scholarly journals Nanoscience Research for Energy Needs. Report of the National Nanotechnology Initiative Grand Challenge Workshop, March 16-18, 2004

2004 ◽  
Author(s):  
P. Alivisatos ◽  
P. Cummings ◽  
J. De Yoreo ◽  
K. Fichthorn ◽  
B. Gates ◽  
...  
Nature ◽  
2007 ◽  
Author(s):  
Lucy Odling-Smee
Keyword(s):  

2006 ◽  
Vol 133 ◽  
pp. 35-35
Author(s):  
D. T. Goodin ◽  
R. W. Petzoldt ◽  
B. A. Vermillion ◽  
D. T. Frey ◽  
N. B. Alexander ◽  
...  

2013 ◽  
Author(s):  
Albert Hornyak ◽  
Shapour Vossoughi
Keyword(s):  

2019 ◽  
Author(s):  
Sukanya Sasmal ◽  
Léa El Khoury ◽  
David Mobley

The Drug Design Data Resource (D3R) Grand Challenges present an opportunity to assess, in the context of a blind predictive challenge, the accuracy and the limits of tools and methodologies designed to help guide pharmaceutical drug discovery projects. Here, we report the results of our participation in the D3R Grand Challenge 4, which focused on predicting the binding poses and affinity ranking for compounds targeting the beta-amyloid precursor protein (BACE-1). Our ligand similarity-based protocol using HYBRID (OpenEye Scientific Software) successfully identified poses close to the native binding mode for most of the ligands with less than 2 A RMSD accuracy. Furthermore, we compared the performance of our HYBRID-based approach to that of AutoDock Vina and Dock 6 and found that HYBRID performed better here for pose prediction. We also conducted end-point free energy estimates on protein-ligand complexes using molecular mechanics combined with generalized Born surface area method (MM-GBSA). We found that the binding affinity ranking based on MM-GBSA scores have poor correlation with the experimental values. Finally, the main lessons from our participation in D3R Grand Challenge 4 suggest that: i) the generation of the macrocycles conformers is a key step for successful pose prediction, ii) the protonation states of the BACE-1 binding site should be treated carefully, iii) the MM-GBSA method could not discriminate well between different predicted binding poses, and iv) the MM-GBSA method does not perform well at predicting protein-ligand binding affinities here.


2014 ◽  
pp. 691-697
Author(s):  
Suleiman José Hassuani

The sugarcane industry for a long time has focused only on the cane juice, its extraction and conversion to sugar. Bagasse was considered a residue and burnt inefficiently to generate steam and power. In the last decades, bagasse gradually started to be converted into energy in a more efficient way, supplying all the sugar industry energy needs (power, and steam) and, in some cases, significant excess electricity has been exported to the grid, becoming another important source of revenue. This motivated several studies of more advanced energy generation systems to boost energy exports. In more recent years, technologies called 2nd and 3rd generation have taken over the scene with many options, promising to convert biomass into more valuable products such as biofuels, chemicals, fertilisers, pellets, etc. Unfilled expectations and opportunities are rising. On the other hand, these technologies are competing for the same biomass, and this has to be considered. The industry has started to question ‘which way to go’, strategy and investment wise. The present study provides a broad scenario for the biomass availability, and its employment, with a close view to the main processes and products that might have an important role in the future of the biomass in the sugarcane industry.


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