High-Performance Cloud Computing for Exhaustive Protein–Protein Docking

ADP_EM: fast exhaustive multi-resolution docking for high-throughput coverage

MOTIVATION Efficient fitting tools are needed to take advantage of a fast growth of atomic models of protein domains from crystallography or comparative modeling, and low-resolution density maps of larger molecular assemblies. Here, we report a novel fitting algorithm for the exhaustive and fast overlay of partial high-resolution models into a low-resolution density map. The method incorporates...

High performance computing in the cloud

A High Performance Cloud-Based Protein-Ligand Docking Prediction Algorithm

The potential of predicting druggability for a particular disease by integrating biological and computer science technologies has witnessed success in recent years. Although the computer science technologies can be used to reduce the costs of the pharmaceutical research, the computation time of the structure-based protein-ligand docking prediction is still unsatisfied until now. Hence, in this ...

High Performance Parallel Computing with Cloud and Cloud Technologies

We present our experiences in applying, developing, and evaluating cloud and cloud technologies. First, we present our experience in applying Hadoop and DryadLINQ to a series of data/compute intensive applications and then compare them with a novel MapReduce runtime developed by us, named CGL-MapReduce and MPI. Preliminary applications are developed for particle physics, bioinformatics, cluster...

Cloud Solutions for High Performance Computing: Oxymoron or Realm?

Preliminary notes In the last years a strong interest of the HPC (High Performance Computing) community raised towards cloud computing. There are many apparent benefits of doing HPC in a cloud, the most important of them being better utilization of computational resources, efficient charge back of used resources and applications, on-demand and dynamic reallocation of computational resources bet...