Protein topology from predicted residue contacts

Taylor, William R.; Jones, David T.; Sadowski, Michael I.

doi:10.1002/pro.2002

Cited by 43 publications

(36 citation statements)

References 60 publications

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“…The extraordinary improvements in DNA sequencing technology, aided by advanced statistical analysis, have now provided the keys to unlock this evolutionary information. Several groups have demonstrated that extracting covariation information from sequences is sufficient not only to estimate which pairs of residues are close in three-dimensional space 15–21 but also to fold a protein to reasonable accuracy 15,22–25 (Table 1). In addition to being predictive of contacts in a protein, these pairs of covarying residues should also be predictive of functional sites (Fig.…”

Section: Covariation and The Problem Of Transitive Correlationsmentioning

confidence: 99%

Protein structure prediction from sequence variation

2012

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Genomic sequences contain rich evolutionary information about functional constraints on macromolecules such as proteins. This information can be efficiently mined to detect evolutionary couplings between residues in proteins and address the long-standing challenge to compute protein three-dimensional structures from amino acid sequences. Substantial progress has recently been made on this problem owing to the explosive growth in available sequences and the application of global statistical methods. In addition to three-dimensional structure, the improved understanding of covariation may help identify functional residues involved in ligand binding, protein-complex formation and conformational changes. We expect computation of covariation patterns to complement experimental structural biology in elucidating the full spectrum of protein structures, their functional interactions and evolutionary dynamics.

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Section: Covariation and The Problem Of Transitive Correlationsmentioning

confidence: 99%

Protein structure prediction from sequence variation

2012

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“…Integrating the DCA-predicted contacts into coarse-grained physical models of proteins such as structure-based models (SBMs) (15)(16)(17)(18)(19) led to predictions on protein-protein interactions (20)(21)(22) as well as tools to aid the prediction of native structures (19,(23)(24)(25). The idea of using predicted contacts to estimate native structures was also explored by other methodologies (26,27). Here, we show that DCA predicts important structural interactions related not only to the native state but also to distinct functional conformational states of a protein, including intermediates.…”

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confidence: 99%

Coevolutionary signals across protein lineages help capture multiple protein conformations

Morcos

Jana

Hwa

et al. 2013

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

175

195

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A long-standing problem in molecular biology is the determination of a complete functional conformational landscape of proteins. This includes not only proteins' native structures, but also all their respective functional states, including functionally important intermediates. Here, we reveal a signature of functionally important states in several protein families, using direct coupling analysis, which detects residue pair coevolution of protein sequence composition. This signature is exploited in a protein structure-based model to uncover conformational diversity, including hidden functional configurations. We uncovered, with high resolution (mean ∼1.9 Å rmsd for nonapo structures), different functional structural states for medium to large proteins (200-450 aa) belonging to several distinct families. The combination of direct coupling analysis and the structure-based model also predicts several intermediates or hidden states that are of functional importance. This enhanced sampling is broadly applicable and has direct implications in protein structure determination and the design of ligands or drugs to trap intermediate states.A s demonstrated by Anfinsen in 1973 (1) for small and intermediate-size proteins, amino acid sequences contain all of the necessary information to determine their native structure and function. In principle, a complete physical understanding of all molecular interactions should be sufficient to uncover not only the proteins' native structures, but also all their respective functional states, including functionally important intermediates. This landscape is required for a complete knowledge of functional mechanisms and therefore it has implications for drug discovery. Advances in computational approaches have been promising in sampling such conformational intermediates (2, 3). However, in general, computational methods are limited by uncertainties in protein models as well as insufficient computational resources to achieve proper sampling. Experimental techniques such as crystallography or NMR spectroscopy have been successful in identifying functional protein structures but only for a fraction of the complete set of known protein sequences (4, 5). Additionally, the determination of functionally important intermediate states using such methods has been challenging due to their transient nature. One idea to confront this challenge is to search for clues in genomic data (6-10). Functional states under conformational selection should leave a trace in the evolutionary history of proteins. Recent results inspired by this hypothesis have led to the development of the powerful "direct coupling analysis" (DCA), which was able to predict a large number of direct structural contacts between residues from sequences alone (11). Other useful methods have been developed to define coupling among residue pairs (12, 13). Others have also looked into correlated electrostatic mutations to study the evolution of protein topology toward minimized interaction frustration (14). Integrating the DCA-predicted co...

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“…Among the most popular are Pearson correlation, maximum likelihood approaches, Bayesian statistics, chi-square-like methods and statistical coupling analysis [10]. In particular, approaches from information theory that use mutual information and Shannon entropy have recently been applied to three-dimensional structure prediction with promising results [1-8]. …”

Section: Box 1 Identification and Interpretation Of Co-evolving Posimentioning

confidence: 99%

“…In this approach, the accuracy of the strongest direct interactions can significantly be improved by sparse inverse covariance estimation, in which the inverse covariance matrix is numerically estimated while iteratively removing weaker interactions. An efficient algorithm known as graphical LASSO has been used to transfer this principle to co-evolutionary correlation problems in order to predict residue contacts [1,4]. …”

Section: Box 1 Identification and Interpretation Of Co-evolving Posimentioning

confidence: 99%

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Deciphering membrane protein structures from protein sequences

et al. 2012

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In 1973, Christian Anfinsen and colleagues postulated that the information required for a protein to fold into its native structure is encoded in its amino acid sequence. Almost 40 years have passed, but determining the threedimensional structure of a large protein solely from its amino acid sequence remains a formidable challenge. In the past decade and a half, remarkable progress in the development of cost-effective high-throughput sequen cing technologies has led to an explosion of genomic sequence information from evolutionarily diverse organisms. Exploiting this information with statistical tools and constraints derived from evolutionary princi ples has begun to show promise toward determining protein structure from sequence.Recently, there has been increasing excitement in predicting protein structure by using co-evolving sites within protein sequences as a structural constraint [1][2][3][4][5][6]. These studies have demonstrated how improved statistical tools and the growing amount of sequence information hold enormous potential to predict the threedimensional structure of proteins (Box 1). Along these lines, Hopf et al. [7] have developed a computational approach that exploits protein sequences to predict membrane protein structures. They demonstrate that the three-dimensional structure of α-helical membrane proteins can be determined with very good accuracy using their algorithm EVfold_membrane [7]. Their approach identifies positions in protein sequences that show correlated patterns of mutation and uses this information as a structural constraint to model membrane protein structures [7]. Ab initio structure prediction of membrane proteinsReliable prediction of structures could have a major impact on our understanding of membrane protein function. This is underscored by the fact that less than 1% of the structures in the Protein Data Bank are of integral membrane proteins despite these comprising over 20% of all genes in mammalian genomes. Membrane proteins are physiologically crucial given their function as a vital communication interface between the intracellular and extracellular environments, and between the cytosol and diverse membrane-bound organelles. Hence, many membrane proteins are pharmacologically important and are potential drug targets. While efforts in structural genomics have led to the elucidation of structures of numerous soluble proteins, determining the structure of membrane proteins remains challenging due to diffi culties involved in their expression, purification and crystallization. Thus, any approach that provides insights into structures of membrane proteins will be very useful in explaining their function.Unlike soluble proteins, membrane proteins predominantly adopt α-helical (as seen in G-protein-coupled receptors) or β-barrel (as seen in porins) structures due to the constraints posed by the lipid environment. Membrane proteins with α-helical structure dominate most cell membranes, while those with β-barrel structure are confined to the outer membrane of bacteria and eukar...

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Protein topology from predicted residue contacts

Cited by 43 publications

References 60 publications

Protein structure prediction from sequence variation

Protein structure prediction from sequence variation

Coevolutionary signals across protein lineages help capture multiple protein conformations

Deciphering membrane protein structures from protein sequences

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