Full-waveform inversion, Part 3: Optimization

This tutorial is the third part of a full-waveform inversion (FWI) tutorial series with a step-by-step walkthrough of setting up forward and adjoint wave equations and building a basic FWI inversion framework. For discretizing and solving wave equations, we use Devito (http://www.opesci.org/devito-public), a Python-based domain-specific language for automated generation of finite-difference code (Lange et al., 2016). The first two parts of this tutorial (Louboutin et al., 2017, 2018) demonstrated how to solve the acoustic wave equation for modeling seismic shot records and how to compute the gradient of the FWI objective function using the adjoint-state method. With these two key ingredients, we will now build an inversion framework that can be used to minimize the FWI least-squares objective function

Collateral and Debt Maturity Choice. A Signaling Model

This paper derives optimal loan policies under asymmetric information where banks offer loan contracts of long and short duration, backed or unbacked with collateral. The main novelty of the paper is that it analyzes a setting in which high quality firms use collateral as a complementary device along with debt maturity to signal their superiority. The least-cost signaling equilibrium depends on the relative costs of the signaling devices, the difference in firm quality and the proportion of good firms in the market. Model simulations suggest a non-monotonic relationship between firm quality and debt maturity, in which high quality firms have both long-term secured debt and short-term secured or non-secured debt.

Keeper-animal interactions: differences between the behaviour of zoo animals affect stockmanship

Stockmanship is a term used to describe the management of animals with a good stockperson someone who does this in a in a safe, effective, and low-stress manner for both the stock-keeper and animals involved. Although impacts of unfamiliar zoo visitors on animal behaviour have been extensively studied, the impact of stockmanship i.e familiar zoo keepers is a new area of research; which could reveal significant ramifications for zoo animal behaviour and welfare. It is likely that different relationships are formed dependant on the unique keeper-animal dyad (human-animal interaction, HAI). The aims of this study were to (1) investigate if unique keeper-animal dyads were formed in zoos, (2) determine whether keepers differed in their interactions towards animals regarding their attitude, animal knowl- edge and experience and (3) explore what factors affect keeper-animal dyads and ultimately influence animal behaviour and welfare. Eight black rhinoceros (Diceros bicornis), eleven Chapman’s zebra (Equus burchellii), and twelve Sulawesi crested black macaques (Macaca nigra) were studied in 6 zoos across the UK and USA. Subtle cues and commands directed by keepers towards animals were identified. The animals latency to respond and the respective behavioural response (cue-response) was recorded per keeper-animal dyad (n=93). A questionnaire was constructed following a five-point Likert Scale design to record keeper demographic information and assess the job satisfaction of keepers, their attitude towards the animals and their perceived relationship with them. There was a significant difference in the animals’ latency to appropriately respond after cues and commands from different keepers, indicating unique keeper-animal dyads were formed. Stockmanship style was also different between keepers; two main components contributed equally towards this: “attitude towards the animals” and “knowledge and experience of the animals”. In this novel study, data demonstrated unique dyads were formed between keepers and zoo animals, which influenced animal behaviour

Novel properties of new phosphatranes and silatranes

The synthesis of the new pro-phosphatranes YP(MeNCH2CH2)3N (Y = lone pair, 0, S, Se, BH3 and CH3+) containing four-coordinate phosphorus, and the tbp phosphatranes YP(MeNCH2CH2)3N (Y = H+, BrCH2 and Cl+) are reported. New azasilatranes of the type YSiRNCH2CH2)3N (R = H, Me, SiMe3; Y = H, OMe, OEt) are also reported and the results of nucleophilic substitution studies of the labile hydrogen on the equatorial nitrogens are given

Benchmarking and Analysis of Protein Docking Performance in Rosetta v3.2

RosettaDock has been increasingly used in protein docking and design strategies in order to predict the structure of protein-protein interfaces. Here we test capabilities of RosettaDock 3.2, part of the newly developed Rosetta v3.2 modeling suite, against Docking Benchmark 3.0, and compare it with RosettaDock v2.3, the latest version of the previous Rosetta software package. The benchmark contains a diverse set of 116 docking targets including 22 antibody-antigen complexes, 33 enzyme-inhibitor complexes, and 60 ‘other’ complexes. These targets were further classified by expected docking difficulty into 84 rigid-body targets, 17 medium targets, and 14 difficult targets. We carried out local docking perturbations for each target, using the unbound structures when available, in both RosettaDock v2.3 and v3.2. Overall the performances of RosettaDock v2.3 and v3.2 were similar. RosettaDock v3.2 achieved 56 docking funnels, compared to 49 in v2.3. A breakdown of docking performance by protein complex type shows that RosettaDock v3.2 achieved docking funnels for 63% of antibody-antigen targets, 62% of enzyme-inhibitor targets, and 35% of ‘other’ targets. In terms of docking difficulty, RosettaDock v3.2 achieved funnels for 58% of rigid-body targets, 30% of medium targets, and 14% of difficult targets. For targets that failed, we carry out additional analyses to identify the cause of failure, which showed that binding-induced backbone conformation changes account for a majority of failures. We also present a bootstrap statistical analysis that quantifies the reliability of the stochastic docking results. Finally, we demonstrate the additional functionality available in RosettaDock v3.2 by incorporating small-molecules and non-protein co-factors in docking of a smaller target set. This study marks the most extensive benchmarking of the RosettaDock module to date and establishes a baseline for future research in protein interface modeling and structure prediction

Protein docking by Rotation-Based Uniform Sampling (RotBUS) with fast computing of intermolecular contact distance and residue desolvation

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions are fundamental for the majority of cellular processes and their study is of enormous biotechnological and therapeutic interest. In recent years, a variety of computational approaches to the protein-protein docking problem have been reported, with encouraging results. Most of the currently available protein-protein docking algorithms are composed of two clearly defined parts: the sampling of the rotational and translational space of the interacting molecules, and the scoring and clustering of the resulting orientations. Although this kind of strategy has shown some of the most successful results in the CAPRI blind test <url>http://www.ebi.ac.uk/msd-srv/capri</url>, more efforts need to be applied. Thus, the sampling protocol should generate a pool of conformations that include a sufficient number of near-native ones, while the scoring function should discriminate between near-native and non-near-native proposed conformations. On the other hand, protocols to efficiently include full flexibility on the protein structures are increasingly needed.</p> <p>Results</p> <p>In these work we present new computational tools for protein-protein docking. We describe here the RotBUS (Rotation-Based Uniform Sampling) method to generate uniformly distributed sets of rigid-body docking poses, with a new fast calculation of the optimal contacting distance between molecules. We have tested the method on a standard benchmark of unbound structures and we can find near-native solutions in 100% of the cases. After applying a new fast filtering scheme based on residue-based desolvation, in combination with FTDock plus pyDock scoring, near-native solutions are found with rank ≤ 50 in 39% of the cases. Knowledge-based experimental restraints can be easily included to reduce computational times during sampling and improve success rates, and the method can be extended in the future to include flexibility of the side-chains.</p> <p>Conclusions</p> <p>This new sampling algorithm has the advantage of its high speed achieved by fast computing of the intermolecular distance based on a coarse representation of the interacting surfaces. In addition, a fast desolvation scoring permits the screening of millions of conformations at low computational cost, without compromising accuracy. The protocol presented here can be used as a framework to include restraints, flexibility and ensemble docking approaches.</p

The eNMR platform for structural biology

The e-NMR project is a European cooperation initiative that aims at providing the bio-NMR user community with a software platform integrating and streamlining the computational approaches necessary for the analysis of bio-NMR data. The e-NMR platform is based on a Grid computational infrastructure. A main focus of the current implementation of the e-NMR platform is on streamlining structure determination protocols. Indeed, to facilitate the use of NMR spectroscopy in the life sciences, the eNMR consortium has set out to provide protocolized services through easy-to-use web interfaces, while still retaining sufficient flexibility to handle specific requests by expert users. Various programs relevant for structural biology applications are already available through the e-NMR portal, including HADDOCK, XPLOR-NIH, CYANA and csRosetta. The implementation of these services, and in particular the distribution of calculations to the GRID infrastructure, has required the development of specific tools. However, the GRID infrastructure is maintained completely transparent to the users. With more than 150 registered users, eNMR is currently the second largest European Virtual Organization in the life sciences

Scoring docking conformations using predicted protein interfaces

BACKGROUND: Since proteins function by interacting with other molecules, analysis of protein-protein interactions is essential for comprehending biological processes. Whereas understanding of atomic interactions within a complex is especially useful for drug design, limitations of experimental techniques have restricted their practical use. Despite progress in docking predictions, there is still room for improvement. In this study, we contribute to this topic by proposing T-PioDock, a framework for detection of a native-like docked complex 3D structure. T-PioDock supports the identification of near-native conformations from 3D models that docking software produced by scoring those models using binding interfaces predicted by the interface predictor, Template based Protein Interface Prediction (T-PIP). RESULTS: First, exhaustive evaluation of interface predictors demonstrates that T-PIP, whose predictions are customised to target complexity, is a state-of-the-art method. Second, comparative study between T-PioDock and other state-of-the-art scoring methods establishes T-PioDock as the best performing approach. Moreover, there is good correlation between T-PioDock performance and quality of docking models, which suggests that progress in docking will lead to even better results at recognising near-native conformations. CONCLUSION: Accurate identification of near-native conformations remains a challenging task. Although availability of 3D complexes will benefit from template-based methods such as T-PioDock, we have identified specific limitations which need to be addressed. First, docking software are still not able to produce native like models for every target. Second, current interface predictors do not explicitly consider pairwise residue interactions between proteins and their interacting partners which leaves ambiguity when assessing quality of complex conformations