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The purpose of this paper is 2-fold. First, we present several extensions to the ONIOM(QM:MM) scheme. In its original formulation, the electrostatic interaction between the regions is included at the classical level. Here we present the extension to electronic embedding. We show how the behavior of ONIOM with electronic embedding can be more stable than QM/MM with electronic embedding. We also investigate the link atom correction, which is implicit in ONIOM but not in QM/MM. Second, we demonstrate some of the practical aspects of ONIOM(QM:MM) calculations. Specifically, we show that the potential surface can be discontinuous when there is bond breaking and forming closer than three bonds from the MM region.

We present an updated and integrated version of our widely used protein-protein docking and binding affinity benchmarks. The benchmarks consist of non-redundant, high quality structures of protein-protein complexes along with the unbound structures of their components. Fifty-five new complexes were added to the docking benchmark, 35 of which have experimentally-measured binding affinities. These updated docking and affinity benchmarks now contain 230 and 179 entries, respectively. In particular, the number of antibody-antigen complexes has increased significantly, by 67% and 74% in the docking and affinity benchmarks, respectively. We tested previously developed docking and affinity prediction algorithms on the new cases. Considering only the top ten docking predictions per benchmark case, a prediction accuracy of 38% is achieved on all 55 cases, and up to 50% for the 32 rigid-body cases only. Predicted affinity scores are found to correlate with experimental binding energies up to r=0.52 overall, and r=0.72 for the rigid complexes.

Hybrid energy methods such as QM/MM and ONIOM, that combine different levels of theory into one calculation, have been very successful in describing large systems. Geometry optimization methods can take advantage of the partitioning of these calculations into a region treated at a quantum mechanical (QM) level of theory and the larger, remaining region treated by an inexpensive method such as molecular mechanics (MM). A series of microiterations can be employed to fully optimize the MM region for each optimization step in the QM region. Cartesian coordinates are used for the MM region and are chosen so that the internal coordinates of the QM region remain constant during the microiterations. The coordinates of the MM region are augmented to permit rigid body translation and rotation of the QM region. This is essential if any atoms in the MM region are constrained, but it also improves the efficiency of unconstrained optimizations. Because of the microiterations, special care is needed for the optimization step in the QM region so that the system remains in the same local valley during the course of the optimization. The optimization methodology with microiterations, constraints, and step-size control are illustrated by calculations on bacteriorhodopsin and other systems.

We present version 3.0 of our publicly available protein-protein docking benchmark. This update includes 40 new test cases, representing a 48% increase from Benchmark 2.0. For all of the new cases, the crystal structures of both binding partners are available. As with Benchmark 2.0, SCOP 1 (Structural Classification of Proteins) was used to remove redundant test cases. The 124 unboundunbound test cases in Benchmark 3.0 are classified into 88 rigid-body cases, 19 medium difficulty cases, and 17 difficult cases, based on the degree of conformational change at the interface upon complex formation. In addition to providing the community with more test cases for evaluating docking methods, the expansion of Benchmark 3.0 will facilitate the development of new algorithms that require a large number of training examples. Benchmark 3.0 is available to the public at

Summary piRNAs guide an adaptive genome defense system that silences transposons during germline development. The Drosophila HP1 homolog Rhino is required for germline piRNA production. We show that Rhino binds specifically to the heterochromatic clusters that produce piRNA precursors, and that binding directly correlates with piRNA production. Rhino co-localizes to germline nuclear foci with Rai1/DXO related protein Cuff and the DEAD box protein UAP56, which are also required for germline piRNA production. RNA sequencing indicates that most cluster transcripts are not spliced, and that rhino, cuff and uap56 mutations increase expression of spliced cluster transcripts over 100 fold. LacI∷Rhino fusion protein binding suppresses splicing of a reporter transgene, and is sufficient to trigger piRNA production from a trans combination of sense and antisense reporters. We therefore propose that Rhino anchors a nuclear complex that suppresses cluster transcript splicing, and speculate that stalled splicing differentiates piRNA precursors from mRNAs.

Five years ago Morokuma and colleagues introduced the IMOMO method, which integrates two molecular orbital methods into one calculation. Since then, the method has been expanded in several ways; it has been generalized to consider up to three methods, and has been unified as the ONIOM method to include both MO and MM combinations. In this review we present the history of the method, a number of chemical problems that we have studied, how to assess IMOMO combinations and partitionings, and our latest efforts that take the method beyond the conventional investigation of ground state energy surfaces. In particular, we emphasize the importance of the S-value test for validation of the ONIOM method/model combinations. The method combination depends much on the properties and accuracies required. Generally speaking, however, if the target level is CCSD(T) or G2, the best choice of low level is MP2. If MP2 or DFT is the target level, HF or eventually semiempirical MO methods are good choices of low level. These methods can be further combined with an outer-most layer of the MM level.

Summary piRNAs silence transposons during germline development. In Drosophila, transcripts from heterochromatic clusters are processed into primary piRNAs in the perinuclear nuage. The nuclear DEAD box protein UAP56 has been previously implicated in mRNA splicing and export, while the DEAD box protein Vasa has an established role in piRNA production and localizes to nuage with the piRNA binding PIWI proteins Ago3 and Aub. We show that UAP56 co-localizes with the cluster-associated HP1 variant Rhino, that nuage granules containing Vasa localize directly across the nuclear envelope from cluster foci containing UAP56 and Rhino, and that cluster transcripts immunoprecipitate with both Vasa and UAP56. Significantly, a charge-substitution mutation that alters a conserved surface residue in UAP56 disrupts co-localization with Rhino, germline piRNA production, transposon silencing, and perinuclear localization of Vasa. We therefore propose that UAP56 and Vasa function in a piRNA-processing compartment that spans the nuclear envelope.