Thomas A. Darden

Current computer simulations of biomolecules typically make use of classical molecular dynamics methods, as a very large number (tens to hundreds of thousands) of atoms are involved over timescales of many nanoseconds. The methodology for treating short-range bonded and van der Waals interactions has matured. However, long-range electrostatic interactions still represent a bottleneck in simulations. In this article, we introduce the basic issues for an accurate representation of the relevant electrostatic interactions. In spite of the huge computational time demanded by most biomolecular systems, it is no longer necessary to resort to uncontrolled approximations such as the use of cutoffs. In particular, we discuss the Ewald summation methods, the fast particle mesh methods, and the fast multipole methods. We also review recent efforts to understand the role of boundary conditions in systems with long-range interactions, and conclude with a short perspective on future trends.

MOTIVATION: We recently introduced a multivariate approach that selects a subset of predictive genes jointly for sample classification based on expression data. We tested the algorithm on colon and leukemia data sets. As an extension to our earlier work, we systematically examine the sensitivity, reproducibility and stability of gene selection/sample classification to the choice of parameters of the algorithm. METHODS: Our approach combines a Genetic Algorithm (GA) and the k-Nearest Neighbor (KNN) method to identify genes that can jointly discriminate between different classes of samples (e.g. normal versus tumor). The GA/KNN method is a stochastic supervised pattern recognition method. The genes identified are subsequently used to classify independent test set samples. RESULTS: The GA/KNN method is capable of selecting a subset of predictive genes from a large noisy data set for sample classification. It is a multivariate approach that can capture the correlated structure in the data. We find that for a given data set gene selection is highly repeatable in independent runs using the GA/KNN method. In general, however, gene selection may be less robust than classification. AVAILABILITY: The method is available at http://dir.niehs.nih.gov/microarray/datamining CONTACT: LI3@niehs.nih.gov

We present an overview of the SIBFA polarizable molecular mechanics procedure, which is formulated and calibrated on the basis of quantum chemistry (QC). It embodies nonclassical effects such as electrostatic penetration, exchange-polarization, and charge transfer. We address the issues of anisotropy, nonadditivity, and transferability by performing parallel QC computations on multimolecular complexes. These encompass multiply H-bonded complexes and polycoordinated complexes of divalent cations. Recent applications to the docking of inhibitors to Zn-metalloproteins are presented next, namely metallo-beta-lactamase, phosphomannoisomerase, and the nucleocapsid of the HIV-1 retrovirus. Finally, toward third-generation intermolecular potentials based on density fitting, we present the development of a novel methodology, the Gaussian electrostatic model (GEM), which relies on ab initio-derived fragment electron densities to compute the components of the total interaction energy. As GEM offers the possibility of a continuous electrostatic model going from distributed multipoles to densities, it allows an inclusion of short-range quantum effects in the molecular mechanics energies. The perspectives of an integrated SIBFA/GEM/QM procedure are discussed.

Statistical properties of the process describing the genealogical history of a random sample of genes are obtained for a class of population genetics models with selection. For models with selection, in contrast to models without selection, the distribution of this process, the coalescent process, depends on the distribution of the frequencies of alleles in the ancestral generations. If the ancestral frequency process can be approximated by a diffusion, then the mean and the variance of the number of segregating sites due to selectively neutral mutations in random samples can be numerically calculated. The calculations are greatly simplified if the frequencies of the alleles are tightly regulated. If the mutation rates between alleles maintained by balancing selection are low, then the number of selectively neutral segregating sites in a random sample of genes is expected to substantially exceed the number predicted under a neutral model.

The alkaline earth metals calcium and magnesium are critically involved in many biomolecular processes. To understand the hydration thermodynamics of these ions, we have performed molecular dynamics simulations using a polarizable potential. Particle-mesh Ewald for point multipoles has been applied to the calculation of electrostatic interactions. The parameters in this model have been determined from an ab initio quantum mechanical calculation of dimer interactions between ions and water. Two methods for ion solvation free energy calculation, free energy perturbation, and the Bennett acceptance ratio have been compared. Both predict results consistent with other theoretical estimations while the Bennett approach leads to a much smaller statistical error. Based on the Born theory and the ion-oxygen radial distribution functions, we estimate the effective size of the ions in solution, concluding that K(+) > Na(+) congruent with Ca(2+) > Mg(2+). There appears to be much stronger perturbation in water structure, dynamics, and dipole moment around the divalent cations than the monovalent K(+) and Na(+). The average water coordination numbers for Ca(2+) and Mg(2+) are 7.3 and 6, respectively. The lifetime of water molecules in the first solvation shell of Mg(2+) is on the order of hundreds of picoseconds, in contrast to only few picoseconds for Ca(2+), K(+), or Na(+).