Calculation of accurate small angle X-ray scattering curves from coarse-grained protein models

<p>Abstract</p> <p>Background</p> <p>Genome sequencing projects have expanded the gap between the amount of known protein sequences and structures. The limitations of current high resolution structure determination methods make it unlikely that this gap will disappear in the near future. Small angle X-ray scattering (SAXS) is an established low resolution method for routinely determining the structure of proteins in solution. The purpose of this study is to develop a method for the efficient calculation of accurate SAXS curves from coarse-grained protein models. Such a method can for example be used to construct a likelihood function, which is paramount for structure determination based on statistical inference.</p> <p>Results</p> <p>We present a method for the efficient calculation of accurate SAXS curves based on the Debye formula and a set of scattering form factors for dummy atom representations of amino acids. Such a method avoids the computationally costly iteration over all atoms. We estimated the form factors using generated data from a set of high quality protein structures. No <it>ad hoc </it>scaling or correction factors are applied in the calculation of the curves. Two coarse-grained representations of protein structure were investigated; two scattering bodies per amino acid led to significantly better results than a single scattering body.</p> <p>Conclusion</p> <p>We show that the obtained point estimates allow the calculation of accurate SAXS curves from coarse-grained protein models. The resulting curves are on par with the current state-of-the-art program CRYSOL, which requires full atomic detail. Our method was also comparable to CRYSOL in recognizing native structures among native-like decoys. As a proof-of-concept, we combined the coarse-grained Debye calculation with a previously described probabilistic model of protein structure, TorusDBN. This resulted in a significant improvement in the decoy recognition performance. In conclusion, the presented method shows great promise for use in statistical inference of protein structures from SAXS data.</p

Wind Turbine Adaptive Blade Integrated Design and Analysis

This project aims to develop efficient and robust tools for optimal design of wind turbine adaptive blades. In general, wind turbine adaptive blade design is an aero-structure coupled design process, in which, the evaluation of aerodynamic performance cannot be carried out precisely without structural deformation analysis of the adaptive blade. However, employing finite element analysis (FEA) based structural analysis commercial packages as part of the aerodynamic objective evaluation process has been proven time consuming and it results in inefficient and redundant design optimisation of adaptive blades caused by elastic-coupled (bend-twist or stretch-twist) iteration. In order to achieve the goal of wind turbine adaptive blade integrated design and analysis, this project is carried out from three aspects. Firstly, a general geometrically linear model for thin-walled composite beams with multi-cell, non-uniform cross-section and arbitrary lay-ups under various types of loadings is developed for implementing structural deformation analysis. After that, this model is validated by a simple box-beam, single- and multi-cell wind turbine blades. Through validation, it denotes that this thin-walled composite beam model is efficient and accurate for predicting the structural deformations compared to FEA based commercial packages (ANSYS). This developed beam model thus provides more probabilities for further investigations of dynamic performance of adaptive blades. Secondly in order to investigate the effects of aero elastic tailoring and implanting elastic coupling on aerodynamic performance of adaptive blades, auxiliary software tools with graphical interfaces are developed via MATLAB codes. Structural/material characteristics and configurations of adaptive blades (i.e. elastic coupling topology, layup configuration and material properties of blade) are defined by these auxiliary software tools. By interfacing these software tools to the structural analysers based on the developed thin-walled composite beam model to an aerodynamic performance evaluator, an integrated design environment is developed. Lastly, by using the developed thin-walled composite beam model as a search platform, the application of the decoupled design method, a method of design of smart aero-structures based on the concept of variable state design parameter, is also extended

Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized

Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge based potentials based on pairwise distances -- so-called "potentials of mean force" (PMFs) -- have been center stage in the prediction and design of protein structure and the simulation of protein folding. However, the validity, scope and limitations of these potentials are still vigorously debated and disputed, and the optimal choice of the reference state -- a necessary component of these potentials -- is an unsolved problem. PMFs are loosely justified by analogy to the reversible work theorem in statistical physics, or by a statistical argument based on a likelihood function. Both justifications are insightful but leave many questions unanswered. Here, we show for the first time that PMFs can be seen as approximations to quantities that do have a rigorous probabilistic justification: they naturally arise when probability distributions over different features of proteins need to be combined. We call these quantities reference ratio distributions deriving from the application of the reference ratio method. This new view is not only of theoretical relevance, but leads to many insights that are of direct practical use: the reference state is uniquely defined and does not require external physical insights; the approach can be generalized beyond pairwise distances to arbitrary features of protein structure; and it becomes clear for which purposes the use of these quantities is justified. We illustrate these insights with two applications, involving the radius of gyration and hydrogen bonding. In the latter case, we also show how the reference ratio method can be iteratively applied to sculpt an energy funnel. Our results considerably increase the understanding and scope of energy functions derived from known biomolecular structures

Financial software on GPUs:between Haskell and Fortran

This paper presents a real-world pricing kernel for financial deriva-tives and evaluates the language and compiler tool chain that would allow expressive, hardware-neutral algorithm implementation and efficient execution on graphics-processing units (GPU). The lan-guage issues refer to preserving algorithmic invariants, e.g., inher-ent parallelism made explicit by map-reduce-scan functional com-binators. Efficient execution is achieved by manually applying a series of generally-applicable compiler transformations that allows the generated-OpenCL code to yield speedups as high as 70 × and 540 × on a commodity mobile and desktop GPU, respectively. Apart from the concrete speed-ups attained, our contributions are twofold: First, from a language perspective, we illustrate that even state-of-the-art auto-parallelization techniques are incapable of discovering all the requisite data parallelism when rendering th