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      Deciphering the structure of the condensin protein complex

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      Proceedings of the National Academy of Sciences
      Proceedings of the National Academy of Sciences

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          Abstract

          Protein assemblies consisting of structural maintenance of chromosomes (SMC) and kleisin subunits are essential for the process of chromosome segregation across all domains of life. Prokaryotic condensin belonging to this class of protein complexes is composed of a homodimer of SMC that associates with a kleisin protein subunit called ScpA. While limited structural data exist for the proteins that comprise the (SMC)–kleisin complex, the complete structure of the entire complex remains unknown. Using an integrative approach combining both crystallographic data and coevolutionary information, we predict an atomic-scale structure of the whole condensin complex, which our results indicate being composed of a single ring. Coupling coevolutionary information with molecular-dynamics simulations, we study the interaction surfaces between the subunits and examine the plausibility of alternative stoichiometries of the complex. Our analysis also reveals several additional configurational states of the condensin hinge domain and the SMC–kleisin interaction domains, which are likely involved with the functional opening and closing of the condensin ring. This study provides the foundation for future investigations of the structure–function relationship of the various SMC–kleisin protein complexes at atomic resolution.

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          Most cited references36

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          Protein 3D Structure Computed from Evolutionary Sequence Variation

          The evolutionary trajectory of a protein through sequence space is constrained by its function. Collections of sequence homologs record the outcomes of millions of evolutionary experiments in which the protein evolves according to these constraints. Deciphering the evolutionary record held in these sequences and exploiting it for predictive and engineering purposes presents a formidable challenge. The potential benefit of solving this challenge is amplified by the advent of inexpensive high-throughput genomic sequencing. In this paper we ask whether we can infer evolutionary constraints from a set of sequence homologs of a protein. The challenge is to distinguish true co-evolution couplings from the noisy set of observed correlations. We address this challenge using a maximum entropy model of the protein sequence, constrained by the statistics of the multiple sequence alignment, to infer residue pair couplings. Surprisingly, we find that the strength of these inferred couplings is an excellent predictor of residue-residue proximity in folded structures. Indeed, the top-scoring residue couplings are sufficiently accurate and well-distributed to define the 3D protein fold with remarkable accuracy. We quantify this observation by computing, from sequence alone, all-atom 3D structures of fifteen test proteins from different fold classes, ranging in size from 50 to 260 residues., including a G-protein coupled receptor. These blinded inferences are de novo, i.e., they do not use homology modeling or sequence-similar fragments from known structures. The co-evolution signals provide sufficient information to determine accurate 3D protein structure to 2.7–4.8 Å Cα-RMSD error relative to the observed structure, over at least two-thirds of the protein (method called EVfold, details at http://EVfold.org). This discovery provides insight into essential interactions constraining protein evolution and will facilitate a comprehensive survey of the universe of protein structures, new strategies in protein and drug design, and the identification of functional genetic variants in normal and disease genomes.
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            Cohesin: its roles and mechanisms.

            The cohesin complex is a major constituent of interphase and mitotic chromosomes. Apart from its role in mediating sister chromatid cohesion, it is also important for DNA double-strand-break repair and transcriptional control. The functions of cohesin are regulated by phosphorylation, acetylation, ATP hydrolysis, and site-specific proteolysis. Recent evidence suggests that cohesin acts as a novel topological device that traps chromosomal DNA within a large tripartite ring formed by its core subunits.
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              Three-dimensional structures of membrane proteins from genomic sequencing.

              We show that amino acid covariation in proteins, extracted from the evolutionary sequence record, can be used to fold transmembrane proteins. We use this technique to predict previously unknown 3D structures for 11 transmembrane proteins (with up to 14 helices) from their sequences alone. The prediction method (EVfold_membrane) applies a maximum entropy approach to infer evolutionary covariation in pairs of sequence positions within a protein family and then generates all-atom models with the derived pairwise distance constraints. We benchmark the approach with blinded de novo computation of known transmembrane protein structures from 23 families, demonstrating unprecedented accuracy of the method for large transmembrane proteins. We show how the method can predict oligomerization, functional sites, and conformational changes in transmembrane proteins. With the rapid rise in large-scale sequencing, more accurate and more comprehensive information on evolutionary constraints can be decoded from genetic variation, greatly expanding the repertoire of transmembrane proteins amenable to modeling by this method. Copyright © 2012 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Proceedings of the National Academy of Sciences
                Proc Natl Acad Sci USA
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                November 20 2018
                November 20 2018
                November 20 2018
                November 01 2018
                : 115
                : 47
                : 11911-11916
                Article
                10.1073/pnas.1812770115
                6255159
                30385633
                8c254506-893d-4ca8-9205-bf8db744fd90
                © 2018

                Free to read

                http://www.pnas.org/site/misc/userlicense.xhtml

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