IntaRNA 2.0: enhanced and customizable prediction of RNA-RNA interactions.
Ontology highlight
ABSTRACT:
SUBMITTER: Mann M
PROVIDER: S-EPMC5570192 | biostudies-literature | 2017 Jul
REPOSITORIES: biostudies-literature
Publications
IntaRNA 2.0: enhanced and customizable prediction of RNA-RNA interactions.
Mann Martin M Wright Patrick R PR Backofen Rolf R
Nucleic acids research 20170701 W1
The IntaRNA algorithm enables fast and accurate prediction of RNA-RNA hybrids by incorporating seed constraints and interaction site accessibility. Here, we introduce IntaRNAv2, which enables enhanced parameterization as well as fully customizable control over the prediction modes and output formats. Based on up to date benchmark data, the enhanced predictive quality is shown and further improvements due to more restrictive seed constraints are highlighted. The extended web interface provides vi ...[more]
Similar Datasets
Project description:The three-dimensional (3-D) structure prediction of proteins, given their amino acid sequence, is addressed using the first principles-based approach ASTRO-FOLD 2.0. The key features presented are: (1) Secondary structure prediction using a novel optimization-based consensus approach, (2) β-sheet topology prediction using mixed-integer linear optimization (MILP), (3) Residue-to-residue contact prediction using a high-resolution distance-dependent force field and MILP formulation, (4) Tight dihedral angle and distance bound generation for loop residues using dihedral angle clustering and non-linear optimization (NLP), (5) 3-D structure prediction using deterministic global optimization, stochastic conformational space annealing, and the full-atomistic ECEPP/3 potential, (6) Near-native structure selection using a traveling salesman problem-based clustering approach, ICON, and (7) Improved bound generation using chemical shifts of subsets of heavy atoms, generated by SPARTA and CS23D. Computational results of ASTRO-FOLD 2.0 on 47 blind targets of the recently concluded CASP9 experiment are presented.
| S-EPMC3462460 | biostudies-literature
Project description:Understanding the base pairing of an RNA sequence provides insight into its molecular structure. By mining suboptimal sampling data, RNAprofiling 1.0 identifies the dominant helices in low-energy secondary structures as features, organizes them into profiles which partition the Boltzmann sample, and highlights key similarities/differences among the most informative, i.e. selected, profiles in a graphical format. Version 2.0 enhances every step of this approach. First, the featured substructures are expanded from helices to stems. Second, profile selection includes low-frequency pairings similar to featured ones. In conjunction, these updates extend the utility of the method to sequences up to length 600, as evaluated over a sizable dataset. Third, relationships are visualized in a decision tree which highlights the most important structural differences. Finally, this cluster analysis is made accessible to experimental researchers in a portable format as an interactive webpage, permitting a much greater understanding of trade-offs among different possible base pairing combinations.
| S-EPMC10081340 | biostudies-literature
Project description:Accurate RNA structure models are crucial for designing small molecule ligands that modulate their functions. This study assesses six standalone RNA 3D structure prediction methods-DeepFoldRNA, RhoFold, BRiQ, FARFAR2, SimRNA and Vfold2, excluding web-based tools due to intellectual property concerns. We focus on reproducing the RNA structure existing in RNA-small molecule complexes, particularly on the ability to model ligand binding sites. Using a comprehensive set of RNA structures from the PDB, which includes diverse structural elements, we found that machine learning (ML)-based methods effectively predict global RNA folds but are less accurate with local interactions. Conversely, non-ML-based methods demonstrate higher precision in modeling intramolecular interactions, particularly with secondary structure restraints. Importantly, ligand-binding site accuracy can remain sufficiently high for practical use, even if the overall model quality is not optimal. With the recent release of AlphaFold 3, we included this advanced method in our tests. Benchmark subsets containing new structures, not used in the training of the tested ML methods, show that AlphaFold 3's performance was comparable to other ML-based methods, albeit with some challenges in accurately modeling ligand binding sites. This study underscores the importance of enhancing binding site prediction accuracy and the challenges in modeling RNA-ligand interactions accurately.
| S-EPMC11260495 | biostudies-literature
Project description:Scansite identifies short protein sequence motifs that are recognized by modular signaling domains, phosphorylated by protein Ser/Thr- or Tyr-kinases or mediate specific interactions with protein or phospholipid ligands. Each sequence motif is represented as a position-specific scoring matrix (PSSM) based on results from oriented peptide library and phage display experiments. Predicted domain-motif interactions from Scansite can be sequentially combined, allowing segments of biological pathways to be constructed in silico. The current release of Scansite, version 2.0, includes 62 motifs characterizing the binding and/or substrate specificities of many families of Ser/Thr- or Tyr-kinases, SH2, SH3, PDZ, 14-3-3 and PTB domains, together with signature motifs for PtdIns(3,4,5)P(3)-specific PH domains. Scansite 2.0 contains significant improvements to its original interface, including a number of new generalized user features and significantly enhanced performance. Searches of all SWISS-PROT, TrEMBL, Genpept and Ensembl protein database entries are now possible with run times reduced by approximately 60% when compared with Scansite version 1.0. Scansite 2.0 allows restricted searching of species-specific proteins, as well as isoelectric point and molecular weight sorting to facilitate comparison of predictions with results from two-dimensional gel electrophoresis experiments. Support for user-defined motifs has been increased, allowing easier input of user-defined matrices and permitting user-defined motifs to be combined with pre-compiled Scansite motifs for dual motif searching. In addition, a new series of Sequence Match programs for non-quantitative user-defined motifs has been implemented. Scansite is available via the World Wide Web at http://scansite.mit.edu.
| S-EPMC168990 | biostudies-literature
Project description:Macromolecular interactions are formed between proteins, DNA and RNA molecules. Being a principle building block in macromolecular assemblies and pathways, the interactions underlie most of cellular functions. Malfunctioning of macromolecular interactions is also linked to a number of diseases. Structural knowledge of the macromolecular interaction allows one to understand the interaction's mechanism, determine its functional implications and characterize the effects of genetic variations, such as single nucleotide polymorphisms, on the interaction. Unfortunately, until now the interactions mediated by different types of macromolecules, e.g. protein-protein interactions or protein-DNA interactions, are collected into individual and unrelated structural databases. This presents a significant obstacle in the analysis of macromolecular interactions. For instance, the homogeneous structural interaction databases prevent scientists from studying structural interactions of different types but occurring in the same macromolecular complex. Here, we introduce DOMMINO 2.0, a structural Database Of Macro-Molecular INteractiOns. Compared to DOMMINO 1.0, a comprehensive database on protein-protein interactions, DOMMINO 2.0 includes the interactions between all three basic types of macromolecules extracted from PDB files. DOMMINO 2.0 is automatically updated on a weekly basis. It currently includes ?1,040,000 interactions between two polypeptide subunits (e.g. domains, peptides, termini and interdomain linkers), ?43,000 RNA-mediated interactions, and ?12,000 DNA-mediated interactions. All protein structures in the database are annotated using SCOP and SUPERFAMILY family annotation. As a result, protein-mediated interactions involving protein domains, interdomain linkers, C- and N- termini, and peptides are identified. Our database provides an intuitive web interface, allowing one to investigate interactions at three different resolution levels: whole subunit network, binary interaction and interaction interface. Database URL: http://dommino.org.
| S-EPMC4733329 | biostudies-literature
Project description:Motivation:Long non-coding RNAs (lncRNAs) are defined as transcripts longer than 200 nt that do not get translated into proteins. Often these transcripts are processed (spliced, capped and polyadenylated) and some are known to have important biological functions. However, most lncRNAs have unknown or poorly understood functions. Nevertheless, because of their potential role in cancer, lncRNAs are receiving a lot of attention, and the need for computational tools to predict their possible mechanisms of action is more than ever. Fundamentally, most of the known lncRNA mechanisms involve RNA-RNA and/or RNA-protein interactions. Through accurate predictions of each kind of interaction and integration of these predictions, it is possible to elucidate potential mechanisms for a given lncRNA. Results:Here, we introduce MechRNA, a pipeline for corroborating RNA-RNA interaction prediction and protein binding prediction for identifying possible lncRNA mechanisms involving specific targets or on a transcriptome-wide scale. The first stage uses a version of IntaRNA2 with added functionality for efficient prediction of RNA-RNA interactions with very long input sequences, allowing for large-scale analysis of lncRNA interactions with little or no loss of optimality. The second stage integrates protein binding information pre-computed by GraphProt, for both the lncRNA and the target. The final stage involves inferring the most likely mechanism for each lncRNA/target pair. This is achieved by generating candidate mechanisms from the predicted interactions, the relative locations of these interactions and correlation data, followed by selection of the most likely mechanistic explanation using a combined P-value. We applied MechRNA on a number of recently identified cancer-related lncRNAs (PCAT1, PCAT29 and ARLnc1) and also on two well-studied lncRNAs (PCA3 and 7SL). This led to the identification of hundreds of high confidence potential targets for each lncRNA and corresponding mechanisms. These predictions include the known competitive mechanism of 7SL with HuR for binding on the tumor suppressor TP53, as well as mechanisms expanding what is known about PCAT1 and ARLn1 and their targets BRCA2 and AR, respectively. For PCAT1-BRCA2, the mechanism involves competitive binding with HuR, which we confirmed using HuR immunoprecipitation assays. Availability and implementation:MechRNA is available for download at https://bitbucket.org/compbio/mechrna. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC6137976 | biostudies-literature
Project description:MOTIVATION:Long-range RNA-RNA interactions (LRIs) play an important role in viral replication, however, only a few of these interactions are known and only for a small number of viral species. Up to now, it has been impossible to screen a full viral genome for LRIs experimentally or in silico Most known LRIs are cross-reacting structures (pseudoknots) undetectable by most bioinformatical tools. RESULTS:We present LRIscan, a tool for the LRI prediction in full viral genomes based on a multiple genome alignment. We confirmed 14 out of 16 experimentally known and evolutionary conserved LRIs in genome alignments of HCV, Tombusviruses, Flaviviruses and HIV-1. We provide several promising new interactions, which include compensatory mutations and are highly conserved in all considered viral sequences. Furthermore, we provide reactivity plots highlighting the hot spots of predicted LRIs. AVAILABILITY AND IMPLEMENTATION:Source code and binaries of LRIscan freely available for download at http://www.rna.uni-jena.de/en/supplements/lriscan/, implemented in Ruby/C?++?and supported on Linux and Windows. CONTACT:manja@uni-jena.de SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
| S-EPMC7189868 | biostudies-literature
Project description:DIMA-the domain interaction map has evolved from a simple web server for domain phylogenetic profiling into an integrative prediction resource combining both experimental data on domain-domain interactions and predictions from two different algorithms. With this update, DIMA obtains greatly improved coverage at the level of genomes and domains as well as with respect to available prediction approaches. The domain phylogenetic profiling method now uses SIMAP as its backend for exhaustive domain hit coverage: 7038 Pfam domains were profiled over 460 completely sequenced genomes. Domain pair exclusion predictions were produced from 83 969 distinct protein-protein interactions obtained from IntAct resulting in 21 513 domain pairs with significant domain pair exclusion algorithm scores. Additional predictions applying the same algorithm to predicted protein interactions from STRING yielded 2378 high-confidence pairs. Experimental data comes from iPfam (3074) and 3did (3034 pairs), two databases identifying domain contacts in solved protein structures. Taken together, these two resources yielded 3653 distinct interacting domain pairs. DIMA is available at http://mips.gsf.de/genre/proj/dima.
| S-EPMC2238836 | biostudies-literature
Project description:Recent studies have revealed that large numbers of non-coding RNAs are transcribed in humans, but only a few of them have been identified with their functions. Identification of the interaction target RNAs of the non-coding RNAs is an important step in predicting their functions. The current experimental methods to identify RNA-RNA interactions, however, are not fast enough to apply to a whole human transcriptome. Therefore, computational predictions of RNA-RNA interactions are desirable, but this is a challenging task due to the huge computational costs involved.Here, we report comprehensive predictions of the interaction targets of lncRNAs in a whole human transcriptome for the first time. To achieve this, we developed an integrated pipeline for predicting RNA-RNA interactions on the K computer, which is one of the fastest super-computers in the world. Comparisons with experimentally-validated lncRNA-RNA interactions support the quality of the predictions. Additionally, we have developed a database that catalogs the predicted lncRNA-RNA interactions to provide fundamental information about the targets of lncRNAs.
| S-EPMC4895283 | biostudies-literature
Project description:Abstract Background Since the preponderance of pediatric gliomas are mutationally ‘bland,’ immune checkpoint inhibitors are unlikely to mediate therapeutic benefit. Alternately, immunologic response can be induced de novo against pediatric gliomas with mRNA cancer vaccines. Messenger RNA represents a paradigm shift in vaccinology (i.e. COVID-19) given its flexibility, commercialization, and propensity to confer rapid protection with only a single vaccine. Objective We sought to develop a new mRNA platform with an optimized backbone for insertion of both personalized and/or “off the shelf’ (i.e. H3K27M) transcripts for rapid induction of anti-tumor activity against pediatric gliomas. Approach We synthesized an mRNA backbone with optimized 5’ and 3’ UTRs for delivery of gene transcripts pertinent to pediatric brain tumors using a lipid-nanoparticle (NP) delivery vehicle. This vaccine utilizes a novel engineering design that layers tumor derived mRNA into a lipid-nanoparticle (NP) “onion-like” or multi-lamellar package. Results We demonstrate immunogenicity of RNA-NPs delivering either personalized glioma mRNA or H3K27M mRNA. RNA-NPs localize to myeloid cells in murine KR158b brain tumors and activate dendritic cells that supplant regulatory intratumoral myeloid populations inducing antigen-recall response with long-term survivor benefit. Our optimized mRNA backbone yielded significantly improved anti-tumor efficacy compared with commercial backbones. We have shown this approach can be refined for co-delivery of immunomodulatory RNAs (i.e. GM-CSF) and/or delivery of siRNAs targeting immunoregulatory axes (PD-L1) in murine brain tumors (GL261). We have since established safety of RNA-NPs in acute/chronic murine GLP toxicity studies without cross-reactivity to normal-brain, and launched a large-animal canine brain tumor trial which demonstrated RNA-NPs to be feasible, safe and immunologically active. Conclusion RNA-NPs reprogram the brain tumor microenvironment while inducing a glioma-specific immune response. We have since received FDA-IND approval for first-in-human trials (IND#BB-19304) in pediatric patients with high-grade gliomas (PNOC020 study, NCT04573140).
| S-EPMC8168117 | biostudies-literature