On the Universe of Protein Folds

Kolodny, Rachel; Pereyaslavets, Leonid B.; Samson, Abraham O.; Levitt, Michael

doi:10.1146/annurev-biophys-083012-130432

Cited by 80 publications

(80 citation statements)

References 146 publications

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“…Estimates for the total number of possible folds range from about 1000 [275], 2000 [276] to 10 000 [277]. Fold classifications are inherently difficult and estimation of the total number of possible folds is sensitive to the definition of a fold (see [278] and references therein). Nonetheless, in most cases, structures of recently sequenced proteins are related to known folds [279], suggesting that the existing PDB structures are probably a near-complete representation of all biologically viable globular protein folds.…”

Section: Applications Of Biophysics-based Models To Understand Proteimentioning

confidence: 99%

Biophysics of protein evolution and evolutionary protein biophysics

Sikosek

Chan

2014

J. R. Soc. Interface.

220

207

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The study of molecular evolution at the level of protein-coding genes often entails comparing large datasets of sequences to infer their evolutionary relationships. Despite the importance of a protein's structure and conformational dynamics to its function and thus its fitness, common phylogenetic methods embody minimal biophysical knowledge of proteins. To underscore the biophysical constraints on natural selection, we survey effects of protein mutations, highlighting the physical basis for marginal stability of natural globular proteins and how requirement for kinetic stability and avoidance of misfolding and misinteractions might have affected protein evolution. The biophysical underpinnings of these effects have been addressed by models with an explicit coarse-grained spatial representation of the polypeptide chain. Sequence-structure mappings based on such models are powerful conceptual tools that rationalize mutational robustness, evolvability, epistasis, promiscuous function performed by 'hidden' conformational states, resolution of adaptive conflicts and conformational switches in the evolution from one protein fold to another. Recently, protein biophysics has been applied to derive more accurate evolutionary accounts of sequence data. Methods have also been developed to exploit sequence-based evolutionary information to predict biophysical behaviours of proteins. The success of these approaches demonstrates a deep synergy between the fields of protein biophysics and protein evolution.

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Section: Applications Of Biophysics-based Models To Understand Proteimentioning

confidence: 99%

Biophysics of protein evolution and evolutionary protein biophysics

Sikosek

Chan

2014

J. R. Soc. Interface.

220

207

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“…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)(2)(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.…”

mentioning

confidence: 99%

“…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%

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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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“…Outro argumento que justifica a aplicação de tal método é a existência aparente de um número finito de enovelamentos adotados pelas proteínas, o qual alguns autores estimam ser entre 1.000 e 10.000 [20,21]. Portanto, com o aumento do número de estruturas resolvidas, acredita-se que futuramente esses enovelamentos estarão completamente representados nos bancos de dados, possibilitando a modelagem de um número cada vez maior de proteínas [22].…”

Section: Enovelamento De Proteínasunclassified

Redes neurais residuais profundas e autômatos celulares como modelos para predição que fornecem informação sobre a formação de estruturas secundárias proteicas

Pereira¹,

Oliveira²

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On the Universe of Protein Folds

Cited by 80 publications

References 146 publications

Biophysics of protein evolution and evolutionary protein biophysics

Biophysics of protein evolution and evolutionary protein biophysics

Global view of the protein universe

Redes neurais residuais profundas e autômatos celulares como modelos para predição que fornecem informação sobre a formação de estruturas secundárias proteicas

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