Representation of the Protein Universe using Classifications, Maps, and Networks

Ben‐Tal, Nir; Kolodny, Rachel

doi:10.1002/ijch.201400001

Cited by 10 publications

(9 citation statements)

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“…Different techniques have previously been explored in order to generate global representations of protein structure space (see, for example, [ 11 ]). Commonly, these approaches utilise structural similarities between protein domains, which produce complex, multi-dimensional data structures.…”

Section: Introductionmentioning

confidence: 99%

“…An alternative is to use networks to capture relationships resulting from significant alignments [ 13 , 22 – 30 ]. Unlike multidimensional scaling approaches, network constructions do not assume that structural similarity between protein domains is transitive [ 11 ]. On the other hand, they do require a score threshold to be set: above which an alignment is considered significant.…”

Section: Introductionmentioning

confidence: 99%

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Structural Bridges through Fold Space

Edwards

Deane

2015

PLoS Comput Biol

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Several protein structure classification schemes exist that partition the protein universe into structural units called folds. Yet these schemes do not discuss how these units sit relative to each other in a global structure space. In this paper we construct networks that describe such global relationships between folds in the form of structural bridges. We generate these networks using four different structural alignment methods across multiple score thresholds. The networks constructed using the different methods remain a similar distance apart regardless of the probability threshold defining a structural bridge. This suggests that at least some structural bridges are method specific and that any attempt to build a picture of structural space should not be reliant on a single structural superposition method. Despite these differences all representations agree on an organisation of fold space into five principal community structures: all-α, all-β sandwiches, all-β barrels, α/β and α + β. We project estimated fold ages onto the networks and find that not only are the pairings of unconnected folds associated with higher age differences than bridged folds, but this difference increases with the number of networks displaying an edge. We also examine different centrality measures for folds within the networks and how these relate to fold age. While these measures interpret the central core of fold space in varied ways they all identify the disposition of ancestral folds to fall within this core and that of the more recently evolved structures to provide the peripheral landscape. These findings suggest that evolutionary information is encoded along these structural bridges. Finally, we identify four highly central pivotal folds representing dominant topological features which act as key attractors within our landscapes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Structural Bridges through Fold Space

Edwards

Deane

2015

PLoS Comput Biol

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“…There are different approaches to forming a global view of the protein universe (18). The most significant efforts are the ones embodied in the hierarchical classifications CATH and SCOP.…”

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confidence: 99%

Global view of the protein universe

Nepomnyachiy

Ben‐Tal

Kolodny

2014

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

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To explore protein space from a global perspective, we consider 9,710 SCOP (Structural Classification of Proteins) domains with up to 70% sequence identity and present all similarities among them as networks: In the "domain network," nodes represent domains, and edges connect domains that share "motifs," i.e., significantly sized segments of similar sequence and structure. We explore the dependence of the network on the thresholds that define the evolutionary relatedness of the domains. At excessively strict thresholds the network falls apart completely; for very lax thresholds, there are network paths between virtually all domains. Interestingly, at intermediate thresholds the network constitutes two regions that can be described as "continuous" versus "discrete." The continuous region comprises a large connected component, dominated by domains with alternating alpha and beta elements, and the discrete region includes the rest of the domains in isolated islands, each generally corresponding to a fold. We also construct the "motif network," in which nodes represent recurring motifs, and edges connect motifs that appear in the same domain. This network also features a large and highly connected component of motifs that originate from domains with alternating alpha/ beta elements (and some all-alpha domains), and smaller isolated islands. Indeed, the motif network suggests that nature reuses such motifs extensively. The networks suggest evolutionary paths between domains and give hints about protein evolution and the underlying biophysics. They provide natural means of organizing protein space, and could be useful for the development of strategies for protein search and design.protein cooccurrence networks | protein similarity networks H ow are proteins related to each other? Which physicochemical considerations affect protein evolution and how? A global view of the protein universe may shed light on these fundamental questions. It could also suggest new strategies for protein search and design (1-3). However, forming a global picture of the protein universe is difficult because we have to piece it together from the many local glimpses that our empirical data and computational tools provide. In other words, a global picture needs to portray the relationships among all proteins, yet we only have evidence of such relationships among several proteins, based on the similarity between their sequences, structures, and functions. The considerable size of the Protein Data Bank (4) also complicates this task.In particular, an intensely debated question is whether protein space is "discrete" or "continuous" (2, 3, 5-10). These terms are loosely defined. Discrete implies that the global picture consists of separate, island-like, structural entities. In the hierarchical protein domains Structural Classification of Proteins (SCOP) (11) these entities are termed "folds," and in the CATH database (12) they are called "topologies." Alternatively, "continuous" implies that the space between these entities is generally populated by...

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“…On the other hand, the highly connected hubs form an integral part of the network, leaving them vulnerable to targeted attacks (Cohen et al, 2001). Translating this graph theory notion to cancer networks, cancer-associated proteins and their interactions are mapped to nodes and their edges (Ben-Tal et al, 2014). Previous studies have suggested scale-free network architectures in glioblastoma (Ladha et al, 2010), gastric cancer (Aggarwal et al, 2006), and colon cancer (Ruan et al, 2006) though these results are based partially or primarily on correlations in gene expression, a surrogate for network edges.…”

Section: Introductionmentioning

confidence: 99%

Scale-free structure of cancer networks and their vulnerability to hub-directed combination therapy

Chen

Zopf

Mettetal

et al. 2020

Preprint

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AbstractBackgroundThe effectiveness of many targeted therapies is limited by toxicity and the rise of drug resistance. A growing appreciation of the inherent redundancies of cancer signaling has led to a rise in the number of combination therapies under development, but a better understanding of the overall cancer network topology would provide a conceptual framework for choosing effective combination partners. In this work, we explore the scale-free nature of cancer protein-protein interaction networks in 14 indications. Scale-free networks, characterized by a power-law degree distribution, are known to be resilient to random attack on their nodes, yet vulnerable to directed attacks on their hubs (their most highly connected nodes).ResultsConsistent with the properties of scale-free networks, we find that lethal genes are associated with ∼5-fold higher protein connectivity partners than non-lethal genes. This provides a biological rationale for a hub-centered combination attack. Our simulations show that combinations targeting hubs can efficiently disrupt 50% of network integrity by inhibiting less than 1% of the connected proteins, whereas a random attack can require inhibition of more than 30% of the connected proteins.ConclusionsWe find that the scale-free nature of cancer networks makes them vulnerable to focused attack on their highly connected protein hubs. Thus, we propose a new strategy for designing combination therapies by targeting hubs in cancer networks that are not associated with relevant toxicity networks.

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Representation of the Protein Universe using Classifications, Maps, and Networks

Cited by 10 publications

References 90 publications

Structural Bridges through Fold Space

Structural Bridges through Fold Space

Global view of the protein universe

Scale-free structure of cancer networks and their vulnerability to hub-directed combination therapy

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