Modulation of the conductance of a 2,2′-bipyridine-functionalized peptidic ion channel by Ni2+

An α-helical amphipathic peptide with the sequence H2N-(LSSLLSL)3-CONH2 was obtained by solid phase synthesis and a 2,2′-bipyridine was coupled to its N-terminus, which allows complexation of Ni2+. Complexation of the 2,2′-bipyridine residues was proven by UV/Vis spectroscopy. The peptide helices were inserted into lipid bilayers (nano black lipid membranes, nano-BLMs) that suspend the pores of porous alumina substrates with a pore diameter of 60 nm by applying a potential difference. From single channel recordings, we were able to distinguish four distinct conductance states, which we attribute to an increasing number of peptide helices participating in the conducting helix bundle. Addition of Ni2+ in micromolar concentrations altered the conductance behaviour of the formed ion channels in nano-BLMs considerably. The first two conductance states appear much more prominent demonstrating that the complexation of bipyridine by Ni2+ results in a considerable confinement of the observed multiple conductance states. However, the conductance levels were independent of the presence of Ni2+. Moreover, from a detailed analysis of the open lifetimes of the channels, we conclude that the complexation of Ni2+ diminishes the frequency of channel events with larger open times

Cortical Columnar Organization Is Reconsidered in Inferior Temporal Cortex

The object selectivity of nearby cells in inferior temporal (IT) cortex is often different. To elucidate the relationship between columnar organization in IT cortex and the variability among neurons with respect to object selectivity, we used optical imaging technique to locate columnar regions (activity spots) and systematically compared object selectivity of individual neurons within and across the spots. The object selectivity of a given cell in a spot was similar to that of the averaged cellular activity within the spot. However, there was not such similarity among different spots (>600 μm apart). We suggest that each cell is characterized by 1) a cell-specific response property that cause cell-to-cell variability in object selectivity and 2) one or potentially a few numbers of response properties common across the cells within a spot, which provide the basis for columnar organization in IT cortex. Furthermore, similarity in object selectivity among cells within a randomly chosen site was lower than that for a cell in an activity spot identified by optical imaging beforehand. We suggest that the cortex may be organized in a region where neurons with similar response properties were densely clustered and a region where neurons with similar response properties were sparsely clustered

HAAD: A Quick Algorithm for Accurate Prediction of Hydrogen Atoms in Protein Structures

Hydrogen constitutes nearly half of all atoms in proteins and their positions are essential for analyzing hydrogen-bonding interactions and refining atomic-level structures. However, most protein structures determined by experiments or computer prediction lack hydrogen coordinates. We present a new algorithm, HAAD, to predict the positions of hydrogen atoms based on the positions of heavy atoms. The algorithm is built on the basic rules of orbital hybridization followed by the optimization of steric repulsion and electrostatic interactions. We tested the algorithm using three independent data sets: ultra-high-resolution X-ray structures, structures determined by neutron diffraction, and NOE proton-proton distances. Compared with the widely used programs CHARMM and REDUCE, HAAD has a significantly higher accuracy, with the average RMSD of the predicted hydrogen atoms to the X-ray and neutron diffraction structures decreased by 26% and 11%, respectively. Furthermore, hydrogen atoms placed by HAAD have more matches with the NOE restraints and fewer clashes with heavy atoms. The average CPU cost by HAAD is 18 and 8 times lower than that of CHARMM and REDUCE, respectively. The significant advantage of HAAD in both the accuracy and the speed of the hydrogen additions should make HAAD a useful tool for the detailed study of protein structure and function. Both an executable and the source code of HAAD are freely available at http://zhang.bioinformatics.ku.edu/HAAD

Subspace Projection Approaches to Classification and Visualization of Neural Network-Level Encoding Patterns

Recent advances in large-scale ensemble recordings allow monitoring of activity patterns of several hundreds of neurons in freely behaving animals. The emergence of such high-dimensional datasets poses challenges for the identification and analysis of dynamical network patterns. While several types of multivariate statistical methods have been used for integrating responses from multiple neurons, their effectiveness in pattern classification and predictive power has not been compared in a direct and systematic manner. Here we systematically employed a series of projection methods, such as Multiple Discriminant Analysis (MDA), Principal Components Analysis (PCA) and Artificial Neural Networks (ANN), and compared them with non-projection multivariate statistical methods such as Multivariate Gaussian Distributions (MGD). Our analyses of hippocampal data recorded during episodic memory events and cortical data simulated during face perception or arm movements illustrate how low-dimensional encoding subspaces can reveal the existence of network-level ensemble representations. We show how the use of regularization methods can prevent these statistical methods from over-fitting of training data sets when the trial numbers are much smaller than the number of recorded units. Moreover, we investigated the extent to which the computations implemented by the projection methods reflect the underlying hierarchical properties of the neural populations. Based on their ability to extract the essential features for pattern classification, we conclude that the typical performance ranking of these methods on under-sampled neural data of large dimension is MDA>PCA>ANN>MGD