Docking optimization, variance and promiscuity for large-scale drug-like chemical space using high performance computing architectures.

There is a continuing need to hasten and improve protein-ligand docking to facilitate the next generation of drug discovery. As the drug-like chemical space reaches into the billions of molecules, increasingly powerful computer systems are required to probe, as well as tackle, the software engineering challenges needed to adapt existing docking programs to use next-generation massively parallel processing systems. We demonstrate docking setup using the wrapper code approach to optimize the DOCK program for large-scale computation as well as docking analysis using variance and promiscuity as examples. Wrappers provide faster docking speeds when compared with the naive multi-threading system MPI-DOCK, making future endeavors in large-scale docking more feasible; in addition, eliminating highly variant or promiscuous compounds will make databases more useful.

PoPLAR: Portal for Petascale Lifescience Applications and Research.

BACKGROUND: We are focusing specifically on fast data analysis and retrieval in bioinformatics that will have a direct impact on the quality of human health and the environment. The exponential growth of data generated in biology research, from small atoms to big ecosystems, necessitates an increasingly large computational component to perform analyses. Novel DNA sequencing technologies and complementary high-throughput approaches--such as proteomics, genomics, metabolomics, and meta-genomics--drive data-intensive bioinformatics. While individual research centers or universities could once provide for these applications, this is no longer the case. Today, only specialized national centers can deliver the level of computing resources required to meet the challenges posed by rapid data growth and the resulting computational demand. Consequently, we are developing massively parallel applications to analyze the growing flood of biological data and contribute to the rapid discovery of novel knowledge. METHODS: The efforts of previous National Science Foundation (NSF) projects provided for the generation of parallel modules for widely used bioinformatics applications on the Kraken supercomputer. We have profiled and optimized the code of some of the scientific community's most widely used desktop and small-cluster-based applications, including BLAST from the National Center for Biotechnology Information (NCBI), HMMER, and MUSCLE; scaled them to tens of thousands of cores on high-performance computing (HPC) architectures; made them robust and portable to next-generation architectures; and incorporated these parallel applications in science gateways with a web-based portal. RESULTS: This paper will discuss the various developmental stages, challenges, and solutions involved in taking bioinformatics applications from the desktop to petascale with a front-end portal for very-large-scale data analysis in the life sciences. CONCLUSIONS: This research will help to bridge the gap between the rate of data generation and the speed at which scientists can study this data. The ability to rapidly analyze data at such a large scale is having a significant, direct impact on science achieved by collaborators who are currently using these tools on supercomputers.

Dynamics of domain coverage of the protein sequence universe.

BACKGROUND: The currently known protein sequence space consists of millions of sequences in public databases and is rapidly expanding. Assigning sequences to families leads to a better understanding of protein function and the nature of the protein universe. However, a large portion of the current protein space remains unassigned and is referred to as its "dark matter". RESULTS: Here we suggest that true size of "dark matter" is much larger than stated by current definitions. We propose an approach to reducing the size of "dark matter" by identifying and subtracting regions in protein sequences that are not likely to contain any domain. CONCLUSIONS: Recent improvements in computational domain modeling result in a decrease, albeit slowly, in the relative size of "dark matter"; however, its absolute size increases substantially with the growth of sequence data.

The effects of dairy components on energy partitioning and metabolic risk in mice: a microarray study.

BACKGROUND/AIM: High-calcium diets modulate energy metabolism and suppress inflammatory stress. These effects are primarily mediated by calcium suppression of calcitriol. We have now investigated the effect of additional components in dairy products [branched-chain amino acids (BCAA) and angiotensin-converting enzyme inhibitors (ACEi)] on adipocyte and muscle metabolism in an animal model of diet-induced obesity. METHODS: aP2-agouti mice were fed four different 70% restricted diets for 6 weeks: basal-restricted diet (0.4% Ca), nonfat dry milk (1.2% Ca), calcium-depleted milk (0.4% Ca), or basal-restricted diet (0.4% Ca) with supplemented BCAA/ACEi. A high-density oligonucleotide microarray approach was used to compare the effects on energy metabolism. RESULTS: Lipogenic genes in adipose tissue were downregulated in the milk group while in muscle protein synthetic pathways were stimulated by the Ca-depleted and low Ca/BCAA/ACEi diets. Pathways involved in inflammation were altered in adipose tissue and muscle by all three diet treatment groups. CONCLUSIONS: The results support our previous findings that calcium and BCAA contribute to the alteration of energy partitioning between adipose tissue and muscle. They provide further evidence for a calcium-independent effect of BCAA and ACEi in energy metabolism and inflammation.

Changes in hepatic gene expression in dogs with experimentally induced nutritional iron deficiency.

BACKGROUND AND OBJECTIVE: We investigated hepatic gene expression in dogs with experimentally induced nutritional iron deficiency (ID). Our hypothesis was that ID would result in decreased hepcidin gene expression, and possibly in altered expression of other genes associated with iron metabolism. METHODS: Liver biopsies were collected from each of 3 dogs before induction of ID, at the point of maximal ID, and after resolution of ID. Using Affymetrix microarray technology and analytical tools specifically designed for microarray data, we identified genes that had at least a 2-fold change in expression in response to ID. Four genes were selected for further analysis by reverse transcriptase PCR (RT-PCR). RESULTS: Dogs with ID had markedly decreased expression of the hepcidin gene (mean decrease of 40-fold for one probe and >100-fold for another probe) and increased expression of the transferrin receptor gene (mean increase of >7-fold). There was also mildly decreased expression of the "similar to calreticulin" gene and a gene of unknown function. Results of RT-PCR analysis were consistent with microarray findings. CONCLUSION: Changes in hepcidin and transferrin receptor gene expression were consistent with the known biology of iron metabolism. The decrease in expression of a gene identified as "similar to calreticulin," while not statistically significant, was consistent with the findings of other investigators that suggest iron plays a role in calreticulin expression.