the secondary structure and salt bridges, are an essential part of improving force fields. Since atomistic simulations can be used to carefully detect the secondary structure and salt bridges and their dynamics, it is an important tool in understanding the mechanism behind the regulation of IDP function by phosphorylation, provided that sufficient accuracy of the force fields is achieved.

#### **4. Materials and Methods**

Fraction of charged residues and κ, a parameter describing how segregated the charged residues are in the sequence [33] were calculated in CIDER [39], by equalizing the phosphorylated residues to other negatively charged residues. The value of κ is normalized against the most segregated sequence for that sequence composition, therefore adopting a value in the range 0–1, where 1 corresponds to the most segregated sequence possible.

The simulations listed in Supplementary Table S3 were performed in GROMACS 2018.4 [40–44], using two different force fields: Amber ff99SB-ILDN [23] with the TIP4P-D [24] water model and parameters for the phosphorylated residues from Homeyer et al. [25] and Steinbrecher et al. [26], and CHARMM36m [27] with the CHARMM-modified TIP3P water model [28]. Initial configurations of the peptides were constructed from the sequence as linear chains using Avogadro 1.2.0 [45], optimizing the structure with the auto-optimization tool. Each peptide was solvated in a rhombic dodecahedron box, having a minimum distance between the peptide and the box edges of 1 nm. Sodium ions were added to neutralize the system, and two systems were also simulated with sodium and chloride ions in a concentration corresponding to 150 mM. Periodic boundary conditions were employed in all directions. The equations of motion were integrated using the Verlet leapfrog algorithm [46] with a time step of 2 fs. Nonbonded interactions were treated with a Verlet list cutoff scheme. The short-range interactions were calculated using neighbor lists with cutoff 1 nm or 1.2 nm, for the Amber and CHARMM force fields, respectively. For the CHARMM force field, the Lennard–Jones interactions were switched off smoothly (force-switch) between 1 nm and 1.2 nm. Long-range dispersion corrections were applied to energy and pressure in the case of the Amber force field. Long-range electrostatic interactions were treated by particle mesh Ewald [47] with a cubic interpolation and a 1.6 Å grid spacing. The LINCS algorithm [48] was used to constrain all bond lengths in the case of Amber and only bonds with hydrogen atoms in the case of CHARMM. The solute and solvent were separately coupled to temperature baths at 298 K using the velocity rescaling thermostat [49] with a 0.1 ps relaxation time. Parrinello–Raman pressure coupling [50] was used to keep the pressure at 1 bar, using a 2 ps relaxation time and 4.5 × 10−<sup>5</sup> bar−<sup>1</sup> isothermal compressibility.

Energy minimization was performed by the steepest descent algorithm until the system converged within the available machine precision. Initiation of five replicates per system with different starting seeds was performed separately in two steps using position restraints on the peptide. The first step was 500 ps of NVT simulation (constant number of particles, volume, and temperature) performed to stabilize the temperature, followed by the second step of 1000 ps of NPT simulation (constant number of particles, pressure, and temperature) to stabilize the pressure. Production runs of the five replicates per system were performed in the NPT ensemble, for at least 1 μs per replicate. The total simulation time per system is stated in Supplementary Table S3. Energies and coordinates were saved every 10 ps. Supplementary Tables S4 and S5 compile a few differences applied to the salt simulations to reduce the computational time.

#### *Analysis*

The convergence and sampling quality were assessed in the following ways. The time evolution of the Rg and the Ree in the simulations were observed for signs of equilibration in the initial stage. Based on this, the first 30 ns were removed from each replicate of bCPP in CHARMM36m and the first 50 ns of each replicate of Tau2 in CHARMM36m before final analysis (see Supplementary Figures S21 and S24). In other systems the equilibration was deemed fast enough to be negligible. The distributions of the Rg and the Ree as well as the energy landscapes were compared between replicates, since similarity indicates sufficient sampling. The autocorrelation function and block average error estimates of the Rg and the Ree in the concatenated simulation were calculated and observed for an estimate of the correlation time and convergence of the error estimates. All this data is presented in the Supplementary Figures S6–S33. Although some systems showed greater dissimilarity between replicates than desired, based on the assessment of the concatenated trajectory, it was deemed sufficiently sampled to allow for a comparison between the force fields.

Rg and Ree were calculated using GROMACS 2018.4 [40–44]. Reported error estimates were calculated using block averaging analysis as implemented in the *gmx analyze* routine in GROMACS. Scattering curves were calculated using CRYSOL Version 2.8.3 [51] with the contrast of the hydration shell being 0.0075 *e*/Å<sup>3</sup> for Amber ff99SB-ILDN+TIP4P-D and 0.02 *e*/Å3 for CHARMM36m, as suggested by [29]. The presented curve is the average over 10,000 equally spaced frames. In Supplementary Figure S1 and Table S2, the effect of different contrasts of the hydration shell is shown for Stath. The quality of fit to the experimental curve is computed as:

$$\chi^2(f,\boldsymbol{c}) = N\_q^{-1} \sum\_{i=1}^{N\_q} \left[ \frac{I\_{\rm ref}(q\_i) - (f I\_{\rm obs}(q\_i) + \boldsymbol{c})}{\sigma\_{\rm ref}(q\_i)} \right]^2 \tag{1}$$

where *N*−<sup>1</sup> *<sup>q</sup>* is the number of points in the reference curve, *I*ref and *I*obs are the reference and observed intensities, respectively, and *σ*ref(*qi*) is the error associated with each data point of the reference curve. The function was minimized using the Nelder–Mead method [52], as implemented in Scipy [53], using linear interpolation to produce *I*obs at the same *q* points as the reference [29]. AUTORG in the ATSAS program [54] was used to determine the Rg from Guinier analysis. The secondary structure was determined using the DSSP program Version 2.2.1 [55] with an extension to detect the polyproline type II structure [56,57]. The MDTraj Python library Version 1.9.3 [58] was used to calculate contact probability and analyze salt bridges. Contact between two residues was defined as when the shortest distance between two atoms < 0.4 nm. Since salt bridges are formed as a result of hydrogen bonding and electrostatic interactions, they were assessed by analyzing the presence of hydrogen bonds based on the criterion in [59], as implemented in MDTraj. Energy landscapes were calculated following the Campos and Baptista approach [60], with the differences described by Henriques et al. [61]. In short, principal component analysis was applied to the Cartesian coordinates of the backbone atoms of the protein, obtained after translational and rotational least squares fitting on the central structure of the simulation. The conditional free energy was calculated from the probability density function in the representation space constructed by the first two principal components, obtained by Gaussian kernel density estimation. The basins and minima were assigned as described by Campos and Baptista [60]. It is worth noting that the first two components were shown to account for 46–60% of the variance, hence not providing a complete picture of the conformational classes, but at least an overview sufficient for comparison between the force fields. Snapshots from the simulations were produced using VMD 1.9.3 [62–64].

**Supplementary Materials:** The following are available online at www.mdpi.com/article/10.3390/ ijms221810174/s1.

**Author Contributions:** Conceptualization, M.S. and E.R.; formal analysis, E.R.; funding acquisition, M.S.; investigation, E.R.; methodology, M.S. and and E.R.; project administration, M.S.; resources, M.S.; supervision, M.S.; validation, M.S. and E.R.; visualization, E.R.; writing, M.S. and E.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** Financial support was given by the Royal Physiographic Society in Lund and the Bertil Andersson foundation.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data is included in the article or Supplementary Materials.

**Acknowledgments:** The simulations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at the Center for Scientific and Technical Computing at Lund University (LUNARC).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Abbreviations**

A99 Amber ff99SB-ILDN with TIP4P-D water C36 CHARMM36m with CHARMM-modified TIP3P water FRET Fluorescence resonance energy transfer IDP Intrinsically disordered protein NMR Nuclear magnetic resonance PPII polyproline type II Rg Radius of gyration Ree End-to-end distance SAXS Small-angle X-ray scattering

#### **References**


**4.2.7 MLST (multilocus sequence typing)**

A recently developed methodology (Maiden et al., 1998) called multilocus sequence typing (MLST) may provide an ideal balance of high discriminatory power and a powerful data analysis capability requiring minimal human input. Multilocus sequence typing (MLST) is a molecular typing strategy that compares DNA sequences from portions of housekeeping or virulence genes and/or rRNA sequences which varies due to mutation or recombination events (Maiden et al., 1998). Nucleotide differences in the individual genes

Laboratory Typing Methods for Diagnostic of

(Chamberlain et al., 1989).

and hence sensitivity of the reaction (Raeymaekers, 1995).

resistance of the polymerase to denaturation (Hengen, 1997).

possibility of cross-contamination, and may complicate automation.

min (Kebelmann-Betzing et al., 1998).

Salmonella Strains, the "Old" Organism That Continued Challenges 363

multiplex reaction is performed for the first time, it is useful to add the primers in equimolar amounts. The results will suggest how the individual primer concentration and other parameters need to be changed. Special attention to primer design parameters such as homology of primers with their target nucleic acid sequences, their length, the GC content, and their concentration have to be considered (Robertson & J., 1998). Ideally, all the primer pairs in a multiplex PCR should enable similar amplification efficiencies for their respective target. This may be achieved through the utilization of primers with nearly identical optimum annealing temperatures and should not display significant homology either internally or to one another (Henegariu et al., 1997). Also, the extension rate of specific primer-target hybrids depends on the activity of the enzyme, availability of essential components such as deoxyribonucleoside triphosphates (dNTPs), and the nature of the target DNA. Thus, the majority of modifications to improve PCR performance have been directed towards the factors affecting annealing and/or extension rates. Therefore, in multiplex PCR, as more loci are simultaneously amplified, the pool of enzyme concentrations, PCR buffer constituents and nucleotides becomes a limiting factor and more time is necessary for the polymerase molecules to complete synthesis of all the products

Variation in concentrations of reaction components above those used in uniplex PCR probably reflects the competitive nature of the PCR process. The desired target DNA can be outcompeted by the more efficient amplification of other targets (including nonspecific products), leading to decreases in the efficiency of the amplification of the desired targets

Various authors recommend dimethyl sulfoxide (DMSO) and glycerol to improve amplification efficiency (higher amount of product) and specificity (no unspecific products) of PCR, when used in concentrations varying between 5%–10% (vol/vol) (Innis & D.H., 1990). Also bovine serum albumin, or betaine, has been reported to be of benefit in multiplex PCRs (Jackson et al., 1996). The components may act to prevent the stalling of DNA polymerization, which can occur through the formation of secondary structures within regions of template DNA during the extension process (Hengen, 1997). Also it can act as destabilizing agents, reducing the melting temperature of GC-rich sequences, or as osmoprotectants, increasing the

A straightforward solution to difficulties encountered in the development of multiplex PCR has been the use of hot start PCR (Chou et al., 1992) and/or nested PCR (Zheng et al., 1995). The former often eliminates nonspecific reactions (particularly production of primer dimers) caused by primer annealing at low temperature (4 to 25°C) before commencement of thermocycling (Chou et al., 1992). The procedure has recently been made more practicable through the use of a nonmechanical hot start methodology which involves the use of a form of Taq polymerase, for example, Ampli Taq Gold (Roche Diagnostics), which is activated only if the reaction mixture is heated in first denaturation step at approximately 94°C for 10

Nested PCR increases the sensitivity and specificity of the test through two independent rounds of amplification using two discrete primer sets. Although this adaptation is undoubtedly effective in most cases, it also considerably complicates the practical application of PCR. The second round of amplification delays results, increases the

are combined and used to determine the differentiation of strains (Yan et al., 2003). MLST provides data similar to those obtained by multilocus enzyme electrophoresis, but in substantively greater detail, because it has the ability to assess individual nucleotide changes rather than to screen for changes in the overall charge and expression of the enzyme under study (Maiden et al., 1998).

This method is extremely useful for long-term epidemiological studies or phylogenetic analyses. Over 230 *Salmonella* isolates were recently characterized by MLST based on sequences from the 16S RNA, pduF, glnA and manB genes (Kotetishvili et al., 2002). These results were compared to PFGE and serotype analysis. MLST was able to differentiate strains better than PFGE, though not all genes performed equally. Among the four loci, only manB demonstrated clusters among the clinical and environmental strains. As expected, the 16S rRNA locus showed significant homogeneity among the isolates and grouped most isolates together.

MLST shows great promise for accurate strain discrimination with data that can be accurately shared between laboratories. However, like FAFLP, the universal appeal of this technique will be improved when automated sequence machinery becomes more affordable and labs can develop familiarity with complicated DNA sequence analysis and statistical software


**1. Introduction**

Aquaculture, the so-called blue biotechnology of the future [1], is the fastest growing food production sector and should continue expanding and diversifying to fulfill the need for good-quality protein of the ∼8.5 billion people that will populate the earth by 2030. Such ascending trajectory of aquaculture will also help to achieve some of the United Nations (UN) sustainable developmental goals (SDGs) [2] that seek a better, less unequal, and more sustainable future for humanity by 2030, specifically in relation to food security, improved nutrition, and poverty alleviation in rural communities in particular. Therefore, aquaculture growth and management of aquatic genetic resources should be done in a sustainable manner, a goal that requires maximizing ecosystem goods and services in line with the Blue Growth Initiative of the Food and Agriculture Organization (FAO) [2]. Aquaculture has expanded at a rate of 5.8% annually (2001–2016), totalizing ~ 80 million of tons

in 2016, worth USD 231.6 billion (mainly finfish, crustacea, mollusk) [2]. Yet, the potential for expansion and diversification is immense considering the diversity of existing marine species [3]. For example, the total number of commercially farmed species increased in the last 10 years from 472 (2006) to 598 (2016) [2]. However, the sustainable aquaculture expansion is currently hampered by the impact of detrimental diseases causing high mortality and economic loss to the sector [4], together with the indiscriminate and inefficient use of antibiotics and chemicals to control them, which causes a negative ecosystem impact [5, 6]. To reevaluate this approach is the challenge ahead, and a good start is the ban imposed to the use of antibiotics by international organizations and consumers, which have stimulated the search for alternative microbial control strategies, like the use of probiotics [6]. Chile is a well-known and competitive salmon and trout producer. In fact, it's the world's second producer of salmon following Norway, and recently it has become a relevant mussel producer [2]. According to the FAO, in 2016 Chile ranked fourth among the world producers of marine and coastal finfish with 726.9 thousand tons (live weight) and the fourth marine mollusk producer (307.4 thousand tons [2]). The relevant aquaculture species are shown in **Figure 1**, with a total of 2162 centers distributed between the so-called Lake District (administrative region X); the northern part of Patagonia, where the salmon boom began; and the Magellan and Antarctic Region (XII). Region X is full of lakes that are intensively exploited as hatcheries for smolt production and also has protected coastal bays, fjords, and estuaries ideal for completing the marine phase of salmonid life cycle, not so far away from hatcheries. This region has 666 registered fish and 1171 mollusk centers. However, due to the ecosystem and disease consequences of the intensive salmon farming (high densities of fish per water volume) [7], the activity has moved to Region XI, with a total of 767 fish centers. In total (fish and mollusk), these centers harvested 1219.739 tons in 2016 [8].

One striking aspect of the farming of exotic salmonid species in Chile is the impressive expansion from the initial 80,000 tons harvested in the early 1990s to the 688,000 tons in 2004 and 900,000 tons in 2014 according to official statistics of the national service of fisheries, SERNAPESCA. In 2017 the production leveled at 791.103 tons with the following production figures by species (2017, tons): Atlantic salmon (*Salmo salar*) 582,350; coho salmon (*Oncorhynchus kisutch*), 134,235; and rainbow trout *(Oncorhynchus mykiss*) 74,518. Such successful story dates back to the last part of the twentieth century with the introduction of rainbow and brown trout

#### **Figure 1.**

*Main species produced by Chilean aquaculture. According to production figures (2017), farming of salmon and trout is by far the main aquaculture activity in Chile, followed by the Chilean mussel (Mytilus chilensis). Others correspond to seaweeds and the Chilean scallop.*

#### *Application of Metagenomics to Chilean Aquaculture DOI: http://dx.doi.org/10.5772/intechopen.86302*

and other salmonids, initially for recreational fisheries and later for aquaculture [9]. However, the industry almost collapsed in 2006–2007 by the outbreak of the deadly virus responsible of the infectious salmon anemia (ISA), a problem that some anticipated due to the unlimited expansion of the industry in an ecosystem with limited carrying capacity [10]. This has raised serious concerns on the environmental standards of an industry making intensive use of coastal, estuaries, and lakes that are shared by multiple users and due to the high rate of antibiotic consumption (0.53 Kg per ton harvested in 2016), one of the highest in the world [7]. The interaction between host (fish), microbiota, and environmental microorganisms could be a key factor to develop rational strategies to improve the productivity by increasing the resistance to infection and reducing the use of antibiotic**s** and the environmental impact of aquaculture. However, in the production scale, the intensive use of water and the utilities of the sector does not match with the lack of scientific research available to deal with the problems caused.

This chapter focuses on Chilean aquaculture to evaluate how metagenomics, a recently developed genomic subdiscipline, is actually contributing, or could potentially contribute, to develop more efficient aquaculture practices in relation to disease control and the environmental burden such practices have brought about. Metagenomics has made possible and cheaper the analysis of the complex genomes of microbial communities to unravel their diversity, dynamics, and functioning in different environments. The application of this tool to the aquaculture microcosm in particular has been reviewed by Martinez-Porchas and Vargas-Albores [11]. One of the benefits of metagenomics is to provide access to unculturable species, the vast majority of diseaserelated microbes in aquaculture whose diversity and function were unknown so far. We first provide an overview of Chilean aquaculture, the microbiological threats faced by the salmon farming industry, mainly the symbiotic and antagonist interrelationship between microbes and farmed animals. The focus is placed on how the gut microbiota of farmed and native species contribute to their fitness and overall performance in production-related traits like disease or stress resistance. Finally, we seek to evaluate the application of metagenomics to monitoring environmental biodiversity and microbial dynamics in a scenario of climate oscillations and other ecosystem perturbations such as the development of harmful algal blooms.


**4. Calculation method of seal characteristics**

The analysis method to calculate the seal characteristics is shown below. During the optimum design calculations, the groove shape should be changed continuously from its original spiral groove shape into other shapes. Therefore, a boundary-fitted coordinate system is adopted as the numerical calculation method [15]. Moreover, a divergence formulation method is implemented. A Reynolds equivalent equation obtained from flow balance as shown in **Figure 3** is used to obtain the pressure distributions on the seal face. This is because the geometry has a step over which there is a discontinuous pressure gradient between the groove and the land areas.

The Reynolds equivalent equation [16] is

$$\mathbf{Q}\_{2I}^{\xi} + \mathbf{Q}\_{1III}^{\xi} - \mathbf{Q}\_{2I}^{\xi} - \mathbf{Q}\_{1IV}^{\xi} + \mathbf{Q}\_{2I}^{\eta} + \mathbf{Q}\_{1II}^{\eta} - \mathbf{Q}\_{2III}^{\eta} - \mathbf{Q}\_{1IV}^{\eta} = \mathbf{Q}^{\Gamma} \tag{7}$$

Subscripts 1, 2, and *I* to *IV* indicate the areas in the control volume shown in **Figure 3**.

where the mass flow rates through the various boundaries are

$$Q^{\xi} = \int\_{\eta\_1}^{\eta\_2} \rho \left( -A\_0 \frac{\partial p}{\partial \xi} + B\_0 \frac{\partial p}{\partial \eta} + D\_0 + E\_0 \right) d\eta \tag{8}$$

$$Q^{\eta} = \int\_{\xi\_1}^{\xi\_2} \rho \left( B\_0 \frac{\partial p}{\partial \xi} - C\_0 \frac{\partial p}{\partial \eta} + F\_0 + G\_0 \right) d\xi \tag{9}$$

**Figure 3.** *Control volume and flow rates.*

*Tribology of Machine Elements - Fundamentals and Applications*

$$Q^{\Gamma} = \int\_{\xi\_1}^{\xi\_2} \int\_{\eta\_1}^{\eta\_2} \frac{\partial(\rho h)}{\partial t} |f\_0| d\eta d\xi \tag{10}$$

The coefficients of *Ao* to *Jo* are

$$A\_o = a\_o \frac{h^3}{12\mu l}, B\_o = b\_o \frac{h^3}{12\mu l}, C\_o = c\_o \frac{h^3}{12\mu l}, D\_o = -\frac{roo\_\eta h}{2} r\_\eta,$$

$$E\_o = \frac{\rho r o\_\eta^2 h^3}{40\mu} r \theta\_\eta, F\_o = \frac{roo\_\eta h}{2} r\_\xi, G\_o = \frac{\rho r o\_\eta^2 h^3}{40\mu} r \theta\_\xi,$$

$$a\_o = (r\theta\_\xi)^2 + r\_\eta^2, b\_o = (r\theta\_\xi)(r\theta\_\eta) + r\_\xi r\_2, c\_o = (r\theta\_\xi)^2 + r\_\xi^2,$$

$$f\_o = r\_\xi (r\theta\_\eta) - r\_\eta (r\theta\_\xi), r\_\xi = \frac{\partial r}{\partial \xi}, r\_\eta = \frac{\partial r}{\partial \eta},$$

$$r\theta\_\xi = r \frac{\partial \theta}{\partial \xi}, r\theta\_\eta = r \frac{\partial \theta}{\partial \eta}$$

Assuming a small amplitude vibration of the seal with frequency *ωf*, the gas film thickness and pressure are expressed as follows.

$$\begin{aligned} h &= h\_0 + \varepsilon e^{j o\_f t} \\ p &= p\_0 + \varepsilon p\_t e^{j o\_f t} \end{aligned} \tag{12}$$

where, *ε* is an amplitude of vibration and *p0* and *pt* express a static component and dynamic pressure components, respectively.

Substituting Eq. (12) into Eq. (7) and neglecting seconds terms of *ε*, the following equations regarding the 0th field and 1st field *ε* are obtained.

$$F\_0(p\_0) = Q\_{200}^\xi + Q\_{1\text{III}0}^\xi - Q\_{2\text{II}0}^\xi - Q\_{1\text{IV}0}^\xi + Q\_{2\text{I}0}^\eta + Q\_{1\text{II}0}^\eta - Q\_{2\text{III}0}^\eta - Q\_{1\text{IV}0}^\eta \tag{13}$$

$$F\_t(p\_t, p\_0) = \mathbf{Q}\_{\text{2lt}}^{\xi} + \mathbf{Q}\_{\text{1IIIt}}^{\xi} - \mathbf{Q}\_{\text{2llt}}^{\xi} - \mathbf{Q}\_{\text{1IVt}}^{\xi} + \mathbf{Q}\_{\text{2lt}}^{\eta} + \mathbf{Q}\_{\text{1IIt}}^{\eta} - \mathbf{Q}\_{\text{2IIIt}}^{\eta} - \mathbf{Q}\_{\text{1IVt}}^{\eta} \tag{14}$$

Discretizing Eqs. (13) and (14), and then solving the equations numerically, the static and dynamic components of the gas pressure fields are obtained. Finally, the gas leakage rate *q* is calculated from the static pressure distributions as

$$q = \int\_0^{2\pi} -\frac{\rho h^3}{12\mu} \frac{\partial p\_0}{\partial r} \bigg|\_{r=r\_i} r d\theta \tag{15}$$

Moreover, assuming the simple vibration model of a dry gas seal shown in **Figure 4**, the dynamic stiffness *K* is obtained from the calculated dynamic pressure distributions as,

$$K = \sqrt{\mathbf{k}^2 + \left(\alpha\_f c\right)^2} \tag{16}$$

$$k = \int\_{0}^{2\pi} \int\_{r\_i}^{r\_s} \text{Re}\left\{-p\_t\right\} r dr d\theta \tag{17}$$

$$\mathcal{L} = \int\_0^{2\pi} \int\_{r\_i}^{r\_o} \operatorname{Im} \{-p\_t\} r dr d\theta \tag{18}$$

*Groove Shape Optimization on Dry Gas Seals DOI: http://dx.doi.org/10.5772/intechopen.103088*

**Figure 4.**

*Simple vibration model of the dry gas seal.*


**Recurrent Neural Network-Based Adaptive Controller Design for Nonlinear Dynamical Systems**

Hong Wei Ge and Guo Zhen Tan\* *College of Computer Science and Technology, Dalian University Of Technology, Dalian, China*


**5. Linked open vocabularies**

Linked Open Vocabularies is a new concept in semantic web ontology. It consists of large amount of dataset around 660 vocabularies as on March 10, 2019. It fully supports the OWL and RDF framework for better management and identification of the right link available in an online database. SPARQL endpoint is easily accessed by using this interface. Properties and classes can be done on the basis of ontology concept that is subject, predicate, and object (https://lov.linkeddata.es/dataset/ lov/). Here all the dataset belongs to the vocabulary content or element type. **Figure 2** represents the Linked Open Vocabularies interface in semantic web for different elements and their relationship.


**Physical Activity and Obesity in Adults**

#### Hidetaka Hamasaki Hidetaka Hamasaki

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/64672

#### **Abstract**

Both obesity and physical inactivity are global health problems responsible for the risk increment of non-communicable diseases. Obese individuals usually cannot perform the recommended level of physical activity because of their low physical fitness and comorbidities. The purpose of this chapter is summarizing and evaluating the effects of physical activity on obesity. The author also focuses on the association between nonexercise activity thermogenesis (NEAT) and obesity. The author has reviewed 13 systematic reviews and meta-analyses of randomized controlled trials investigating the effects of physical activity on obesity. Exercise is essential for the management of obesity. However, exercise alone is not sufficient for long-term weight loss and improving cardiovascular disease (CVD) risk factors. Diet seems to be more effective for treating obesity than exercise. On the other hand, exercise improves cardiorespiratory fitness and skeletal muscle fitness, which leads to prevent sarcopenic obesity in the elderly. Exercise therapy should be performed in conjunction with diet therapy to improve obesity. NEAT is the main determinant of variability in daily energy expenditure, which considerably contributes to weight change in humans. The current evidence regarding NEAT is limited; however, NEAT appears to be effective for the management of metabolic diseases as well as weight loss. To reveal the optimal mode of physical activity and to elucidate the effects of NEAT on health beyond weight lowering, further welldesigned studies are warranted.

**Keywords:** obesity, physical activity, exercise, systematic review and meta-analysis, non-exercise activity thermogenesis