An Effective Protein Multiple Structure Alignment Using Parallel Computing Multiple spatial alignments of protein structures are an important tool of structural biology. Analysis of protein structures allows us to establish their homology; i.e., the origin from a common ancestor. The rapid growth in the number of known protein structures determines the requirements for the speed of the spatial alignment algorithms. This paper proposes a strategy for using parallel computations to efficiently construct multiple spatial equalizations using multicore cluster system. The developed algorithm is based on the wellproven sequential method of spatial alignment of Multiple Alignments with Translations and Twists (MATT). Results show that the best speedup (38.44) and the least difference between the experimental and theoretical efficiency (0.01) was obtained. The speedup and efficiency based on (128) nodes have been evaluated using LinkSCEEM2 systems at Bibliotheca Alexandrina, Egypt.

Largescale Parallel Design for CryoEM Structure Determination on Heterogeneous Manycore Architectures CryoEM structure determination is the most important research area in structural biology. With the development of cryoelectron microscopy, the resolution has been enhanced significantly, which leads to the huge computation to reconstruct the biomolecule in recent years. In this paper, we present a largescale parallel design for CryoEM structure determination on heterogeneous manycore architectures. A novel task parallel strategy is proposed to reduce the redundant computation and improve the scalability on largescale systems. Further, We distribute the reconstruction model to each node and rearrange the data layout to achieve high parallel efficiency and reduce the memory footprint. The proposed comprehensive parallel design shows highly parallel efficiency and scalability on largescale heterogeneous architectures, which could significantly accelerate the whole period of CryoEM structure determination process.