Structure of eIF3b RNA Recognition Motif and Its Interaction with eIF3j

ElAntak, Latifa; Tzakos, Andreas G.; Locker, Nicolas; Lukavsky, Peter J.

doi:10.1074/jbc.m610860200

Cited by 55 publications

(44 citation statements)

References 39 publications

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“…S7B in the supplemental material). They were all down to ∼10 to 15% with the exception of eIF3b (down to ∼25%), which was previously shown to interact with the eIF3j-bound 40S subunits on its own (40). Since the distribution of module ii in the gradients was practically unchanged, we conclude that all we see in the 40S fractions corresponds to the intact 12-subunit eIF3 (with the exception of eIF3b).…”

Section: Resultsmentioning

confidence: 78%

Functional and Biochemical Characterization of Human Eukaryotic Translation Initiation Factor 3 in Living Cells

Wagner

Herrmannová

Malı́k

et al. 2014

Molecular and Cellular Biology

130

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The main role of the translation initiation factor 3 (eIF3) is to orchestrate formation of 43S-48S preinitiation complexes (PICs). Until now, most of our knowledge on eIF3 functional contribution to regulation of gene expression comes from yeast studies. Hence, here we developed several novel in vivo assays to monitor the integrity of the 13-subunit human eIF3 complex, defects in assembly of 43S PICs, efficiency of mRNA recruitment, and postassembly events such as AUG recognition. We knocked down expression of the PCI domain-containing eIF3c and eIF3a subunits and of eIF3j in human HeLa and HEK293 cells and analyzed the functional consequences. Whereas eIF3j downregulation had barely any effect and eIF3a knockdown disintegrated the entire eIF3 complex, eIF3c knockdown produced a separate assembly of the a, b, g, and i subunits (closely resembling the yeast evolutionary conserved eIF3 core), which preserved relatively high 40S binding affinity and an ability to promote mRNA recruitment to 40S subunits and displayed defects in AUG recognition. Both eIF3c and eIF3a knockdowns also severely reduced protein but not mRNA levels of many other eIF3 subunits and indeed shut off translation. We propose that eIF3a and eIF3c control abundance and assembly of the entire eIF3 and thus represent its crucial scaffolding elements critically required for formation of PICs.

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Section: Resultsmentioning

confidence: 78%

Functional and Biochemical Characterization of Human Eukaryotic Translation Initiation Factor 3 in Living Cells

Wagner

Herrmannová

Malı́k

et al. 2014

Molecular and Cellular Biology

130

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“…S2). Taken together, these observations may indicate that, in addition to its role in RNA binding, CRM2 functions in protein-protein interactions as well, as was also shown in the case of several RRM domains in "multiple RRM" proteins (33)(34)(35)(36). However, because no experimental data existed until now to support CRM2 roles in protein-protein interactions (i.e.…”

Section: Discussionmentioning

confidence: 72%

Characterization of the Molecular Basis of Group II Intron RNA Recognition by CRS1-CRM Domains

Keren¹,

Klipcan²,

Bezawork-Geleta

et al. 2008

Journal of Biological Chemistry

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CRM (chloroplast RNA splicing and ribosome maturation) is a recently recognized RNA-binding domain of ancient origin that has been retained in eukaryotic genomes only within the plant lineage. Whereas in bacteria CRM domains exist as single domain proteins involved in ribosome maturation, in plants they are found in a family of proteins that contain between one and four repeats. Several members of this family with multiple CRM domains have been shown to be required for the splicing of specific plastidic group II introns. Detailed biochemical analysis of one of these factors in maize, CRS1, demonstrated its high affinity and specific binding to the single group II intron whose splicing it facilitates, the plastid-encoded atpF intron RNA. Through its association with two intronic regions, CRS1 guides the folding of atpF intron RNA into its predicted "catalytically active" form. To understand how multiple CRM domains cooperate to achieve high affinity sequence-specific binding to RNA, we analyzed the RNA binding affinity and specificity associated with each individual CRM domain in CRS1; whereas CRM3 bound tightly to the RNA, CRM1 associated specifically with a unique region found within atpF intron domain I. CRM2, which demonstrated only low binding affinity, also seems to form specific interactions with regions localized to domains I, III, and IV. We further show that CRM domains share structural similarities and RNA binding characteristics with the well known RNA recognition motif domain.

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“…M ILP , the precision increases from (7,9,10) to (8,9,10) as also seen by a Pearson correlation coecient of -0.51 between the mean false positive count per score, and the score (of a solution). ⊲Overall assessment.…”

Section: Resultsmentioning

confidence: 83%

“…NB: **The assignment of contacts was done manually [26] (8,9,10) of the ensemble of solutions S, denoted P MILP;C (S), by the triple (min, median, max) of the precisions of the solutions S ∈ S. The score of a contact appearing in a solution is the number of solutions from S containing it, and its signed score is its score multiplied by ±1 depending on whether it is a true or false positive w.r.t C. The score of a solution S ∈ S is the sum of the scores of its contacts. Finally, a consensus solution is a solution achieving the maximum score over S, the set of all such solutions being denoted S cons.…”

Section: Assessment Methodsmentioning

confidence: 99%

“…Owing to the reliability of the three sets of contacts C Xtal , C Dim , and C XL , for a given assembly, we dene the interface contacts C Exp = C Xtal ∪ C Dim ∪ C XL . (Rpn8, Rpn9) [12] (Rpn8, Rpn11) [12] (Rpn3, Rpn5) [21] (Rpn3, Rpn11) [14] T AP, M S 1 , M S 2 , P artial Denaturation [26] Immuno − precipitation [26] (b, f ) N M R T itrations [9] (b, j)…”

Section: Pairwise Contacts Within Macro-molecular Complexesmentioning

confidence: 99%

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Connectivity Inference in Mass Spectrometry Based Structure Determination

Agarwal

Araújo

Caillouet

et al. 2013

Lecture Notes in Computer Science

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Abstract:We consider the following Minimum Connectivity Inference problem (MCI), which arises in structural biology: given vertex sets V i ⊆ V, i ∈ I, nd the graph G = (V, E) minimizing the size of the edge set E, such that the sub-graph of G induced by each V i is connected. This problem arises in structural biology, when one aims at nding the pairwise contacts between the proteins of a protein assembly, given the lists of proteins involved in sub-complexes. We present four contributions. First, using a reduction of set cover, we establish that MCI is APX-hard. Second, we show how to solve the problem to optimality using a mixed integer linear programming formulation (MILP). Third, we develop a greedy algorithm based on union-nd data structures (Greedy), yielding a 2(log 2 |V | + log 2 κ)-approximation, with κ the maximum number of subsets V i a vertex belongs to. Fourth, application-wise, we use the MILP and the greedy heuristic to solve the aforementioned connectivity inference problem in structural biology. We show that the solutions of MILP and Greedy are more parsimonious than those reported by the algorithm initially developed in biophysics, which are not qualied in terms of optimality. Since MILP outputs a set of optimal solutions, we introduce the notion of consensus solution. Using assemblies whose pairwise contacts are known exhaustively, we show an almost perfect agreement between the contacts predicted by our algorithms and the experimentally determined ones, especially for consensus solutions. Key-words: Connectivity Inference Connected induced sub-graphs, network design, APX-hard, Mixed integer linear program, Greedy algorithm, Mass spectrometry, Protein assembly, Structural biology, Biophysics, Molecular machines * INRIA Sophia-Antipolis -Méditerranée † Univ. Nice Sophia Antipolis, CNRS, I3S, UMR 7271, 06900 Sophia Antipolis, France ‡ Correspondence to Frederic.Cazals@inria.fr or to David.Coudert@inria.fr Inférence de la connectivité pour la détermination de structure en spectrométrie de masse Résumé : Nous considérons le problème d'Inférence de Connectivité Minimale (Minimum Connectivity Inference ou MCI) qui se pose en biologie structurale: étant donnés des ensembles de sommets V i ⊆ V, i ∈ I, trouver le graphe G = (V, E) minimisant la taille de l'ensemble des arêtes E, de telle sorte que le sous-graphe de G induit par chaque ensemble V i soit connexe. Ce problème se pose en biologie structurale pour la determination des contacts plausibles entre les protéines d'un assemblage à partir des listes de protéines présentes dans des sous-complexes. Nous présentons quatre contributions.Premièrement, nous montrons que le problème MCI est APX-hard en utilisant une réduction de set cover. Deuxièmement, nous présentons une formulation en programme linéaire mixte (MILP) permettant de résoudre MCI de façon optimale. Troisièmement, nous proposons un algorithme glouton (Greedy) basé sur des structures de données Union-Find. Nous montrons que cet algorithme est une 2(log 2 |V | + log 2 κ)-approximation de l'op...

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Structure of eIF3b RNA Recognition Motif and Its Interaction with eIF3j

Cited by 55 publications

References 39 publications

Functional and Biochemical Characterization of Human Eukaryotic Translation Initiation Factor 3 in Living Cells

Functional and Biochemical Characterization of Human Eukaryotic Translation Initiation Factor 3 in Living Cells

Characterization of the Molecular Basis of Group II Intron RNA Recognition by CRS1-CRM Domains

Connectivity Inference in Mass Spectrometry Based Structure Determination

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