We present an implementation of generalized Born implicit solvent all-atom classical molecular dynamics (MD) within the AMBER program package that runs entirely on CUDA enabled NVIDIA graphics processing units (GPUs). We discuss the algorithms that are used to exploit the processing power of the GPUs and show the performance that can be achieved in comparison to simulations on conventional CPU clusters. The implementation supports three different precision models in which the contributions to the forces are calculated in single precision floating point arithmetic but accumulated in double precision (SPDP), or everything is computed in single precision (SPSP) or double precision (DPDP). In addition to performance, we have focused on understanding the implications of the different precision models on the outcome of implicit solvent MD simulations. We show results for a range of tests including the accuracy of single point force evaluations and energy conservation as well as structural properties pertainining to protein dynamics. The numerical noise due to rounding errors within the SPSP precision model is sufficiently large to lead to an accumulation of errors which can result in unphysical trajectories for long time scale simulations. We recommend the use of the mixed-precision SPDP model since the numerical results obtained are comparable with those of the full double precision DPDP model and the reference double precision CPU implementation but at significantly reduced computational cost. Our implementation provides performance for GB simulations on a single desktop that is on par with, and in some cases exceeds, that of traditional supercomputers.

To understand further, and to utilize, the interactions across protein-protein interfaces, we carried out an analysis of the hydrogen bonds and of the salt bridges in a collection of 319 non-redundant protein-protein interfaces derived from high-quality X-ray structures. We found that the geometry of the hydrogen bonds across protein interfaces is generally less optimal and has a wider distribution than typically observed within the chains. This difference originates from the more hydrophilic side chains buried in the binding interface than in the folded monomer interior. Protein folding differs from protein binding. Whereas in folding practically all degrees of freedom are available to the chain to attain its optimal configuration, this is not the case for rigid binding, where the protein molecules are already folded, with only six degrees of translational and rotational freedom available to the chains to achieve their most favorable bound configuration. These constraints enforce many polar/charged residues buried in the interface to form weak hydrogen bonds with protein atoms, rather than strongly hydrogen bonding to the solvent. Since interfacial hydrogen bonds are weaker than the intra-chain ones to compete with the binding of water, more water molecules are involved in bridging hydrogen bond networks across the protein interface than in the protein interior. Interfacial water molecules both mediate non-complementary donor-donor or acceptor-acceptor pairs, and connect non-optimally oriented donor-acceptor pairs. These differences between the interfacial hydrogen bonding patterns and the intra-chain ones further substantiate the notion that protein complexes formed by rigid binding may be far away from the global minimum conformations. Moreover, we summarize the pattern of charge complementarity and of the conservation of hydrogen bond network across binding interfaces. We further illustrate the utility of this study in understanding the specificity of protein-protein associations, and hence in docking prediction and molecular (inhibitor) design.

Avian influenza virus subtype H5N1 is a potential pandemic threat with human-adapted strains resistant to antiviral drugs. Although virtual screening (VS) against a crystal or relaxed receptor structure is an established method to identify potential inhibitors, the more dynamic changes within binding sites are neglected. To accommodate full receptor flexibility, we use AutoDock4 to screen the NCI diversity set against representative receptor ensembles extracted from explicitly solvated molecular dynamics simulations of the neuraminidase system. The top hits are redocked to the entire nonredundant receptor ensemble and rescored using the relaxed complex scheme (RCS). Of the 27 top hits reported, half ranked very poorly if only crystal structures are used. These compounds target the catalytic cavity as well as the newly identified 150- and 430-cavities, which exhibit dynamic properties in electrostatic surface and geometric shape. This ensemble-based VS and RCS approach may offer improvement over existing strategies for structure-based drug discovery.

An alarming trend in malware attacks is that they are armed with stealthy techniques to detect, evade, and subvert malware detection facilities of the victim. On the defensive side, a fundamental limitation of traditional host-based anti-malware systems is that they run inside the very hosts they are protecting ("in the box"), making them vulnerable to counter-detection and subversion by malware. To address this limitation, recent solutions based on virtual machine (VM) technologies advocate placing the malware detection facilities outside of the protected VM ("out of the box"). However, they gain tamper resistance at the cost of losing the native, semantic view of the host which is enjoyed by the "in the box" approach, thus leading to a technical challenge known as the semantic gap.In this paper, we present the design, implementation, and evaluation of VMwatcher -an "out-of-the-box" approach that overcomes the semantic gap challenge. A new technique called guest view casting is developed to systematically reconstruct internal semantic views (e.g., files, processes, and kernel modules) of a VM from the outside in a non-intrusive manner. Specifically, the new technique casts semantic definitions of guest OS data structures and functions on virtual machine monitor (VMM)-level VM states, so that the semantic view can be reconstructed. With the semantic gap bridged, we identify two unique malware detection capabilities: (1) view comparison-based malware detection and its demonstration in rootkit detection and (2) "out-of-the-box" deployment of hostbased anti-malware software with improved detection accuracy and tamper-resistance. We have implemented a proof-of-concept prototype on both Linux and Windows platforms and our experimental results with real-world malware, including elusive kernel-level rootkits, demonstrate its practicality and effectiveness.