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Register a new userRelevance of the speed and direction of pulling in simple modular proteins
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A theoretical analysis of the unfolding pathway of simple modular proteins in length- controlled pulling experiments is put forward. Within this framework, we predict the first module to unfold in a chain of identical units, emphasizing the ranges of pulling speeds in which we expect our theory to hold. These theoretical predictions are checked by means of steered molecular dynamics of a simple construct, specifically a chain composed of two coiled-coils motives, where anisotropic features are revealed. These simulations also allow us to give an estimate for the range of pulling velocities in which our theoretical approach is valid.
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The convergent interests of different scientific disciplines, from biochemistry to electronics, toward the investigation of protein electrical properties, has promoted the development of a novel bailiwick, the so called proteotronics. The main aim of proteotronics is to propose and achieve innovative electronic devices, based on the selective action of specific proteins. This paper gives a sketch of the fields of applications of proteotronics, by using as significant example the detection of a specific odorant molecule carried out by an olfactory receptor. The experiment is briefly reviewed and its theoretical interpretation given. Further experiments are envisioned and expected results discussed in the perspective of an experimental validation.
We explore the dynamical large-deviations of a lattice heteropolymer model of a protein by means of path sampling of trajectories. We uncover the existence of non-equilibrium dynamical phase-transitions in ensembles of trajectories between active and inactive dynamical phases, whose nature depends on properties of the interaction potential. When the full heterogeneity of interactions due to the amino-acid sequence is preserved, as in a fully interacting model or in a heterogeneous version of the G={o} model where only native interactions are considered, the transition is between the equilibrium native state and a highly native but kinetically trapped state. In contrast, for the homogeneous G={o} model, where there is a single native energy and the sequence plays no role, the dynamical transition is a direct consequence of the static bi-stability between unfolded and native states. In the heterogeneous case the native-active and native-inactive states, despite their static similarity, have widely varying dynamical properties, and the transition between them occurs even in lattice proteins whose sequences are designed to make them optimal folders.
Recent literatures reported blue-green emission from amyloid fibril as exclusive signature of fibril formation. This unusual visible luminescence is regularly used to monitor fibril growth. Blue-green emission has also been observed in crystalline protein and in solution. However, the origin of this emission is not known exactly. Our spectroscopic study of serum proteins reveals that the blue-green emission is a property of protein monomer. Evidences suggest that semiconductor-like band structure of proteins with the optical band-gap in the visible region is possibly the origin of this phenomenon. We show here that the band structure of proteins is primarily the result of electron delocalization through the peptide chain, rather than through the hydrogen bond network in secondary structure.
Deep Learning (DL) algorithms hold great promise for applications in the field of computational biophysics. In fact, the vast amount of available molecular structures, as well as their notable complexity, constitutes an ideal context in which DL-based approaches can be profitably employed. To express the full potential of these techniques, though, it is a prerequisite to express the information contained in the molecules atomic positions and distances in a set of input quantities that the network can process. Many of the molecular descriptors devised insofar are effective and manageable for relatively small structures, but become complex and cumbersome for larger ones. Furthermore, most of them are defined locally, a feature that could represent a limit for those applications where global properties are of interest. Here, we build a deep learning architecture capable of predicting non-trivial and intrinsically global quantities, that is, the eigenvalues of a proteins lowest-energy fluctuation modes. This application represents a first, relatively simple test bed for the development of a neural network approach to the quantitative analysis of protein structures, and demonstrates unexpected use in the identification of mechanically relevant regions of the molecule.
Recently, a growing interest has been addressed to the electrical properties of bacteriorhodopsin (bR), a protein belonging to the transmembrane protein family. To take into account the structure-dependent nature of the current, in a previous set of papers we suggested a mechanism of sequential tunneling among neighbouring amino acids. As a matter of fact, it is well accepted that, when irradiated with green light, bR undergoes a conformational change at a molecular level. Thus, the role played by the protein tertiary-structure in modeling the charge transfer cannot be neglected. The aim of this paper is to go beyond previous models, in the framework of a new branch of electronics, we called proteotronics, which exploits the ability to use proteins as reliable, well understood materials, for the development of novel bioelectronic devices. In particular, the present approach assumes that the conformational change is not the unique transformation that the protein undergoes when irradiated by light. Instead, the light can also promote a free-energy increase of the protein state that, in turn, should modify its internal degree of connectivity, here described by the change in the value of an interaction radius associated with the physical interactions among amino acids. The implemented model enables us to achieve a better agreement between theory and experiments in the region of a low applied bias by preserving the level of agreement at high values of applied bias. Furthermore, results provide new insights on the mechanisms responsible for bR photoresponse.
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