DIVE: a data intensive visualization engine.

SUMMARY: Modern scientific investigation is generating increasingly larger datasets, yet analyzing these data with current tools is challenging. DIVE is a software framework intended to facilitate big data analysis and reduce the time to scientific insight. Here, we present features of the framework and demonstrate DIVE's application to the Dynameomics project, looking specifically at two proteins. AVAILABILITY AND IMPLEMENTATION: Binaries and documentation are available at http://www.dynameomics.org/DIVE/DIVESetup.exe.

Structural consequences of mutations to the α-tocopherol transfer protein associated with the neurodegenerative disease ataxia with vitamin E deficiency.

The α-tocopherol transfer protein (α-TTP) is a liver protein that transfers α-tocopherol (vitamin E) to very-low-density lipoproteins (VLDLs). These VLDLs are then circulated throughout the body to maintain blood α-tocopherol levels. Mutations to the α-TTP gene are associated with ataxia with vitamin E deficiency, a disease characterized by peripheral nerve degeneration. In this study, molecular dynamics simulations of the E141K and R59W disease-associated mutants were performed. The mutants displayed disruptions in and around the ligand-binding pocket. Structural analysis and ligand docking to the mutant structures predicted a decreased affinity for α-tocopherol. To determine the detailed mechanism of the mutation-related changes, we developed a new tool called ContactWalker that analyzes contact differences between mutant and wild-type proteins and highlights pathways of altered contacts within the mutant proteins. Taken together, our findings are in agreement with experiment and suggest structural explanations for the weakened ability of the mutants to bind and carry α-tocopherol.

Tumorigenic p53 mutants undergo common structural disruptions including conversion to α-sheet structure.

The p53 protein is a commonly studied cancer target because of its role in tumor suppression. Unfortunately, it is susceptible to mutation-associated loss of function; approximately 50% of cancers are associated with mutations to p53, the majority of which are located in the central DNA-binding domain. Here, we report molecular dynamics simulations of wild-type (WT) p53 and 20 different mutants, including a stabilized pseudo-WT mutant. Our findings indicate that p53 mutants tend to exacerbate latent structural-disruption tendencies, or vulnerabilities, already present in the WT protein, suggesting that it may be possible to develop cancer therapies by targeting a relatively small set of structural-disruption motifs rather than a multitude of effects specific to each mutant. In addition, α-sheet secondary structure formed in almost all of the proteins. α-Sheet has been hypothesized and recently demonstrated to play a role in amyloidogenesis, and its presence in the reported p53 simulations coincides with the recent re-consideration of cancer as an amyloid disease.

An in silico algorithm for identifying stabilizing pockets in proteins: test case, the Y220C mutant of the p53 tumor suppressor protein.

The p53 tumor suppressor protein performs a critical role in stimulating apoptosis and cell cycle arrest in response to oncogenic stress. The function of p53 can be compromised by mutation, leading to increased risk of cancer; approximately 50% of cancers are associated with mutations in the p53 gene, the majority of which are in the core DNA-binding domain. The Y220C mutation of p53, for example, destabilizes the core domain by 4 kcal/mol, leading to rapid denaturation and aggregation. The associated loss of tumor suppressor functionality is associated with approximately 75 000 new cancer cases every year. Destabilized p53 mutants can be 'rescued' and their function restored; binding of a small molecule into a pocket on the surface of mutant p53 can stabilize its wild-type structure and restore its function. Here, we describe an in silico algorithm for identifying potential rescue pockets, including the algorithm's integration with the Dynameomics molecular dynamics data warehouse and the DIVE visual analytics engine. We discuss the results of the application of the method to the Y220C p53 mutant, entailing finding a putative rescue pocket through MD simulations followed by an in silico search for stabilizing ligands that dock into the putative rescue pocket. The top three compounds from this search were tested experimentally and one of them bound in the pocket, as shown by nuclear magnetic resonance, and weakly stabilized the mutant.

DIVE: a graph-based visual-analytics framework for big data.

The need for data-centric scientific tools is growing; domains such as biology, chemistry, and physics are increasingly adopting computational approaches. So, scientists must deal with the challenges of big data. To address these challenges, researchers built a visual-analytics platform named DIVE (Data Intensive Visualization Engine). DIVE is a data-agnostic, ontologically expressive software framework that can stream large datasets at interactive speeds. In particular, DIVE makes novel contributions to structured-data-model manipulation and high-throughput streaming of large, structured datasets.

Analyzing disease-associated protein structures with visual analytics.

Protein function is related to protein structure, and understanding disease-associated structural changes is critical both to understanding protein-mediated illness and to developing clinical therapies. We have developed a visual analytics tool called ContactWalker that uses molecular dynamics simulations to investigate the structural and physical differences between wild type and disease-associated protein structures. This tool has been used successfully to characterize the effects of the disease ataxia with vitamin E deficiency (AVED). We are now beginning to investigate mutations to other disease-associated proteins including the tumor suppressor protein p53.

Dynameomics: a comprehensive database of protein dynamics.

The dynamic behavior of proteins is important for an understanding of their function and folding. We have performed molecular dynamics simulations of the native state and unfolding pathways of over 2000 protein/peptide systems (approximately 11,000 independent simulations) representing the majority of folds in globular proteins. These data are stored and organized using an innovative database approach, which can be mined to obtain both general and specific information about the dynamics and folding/unfolding of proteins, relevant subsets thereof, and individual proteins. Here we describe the project in general terms and the type of information contained in the database. Then we provide examples of mining the database for information relevant to protein folding, structure building, the effect of single-nucleotide polymorphisms, and drug design. The native state simulation data and corresponding analyses for the 100 most populated metafolds, together with related resources, are publicly accessible through http://www.dynameomics.org.