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The dependence of hydrogen-bond interaction energies between identical amides (two formamides and two N-methylacetamides) on the hydrogen bond length (r O···H), the two hydrogen bond angles (θCOH and θNHO), and the dihedral between the two amides (ΦCNCN) has been assessed by semiempirical calculations (SAM1 with single point transfers to AM1/SM2.1 aqueous solvation calculations). Ab initio calculations (MP2/6-31+G(d,p)//HF/6-31+G(d,p)) at given values of ΦCNCN and θCOH predict the same change in interaction energies with changes in θNHO as the semiempirical calculations. With formamide, hydrogen-bond interaction energies are independent of the dihedral angle ΦCNCN when θCOH and θNHO deviate less than 40° from 180°. Most importantly, the increased interaction energies at θCOH and θNHO below 140° and above 220° are found to be associated with steric interference between the carbonyl oxygen of the hydrogen-bond acceptor and the amide nitrogen of the hydrogen-bond donor. Comparing formamide and N-methylacetamide, the angle requirements (θCOH, θNHO, and ΦCNCN) of favorable hydrogen-bond interaction energies are much more stringent for the latter due to the steric effects of the methyl substituents. In summary, by both semiempirical SAM1 and ab initio MP2/6-31+G(d,p)//HF/6-31+G(d,p) calculations, the strength of amide hydrogen bonding in the absence of steric hindrance is essentially independent of the angles defining the hydrogen bond.

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Efficient design of hardware and software for large-scale parallel execution requires detailed understanding of the interactions between the application, computer, and network. The authors have developed a macro-scale simulator (SST/macro) that permits the coarse-grained study of distributed-memory applications. In the presented work, applications using the Message Passing Interface (MPI) are simulated; however, the simulator is designed to allow inclusion of other programming models. The simulator is driven from either a trace file or a skeleton application. Trace files can be either a standard format (Open Trace Format) or a more detailed custom format (DUMPI). The simulator architecture is modular, allowing it to easily be extended with additional network models, trace file formats, and more detailed processor models. This paper describes the design of the simulator, provides performance results, and presents studies showing how application performance is affected by machine characteristics.

Efficient design of hardware and software for large-scale parallel execution requires detailed understanding of the interactions between the application, computer, and network. The authors have developed a macro-scale simulator (SST/macro) that permits the coarse-grained study of distributed-memory applications. In the presented work, applications using the Message Passing Interface (MPI) are simulated; however, the simulator is designed to allow inclusion of other programming models. The simulator is driven from either a trace file or a skeleton application. Trace files can be either a standard format (Open Trace Format) or a more detailed custom format (DUMPI). The simulator architecture is modular, allowing it to easily be extended with additional network models, trace file formats, and more detailed processor models. This paper describes the design of the simulator, provides performance results, and presents studies showing how application performance is affected by machine characteristics.

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In this work we introduce a postprocessing filter (PostDOCK) that distinguishes true binding ligand-protein complexes from docking artifacts (that are created by DOCK 4.0.1). PostDOCK is a pattern recognition system that relies on (1) a database of complexes, (2) biochemical descriptors of those complexes, and (3) machine learning tools. We use the protein databank (PDB) as the structural database of complexes and create diverse training and validation sets from it based on the "families of structurally similar proteins" (FSSP) hierarchy. For the biochemical descriptors, we consider terms from the DOCK score, empirical scoring, and buried solvent accessible surface area. For the machine-learners, we use a random forest classifier and logistic regression. Our results were obtained on a test set of 44 structurally diverse protein targets. Our highest performing descriptor combinations obtained approximately 19-fold enrichment (39 of 44 binding complexes were correctly identified, while only allowing 2 of 44 decoy complexes), and our best overall accuracy was 92%.