Currently the Protein Data Bank (PDB) contains over 18,000 structures that contain a metal ion including Na, Mg, K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Pd, Ag, Cd, Ir, Pt, Au, and Hg. In general, carrying out classical molecular dynamics (MD) simulations of metalloproteins is a convoluted and time consuming process. Herein, we describe MCPB (Metal Center Parameter Builder), which allows one, to conveniently and rapidly incorporate metal ions using the bonded plus electrostatics model (Hoops et al., J. Am. Chem. Soc. 1991, 113, 8262-8270) into the AMBER Force Field (FF). MCPB was used to develop a Zinc FF, ZAFF, which is compatible with the existing AMBER FFs. The PDB was mined for all Zn containing structures with most being tetrahedrally bound. The most abundant primary shell ligand combinations were extracted and FFs were created. These include Zn bound to CCCC, CCCH, CCHH, CHHH, HHHH, HHHO, HHOO, HOOO, HHHD, and HHDD (O = water and the remaining are 1 letter amino acid codes). Bond and angle force constants and RESP charges were obtained from B3LYP/6-31G* calculations of model structures from the various primary shell combinations. MCPB and ZAFF can be used to create FFs for MD simulations of metalloproteins to study enzyme catalysis, drug design and metalloprotein crystal refinement.

Computational chemists have long demonstrated great interest in finding ways to reliably and accurately predict the molecular properties for transition metal containing complexes. This manuscript is a continuation of our validation efforts of Density Functional Theory (DFT) methods when applied to transition metal containing systems (K. E. Riley; K. M. Merz, Jr. J. Phys. Chem. 2007, 111, 6044-6053). In our previous work we examined DFT using all-electron basis sets, but approaches incorporating effective core potentials (ECPs) are effective in reducing computational expense. With this in mind, our efforts were expanded to include evaluation of the performance of the basis set derived to approximate such an approach as well on the same set of density functionals. Indeed, employing an ECP basis such as LANL2DZ for transition metals, while using all-electron basis sets for all other non-transition-metal atoms has become more and more popular in computations on transition metal containing systems. In this study, we assess the performance of twelve different DFT functionals, from GGA, hybrid-GGA, meta-GGA and hybrid-meta-GGA classes respectively, along with the 6-31+G** + LANL2DZ (on the transition metal) mixed basis set on predicting two important molecular properties: heats of formation and ionization potentials, for 94 and 58 systems containing first row transition metals from Ti to Zn, which are all in the third row of the periodic table. An interesting note is that the inclusion of the exact exchange term in density functional methods generally increases the accuracy of ionization potentials prediction for the hybrid-GGA methods but decreases the reliability of determining the heats of formation for transition metal containing complexes for all hybrid density functional methods. The hybrid-GGA functional B3LYP gives the best performance on predicting the ionization potentials while the meta-GGA functional TPSSTPSS provides the most reliable and accurate results for heats of formation calculations. TPSSTPSS, a meta-GGA functional, which was constructed from first principles and subject to known exact constraints just like in an "ab initio" way, is successful in predicting both the ionization potentials and the heats of formation for transition metal containing systems.

RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-atomistic detail at macroscopic temporal and spatial scales, which is not possible with conventional computational or experimental techniques. We demonstrate here a multiscale simulation infrastructure that uses machine learning to create a scale-bridging ensemble of over 100,000 simulations of active wild-type KRAS on a complex, asymmetric membrane. Initialized and validated with experimental data (including a new structure of active wild-type KRAS), these simulations represent a substantial advance in the ability to characterize RAS-membrane biology. We report distinctive patterns of local lipid composition that correlate with interfacially promiscuous RAS multimerization. These lipid fingerprints are coupled to RAS dynamics, predicted to influence effector binding, and therefore may be a mechanism for regulating cell signaling cascades.