Three systems, LDHA:0SN, LDHA:2B4, and LDHA:NHIS, could hold the mobile loop in the closed conformation. Additionally, the mobile loop displayed larger fluctuations in the open conformation than in the closed conformation, which is probably caused by a much larger conformational space available for the loop open state. It follows that bringing the mobile loop to the closed conformation causes an entropic penalty. This could partially explain the comparable binding affinities of 0SN and 1E4, even though 0SN possesses more polar interactions. Similarly, the ionic interactions with Arg111 were shown to significantly reduce the mobility of 1E4 and surrounding A-site residues, including Arg111; the incurred entropic penalty would offset the enthalpy gain from such strong ionic interactions. Since Arg111 is largely exposed to bulk solvent, polar water molecules can also compete with the inhibitor in interacting with Arg111. Notably, similar ionic interactions in the LDHA:1E7 complex appeared to be unstable, suggesting little free energy gain from this interaction. No significant correlation between the dynamics of ligand binding, as revealed by RMSF values of binding site residues and ligands as well as the percentage existence of polar interactions, and experimental binding affinities was found. For example, the binding of 1E4 incurred much larger fluctuations with smaller percentage existence of polar interactions than that of 0SN, but their experimental binding affinities are 
roughly the same, with 1E4 being slightly higher. The same phenomenon was observed for A-site binders 1E7 and AJ1. Likewise, the number of strong polar interactions or contactsdoes not predict the strength of binding. Hence, conventional MD simulations appear to be incapable of discriminating LDHA inhibitors of different binding strengths. To resolve this issue, we resorted to steered MD simulations, which can qualitatively discern inhibitors of largely different binding affinities. Steered MD simulations have demonstrated the effects of different initial conformations of the mobile loopand different sites of bindingon the difficulty of pulling. Considering these effects, our pulling results correlated well with experimental binding affinities and were able to distinguish inhibitors with a small 4 kJ mol21 DGdissoc difference, despite their different dynamics and modes of binding. Although DPMF values, calculated from exponential averages of SP600125 JNK inhibitor non-equilibrium work, largely depend on rarely sampled trajectories with small dissipated work, the work and peak force were able to qualitatively discriminate inhibitors of the same binding site and initial loop conformation. Other computational approaches such as umbrella BU 4061T sampling can yield a better estimate of free binding energy.Nevertheless, steered MD simulations provide a more convenient set-up with much less computational cost for ranking inhibitors with respect to relative binding affinities. Our steered MD simulations also suggest that NHI is more likely to bind in the A-site by comparison of relative difficulties in pulling, even though NHI binding models in both the A-site and the S-site, generated from conventional MD simulations, can explain its experimental structure-activity relationships.After all, NHI behaved differently in the S-site from other inhibitors that have only one carboxylate group within the S-site, in that NHI could hold the mobile loop closed by interacting with Arg105 for most of the time while others could not. The binding of NHI at the A-site also agrees with preliminary NMR and crystallographic data.On the other hand, our attempts to obtain possible binding modes of FX11 were unsuccessful.