Study Of Transmembrane Proteins Research Articles

Dimensionality Reduction of High Dimensional Time Series based on Artificial Neural Network

Molecular dynamics is a molecular simulation method which relies on Newtonian mechanics to simulate the motion of molecular system. In this method, some differential equations are integrated, and the results of integration are further processed to obtain the trajectory or momentum evolution process of some particles controlled by dynamic equations, and the technology of extracting the equilibrium state, motion process or related properties of classical particle system can be used. Through molecular dynamics simulation, we can obtain a series of properties of the system, which are widely used in experimental verification, theoretical derivation and other scenarios. Because it can obtain the dynamic state of macromolecules to make up for the limitations of these properties, it is widely used in the study of transmembrane proteins, polypeptide chains and other systems in life sciences. Through the kinetic path reduction of these systems, we can intuitively understand the characteristics of molecular folding, molecular motion and specific binding, which can play a very important role in the study of proteins and peptides. However, due to the characteristics of high-dimensional time series obtained by molecular dynamics simulation, it is difficult for us to pay attention to the collective state or characteristic process of the whole system in a non-equilibrium state or slow process. This is due to the difficulty in data processing and the difficulty in obtaining its characteristic function. This makes it very difficult to study the dynamic process of the whole system, especially the dynamic process at the intermediate non-equilibrium moment. It is difficult to solve this kind of problem by conventional methods, and only a few special simple systems can be solved by experience. Therefore, it is of great significance to find a method to obtain the characteristic function of the system through the trajectory obtained by molecular dynamics, and then reduce the molecular dynamics path. In order to solve this scientific problem, researchers focus on machine learning. In this study, machine learning method will be used to solve the overall non-equilibrium state of the system or the collective state of the slow process in molecular dynamics simulation. Firstly, we use this method to solve a simple one-dimensional four well model. By this method, we obtain a series of characteristic functions describing the motion process of the model. By sorting the eigenvalue contributions, we obtain some main characteristic functions describing the system. It includes the motion description of Markov smooth transition state and the motion description of four potential wells. At the same time, we use the traditional transition probability matrix to calculate. The difference between the characteristic function obtained by machine learning and the traditional method is very small, but the calculation method is simpler and more universal. After that, we apply the method to the actual scene. By solving the molecular dynamics simulation of alanine dipeptide structure in polymer protein molecule, the characteristic function of dihedral angle folding of alanine dipeptide structure was preliminarily calculated. The results were consistent with the traditional method.

There is no overkill in biochemistry

Har Gobind Khorana pioneered study of transmembrane proteins at highest resolution possible through the application of biochemical techniques. His research on bacteriorhodopsin, a retinal based light driven proton pump from Halobacterium halobium spanned a range of ground breaking studies from membrane reconstitution, complete denaturation and refolding of a polytopic membrane protein, gene synthesis combined with genetically-directed protein modification and protein dynamic/kinetic analysis. His courage for tackling tough biochemical problems through enormous enthusiasm, building of a multi disciplinary team and maintain focus is a lesson for generations to come.

The Role of Stacking Interactions in Complexes of Proteins with Adenine and Guanine Fragments of Ligands

The biological function of proteins is closely connected to interactions with their ligands and substrates. Proteins acting as receptors and enzymes bind these small molecules. Knowledge of the molecular mechanisms of protein-ligand interactions, particularly in the spatial structure of the protein-ligand complex, is a prerequisite for understanding the structure-functional properties of proteins and their role in biochemical pathways in the living cell. The availability of such a structure serves as a basis for rational drug design projects and greatly assists the search for new inhibitors (the ligands of certain protein-targets in an organism). Experimental tools for determining the spatial structure of proteins and their complexes with ligands (such as X-ray crystallography or NMR spectroscopy) have particular limitations. Even if the structure of a protein is available, determining the structure of its complex with ligands may be experimentally demanding. Problems with purification and crystallization become especially difficult in studies of transmembrane proteins, which include a biologically important class of G-protein coupled receptors. However, recent successes in determining the structure of beta-adrenergic and adenosine receptors [1] are cause for optimism. The technical difficulties restraining experimental methods stimulated computational molecular modeling. One of them (molecular docking) is a method aimed at predicting the spatial structure of a protein-ligand complex by docking a ligand molecule into the known atomic-resolution structure of a protein-binding site and estimating the reliability of the results. Nowadays, molecular docking has become an integral part of both fundamental studies aimed at understanding the structure-functional role of protein amino acids and applied drug-design programs [2, 3]. Docking approaches are further improved by implementing new algorithms of the conformational search and new scoring functions (methods to estimate the free energy of ligand binding). Scoring functions may include either components of molecular mechanics force fields [2] or empirical terms, e.g. hydrogen bonds described by their geometrical parameters [4]. In this work we studied stacking interactions, which usually are not properly taken into account in widely used scoring functions.

Rapid Immunosorting of Transmembrane Proteins of Lymphocytes from a cDNA Expression Library of COS-1 Cells

The proteins on lymphocyte surface play important roles in a wide range of immunological processes, but the profile and characterization of surface proteins remain to be further investigated, among which the method for fast screening of surface proteins needs to be established. In this study, a conventional cDNA clone library of hepatic lymphocytes from C57BL/6 mouse was constructed, and then the cDNA was inserted into a recombinant expression vector pSecTag-attR with a signal peptide and tag protein for fluorescence screening. The recombinant cDNA expression library was transfected into COS-1 cells, and the transfected cells with the expressed membrane proteins were labeled by fluorescence antibodies and isolated by fluorescence activated cell sorting. After two cycles of sorting, the purity of fluorescence positive cells with membrane proteins was up to 98%, and the representative membrane molecules on lymphocytes such as CD3, CD4, CD8, NK1.1 and NKG2D were detected in the library. These results demonstrated that the cDNA expression library containing transmembrane proteins provided an efficient and fast tool for the study of transmembrane proteins on hepatic lymphocytes.

Solubilization of Lymphocytes

Purification and study of transmembrane proteins require isolation of these structures from their lipid environment. This isolation is carried out through the use of detergents. In this unit, several approaches to solubilization of membrane proteins are presented. Solubilization of whole lymphocytes, using conditions aimed at minimizing the disruption of protein-protein interactions, is described with an optional step that may be useful when the disruption of protein interactions is desired as part of a purification protocol. In some situations, it may be desirable to purify membranes prior to their solubilization or to determine the physical relationship between proteins, which can be accomplished by a cross-linking.