Reparameterization of RNA χ Torsion Parameters for the AMBER Force Field and Comparison to NMR Spectra for Cytidine and Uridine

A reparameterization of the torsional parameters for the glycosidic dihedral angle, χ, for the AMBER99 force field in RNA nucleosides is used to provide a modified force field, AMBER99χ. Molecular dynamics simulations of cytidine, uridine, adenosine, and guanosine in aqueous solution using the AMBER99 and AMBER99χ force fields are compared with NMR results. For each nucleoside and force field, 10 individual molecular dynamics simulations of 30 ns each were run. For cytidine with AMBER99χ force field, each molecular dynamics simulation time was extended to 120 ns for convergence purposes. Nuclear magnetic resonance (NMR) spectroscopy, including one-dimensional (1D) 1H, steady-state 1D 1H nuclear Overhauser effect (NOE), and transient 1D 1H NOE, was used to determine the sugar puckering and preferred base orientation with respect to the ribose of cytidine and uridine. The AMBER99 force field overestimates the population of syn conformations of the base orientation and of C2′-endo sugar puckering of the pyrimidines, while the AMBER99χ force field’s predictions are more consistent with NMR results. Moreover, the AMBER99 force field prefers high anti conformations with glycosidic dihedral angles around 310° for the base orientation of purines. The AMBER99χ force field prefers anti conformations around 185°, which is more consistent with the quantum mechanical calculations and known 3D structures of folded ribonucleic acids (RNAs). Evidently, the AMBER99χ force field predicts the structural characteristics of ribonucleosides better than the AMBER99 force field and should improve structural and thermodynamic predictions of RNA structures

Neural network model of the primary visual cortex: From functional architecture to lateral connectivity and back

The role of intrinsic cortical dynamics is a debatable issue. A recent optical imaging study (Kenet et al., 2003) found that activity patterns similar to orientation maps (OMs), emerge in the primary visual cortex (V1) even in the absence of sensory input, suggesting an intrinsic mechanism of OM activation. To better understand these results and shed light on the intrinsic V1 processing, we suggest a neural network model in which OMs are encoded by the intrinsic lateral connections. The proposed connectivity pattern depends on the preferred orientation and, unlike previous models, on the degree of orientation selectivity of the interconnected neurons. We prove that the network has a ring attractor composed of an approximated version of the OMs. Consequently, OMs emerge spontaneously when the network is presented with an unstructured noisy input. Simulations show that the model can be applied to experimental data and generate realistic OMs. We study a variation of the model with spatially restricted connections, and show that it gives rise to states composed of several OMs. We hypothesize that these states can represent local properties of the visual scene