Correlated mutations in models of protein sequences: phylogenetic and structural
 effects

Lapedes, Alan S.; Giraud, B. G.; Liu, LonChang; Stormo, Gary D.

doi:10.1214/lnms/1215455556

Cited by 122 publications

(146 citation statements)

References 24 publications

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“…More complex variants such as A affects D and D affects B are not considered; these are known as 'indirect effects', and have proved problematic in studies of covariance (e.g. in protein folding based on phylogenetics, 475 although considerable progress is being made in disentangling them [476][477][478][479][480] ).…”

Section: Cytokines Linking Inflammation Hypercoagulability and Hypofmentioning

confidence: 99%

The simultaneous occurrence of both hypercoagulability and hypofibrinolysis in blood and serum during systemic inflammation, and the roles of iron and fibrin(ogen)

Kell

Pretorius

2014

Integrative Biology

127

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Although the two phenomena are usually studied separately, we summarise a considerable body of literature to the effect that a great many diseases involve (or are accompanied by) both an increased tendency for blood to clot (hypercoagulability) and the resistance of the clots so formed (hypofibrinolysis) to the typical, 'healthy' or physiological lysis. We concentrate here on the terminal stages of fibrin formation from fibrinogen, as catalysed by thrombin. Hypercoagulability goes hand in hand with inflammation, and is strongly influenced by the fibrinogen concentration (and vice versa); this can be mediated via interleukin-6. Poorly liganded iron is a significant feature of inflammatory diseases, and hypofibrinolysis may change as a result of changes in the structure and morphology of the clot, which may be mimicked in vitro, and may be caused in vivo, by the presence of unliganded iron interacting with fibrin(ogen) during clot formation. Many of these phenomena are probably caused by electrostatic changes in the iron-fibrinogen system, though hydroxyl radical (OH ) formation can also contribute under both acute and (more especially) chronic conditions. Many substances are known to affect the nature of fibrin polymerised from fibrinogen, such that this might be seen as a kind of bellwether for human or plasma health. Overall, our analysis demonstrates the commonalities underpinning a variety of pathologies as seen in both hypercoagulability and hypofibrinolysis, and offers opportunities for both diagnostics and therapies.

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Section: Cytokines Linking Inflammation Hypercoagulability and Hypofmentioning

confidence: 99%

The simultaneous occurrence of both hypercoagulability and hypofibrinolysis in blood and serum during systemic inflammation, and the roles of iron and fibrin(ogen)

Kell

Pretorius

2014

Integrative Biology

127

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“…‡ wingreen@princeton.edu contact [2-4], as correlation can also stem from other effects such as phylogeny and indirect interactions. Fortunately, global statistical models provide a means to disentangle direct and indirect interactions [5][6][7]. In particular, the maximum entropy principle [8] specifies the least-structured global statistical model consistent with the one-and two-point statistics of an alignment [5].…”

Section: Introductionmentioning

confidence: 99%

“…Fortunately, global statistical models provide a means to disentangle direct and indirect interactions [5][6][7]. In particular, the maximum entropy principle [8] specifies the least-structured global statistical model consistent with the one-and two-point statistics of an alignment [5]. This approach has recently been used with success to determine three-dimensional protein structures from sequences [9][10][11], to predict mutational effects [12][13][14], and to find residue contacts between known interaction partners [7,[15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Inferring interaction partners from protein sequences

Bitbol

Dwyer

Colwell

et al. 2016

Preprint

171

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Specific protein-protein interactions are crucial in the cell, both to ensure the formation and stability of multi-protein complexes, and to enable signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interaction partners. Hence, the sequences of interacting partners are correlated. Here we exploit these correlations to accurately identify which proteins are specific interaction partners from sequence data alone. Our general approach, which employs a pairwise maximum entropy model to infer direct couplings between residues, has been successfully used to predict the three-dimensional structures of proteins from sequences. Building on this approach, we introduce an iterative algorithm to predict specific interaction partners from among the members of two protein families. We assess the algorithm's performance on histidine kinases and response regulators from bacterial two-component signaling systems. The algorithm proves successful without any a priori knowledge of interaction partners, yielding a striking 0.93 true positive fraction on our complete dataset, and we uncover the origin of this surprising success. Finally, we discuss how our method could be used to predict novel protein-protein interactions. INTRODUCTIONMany key cellular processes are carried out by interacting proteins. For instance, transient protein-protein interactions determine signaling pathways, and their specificity ensures proper signal transduction. Hence, mapping specific protein-protein interactions is central to a systems-level understanding of cells, and has broad applications to areas such as drug targeting. High-throughput experimental methods have recently elucidated a substantial fraction of protein-protein interactions in a few model organisms [1], but experimental approaches remain challenging. Meanwhile, major progress in sequencing has led to an explosion of available sequence data. Can we exploit this abundant new sequence data to identify specific protein-protein interaction partners?Specific interactions between proteins imply evolutionary constraints on the interacting partners. For instance, mutation of a contact residue in one partner generically impairs binding, but may be compensated by a complementary mutation in the other partner. This co-evolution of interaction partners results in a correlation of their amino-acid sequences. Similar correlations exist within single proteins, between amino acids that are in contact in the folded protein. However, the simple fact of a correlation between residues in a multiple sequence alignment is only weakly predictive of a three-dimensional * anne-florence.bitbol@upmc.fr † ljc37@cam.ac.uk; L.J.C. and N.S.W. contributed equally to this work. ‡ wingreen@princeton.edu contact [2-4], as correlation can also stem from other effects such as phylogeny and indirect interactions. Fortunately, global statistical models provide a means to disentangle direct and indirect interactions [5][6][7]. In particular, the maximum entropy pri...

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“…Fortunately, global statistical models allow direct and indirect interactions to be disentangled (5-7). In particular, the maximum entropy principle (8) specifies the least-structured global statistical model consistent with the one-and two-point statistics of an alignment (5). This approach has recently been used with success to determine 3D protein structures from sequences (9)(10)(11), to predict mutational effects (12)(13)(14), and to find residue contacts between known interaction partners (7,(15)(16)(17)(18)(19).…”

mentioning

confidence: 99%

Inferring interaction partners from protein sequences

Bitbol

Dwyer

Colwell

et al. 2016

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

134

188

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Specific protein−protein interactions are crucial in the cell, both to ensure the formation and stability of multiprotein complexes and to enable signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interaction partners, causing their sequences to be correlated. Here we exploit these correlations to accurately identify, from sequence data alone, which proteins are specific interaction partners. Our general approach, which employs a pairwise maximum entropy model to infer couplings between residues, has been successfully used to predict the 3D structures of proteins from sequences. Thus inspired, we introduce an iterative algorithm to predict specific interaction partners from two protein families whose members are known to interact. We first assess the algorithm's performance on histidine kinases and response regulators from bacterial twocomponent signaling systems. We obtain a striking 0.93 true positive fraction on our complete dataset without any a priori knowledge of interaction partners, and we uncover the origin of this success. We then apply the algorithm to proteins from ATP-binding cassette (ABC) transporter complexes, and obtain accurate predictions in these systems as well. Finally, we present two metrics that accurately distinguish interacting protein families from noninteracting ones, using only sequence data. proteins. For instance, specific protein−protein interactions ensure proper signal transduction in various pathways. Hence, mapping specific protein−protein interactions is central to a systems-level understanding of cells, and has broad applications to areas such as drug targeting. High-throughput experiments have recently elucidated a substantial fraction of protein−protein interactions in a few model organisms (1), but such experiments remain challenging. Meanwhile, there has been an explosion of available sequence data. Can we exploit this abundant new sequence data to identify specific protein−protein interaction partners?Specific interactions between proteins imply evolutionary constraints on the interacting partners. For instance, mutation of a contact residue in one partner generally impairs binding, but may be compensated by a complementary mutation in the other partner. This coevolution of interaction partners results in correlations between their amino acid sequences. Similar correlations exist within single proteins, for example, between amino acids that are in contact in the folded protein. However, the simple fact of a correlation between residues in a multiple sequence alignment is only weakly predictive of a 3D contact (2-4), as correlation can also stem from indirect effects. Fortunately, global statistical models allow direct and indirect interactions to be disentangled (5-7). In particular, the maximum entropy principle (8) specifies the least-structured global statistical model consistent with the one-and two-point statistics of an alignment (5). This approach has recently been used with success to determine 3D ...

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Correlated mutations in models of protein sequences: phylogenetic and structural effects

Cited by 122 publications

References 24 publications

The simultaneous occurrence of both hypercoagulability and hypofibrinolysis in blood and serum during systemic inflammation, and the roles of iron and fibrin(ogen)

The simultaneous occurrence of both hypercoagulability and hypofibrinolysis in blood and serum during systemic inflammation, and the roles of iron and fibrin(ogen)

Inferring interaction partners from protein sequences

Inferring interaction partners from protein sequences

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