scholarly journals De novo computational RNA modeling into cryoEM maps of large ribonucleoprotein complexes

2018 ◽  
Author(s):  
Kalli Kappel ◽  
Shiheng Liu ◽  
Kevin P. Larsen ◽  
Georgios Skiniotis ◽  
Elisabetta Viani Puglisi ◽  
...  
Keyword(s):  
Rna Splicing ◽  
Structure Prediction ◽  
Model Building ◽  
De Novo ◽  
Protein Complexes ◽  
Real Space ◽  
Model Interpretation ◽  
Ribonucleoprotein Complexes ◽  
Mitochondrial Ribosome ◽  
Rna Protein Complexes

AbstractRNA-protein assemblies carry out many critical biological functions including translation, RNA splicing, and telomere extension. Increasingly, cryo-electron microscopy (cryoEM) is used to determine the structures of these complexes, but nearly all maps determined with this method have regions in which the local resolution does not permit manual coordinate tracing. Because RNA coordinates typically cannot be determined by docking crystal structures of separate components and existing structure prediction algorithms cannot yet model RNA-protein complexes, RNA coordinates are frequently omitted from final models despite their biological importance. To address these omissions, we have developed a new framework for De novo Ribonucleoprotein modeling in Real-space through Assembly of Fragments Together with Electron density in Rosetta (DRRAFTER). We show that DRRAFTER recovers near-native models for a diverse benchmark set of small RNA-protein complexes, as well as for large RNA-protein machines, including the spliceosome, mitochondrial ribosome, and CRISPR-Cas9-sgRNA complexes where the availability of both high and low resolution maps enable rigorous tests. Blind tests on yeast U1 snRNP and spliceosomal P complex maps demonstrate that the method can successfully build RNA coordinates in real-world modeling scenarios. Additionally, to aid in final model interpretation, we present a method for reliable in situ estimation of DRRAFTER model accuracy. Finally, we apply this method to recently determined maps of telomerase, the HIV-1 reverse transcriptase initiation complex, and the packaged MS2 genome, demonstrating that DRRAFTER can be used to accelerate accurate model building in challenging cases.

scholarly journals Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes

Genes ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 432 ◽  
Author(s):  
Chandran Nithin ◽  
Pritha Ghosh ◽  
Janusz Bujnicki
Keyword(s):  
Rna Splicing ◽  
Structure Prediction ◽  
Protein Complexes ◽  
3D Structure ◽  
Structural Models ◽  
Computational Prediction ◽  
Computational Docking ◽  
3D Structure Prediction ◽  
Rna Protein Complexes

RNA-protein (RNP) interactions play essential roles in many biological processes, such as regulation of co-transcriptional and post-transcriptional gene expression, RNA splicing, transport, storage and stabilization, as well as protein synthesis. An increasing number of RNP structures would aid in a better understanding of these processes. However, due to the technical difficulties associated with experimental determination of macromolecular structures by high-resolution methods, studies on RNP recognition and complex formation present significant challenges. As an alternative, computational prediction of RNP interactions can be carried out. Structural models obtained by theoretical predictive methods are, in general, less reliable compared to models based on experimental measurements but they can be sufficiently accurate to be used as a basis for to formulating functional hypotheses. In this article, we present an overview of computational methods for 3D structure prediction of RNP complexes. We discuss currently available methods for macromolecular docking and for scoring 3D structural models of RNP complexes in particular. Additionally, we also review benchmarks that have been developed to assess the accuracy of these methods.

Automatedde novophasing and model building of coiled-coil proteins

2015 ◽  
Vol 71 (3) ◽  
pp. 606-614 ◽  
Author(s):  
Sebastian Rämisch ◽  
Robert Lizatović ◽  
Ingemar André
Keyword(s):  
Structure Prediction ◽  
Initial Phase ◽  
Model Building ◽  
De Novo ◽  
Protein Complexes ◽  
Coiled Coil ◽  
Coiled Coils ◽  
Phase Estimation ◽  
Molecular Replacement ◽  
Experimental Phasing

Models generated byde novostructure prediction can be very useful starting points for molecular replacement for systems where suitable structural homologues cannot be readily identified. Protein–protein complexes andde novo-designed proteins are examples of systems that can be challenging to phase. In this study, the potential ofde novomodels of protein complexes for use as starting points for molecular replacement is investigated. The approach is demonstrated using homomeric coiled-coil proteins, which are excellent model systems for oligomeric systems. Despite the stereotypical fold of coiled coils, initial phase estimation can be difficult and many structures have to be solved with experimental phasing. A method was developed for automatic structure determination of homomeric coiled coils from X-ray diffraction data. In a benchmark set of 24 coiled coils, ranging from dimers to pentamers with resolutions down to 2.5 Å, 22 systems were automatically solved, 11 of which had previously been solved by experimental phasing. The generated models contained 71–103% of the residues present in the deposited structures, had the correct sequence and had freeRvalues that deviated on average by 0.01 from those of the respective reference structures. The electron-density maps were of sufficient quality that only minor manual editing was necessary to produce final structures. The method, namedCCsolve, combines methods forde novostructure prediction, initial phase estimation and automated model building into one pipeline.CCsolveis robust against errors in the initial models and can readily be modified to make use of alternative crystallographic software. The results demonstrate the feasibility ofde novophasing of protein–protein complexes, an approach that could also be employed for other small systems beyond coiled coils.

scholarly journals LZerD Protein-Protein Docking Webserver Enhanced With de novo Structure Prediction

2021 ◽  
Vol 8 ◽  
Author(s):  
Charles Christoffer ◽  
Vijay Bharadwaj ◽  
Ryan Luu ◽  
Daisuke Kihara
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
De Novo ◽  
Protein Complexes ◽  
Protein Sequences ◽  
Data Bank ◽  
Protein Docking ◽  
Functional Mechanisms ◽  
Established Technique

Protein-protein docking is a useful tool for modeling the structures of protein complexes that have yet to be experimentally determined. Understanding the structures of protein complexes is a key component for formulating hypotheses in biophysics regarding the functional mechanisms of complexes. Protein-protein docking is an established technique for cases where the structures of the subunits have been determined. While the number of known structures deposited in the Protein Data Bank is increasing, there are still many cases where the structures of individual proteins that users want to dock are not determined yet. Here, we have integrated the AttentiveDist method for protein structure prediction into our LZerD webserver for protein-protein docking, which enables users to simply submit protein sequences and obtain full-complex atomic models, without having to supply any structure themselves. We have further extended the LZerD docking interface with a symmetrical homodimer mode. The LZerD server is available at https://lzerd.kiharalab.org/.

scholarly journals Dead or alive: DEAD-box ATPases as regulators of ribonucleoprotein complex condensation

2021 ◽  
Vol 402 (5) ◽  
pp. 653-661
Author(s):  
Karsten Weis
Keyword(s):  
Rna Processing ◽  
Atp Hydrolysis ◽  
Protein Complexes ◽  
Dependent Manner ◽  
Rna Helicases ◽  
Ribonucleoprotein Complexes ◽  
Dead Box ◽  
Rna Duplexes ◽  
Rna Protein Complexes

Abstract DEAD-box ATPase proteins are found in all clades of life and have been associated with a diverse array of RNA-processing reactions in eukaryotes, bacteria and archaea. Their highly conserved core enables them to bind RNA, often in an ATP-dependent manner. In the course of the ATP hydrolysis cycle, they undergo conformational rearrangements, which enable them to unwind short RNA duplexes or remodel RNA-protein complexes. Thus, they can function as RNA helicases or chaperones. However, when their conformation is locked, they can also clamp RNA and create ATP-dependent platforms for the formation of higher-order ribonucleoprotein complexes. Recently, it was shown that DEAD-box ATPases globally regulate the phase-separation behavior of RNA-protein complexes in vitro and control the dynamics of RNA-containing membraneless organelles in both pro- and eukaryotic cells. A role of these enzymes as regulators of RNA-protein condensates, or ‘condensases’, suggests a unifying view of how the biochemical activities of DEAD-box ATPases are used to keep cellular condensates dynamic and ‘alive’, and how they regulate the composition and fate of ribonucleoprotein complexes in different RNA processing steps.

scholarly journals Circular RNAs: An emerging type of RNA in cancer

2017 ◽  
Vol 30 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Li-Dan Hou ◽  
Jing Zhang
Keyword(s):  
Rna Splicing ◽  
Protein Complexes ◽  
Predictive Biomarkers ◽  
Circular Rnas ◽  
Splice Donor ◽  
Associated Proteins ◽  
Non Coding Rnas ◽  
Rna Protein Complexes ◽  
Microrna Sponges ◽  
New Strategies

Circular RNAs (circRNAs), a novel type of widespread and diverse endogenous non-coding RNAs (ncRNAs), which are different from the linear RNAs, form a covalently closed continuous loop without 5’ or 3’ polarities. The majority of circRNAs are abundant, conserved and stable across different species, and exhibit tissue/developmental-stage-specific characteristics. They are generated primarily through a type of alternative RNA splicing called “back-splicing,” in which a downstream splice donor is joined to an upstream splice acceptor through splice skipping or direct splice. Recent studies have discovered circRNAs function as microRNA sponges, binding with RNA-associated proteins to form RNA–protein complexes and then regulating gene transcription and translation into polypeptides. Emerging evidence indicates that circRNAs play important roles in the regulation of the development and progression of multiple cancers by serving as potential diagnostic and predictive biomarkers involved in tumor growth and invasion and providing new strategies for cancer diagnosis and targeted therapy. In this review, we briefly delineate the diversity and characteristics of circRNAs and discuss the highlights of the biogenesis of circRNAs and their potential functions in tumor.

scholarly journals Sampling native-like structures of RNA-protein complexes through Rosetta folding and docking

2018 ◽  
Author(s):  
Kalli Kappel ◽  
Rhiju Das
Keyword(s):  
Conformational Changes ◽  
Posttranscriptional Regulation ◽  
De Novo ◽  
Protein Complexes ◽  
Regulation Of Gene Expression ◽  
Low Resolution ◽  
Human Telomerase ◽  
Nucleotide Resolution ◽  
Rna Protein Complexes ◽  
Resolution Data

AbstractRNA-protein complexes underlie numerous cellular processes including translation, splicing, and posttranscriptional regulation of gene expression. The structures of these complexes are crucial to their functions but often elude high-resolution structure determination. Computational methods are needed that can integrate low-resolution data for RNA-protein complexes while modeling de novo the large conformational changes of RNA components upon complex formation. To address this challenge, we describe a Rosetta method called RNP-denovo to simultaneously fold and dock RNA to a protein surface. On a benchmark set of structurally diverse RNA-protein complexes that are not solvable with prior strategies, this fold-and-dock method consistently sampled native-like structures with better than nucleotide resolution. We revisited three past blind modeling challenges in which previous methods gave poor results: human telomerase, an RNA methyltransferase with a ribosomal RNA domain, and the spliceosome. When coupled with the same sparse FRET, cross-linking, and functional data used in previous work, RNP-denovo gave models with significantly improved accuracy. These results open a route to computationally modeling global folds of RNA-protein complexes from low-resolution data.

scholarly journals AlphaDesign: A de novo protein design framework based on AlphaFold

2021 ◽  
Author(s):  
Michael Jendrusch ◽  
Jan O. Korbel ◽  
S. Kashif Sadiq
Keyword(s):  
Protein Design ◽  
Structure Prediction ◽  
Structural Integrity ◽  
De Novo ◽  
Protein Complexes ◽  
Protein Structures ◽  
Higher Order ◽  
Design Framework ◽  
Oligomeric State ◽  
Dynamics Simulations

De novo protein design is a longstanding fundamental goal of synthetic biology, but has been hindered by the difficulty in reliable prediction of accurate high-resolution protein structures from sequence. Recent advances in the accuracy of protein structure prediction methods, such as AlphaFold (AF), have facilitated proteome scale structural predictions of monomeric proteins. Here we develop AlphaDesign, a computational framework for de novo protein design that embeds AF as an oracle within an optimisable design process. Our framework enables rapid prediction of completely novel protein monomers starting from random sequences. These are shown to adopt a diverse array of folds within the known protein space. A recent and unexpected utility of AF to predict the structure of protein complexes, further allows our framework to design higher-order complexes. Subsequently a range of predictions are made for monomers, homodimers, heterodimers as well as higher-order homo-oligomers -trimers to hexamers. Our analyses also show potential for designing proteins that bind to a pre-specified target protein. Structural integrity of predicted structures is validated and confirmed by standard ab initio folding and structural analysis methods as well as more extensively by performing rigorous all-atom molecular dynamics simulations and analysing the corresponding structural flexibility, intramonomer and interfacial amino-acid contacts. These analyses demonstrate widespread maintenance of structural integrity and suggests that our framework allows for fairly accurate protein design. Strikingly, our approach also reveals the capacity of AF to predict proteins that switch conformation upon complex formation, such as involving switches from α-helices to β-sheets during amyloid filament formation. Correspondingly, when integrated into our design framework, our approach reveals de novo design of a subset of proteins that switch conformation between monomeric and oligomeric state.

scholarly journals Partitioning of ribonucleoprotein complexes from the cellular actin cortex

2021 ◽  
Author(s):  
Isaac Angert ◽  
Siddarth Reddy Karuka ◽  
Louis Mansky ◽  
Joachim Mueller
Keyword(s):  
Plasma Membrane ◽  
Cell Mechanics ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Protein Complexes ◽  
Cell Cortex ◽  
Actin Cortex ◽  
Ribonucleoprotein Complexes ◽  
Microscopy Method ◽  
Rna Protein Complexes

The cell cortex plays a crucial role in cell mechanics, signaling, and development. However, little is known about the influence of the cortical meshwork on the spatial distribution of cytoplasmic biomolecules. Here, we describe a new fluorescence microscopy method to infer the intracellular distribution of labeled biomolecules with sub-resolution accuracy. Unexpectedly, we find that RNA-binding proteins are partially excluded from the cytoplasmic volume adjacent to the plasma membrane that corresponds to the actin cortex. Complementary diffusion measurements of RNA-protein complexes suggest that a rudimentary model based on excluded volume interactions can explain this partitioning effect. Our results suggest the actin cortex meshwork may play a role in regulating the biomolecular content of the volume immediately adjacent to the plasma membrane.

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