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Biotin-labeled proteins are widely used as tools to study protein–protein interactions and proximity in living cells. Proteomic methods broadly employ proximity-labeling technologies based on protein biotinylation in order to investigate the transient encounters of biomolecules in subcellular compartments. Biotinylation is a post-translation modification in which the biotin molecule is attached to lysine or tyrosine residues. So far, biotin-based technologies proved to be effective instruments as affinity and proximity tags. However, the influence of biotinylation on aspects such as folding, binding, mobility, thermodynamic stability, and kinetics needs to be investigated. Here, we selected two proteins [biotin carboxyl carrier protein (BCCP) and FKBP3] to test the influence of biotinylation on thermodynamic and kinetic properties. Apo (without biotin) and holo (biotinylated) protein structures were used separately to generate all-atom structure-based model simulations in a wide range of temperatures. Holo BCCP contains one biotinylation site, and FKBP3 was modeled with up to 23 biotinylated lysines. The two proteins had their estimated thermodynamic stability changed by altering their energy landscape. In all cases, after comparison between the apo and holo simulations, differences were observed on the free-energy profiles and folding routes. Energetic barriers were altered with the density of states clearly showing changes in the transition state. This study suggests that analysis of large-scale datasets of biotinylation-based proximity experiments might consider possible alterations in thermostability and folding mechanisms imposed by the attached biotins. [ABSTRACT FROM AUTHOR]

The stochastic drift-diffusion (DrDiff) theory is an approach used to characterize the dynamical properties of simulation data. With new features in transition times analyses, the framework characterized the thermodynamic free-energy profile [F(Q)], the folding time (τf), and transition path time (τTP) by determining the coordinate-dependent drift-velocity [ v (Q)] and diffusion [D(Q)] coefficients from trajectory time traces. In order to explore the DrDiff approach and to tune it with two other methods (Bayesian analysis and fep1D algorithm), a numerical integration of the Langevin equation with known D(Q) and F(Q) was performed and the inputted coefficients were recovered with success by the diffusion models. DrDiff was also applied to investigate the prion protein (PrP) kinetics and thermodynamics by analyzing folding/unfolding simulations. The protein structure-based model, the well-known G o ¯ -model, was employed in a coarse-grained Cα level to generate long constant-temperature time series. PrP was chosen due to recent experimental single-molecule studies in D and τTP that stressed the importance and the difficulty of probing these quantities and the rare transition state events related to prion misfolding and aggregation. The PrP thermodynamic double-well F(Q) profile, the "X" shape of τf(T), and the linear shape of τTP(T) were predicted with v (Q) and D(Q) obtained by the DrDiff algorithm. With the advance of single-molecule techniques, the DrDiff framework might be a useful ally for determining kinetic and thermodynamic properties by analyzing time observables of biomolecular systems. The code is freely available at https://github.com/ronaldolab/DrDiff. [ABSTRACT FROM AUTHOR]

The dynamics of biological polymers, including proteins, RNA, and DNA, occur in very high-dimensional spaces. Many naturally occurring polymers can navigate a vast phase space and rapidly find their lowest free energy (folded) state. Thus, although the search process is stochastic, it is not completely random. Instead, it is best described in terms of diffusion along a downhill energy landscape. In this context, there have been many efforts to use simplified representations of the energetics, for which the potential energy is chosen to be a relatively smooth function with a global minimum that corresponds to the folded state. That is, instead of including every type of physical interaction, the broad characteristics of the landscape are encoded in approximate energy functions. We describe a particular class of models, called structure-based models, that can be used to explore the diffusive properties of biomolecular folding and conformational rearrangements. These energy functions may be regarded as the spherical cow for modeling molecular biophysics. We discuss the physical principles underlying these models and provide an entry-level tutorial, which may be adapted for use in curricula for physics and non-physics majors. [ABSTRACT FROM AUTHOR]

The daily use of electronic devices, which are mostly based on semiconductor diodes and transistors, has become very common. The understanding of some concepts related to semiconductor materials is very important to achieve a better comprehension of these devices. Although this statement appears in the official documents and in national Brazilian curricular parameters (PCN), in practice there is a small number of proposals in this direction. Thus, the purpose of this article was to describe the use of a sequence with four short videos to teach introductory concepts of semiconductors at high-school level. These videos were shown to two classes of third grade of high school, with a total of 72 students. After each video, we gave the students a questionnaire with 16 Likert items, in order to measure the students' attitudes regarding some concepts discussed in the videos. Analyzing the questionnaire results, we saw the effectiveness of the proposal, mainly with respect to the students motivation to study this subject. The majority of concepts were well assimilated, and the few difficulties were in agreement with literature. This suggests that the use of short videos is a strategy that can be more used, mainly due its versatility and very limited need of technical resources. [ABSTRACT FROM AUTHOR]