Exploring angular distance in protein-protein docking algorithms

We present a two-stage hybrid-resolution approach for rigid-body protein-protein docking. The first stage is carried out at low-resolution (15 degrees ) angular sampling. In the second stage, we sample promising regions from the first stage at a higher resolution of 6 degrees . The hybrid-resolution approach produces the same results as a 6 degrees uniform sampling docking run, but uses only 17% of the computational time. We also show that the angular distance can be used successfully in clustering and pruning algorithms, as well as the characterization of energy funnels. Traditionally the root-mean-square-distance is used in these algorithms, but the evaluation is computationally expensive as it depends on both the rotational and translational parameters of the docking solutions. In contrast, the angular distances only depend on the rotational parameters, which are generally fixed for all docking runs. Hence the angular distances can be pre-computed, and do not add computational time to the post-processing of rigid-body docking results

Using the ONIOM hybrid method to apply equation of motion CCSD to larger systems: Benchmarking and comparison with time-dependent density functional theory, configuration interaction singles, and time-dependent Hartree–Fock

Equation of motion coupled-cluster singles and doubles (EOM-CCSD) is one of the most accurate computational methods for the description of one-electron vertical transitions. However, its O(N6) scaling, where N is the number of basis functions, often makes the study of molecules larger than 10–15 heavy atoms prohibitive. In this work we investigate how accurately less expensive methods can approximate the EOM-CCSD results. We focus on our own N-layer integrated molecular orbital molecular mechanics (ONIOM) hybrid scheme, where the system is partitioned into regions which are treated with different levels of theory. For our set of benchmark calculations, the comparison of conventional configuration interaction singles (CIS), time-dependent Hartree–Fock (TDHF), and time-dependent density functional theory (TDDFT) methods and ONIOM (with different low level methods) showed that the best accuracy-computational time combination is obtained with ONIOM(EOM:TDDFT), which has a rms of the error with respect to the conventional EOM-CCSD of 0.06 eV, compared with 0.47 eV of the conventional TDDFT

The HP1 Homolog Rhino Anchors a Nuclear Complex that Suppresses piRNA Precursor Splicing

SummarypiRNAs 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 colocalizes 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

IRaPPA: information retrieval based integration of biophysical models for protein assembly selection

Motivation: In order to function, proteins frequently bind to one another and form 3D assemblies. Knowledge of the atomic details of these structures helps our understanding of how proteins work together, how mutations can lead to disease, and facilitates the designing of drugs which prevent or mimic the interaction. Results: Atomic modeling of protein-protein interactions requires the selection of near-native structures from a set of docked poses based on their calculable properties. By considering this as an information retrieval problem, we have adapted methods developed for Internet search ranking and electoral voting into IRaPPA, a pipeline integrating biophysical properties. The approach enhances the identification of near-native structures when applied to four docking methods, resulting in a near-native appearing in the top 10 solutions for up to 50% of complexes benchmarked, and up to 70% in the top 100. Availability and Implementation: IRaPPA has been implemented in the SwarmDock server ( http://bmm.crick.ac.uk/ approximately SwarmDock/ ), pyDock server ( http://life.bsc.es/pid/pydockrescoring/ ) and ZDOCK server ( http://zdock.umassmed.edu/ ), with code available on request. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online

Updates to the Integrated Protein–Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2

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 10 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.Peer ReviewedPostprint (author's final draft

Community-Wide Assessment of Protein-Interface Modeling Suggests Improvements to Design Methodology

The CAPRI and CASP prediction experiments have demonstrated the power of community wide tests of methodology in assessing the current state of the art and spurring progress in the very challenging areas of protein docking and structure prediction. We sought to bring the power of community wide experiments to bear on a very challenging protein design problem that provides a complementary but equally fundamental test of current understanding of protein-binding thermodynamics. We have generated a number of designed protein-protein interfaces with very favorable computed binding energies but which do not appear to be formed in experiments, suggesting there may be important physical chemistry missing in the energy calculations. 28 research groups took up the challenge of determining what is missing: we provided structures of 87 designed complexes and 120 naturally occurring complexes and asked participants to identify energetic contributions and/or structural features that distinguish between the two sets. The community found that electrostatics and solvation terms partially distinguish the designs from the natural complexes, largely due to the non-polar character of the designed interactions. Beyond this polarity difference, the community found that the designed binding surfaces were on average structurally less embedded in the designed monomers, suggesting that backbone conformational rigidity at the designed surface is important for realization of the designed function. These results can be used to improve computational design strategies, but there is still much to be learned; for example, one designed complex, which does form in experiments, was classified by all metrics as a non-binder