The equilibrium unfolding of hen lysozyme at pH 2 was studied as a function of pressure (0.1~700 MPa) and temperature (−10°C~50°C) using Trp fluorescence as monitor supplemented by variable pressure ¹H NMR spectroscopy (0.1~400 MPa). The unfolding profiles monitored by the two methods allowed the two-state equilibrium analysis between the folded (N) and unfolded (U) conformers. The free energy differences ΔG (=G_U−G_N) were evaluated from changes in the wavelength of maximum fluorescence intensity (λ_max) as a function of pressure and temperature. The dependence of ΔG on temperature exhibits concave curvatures against temperature, showing positive heat capacity changes (ΔC_p=C_pU−C_pN=1.8−1.9 kJ mol^–1 deg^–1) at all pressures studied (250~400 MPa), while the temperature T_S for maximal ΔG increased from about 10°C at 250 MPa to about 40°C at 550 MPa. The dependence of ΔG on pressure gave negative volume changes (ΔV=V_U−V_N) upon unfolding at all temperatures studied (−86~−17 ml mol^–1 for −10°C~50°C), which increase significantly with increasing temperature, giving a positive expansivity change (Δα~1.07 ml mol^–1 deg^–1). A phase-diagram between N and U (for ΔG=0) is drawn of hen lysozyme at pH 2 on the pressure-temperature plane. Finally, a three-dimensional free energy landscape (ΔG) is presented on the p-T plane.

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A model of muscle contraction is proposed, assuming loose coupling between power strokes and ATP hydrolysis of a myosin head. The energy borrowing mechanism is introduced in a cross-bridge cycle that borrows energy from the environment to cover the necessary energy for enthalpy production during sliding movement. Important premises for modeling are as follows: 1) the interaction area where a myosin head slides is supposed to be on an actin molecule; 2) the actomyosin complex is assumed to generate force F(θ), which slides the myosin head M* in the interaction area; 3) the direction of the force F(θ) varies in proportion to the load P; 4) the energy supplied by ATP hydrolysis is used to retain the myosin head in the high-energy state M*, and is not used for enthalpy production; 5) the myosin head enters a hydration state and dehydration state repeatedly during the cross-bridge cycle. The dehydrated myosin head recovers its hydrated state by hydration in the surrounding medium; 6) the energy source for work and heat production liberated by the AM* complex is of external origin. On the basis of these premises, the model adequately explains the experimental results observed at various levels in muscular samples: 1) twist in actin filaments observed in shortening muscle fibers; 2) the load-velocity relationship in single muscle fiber; 3) energy balance among enthalpy production, the borrowed energy and the energy supplied by ATP hydrolysis during muscle contraction. Force F(θ) acting on the myosin head is depicted.

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Experimental probing of a protein-folding energy landscape can be challenging, and energy landscapes comprising multiple intermediates have not yet been defined. Here, we quasi-statically unfolded single molecules of staphylococcal nuclease by constant-rate mechanical stretching with a feedback positioning system. Multiple discrete transition states were detected as force peaks, and only some of the multiple transition states emerged stochastically in each trial. This finding was confirmed by molecular dynamics simulations, and agreed with another result of the simulations which showed that individual trajectories took highly heterogeneous pathways. The presence of Ca²⁺ did not change the location of the transition states, but changed the frequency of the emergence. Transition states emerged more frequently in stabilized domains. The simulations also confirmed this feature, and showed that the stabilized domains had rugged energy surfaces. The mean energy required per residue to disrupt secondary structures was a few times the thermal energy (1–3 k_BT), which agreed with the stochastic feature. Thus, single-molecule quasi-static measurement has achieved notable success in detecting stochastic features of a huge number of possible conformations of a protein.

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A statistical model of protein families, called profile conditional random fields (CRFs), is proposed. This model may be regarded as an integration of the profile hidden Markov model (HMM) and the Finkelstein-Reva (FR) theory of protein folding. While the model structure of the profile CRF is almost identical to the profile HMM, it can incorporate arbitrary correlations in the sequences to be aligned to the model. In addition, like in the FR theory, the profile CRF can incorporate long-range pairwise interactions between model states via mean-field-like approximations. We give the detailed formulation of the model, self-consistent approximations for treating long-range interactions, and algorithms for computing partition functions and marginal probabilities. We also outline the methods for the global optimization of model parameters as well as a Bayesian framework for parameter learning and selection of optimal alignments.

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Flagellar motor proteins, MotA/B and PomA/B, are essential for the motility of Escherichia coli and Vibrio alginolyticus, respectively. Those complexes work as a H⁺ and a Na⁺ channel, respectively and play important roles in torque generation as the stators of the flagellar motors. Although Asp32 of MotB and Asp24 of PomB are believed to function as ion binding site(s), the ion flux pathway from the periplasm to the cytoplasm is still unclear. Conserved residues, Ala39 of MotB and Cys31 of PomB, are located on the same sides as Asp32 of MotB and Asp24 of PomB, respectively, in a helical wheel diagram. In this study, a series of mutations were introduced into the Ala39 residue of MotB and the Cys31 residue of PomB. The motility of mutant cells were markedly decreased as the volume of the side chain increased. The loss of function due to the MotB(A39V) and PomB(L28A/C31A) mutations was suppressed by mutations of MotA(M206S) and PomA(L183F), respectively, and the increase in the volume caused by the MotB(A39V) mutation was close to the decrease in the volume caused by the MotA(M206S) mutation. These results demonstrate that Ala39 of MotB and Cys31 of PomB form part of the ion flux pathway and pore with Met206 of MotA and Leu183 of PomA in the MotA/B and PomA/B stator units, respectively.

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Proteins with wholly or partly denatured structures in vivo are called intrinsically disordered or natively unfolded proteins (NUPs). Functional importance of NUPs was revealed by NMR studies as first reviewed by P. Wright in 1999. Since then, computational analyses on NUPs have also been intensively carried out to predict that approximately one third of eukaryotic proteins are NUPs. I will start this overview with the question why it took so long to identify NUPs as an important subject of protein science, and then move on to several issues such as, whether or not NUPs are specific to eukaryotes, what a particularly higher fraction of NUPs existing in the nucleus means, and what evolutionary implications NUPs have.

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It is generally difficult to understand the rates of human mortality from biological and biophysical standpoints because there are no cohorts or genetic homogeneity; in addition, information is limited regarding the various causes of death, such as the types of accidents and diseases. Despite such complexity, Gompertz’s rule is useful in humans. Thus, to characterize the rates of mortality from a demographic viewpoint, it would be interesting to research a single disease in one of the simplest organisms, the nematode C. elegans, which dies naturally under identically controlled circumstances without predators. Here, we report an example of the fact that heterogeneity on survival and mortality is observed through a single disease in a cohort of 100% genetically identical (isogenic) nematodes. Under the observed heterogeneity, we show that the diffusion theory, as a biophysical model, can precisely analyze the heterogeneity and conveniently estimate the degree of penetrance of a lifespan gene from the biodemographic data. In addition, we indicate that heterogeneity models are effective for the present heterogeneous data.

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