A post-process to estimate an approximated minimal free energy path based on local centroids

Conformational flexibility driving charge-selective substrate translocation across a bacterial transporter

Bacterial membrane porins facilitate the translocation of small molecules while restricting large molecules, and this mechanism remains elusive at the molecular level. Here, we investigate the selective uptake of large cyclic sugars across an unusual passive membrane transporter, CymA, comprising a charged zone and a constricting N terminus segment. Using a combination of electrical recordings, protein mutagenesis and molecular dynamics simulations, we establish substrate translocation across CymA governed by the electrostatic pore properties and conformational dynamics of the constriction segment. Notably, we show that the variation in pH of the environment resulted in reversible modulation of the substrate binding site in the pore, thereby regulating charge-selective transport of cationic, anionic and neutral cyclic sugars. The quantitative kinetics of cyclic sugar translocation across CymA obtained in electrical recordings at different pHs are comparable with molecular dynamics simulations that revealed the transport pathway, energetics and favorable affinity sites in the pore for substrate binding. We further define the molecular basis of cyclic sugar translocation and establish that the constriction segment is flexible and can reside inside or outside the pore, regulating substrate translocation distinct from the ligand-gated transport mechanism. Our study provides novel insights into energy-independent large molecular membrane transport for targeted drug design strategies.

Structural Validation by the G-Factor Properly Regulates Boost Potentials Imposed in Conformational Sampling of Proteins

Free energy landscapes (FELs) of proteins are indispensable for evaluating thermodynamic properties. Molecular dynamics (MD) simulation is a computational method for calculating FELs; however, conventional MD simulation frequently fails to search a broad conformational subspace due to its accessible timescale, which results in the calculation of an unreliable FEL. To search a broad subspace, an external bias can be imposed on a protein system, and biased sampling tends to cause a strong perturbation that might collapse the protein structures, indicating that the strength of the external bias should be properly regulated. This regulation can be challenging, and empirical parameters are frequently employed to impose an optimal bias. To address this issue, several methods regulate the external bias by referring to system energies. Herein, we focused on protein structural information for this regulation. In this study, a well-established structural indicator (the G-factor) was used to obtain structural information. Based on the G-factor, we proposed a scheme for regulating biased sampling, which is referred to as a G-factor-based external bias limiter (GERBIL). With GERBIL, the configurations were structurally validated by the G-factor during biased sampling. As an example of biased sampling, an accelerated MD (aMD) simulation was adopted in GERBIL (aMD-GERBIL), whereby the aMD simulation was repeatedly performed by increasing the strength of the boost potential. Furthermore, the configurations sampled by the aMD simulation were structurally validated by their G-factor values, and aMD-GERBIL stopped increasing the strength of the boost potential when the sampled configurations were regarded as low-quality (collapsed) structures. This structural validation is regarded as a "Brake" of the boost potential. For demonstrations, aMD-GERBIL was applied to globular proteins (ribose binding and maltose-binding proteins) to promote their large-amplitude open-closed transitions and successfully identify their domain motions.