Aquaria: simplifying discovery and insight from protein structures

O’Donoghue, Seán I.; Sabir, Kenneth; Kalemanov, Maria; Stolte, Christian; Wellmann, Benjamin; Ho, Vivian; Roos, Manfred; Perdigão, Nelson; Buske, Fabian A.; Heinrich, Julian; Rost, Burkhard; Schafferhans, Andrea

doi:10.1038/nmeth.3258

Cited by 62 publications

(72 citation statements)

References 7 publications

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“…Fig. 1A shows how we mapped the dark proteome: for each Swiss-Prot sequence, each residue was categorized as "not dark" if it was aligned to a PDB entry in Aquaria (19) and as "dark" otherwise (SI Methods). This definition partly underestimates the dark proteome, because Aquaria includes very remote homologies [found using HHblits (22)] and uses all PDB entries, including low-quality structures from electron microscopy (EM) or NMR spectroscopy.…”

Section: Resultsmentioning

confidence: 99%

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Unexpected features of the dark proteome

Perdigão

Heinrich

Stolte

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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We surveyed the "dark" proteome-that is, regions of proteins never observed by experimental structure determination and inaccessible to homology modeling. For 546,000 Swiss-Prot proteins, we found that 44-54% of the proteome in eukaryotes and viruses was dark, compared with only ∼14% in archaea and bacteria. Surprisingly, most of the dark proteome could not be accounted for by conventional explanations, such as intrinsic disorder or transmembrane regions. Nearly half of the dark proteome comprised dark proteins, in which the entire sequence lacked similarity to any known structure. Dark proteins fulfill a wide variety of functions, but a subset showed distinct and largely unexpected features, such as association with secretion, specific tissues, the endoplasmic reticulum, disulfide bonding, and proteolytic cleavage. Dark proteins also had short sequence length, low evolutionary reuse, and few known interactions with other proteins. These results suggest new research directions in structural and computational biology. structure prediction | protein disorder | transmembrane proteins | secreted proteins | unknown unknowns T he Protein Data Bank (PDB) (1) of experimentally determined macromolecular structures recently surpassed 110,000 entries-a landmark in understanding the molecular machinery of life. Structure determination lags far behind DNA sequencing, but high-throughput computational modeling (2, 3) can leverage the PDB to provide accurate structural predictions for a large fraction of protein sequences. Thus, structural data now scale with se-

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Section: Resultsmentioning

confidence: 99%

“…(Unfortunately, however, Khafizov et al used only a few model organisms.) We recently announced Aquaria (19), which provides a median of 35 sequence-to-structure alignments for each Swiss-Prot sequence, a depth of structural data not available from other resources.…”

mentioning

confidence: 99%

Unexpected features of the dark proteome

Perdigão

Heinrich

Stolte

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

170

211

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“…However, there is still the need for a browser that allows the user to visualize 3D models alongside genome-wide datasets. Examples of browsers from other fields clearly show the utility of integrating these disparate data types, e.g., MulteeSum for gene expression [85] or Aquaria for proteins [86]. Key to advancing the development of these types of tools is the creation of standard formats for interchange of genomic spatial datasets.…”

Section: Visualizing the Modelsmentioning

confidence: 99%

Restraint‐based three‐dimensional modeling of genomes and genomic domains

et al. 2015

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a b s t r a c tChromosomes are large polymer molecules composed of nucleotides. In some species, such as humans, this polymer can sum up to meters long and still be properly folded within the nuclear space of few microns in size. The exact mechanisms of how the meters long DNA is folded into the nucleus, as well as how the regulatory machinery can access it, is to a large extend still a mystery. However, and thanks to newly developed molecular, genomic and computational approaches based on the Chromosome Conformation Capture (3C) technology, we are now obtaining insight on how genomes are spatially organized. Here we review a new family of computational approaches that aim at using 3C-based data to obtain spatial restraints for modeling genomes and genomic domains.

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“…Protein structural biology is still far from complete, as many proteins still have little or no experimentally determined structural information. To address this, researchers are using high-throughput homology modeling to systematically compare all known protein sequences against all experimentally determined structures, resulting in over 100 million model structures (63). Allowing researchers to effectively explore and benefit from such large data sets requires carefully tailored visualization tools (63) that use the overview/details strategy, as well as alternative views connected via brushing and linking (Figures 6d,e) (64).…”

Section: Protein Structuresmentioning

confidence: 99%

Visualization of Biomedical Data

O’Donoghue

Baldi

Clark

et al. 2018

Annu. Rev. Biomed. Data Sci.

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The rapid increase in volume and complexity of biomedical data requires changes in research, communication, and clinical practices. This includes learning how to effectively integrate automated analysis with high–data density visualizations that clearly express complex phenomena. In this review, we summarize key principles and resources from data visualization research that help address this difficult challenge. We then survey how visualization is being used in a selection of emerging biomedical research areas, including three-dimensional genomics, single-cell RNA sequencing (RNA-seq), the protein structure universe, phosphoproteomics, augmented reality–assisted surgery, and metagenomics. While specific research areas need highly tailored visualizations, there are common challenges that can be addressed with general methods and strategies. Also common, however, are poor visualization practices. We outline ongoing initiatives aimed at improving visualization practices in biomedical research via better tools, peer-to-peer learning, and interdisciplinary collaboration with computer scientists, science communicators, and graphic designers. These changes are revolutionizing how we see and think about our data.

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Aquaria: simplifying discovery and insight from protein structures

Cited by 62 publications

References 7 publications

Unexpected features of the dark proteome

Unexpected features of the dark proteome

Restraint‐based three‐dimensional modeling of genomes and genomic domains

Visualization of Biomedical Data

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