Is protein classification necessary? Toward alternative approaches to function annotation

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...

Section: Discussionmentioning

confidence: 99%

“…2,5,6,9,[13][14][15]. If such similarities are abundant, then one must account for them when organizing and searching proteins (5,8,16). In support of the abundance of such similarities is the remarkable success of structure prediction methods that piece together predictions of protein fragments or larger protein segments (e.g., ref.…”

mentioning

confidence: 99%

Global view of the protein universe

Nepomnyachiy

Ben‐Tal

Kolodny

2014

“…Although in the latter case one is typically restricted to studying sets of proteins of the same label (e.g., fold), maps display structural similarities among all proteins in a single representation. Indeed, it was previously noted that considering all structural similarities is advantageous for studying protein structure and function (18,19). The efficiency of our method renders the calculation of maps for the full Protein Data Bank (PDB) possible, as opposed to only a sparse sample from it.…”

Section: Discussionmentioning

confidence: 99%

Maps of protein structure space reveal a fundamental relationship between protein structure and function

Osadchy

Kolodny

2011

To study the protein structure-function relationship, we propose a method to efficiently create three-dimensional maps of structure space using a very large dataset of >30,000 Structural Classification of Proteins (SCOP) domains. In our maps, each domain is represented by a point, and the distance between any two points approximates the structural distance between their corresponding domains. We use these maps to study the spatial distributions of properties of proteins, and in particular those of local vicinities in structure space such as structural density and functional diversity. These maps provide a unique broad view of protein space and thus reveal previously undescribed fundamental properties thereof. At the same time, the maps are consistent with previous knowledge (e.g., domains cluster by their SCOP class) and organize in a unified, coherent representation previous observation concerning specific protein folds. To investigate the function-structure relationship, we measure the functional diversity (using the Gene Ontology controlled vocabulary) in local structural vicinities. Our most striking finding is that functional diversity varies considerably across structure space: The space has a highly diverse region, and diversity abates when moving away from it. Interestingly, the domains in this region are mostly alpha/beta structures, which are known to be the most ancient proteins. We believe that our unique perspective of structure space will open previously undescribed ways of studying proteins, their evolution, and the relationship between their structure and function. global map of protein universe | protein function prediction | protein structure universe I nvestigating protein structure space and its relationship to function space is a fundamental scientific challenge. Characterizing this relationship may also carry practical implications to protein function prediction, whereby one wishes to infer the biological role of a protein from its structure [as is the case with many of the structures solved in the high-throughput pipeline of the Structural Genomics projects (1, 2)]. One way to approach this challenge is to represent protein structure space by three-dimensional maps. Maps of structure space were first introduced by Holm and Sander (3) and were later used by Kim and colleagues (4-6). To calculate their maps, they first calculate the structural similarity between all pairs of protein structures. Then, they use multidimensional scaling (MDS) to find a collection of points in three dimensions, each of which corresponds to a protein, and where the distance between any two points depends on the structural similarity of the proteins they represent. Such a representation provides a comprehensive visual view of structure space, which is not constrained by a hierarchical system such as the Structural Classification of Proteins (SCOP) (7).We propose an efficient way to calculate maps of protein structure space, using the recently introduced FragBag model (8). Using FragBag, we represent each structure...

“…[6][7][8]. One may view the collection of all possible tertiary structures as a protein structural space.…”

mentioning

confidence: 99%

Structural space of protein–protein interfaces is degenerate, close to complete, and highly connected

Gao

Skolnick

2010

142

133

At the heart of protein-protein interactions are protein-protein interfaces where the direct physical interactions occur. By developing and applying an efficient structural alignment method, we study the structural similarity of representative protein-protein interfaces involving interactions between dimers. Even without structural similarity between individual monomers that form dimeric complexes, ∼90% of native interfaces have a close structural neighbor with similar backbone C α geometry and interfacial contact pattern. About 80% of the interfaces form a dense network, where any two interfaces are structurally related using a transitive set of at most seven intermediate interfaces. The degeneracy of interface space is largely due to the packing of compact, hydrogen-bonded secondary structure elements. This packing generates relatively flat interacting surfaces whose geometries are highly degenerate. Comparative study of artificial and native interfaces argues that the library of protein interfaces is close to complete and comprised of roughly 1,000 distinct interface types. In contrast, the number of possible quaternary structures of dimers is estimated to be about 10 4 times larger; thus, an experimentally determined database of all representative quaternary structures is not likely in the near future. Nevertheless, one could in principle exploit the completeness of protein interfaces to predict most dimeric quaternary structures. Finally, our results provide a structural explanation for the prevalence of promiscuous protein interactions. By side-chain packing adjustments, we illustrate how multiprotein specificity can be attained at a promiscuous interface.