Stochastic Sampling of Structural Contexts Improves the Scalability and Accuracy of RNA 3D Module Identification

Sarrazin-Gendron, Roman; Yao, Hua-Ting; Reinharz, Vladimir; Oliver, Carlos G.; Ponty, Yann; Waldispühl, Jérôme

doi:10.1101/834762

Cited by 4 publications

(5 citation statements)

References 36 publications

(52 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The prediction of 3D modules has been shown to improve this class of methods by providing more informative fragments, namely in RNA-MoIP [ 7 ]. Further progress has since been made in this direction with recent improvements in RNA 3D modules identification in sequences [ 34 , 35 ].…”

Section: Resultsmentioning

confidence: 99%

Finding recurrent RNA structural networks with fast maximal common subgraphs of edge-colored graphs

et al. 2021

Self Cite

View full text Add to dashboard Cite

RNA tertiary structure is crucial to its many non-coding molecular functions. RNA architecture is shaped by its secondary structure composed of stems, stacked canonical base pairs, enclosing loops. While stems are precisely captured by free-energy models, loops composed of non-canonical base pairs are not. Nor are distant interactions linking together those secondary structure elements (SSEs). Databases of conserved 3D geometries (a.k.a. modules) not captured by energetic models are leveraged for structure prediction and design, but the computational complexity has limited their study to local elements, loops. Representing the RNA structure as a graph has recently allowed to expend this work to pairs of SSEs, uncovering a hierarchical organization of these 3D modules, at great computational cost. Systematically capturing recurrent patterns on a large scale is a main challenge in the study of RNA structures. In this paper, we present an efficient algorithm to compute maximal isomorphisms in edge colored graphs. We extend this algorithm to a framework well suited to identify RNA modules, and fast enough to considerably generalize previous approaches. To exhibit the versatility of our framework, we first reproduce results identifying all common modules spanning more than 2 SSEs, in a few hours instead of weeks. The efficiency of our new algorithm is demonstrated by computing the maximal modules between any pair of entire RNA in the non-redundant corpus of known RNA 3D structures. We observe that the biggest modules our method uncovers compose large shared sub-structure spanning hundreds of nucleotides and base pairs between the ribosomes of Thermus thermophilus, Escherichia Coli, and Pseudomonas aeruginosa.

show abstract

Section: Resultsmentioning

confidence: 99%

Finding recurrent RNA structural networks with fast maximal common subgraphs of edge-colored graphs

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…First, motifs can be inserted now based on a perfect sequence match. More advanced probabilistic techniques, as RMDetect [46], JAR3D [47] or BayesPairing [48], would allow to integrate a more rigorous term in the objective function, as match motifs with altered sequence, increasing diversity and therefore the range of predictable structure. Second, the database of motifs only incorporates loops (i.e., hairpin, interior loops, multi-loops).…”

Section: Discussionmentioning

confidence: 99%

Concurrent prediction of RNA secondary structures with pseudoknots and local 3D motifs in an Integer Programming framework

Loyer

Reinharz

2023

Preprint

Self Cite

View full text Add to dashboard Cite

The prediction of RNA structure canonical base pairs from a single sequence, especially pseudoknotted ones, remains challenging in a thermodynamic models that approximates the energy of the local 3D motifs joining canonical stems. It has become more and more apparent in recent years that the structural motifs in the loops, composed of non-canonical interactions, are essential for the final shape of the molecule enabling its multiple functions. Our capacity to predict accurate 3D structures is also limited when it comes to the organization of the large intricate network of interactions that form inside those loops. Results: We previously developed the integer programming framework ourprog (RNA Motifs over Integer Programming) to reconcile RNA secondary structure and local 3D motif information available in databases. We further develop our model to now simultaneously predict the canonical base pairs (with pseudoknots) from base pair probability matrices with or without alignment. We benchmarked our new method over the all non-redundant RNAs below 150 nucleotides. We show that the joined prediction of canonical base pairs structure and local conserved motifs (i) improves the ratio of well predicted interactions in the secondary structure, (ii) predicts well canonical and Wobble pairs at the location where motifs are inserted, (iii) is greatly improved with evolutionnary information and (iv) can recover kink-turn locations.

show abstract

“…Despite the fact that RNA molecules can adopt complex structures, dedicated graph representation learning techniques have been scarce, with some recent works beginning to leverage graph related learning techniques to predict RNA folding (Chen et al, 2020;Singh et al, 2019) and to represent RNA molecular structures (Yan et al, 2020;Oliver et al, 2020). Prior to our work, the design of RNA has mostly focused on the inverse design problem, which is to conditionally generate an RNA sequence whose MFE secondary structure corresponds to an input secondary structure.…”

Section: Related Workmentioning

confidence: 99%

Neural representation and generation for RNA secondary structures

Yan,

Hamilton,

Blanchette

2021

Preprint

View full text Add to dashboard Cite

Our work is concerned with the generation and targeted design of RNA, a type of genetic macromolecule that can adopt complex structures which influence their cellular activities and functions. The design of large scale and complex biological structures spurs dedicated graph-based deep generative modeling techniques, which represents a key but underappreciated aspect of computational drug discovery. In this work, we investigate the principles behind representing and generating different RNA structural modalities, and propose a flexible framework to jointly embed and generate these molecular structures along with their sequence in a meaningful latent space. Equipped with a deep understanding of RNA molecular structures, our most sophisticated encoding and decoding methods operate on the molecular graph as well as the junction tree hierarchy, integrating strong inductive bias about RNA structural regularity and folding mechanism such that high structural validity, stability and diversity of generated RNAs are achieved. Also, we seek to adequately organize the latent space of RNA molecular embeddings with regard to the interaction with proteins, and targeted optimization is used to navigate in this latent space to search for desired novel RNA molecules.

show abstract

Stochastic Sampling of Structural Contexts Improves the Scalability and Accuracy of RNA 3D Module Identification

Cited by 4 publications

References 36 publications

Finding recurrent RNA structural networks with fast maximal common subgraphs of edge-colored graphs

Finding recurrent RNA structural networks with fast maximal common subgraphs of edge-colored graphs

Concurrent prediction of RNA secondary structures with pseudoknots and local 3D motifs in an Integer Programming framework

Neural representation and generation for RNA secondary structures

Contact Info

Product

Resources

About