Many proteins can be partially or completely disordered under physiological conditions. Structural characterization of these disordered states using experimental methods can be challenging, since they are composed of a structurally heterogeneous ensemble of conformations rather than a single dominant conformation. Molecular dynamics (MD) simulations should in principle provide an ideal tool for elucidating the composition and behavior of disordered states at an atomic level of detail. Unfortunately, MD simulations using current physics-based models tend to produce disordered-state ensembles that are structurally too compact relative to experiments. We find that the water models typically used in MD simulations significantly underestimate London dispersion interactions, and speculate that this may be a possible reason for these erroneous results. To test this hypothesis, we create a new water model, TIP4P-D, that approximately corrects for these deficiencies in modeling water dispersion interactions while maintaining compatibility with existing physics-based models. We show that simulations of solvated proteins using this new water model typically result in disordered states that are substantially more expanded and in better agreement with experiment. These results represent a significant step toward extending the range of applicability of MD simulations to include the study of (partially or fully) disordered protein states.

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We introduce a quantum mechanical polarizable force field (QMPFF) fitted solely to QM data at the MP2͞aTZ(-hp) level. Atomic charge density is modeled by point-charge nuclei and floating exponentially shaped electron clouds. The functional form of interaction energy parallels quantum mechanics by including electrostatic, exchange, induction, and dispersion terms. Separate fitting of each term to the counterpart calculated from high-quality QM data ensures high transferability of QMPFF parameters to different molecular environments, as well as accurate fit to a broad range of experimental data in both gas and liquid phases. QMPFF, which is much more efficient than ab initio QM, is optimized for the accurate simulation of biomolecular systems and the design of drugs.drug design ͉ quantum mechanics A ccurate simulation of intermolecular interactions is essential in computational studies of chemical and biological systems ranging from multimer spectroscopy in molecular beams, atomsurface interactions, and catalyzed chemical reactions to protein folding and rational drug design. The most reliable and consistent means for such simulations would be to directly use quantum mechanics. However, this is much too computationally demanding, mandating instead the use of a force field, in which the molecular potential surface is approximated by simple analytical formulas. Commonly used force fields including CHARMM, OPLS-AA, MMFF, and AMBER (1-4) originated with Lifson's and Warshel's (5) consistent force field; they all use two basic types of interactions, bonded and nonbonded. The bonded terms are usually modeled formally as functions of stretching, bending, and torsion, whereas the nonbonded components are more physically grounded and involve electrostatic and van der Waals potentials. Electrostatics is described in terms of fixed point charges, and the van der Waals interaction is usually approximated by the classical Leonard-Jones ''12-6'' potential or its modifications. Empirical parameters that shape the various functional forms are found by fitting to low-level quantum mechanical (QM) and͞or experimental data for simple molecules and their interactions in the solid and liquid phases.Although such force fields have been quite successful in modeling a wide variety of molecular systems, there are significant problems in simulation of liquid-phase solutes (6). These force fields have many possible defects including oversimplified treatment of bonded interactions and approximation of charge distributions by point charges with consequent neglect of charge penetration effects, nonadiabatic motions, and other QM features of intra-and intermolecular interactions. However, the most serious defect is recognized to be the failure to incorporate electronic polarization at a fundamental level, which is especially important in a polar medium such as water. To allow for the effects of polarization, the standard nonpolarizable force fields fit the mean field of the liquid by artificially increased dipole moments, deformed molecular geo...

Noncovalent interactions are of fundamental importance across the disciplines of chemistry, materials science, and biology. Quantum chemical calculations on noncovalently bound complexes, which allow for the quantification of properties such as binding energies and geometries, play an essential role in advancing our understanding of, and building models for, a vast array of complex processes involving molecular association or self-assembly. Because of its relatively modest computational cost, second-order Møller-Plesset perturbation (MP2) theory is one of the most widely used methods in quantum chemistry for studying noncovalent interactions. MP2 is, however, plagued by serious errors due to its incomplete treatment of electron correlation, especially when modeling van der Waals interactions and π-stacked complexes. Here we present spin-network-scaled MP2 (SNS-MP2), a new semi-empirical MP2-based method for dimer interaction-energy calculations. To correct for errors in MP2, SNS-MP2 uses quantum chemical features of the complex under study in conjunction with a neural network to reweight terms appearing in the total MP2 interaction energy. The method has been trained on a new data set consisting of over 200 000 complete basis set (CBS)-extrapolated coupled-cluster interaction energies, which are considered the gold standard for chemical accuracy. SNS-MP2 predicts gold-standard binding energies of unseen test compounds with a mean absolute error of 0.04 kcal mol (root-mean-square error 0.09 kcal mol), a 6- to 7-fold improvement over MP2. To the best of our knowledge, its accuracy exceeds that of all extant density functional theory- and wavefunction-based methods of similar computational cost, and is very close to the intrinsic accuracy of our benchmark coupled-cluster methodology itself. Furthermore, SNS-MP2 provides reliable per-conformation confidence intervals on the predicted interaction energies, a feature not available from any alternative method.

The role of many-body (MB) dispersion forces have been analyzed for strands, films, and cubic lattices in the framework of a model Hamiltonian that allows exact solution of the multiparticle Shrodinger equation. For the systems investigated the MB contribution may be as large as 7% of specific dispersion energy and 11% of solvation energy. Nonadditivity becomes particularly important for aggregation in solution, where its effect may be several times larger than the pairwise contribution. For all systems considered, the three-body Axilrod-Teller approximation was insufficient to predict the magnitude and in some cases even the sign of the full MB effect.

Advances in computational chemistry create an ongoing need for larger and higher-quality datasets that characterize noncovalent molecular interactions. We present three benchmark collections of quantum mechanical data, covering approximately 3,700 distinct types of interacting molecule pairs. The first collection, which we refer to as DES370K, contains interaction energies for more than 370,000 dimer geometries. These were computed using the coupled-cluster method with single, double, and perturbative triple excitations [CCSD(T)], which is widely regarded as the gold-standard method in electronic structure theory. Our second benchmark collection, a core representative subset of DES370K called DES15K, is intended for more computationally demanding applications of the data. Finally, DES5M, our third collection, comprises interaction energies for nearly 5,000,000 dimer geometries; these were calculated using SNS-MP2, a machine learning approach that provides results with accuracy comparable to that of our coupled-cluster training data. These datasets may prove useful in the development of density functionals, empirically corrected wavefunction-based approaches, semi-empirical methods, force fields, and models trained using machine learning methods.

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The novel neutral gallium cluster compounds [Ga18R*8] (1) and [Ga22R*8] (2) are obtained by warming up a metastable solution of gallium(I) bromide in THF/C6H5CH3 after addition of equimolar amounts of supersilyl sodium NaR* from -78 degrees C to room temperature (R* = SitBu3 = supersilyl). From X-ray structure analyses, the observed arrangements of the 18 and 22 Ga atoms in 1 and 2, respectively, are comparable with an 18 atom section of the beta-Ga modification, or show at least some kind of relationship to a 22 atom section of the Ga-III modification. This allows a description of both the clusters as metalloid. The topology of the atoms in 2 is also well explained by the Wade-Mingos rules as an eightfold capped closo-Ga14 cluster, whereby the Ga atoms of Ga14 occupy the center and the corners of a cuboctahedron with one Ga3 face replaced by a Ga4 face. Some concepts are presented about the formation mechanism, the cluster growth, and the metalloid character of the two Ga cluster compounds.