Yue Yang

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 study is a continuation of our validation efforts of density functional theory (DFT) methods when applied to transition-metal-containing systems (Riley, K.E.; Merz, K. M., 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 (Los Alamos National Laboratory 2 double zeta) 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 12 different DFT functionals, from the GGA (generalized gradient approximation), hybrid-GGA, meta-GGA, and hybrid-meta-GGA classes, respectively, along with the 6-31+G** + LANL2DZ (on the transition metal) mixed basis set in 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 potential 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 in predicting the ionization potentials, while the meta-GGA functional TPSSTPSS provides the most reliable and accurate results for heat 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.

Summary form only given. Conforming to the underlying memory consistency rules is a fundamental requirement for implementing shared memory systems and developing multiprocessor programs. In order to promote understanding and enable automated verification, it is highly desirable that a memory model specification be both declarative and executable. We present a specification framework called Nemos (Nonoperational yet Executable Memory Ordering Specifications), which supports precise specification and automatic execution in the same framework. We employ a uniform notation based on predicate logic to define shared memory semantics in an axiomatic as well as compositional style. We also apply constraint logic programming and SAT solving to make the axiomatic specifications executable for memory model analysis. To illustrate our approach, we formalize a collection of classical memory models, including sequential consistency, coherence, PRAM, causal consistency, and processor consistency.

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.

Atomistic Molecular Dynamics (MD) simulations provide researchers the ability to model biomolecular structures such as proteins and their interactions with drug-like small molecules with greater spatiotemporal resolution than is otherwise possible using experimental methods. MD simulations are notoriously expensive computational endeavors that have traditionally required massive investment in specialized hardware to access biologically relevant spatiotemporal scales. Our goal is to summarize the fundamental algorithms that are employed in the literature to then highlight the challenges that have affected accelerator implementations in practice. We consider three broad categories of accelerators: Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application Specific Integrated Circuits (ASICs). These categories are comparatively studied to facilitate discussion of their relative trade-offs and to gain context for the current state of the art. We conclude by providing insights into the potential of emerging hardware platforms and algorithms for MD.