NCACO-score: An effective main-chain dependent scoring function for structure modeling

<p>Abstract</p> <p>Background</p> <p>Development of effective scoring functions is a critical component to the success of protein structure modeling. Previously, many efforts have been dedicated to the development of scoring functions. Despite these efforts, development of an effective scoring function that can achieve both good accuracy and fast speed still presents a grand challenge.</p> <p>Results</p> <p>Based on a coarse-grained representation of a protein structure by using only four main-chain atoms: N, Cα, C and O, we develop a knowledge-based scoring function, called NCACO-score, that integrates different structural information to rapidly model protein structure from sequence. In testing on the Decoys'R'Us sets, we found that NCACO-score can effectively recognize native conformers from their decoys. Furthermore, we demonstrate that NCACO-score can effectively guide fragment assembly for protein structure prediction, which has achieved a good performance in building the structure models for hard targets from CASP8 in terms of both accuracy and speed.</p> <p>Conclusions</p> <p>Although NCACO-score is developed based on a coarse-grained model, it is able to discriminate native conformers from decoy conformers with high accuracy. NCACO is a very effective scoring function for structure modeling.</p

Bis(1,10-phenanthroline-κ2 N,N′)(sulfato-κ2 O,O′)zinc(II) propane-1,3-diol solvate

In the title compound, [Zn(SO4)(C12H8N2)2]·C3H8O2, the Zn2+ ion (site symmetry 2) is coordinated by two chelating 1,10-phenanthroline ligands and an O,O′-bidentate sulfate ion (S site symmetry 2), resulting in a distorted cis-ZnO2N4 octa­hedral geometry for the metal ion. The complete propane-1,3-diol mol­ecule is generated by crystallographic twofold symmetry and two O—H⋯O hydrogen bonds are formed with the uncoordinated O atoms of the sulfate group

Research into container reshuffling and stacking problems in container terminal yards

Container stacking and reshuffling are important issues in the management of operations in a container terminal. Minimizing the number of reshuffles can increase productivity of the yard cranes and the efficiency of the terminal. In this research, the authors improve the existing static reshuffling model, develop five effective heuristics, and analyze the performance of these algorithms. A discrete-event simulation model is developed to animate the stacking, retrieving, and reshuffling operations and to test the performance of the proposed heuristics and their extended versions in a dynamic environment with arrivals and retrievals of containers. The experimental results for the static problem show that the improved model can solve the reshuffling problem more quickly than the existing model and the proposed extended heuristics are superior to the existing ones. The experimental results for the dynamic problem show that the results of the extended versions of the five proposed heuristics are superior or similar to the best results of the existing heuristics and consume very little time