The accuracy of atomistic biomolecular modeling and simulation studies depend on the accuracy of the input structures. Preparing these structures for an atomistic modeling task, such as molecular dynamics (MD) simulation, can involve the use of a variety of different tools for: correcting errors, adding missing atoms, filling valences with hydrogens, predicting pK values for titratable amino acids, assigning predefined partial charges and radii to all atoms, and generating force field parameter/topology files for MD. Identifying, installing and effectively using the appropriate tools for each of these tasks can be difficult for novice and time-consuming for experienced users. H++ (http://biophysics.cs.vt.edu/) is a free open-source web server that automates the above key steps in the preparation of biomolecular structures for molecular modeling and simulations. H++ also performs extensive error and consistency checking, providing error/warning messages together with the suggested corrections. In addition to numerous minor improvements, the latest version of H++ includes several new capabilities and options: fix erroneous (flipped) side chain conformations for HIS, GLN and ASN, include a ligand in the input structure, process nucleic acid structures and generate a solvent box with specified number of common ions for explicit solvent MD.

The generalized Born model (GB) provides a reasonably accurate and computationally efficient way to compute the electrostatic component (∆G el ) of the solvation free energy. In this work, we have developed a method to compute effective Born radii, which is intended to address the known secondary structure bias of the GB model reported earlier (Roe et al. J. Phys. Chem. B, 2007, 111, 1846-1857. Our analytical approach, termed AR6, is based on the |r| -6 (R6) integration over an approximation to molecular volume. Within the approach, several computationally efficient corrections to the pairwise VDW-volume integration are combined to closely approximate the true molecular volume in the vicinity of each atom. The accuracy of the AR6 model in predicting relative ∆G el is tested on four conformational states of alanine decapeptide. Changes in ∆G el estimated by AR6 between various pairs of conformational states have the same RMS error relative to the explicit solvent, as do the corresponding numerical PB values; at the same time, the RMS error of the proposed model is 2 times lower than that of the popular GB_OBC model from the AMBER package. Tests against the PB treatment on 22 biomolecular structures including proteins and DNA show that the relative error of ∆G el is 0.58%; the RMS error of ∆G el computed by AR6 is 3 times lower than the corresponding value for GB_OBC. However, the computational efficiencies of the AR6 and GB_OBC models are comparable. A variant of the R6 model, NSR6, based on numerically exact integration over triangulated molecular surface is tested on a "challenge" set of small drug-like molecules (Nicholls et al.

Boolean network models of molecular regulatory networks have been used successfully in computational systems biology. The Boolean functions that appear in published models tend to have special properties, in particular the property of being nested canalizing, a concept inspired by the concept of canalization in evolutionary biology. It has been shown that networks comprised of nested canalizing functions have dynamic properties that make them suitable for modeling molecular regulatory networks, namely a small number of (large) attractors, as well as relatively short limit cycles.This paper contains a detailed analysis of this class of functions, based on a novel normal form as polynomial functions over the Boolean field. The concept of layer is introduced that stratifies variables into different classes depending on their level of dominance. Using this layer concept a closed form formula is derived for the number of nested canalizing functions with a given number of variables. Additional metrics considered include Hamming weight, the activity number of any variable, and the average sensitivity of the function. It is also shown that the average sensitivity of any nested canalizing function is between 0 and 2. This provides a rationale for why nested canalizing functions are stable, since a random Boolean function in n variables has average sensitivity n 2 . The paper also contains experimental evidence that the layer number is an important factor in network stability.

Modeling stochasticity in gene regulatory networks is an important and complex problem in molecular systems biology. To elucidate intrinsic noise, several modeling strategies such as the Gillespie algorithm have been used successfully. This article contributes an approach as an alternative to these classical settings. Within the discrete paradigm, where genes, proteins, and other molecular components of gene regulatory networks are modeled as discrete variables and are assigned as logical rules describing their regulation through interactions with other components. Stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update rule guarantee activation or degradation there is a probability that the process will not occur due to stochastic effects. This approach allows a finer analysis of discrete models and provides a natural setup for cell population simulations to study cell-to-cell variability. We applied our methods to two of the most studied regulatory networks, the outcome of lambda phage infection of bacteria and the p53-mdm2 complex.