Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information

132

188

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 ...

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Inferring interaction partners from protein sequences

Bitbol

Dwyer

Colwell

et al. 2016

132

188

“…(19), the mammal Bos taurus (20), and the bacterium Paracoccus denitrificans (21). Identification of covarying pairs of residues in the sequences of homologous proteins, also known as evolutionary couplings, can identify pairs of residues that are likely to interact physically (22)(23)(24)(25). A model was proposed for the a subunit from the mammalian ATP synthase using data from evolutionary covariance in conjunction with the cryo-EM density map (20).…”

Section: Significancementioning

confidence: 99%

Models for the a subunits of the Thermus thermophilus V/A-ATPase and Saccharomyces cerevisiae V-ATPase enzymes by cryo-EM and evolutionary covariance

Schep

Zhao

Rubinstein

2016

Rotary ATPases couple ATP synthesis or hydrolysis to proton translocation across a membrane. However, understanding proton translocation has been hampered by a lack of structural information for the membrane-embedded a subunit. The V/A-ATPase from the eubacterium Thermus thermophilus is similar in structure to the eukaryotic V-ATPase but has a simpler subunit composition and functions in vivo to synthesize ATP rather than pump protons. We determined the T. thermophilus V/A-ATPase structure by cryo-EM at 6.4 Å resolution. Evolutionary covariance analysis allowed tracing of the a subunit sequence within the map, providing a complete model of the rotary ATPase. Comparing the membraneembedded regions of the T. thermophilus V/A-ATPase and eukaryotic V-ATPase from Saccharomyces cerevisiae allowed identification of the α-helices that belong to the a subunit and revealed the existence of previously unknown subunits in the eukaryotic enzyme. Subsequent evolutionary covariance analysis enabled construction of a model of the a subunit in the S. cerevisae V-ATPase that explains numerous biochemical studies of that enzyme. Comparing the two a subunit structures determined here with a structure of the distantly related a subunit from the bovine F-type ATP synthase revealed a conserved pattern of residues, suggesting a common mechanism for proton transport in all rotary ATPases.cryo-EM | evolutionary covariance | V-ATPase | V/A-ATPase | structure V acuolar H + -ATPases (V-ATPases) are large membrane protein complexes responsible for the acidification of intracellular compartments in eukaryotes. These complexes are essential for processes including receptor-mediated endocytosis, coupled transport, and lysosomal degradation (1). V-ATPases localize to the plasma membrane of some specialized cells where they participate in bone resorption by osteoclasts (2) and urine acidification by the α-intercalated cells of the kidney (3). Malfunction or misregulation of these enzymes results in numerous disorders, including osteopetrosis (4) and renal tubular acidosis (5, 6). Expression of V-ATPases on the surfaces of some tumor cells results in increased tumor invasion and metastasis (7). The V/A-ATPase from the thermophilic eubacterium Thermus thermophilus is homologous to the eukaryotic V-ATPase and has a similar overall structure, but is smaller and simpler and functions in vivo as an ATP synthase. V-and V/A-ATPases have similar subunit folds and arrangements of subunits (8-11). The simplified architecture and superior stability of the T. thermophilus V/A-ATPase make it an ideal model to study the structure and function of V-ATPases. Both enzymes are composed of a soluble V 1 catalytic region and a membrane-embedded V O region. The V/A-ATPase subunits I, L, and C in T. thermophilus are homologous to the a, c, and d subunits in eukaryotic V-ATPases, respectively, and, for clarity, the eukaryotic subunit names are used here. With this convention, the T. thermophilus V/A-ATPase contains subunits A 3 B 3 DE 2 FG 2 ac 12 d whereas the eukaryot...

“…DCA has been used to identify interacting protein families (21,25). Based again on the availability of large joint MSA, only pairs of families showing significant interprotein coevolution are expected to interact.…”

Section: Simultaneous Identification Of Interacting Families and Specmentioning

confidence: 99%

“…contacts between bacterial signal transduction proteins, to help to assemble protein complexes (15,16) and shed light on interaction specificity (17,18). The applicability of DCA and related coevolutionary approaches (19,20) to protein−protein interactions far beyond the signaling system has been recently established (21,22).…”

mentioning

confidence: 99%

Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis

Gueudré

Baldassi

Zamparo

et al. 2016

125

173

Understanding protein−protein interactions is central to our understanding of almost all complex biological processes. Computational tools exploiting rapidly growing genomic databases to characterize protein−protein interactions are urgently needed. Such methods should connect multiple scales from evolutionary conserved interactions between families of homologous proteins, over the identification of specifically interacting proteins in the case of multiple paralogs inside a species, down to the prediction of residues being in physical contact across interaction interfaces. Statistical inference methods detecting residue−residue coevolution have recently triggered considerable progress in using sequence data for quaternary protein structure prediction; they require, however, large joint alignments of homologous protein pairs known to interact. The generation of such alignments is a complex computational task on its own; application of coevolutionary modeling has, in turn, been restricted to proteins without paralogs, or to bacterial systems with the corresponding coding genes being colocalized in operons. Here we show that the direct coupling analysis of residue coevolution can be extended to connect the different scales, and simultaneously to match interacting paralogs, to identify interprotein residue−residue contacts and to discriminate interacting from noninteracting families in a multiprotein system. Our results extend the potential applications of coevolutionary analysis far beyond cases treatable so far.A lmost all biological processes depend on interacting proteins.Understanding protein−protein interactions is therefore key to our understanding of complex biological systems. In this context, at least two questions are of interest: First, the question "who with whom," i.e., which proteins interact; this concerns the networks connecting specific proteins inside one organism, but alsoin the context of this article-the evolutionary perspective of protein−protein interactions, which are conserved across different species. Their coevolution is at the basis of many modern computational techniques for characterizing protein−protein interactions. The second question is the question "how" proteins interact with each other, in particular, which residues are involved in the interaction interfaces, and which residues are in contact across the interfaces. Such knowledge may provide important mechanistic insight into questions related to interaction specificity or competitive interaction with partially shared interfaces.The experimental identification of protein−protein interactions is an arduous task (for reviews, cf. refs. 1 and 2): High-throughput techniques that aim to identify protein−protein interactions in vivo or in vitro are well documented and include large-scale yeast two-hybrid assays and protein affinity mass spectrometry assays. Such large-scale efforts have revealed useful information but are hampered by high false positive and false negative error rates. Structural approaches based on protein cocrystalli...