Nothing about protein structure classification makes sense except in the light of evolution

Valas, Ruben E.; Yang, Song; Bourne, Philip E.

doi:10.1016/j.sbi.2009.03.011

Cited by 31 publications

(28 citation statements)

References 67 publications

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“…While it is generally admitted that structures change at a slower pace than sequences do, evidence has accumulated in recent years supporting that protein structures are not invariant and, therefore, that they may change during the course of evolution (Grishin, 2001; Murzin, 2008; Sikosek et al, 2012; Taylor, 2007; Tokuriki and Tawfik, 2009; Valas et al, 2009). In fact, due to the so-called shape-covering properties of the mapping of sequence into structure (Caetano-Anolles et al, 2009), different structures may be just a few mutational steps away in sequence space, as has been experimentally demonstrated (Cordes et al, 1999; He et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the identification of basic principles of structure evolution may be difficult to extract from the study of extant protein structures(Caetano-Anolles et al, 2009; Murzin, 2008). Consequently, many current fold classifications are phenetic (based on a metric of structure similarity) and the viability of phyletic classifications (based on evolutionary relationships) remains an open issue (Murzin, 2008; Valas et al, 2009). As a result, age estimates for protein folds are uncertain and are based upon indirect methods, such as the census of (assigned) folds in genomes (Caetano-Anolles et al, 2009; Winstanley et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

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Conservation of Protein Structure over Four Billion Years

et al. 2013

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SUMMARY Little is known with certainty about the evolution of protein structures in general and the degree of protein structure conservation over planetary time scales in particular. Here we report the X-ray crystal structures of seven laboratory resurrections of Precambrian thioredoxins dating back up to ~4 billion years before present. Despite considerable sequence differences compared with extant enzymes, the ancestral proteins display the canonical thioredoxin fold while only small structural changes have occurred over 4 billion years. This remarkable degree of structure conservation since a time near the last common ancestor of life supports a punctuated-equilibrium model of structure evolution in which the generation of new folds occurs over comparatively short periods of time and is followed by long periods of structural stasis.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Conservation of Protein Structure over Four Billion Years

et al. 2013

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“…When such a scheme is coupled with one of many possible methods that create hierarchical clusters based on pairwise distances [31], the result is a fully automatic, unsupervised partitioning of protein structural domains into hierarchical classification systems. Such “bottom-up” protein structure classifications, as they are called in [35], have been previously designed based on VAST [20], [11], Dali [17], [18], [16], and others [39], and have both practical and theoretical appeal. Practically, removing a human expert speeds the assignment of new protein structures to clusters.…”

Section: Introductionmentioning

confidence: 99%

Touring Protein Space with Matt

Daniels

Kumar

Cowen

et al. 2012

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Using the Matt structure alignment program, we take a tour of protein space, producing a hierarchical clustering scheme that divides protein structural domains into clusters based on geometric dissimilarity. While it was known that purely structural, geometric, distance-based measures of structural similarity, such as Dali/FSSP, could largely replicate hand-curated schemes such as SCOP at the family level, it was an open question as to whether any such scheme could approximate SCOP at the more distant superfamily and fold levels. We partially answer this question in the affirmative, by designing a clustering scheme based on Matt that approximately matches SCOP at the superfamily level, and demonstrates qualitative differences in performance between Matt and DaliLite. Implications for the debate over the organization of protein fold space are discussed. Based on our clustering of protein space, we introduce the Mattbench benchmark set, a new collection of structural alignments useful for testing sequence aligners on more distantly homologous proteins.

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“…In particular, an intensely debated question is whether protein space is "discrete" or "continuous" (2,3,(5)(6)(7)(8)(9)(10). These terms are loosely defined.…”

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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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Nothing about protein structure classification makes sense except in the light of evolution

Cited by 31 publications

References 67 publications

Conservation of Protein Structure over Four Billion Years

Conservation of Protein Structure over Four Billion Years

Touring Protein Space with Matt

Global view of the protein universe

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