Protein complexes: structure prediction challenges for the 21st century

Aloy, Patrick; Pichaud, Matthieu; Russell, Robert B.

doi:10.1016/j.sbi.2005.01.012

Cited by 83 publications

(64 citation statements)

References 39 publications

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“…When experimental structural data are absent or incomplete, template-based homology modeling of protein complexes represents the most reliable option (5,6). Similarly to modeling of tertiary structure for single-chain proteins, homology modeling of protein-protein interactions follows a conservation-based approach, in which the quaternary structure of one or more experimentally solved complexes with enough sequence similarity to a target complex (the templates) is projected onto the target.…”

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confidence: 99%

Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone

Rodriguez-Rivas

Marsili

Juan

et al. 2016

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

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Protein-protein interactions are fundamental for the proper functioning of the cell. As a result, protein interaction surfaces are subject to strong evolutionary constraints. Recent developments have shown that residue coevolution provides accurate predictions of heterodimeric protein interfaces from sequence information. So far these approaches have been limited to the analysis of families of prokaryotic complexes for which large multiple sequence alignments of homologous sequences can be compiled. We explore the hypothesis that coevolution points to structurally conserved contacts at protein-protein interfaces, which can be reliably projected to homologous complexes with distantly related sequences. We introduce a domain-centered protocol to study the interplay between residue coevolution and structural conservation of protein-protein interfaces. We show that sequencebased coevolutionary analysis systematically identifies residue contacts at prokaryotic interfaces that are structurally conserved at the interface of their eukaryotic counterparts. In turn, this allows the prediction of conserved contacts at eukaryotic protein-protein interfaces with high confidence using solely mutational patterns extracted from prokaryotic genomes. Even in the context of high divergence in sequence (the twilight zone), where standard homology modeling of protein complexes is unreliable, our approach provides sequencebased accurate information about specific details of protein interactions at the residue level. Selected examples of the application of prokaryotic coevolutionary analysis to the prediction of eukaryotic interfaces further illustrate the potential of this approach.coevolution | protein-protein interaction | protein complex | homology modeling | contact prediction C ells function as a remarkably synchronized orchestra of finely tuned molecular interactions, and establishing this molecular network has become a major goal of molecular biology. Important methodological and technical advances have led to the identification of a large number of novel protein-protein interactions and to major contributions to our understanding of the functioning of cells and organisms (1, 2). In contrast, and despite relevant advances in EM (3) and crystallography (4), the molecular details of a large number of interactions remain unknown.When experimental structural data are absent or incomplete, template-based homology modeling of protein complexes represents the most reliable option (5, 6). Similarly to modeling of tertiary structure for single-chain proteins, homology modeling of protein-protein interactions follows a conservation-based approach, in which the quaternary structure of one or more experimentally solved complexes with enough sequence similarity to a target complex (the templates) is projected onto the target. Templatebased techniques have provided models for a large number of protein complexes with structurally solved homologous complexes (7-10). Unfortunately, proteins involved in homologous protein dimers tend to systema...

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confidence: 99%

Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone

Rodriguez-Rivas

Marsili

Juan

et al. 2016

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

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“…The problems arise when there are multiple possibilities for an interaction between two subunits or when combining several binary interactions into larger complexes leads to unrealistic structural clashes (i.e., two proteins on top of each other). Tackling these problems represents the next challenge for structure prediction algorithms [27].…”

Section: Complex Assembly From Binary Interactionsmentioning

confidence: 99%

Structure‐based systems biology: a zoom lens for the cell

Aloy

Russell

2005

FEBS Letters

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Systems biology seeks to explain complex biological systems, such as the cell, through the integration of many different types of information. Here, we discuss how the incorporation of high-resolution structural data can provide key molecular details often necessary to understand the complex connection between individual molecules and cell behavior. We suggest a process of zooming on the cell, from global networks through pathways to the precise atomic contacts at the interfaces of interacting proteins.

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“…However, to characterize the interactions with respect to their structural and chemical properties and in particular to understand how the function is exerted, it is essential to include structural information in the networks. [1][2][3][4][5][6][7][8][9][10] To date, in structural network studies, attempts have focused on elucidating the nature of the interactions and on predicting new protein interactions, [11][12][13][14][15] affinities and kinetic constants. 9 In the pioneering works of Aloy and Russell, 1-4 structural information was used to interpret the molecular details of the interactions and to model the binary interactions.…”

Section: The Concept and Introductionmentioning

confidence: 99%

Forward Genetics Approach in Genomics: Functional Identication of Unknown Genes

2016

Omics

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Inspection of protein-protein interaction maps illustrates that a hub protein can interact with a very large number of proteins, reaching tens and even hundreds. Since a single protein cannot interact with such a large number of partners at the same time, this presents a challenge: can we figure out which interactions can occur simultaneously and which are mutually excluded? Addressing this question adds a fourth dimension into interaction maps: that of time. Including the time dimension in structural networks is an immense asset; time dimensionality transforms network node-and-edge maps into cellular processes, assisting in the comprehension of cellular pathways and their regulation. While the time dimensionality can be further enhanced by linking protein complexes to time series of mRNA expression data, current robust, network experimental data are lacking. Here we outline how, using structural data, efficient structural comparison algorithms and appropriate datasets and filters can assist in getting an insight into time dimensionality in interaction networks; in predicting which interactions can and cannot co-exist; and in obtaining concrete predictions consistent with experiment. As an example, we present p53-linked processes.

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Protein complexes: structure prediction challenges for the 21st century

Cited by 83 publications

References 39 publications

Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone

Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone

Structure‐based systems biology: a zoom lens for the cell

Forward Genetics Approach in Genomics: Functional Identication of Unknown Genes

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Protein complexes: structure prediction challenges for the 21st century

Cited by 83 publications

References 39 publications

Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone

Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone

Structure‐based systems biology: a zoom lens for the cell

Forward Genetics Approach in Genomics: Functional Identication of Unknown Genes

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Forward Genetics Approach in Genomics: Functional Identication of Unknown Genes