Biology subjects

Baulin, E. F.

Publications and source records attributed to Baulin, E. F..

9 recordsLinked to original sources

bioRxiv2026

Revisiting the base pair maximization approach for RNA secondary structure prediction with SQUARNA

Non-coding RNAs play diverse roles in a wide range of cellular processes, with their spatial structure being pivotal to their function. RNA secondary structure is a key determinant of its overall fold. Given the scarcity of experimentally determined RNA 3D structures, understanding secondary structure is vital for discerning RNA function. Currently, there is no universally effective solution for de novo RNA secondary structure prediction. Existing methods are becoming increasingly complex without marked improvements in accuracy and often overlook critical features such as pseudoknots and alternative folds. Here, we introduce SQUARNA, a new approach to de novo RNA secondary structure prediction that is suitable for both individual RNA analysis and large-scale structural searches. SQUARNA revisits the concept of base pair maximization and develops it into a stem maximization idea coupled with the widely used free energy minimization (MFE) framework. SQUARNA can predict alternative structures and handle pseudoknots of arbitrary complexity. Benchmarking shows that SQUARNA outperforms existing methods, including deep learning models, in both single-sequence and alignment-based RNA secondary structure prediction. SQUARNA seamlessly integrates sequence and alignment information with experimental data, such as residue reactivities obtained by chemical probing, as well as other structural restraints, including automated searches for Rfam database templates, G-quadruplex patterns, and protein-binding motifs. SQUARNA is available as a standalone tool at https://github.com/febos/SQUARNA and as a web server at https://larnal.imol.institute.

bioinformatics↗

bioRxiv2025

Blind Prediction of Complex Water and Ion Ensembles Around RNA in CASP16

Biomolecules rely on water and ions for stable folding, but these interactions are often transient, dynamic, or disordered and thus hidden from experiments and evaluation challenges that represent biomolecules as single, ordered structures. Here, we compare blindly predicted ensembles of water and ion structure to the cryo-EM densities observed around the Tetrahymena ribozyme at 2.2-2.3 [A] resolution, collected through target R1260 in the CASP16 competition. 26 groups participated in this solvation cryo-ensemble prediction challenge, submitting over 350 million atoms in total, offering the first opportunity to compare blind predictions of dynamic solvent shell ensembles to cryo-EM density. Predicted atomic ensembles were converted to density through local alignment and these densities were compared to the cryo-EM densities using Pearson correlation, Spearman correlation, mutual information, and precision-recall curves. These predictions show that an ensemble representation is able to capture information of transient or dynamic water and ions better than traditional atomic models, but there remains a large accuracy gap to the performance ceiling set by experimental uncertainty. Overall, molecular dynamics approaches best matched the cryo-EM density, with blind predictions from bussilab_plain_md, SoutheRNA, bussilab_replex, coogs2, and coogs3 outperforming the baseline molecular dynamics prediction. This study indicates that simulations of water and ions can be quantitatively evaluated with cryo-EM maps. We propose that further community-wide blind challenges can drive and evaluate progress in modeling water, ions and other previously hidden components of biomolecular systems.

biophysics↗

bioRxiv2025

Exploring GNRA tetraloop-like motifs in nucleic acid 3D structures

Structured nucleic acids play key roles in a variety of molecular mechanisms and have numerous applications in medicine and biotechnology. Stem-loops with specific tetraloop sequences, such as UNCG and GNRA, are among the most well-studied recurrent building blocks of structured RNAs, known to adopt very stable structures. The GNRA tetraloop motif frequently engages in long-range interactions with specific RNA receptors and can also be recognized by proteins. While several examples of GNRA-like conformations have been reported in non-tetraloop strands, no systematic survey has been conducted to explore the landscape of these variants. In this work, we report a comprehensive survey of GNRA tetraloop-like motifs within known 3D structures of nucleic acids, without imposing restraints on sequence, loop type, backbone topology, or interactions involved. We identified twelve recurrent backbone topology variants of the motif, including all five possible two-strand variants and six three-strand variants. Among the pentaloop variants, we observed four different locations of the looped-out residues, with three of them directly interacting with proteins. These GNRA-like motifs highlight the versatility of favorable RNA 3D conformations to fit in diverse backbone contexts and modulate intermolecular interactions, which can be leveraged in RNA motif design.

bioinformatics↗

bioRxiv2025

ClaPNAC: a Classifier of Protein - Nucleic Acid Contacts

Nucleic acid-protein interactions are fundamental to many cellular processes, including genome stability and replication, and the regulation of gene expression. They rely on the formation of specific contacts between amino acid and nucleotide residues. Accurate detection and classification of these inter-residue contacts in both experimentally determined structures and computationally predicted models of nucleic acid-protein complexes are crucial for achieving a comprehensive, molecular-level understanding of nucleic acid-protein recognition mechanisms. Here, we present ClaPNAC, a classifier that annotates 3D structures of nucleic acid-protein complexes with pairwise contacts. It distinguishes stacking interactions (involving the faces of nucleobases), pseudo pairs (involving the Watson-Crick, Hoogsteen, or sugar edge of the base), and phosphate and ribose interactions on the nucleoside/nucleotide side, as well as side chain and backbone interactions on the amino acid side. ClaPNAC is based on a geometric approach, extending our previous method ClaRNA and uses a database of pre-classified doublets extracted from experimentally solved RNA-protein complex structures. Unlike other tools that focus on specific chemical interactions such as hydrogen bonds or van der Waals contacts, ClaPNAC relies on the geometry of the interacting residues. It is applicable to both RNA- and DNA-protein complexes, and it can also operate on isolated nucleosides/nucleotides and amino acids. It can be extended to support additional interaction types and higher-order spatial relationships. ClaPNAC constitutes a notable advancement in the characterization of amino acid-nucleotide interactions by providing a comprehensive classification framework that deepens our insight into these fundamental biological processes. Its applicability to 3D structure prediction and validation highlights its value as a resource for researchers in structural biology.

molecular biology↗

bioRxiv2024

ARTEM: a method for RNA tertiary motif identification with backbone permutations, and its example application to kink-turn-like motifs

The functions of non-coding RNAs are largely defined by their three-dimensional structures. RNA 3D structure is organized hierarchically and consists of recurrent building blocks called tertiary motifs. The computational problem of RNA tertiary motif search remains largely unsolved, as standard approaches are restrained by sequence, interaction network, or backbone topology. We developed the ARTEM superposition algorithm, which is free from these limitations. Here, we present a version of ARTEM that allows automated searches of RNA and DNA structure databases to identify 3D structure motifs. We exemplify it by a search of motifs isosteric to the kink-turn motif. This widespread motif plays a role in many aspects of RNA function, and its mutations are known to cause several human syndromes. With ARTEM, we discovered two new kink-turn topologies, multiple no-kink variants of the motif, and showed that a ribosomal junction in bacteria forms either a kink or a no-kink variant depending on the species. Additionally, we identified kink-turns in the catalytic core of group II introns, whose structures have not previously been characterized as containing kink-turns. ARTEM opens a fundamentally new way to study RNA and DNA 3D folds and motifs and analyze their correlations and variations.

bioinformatics↗

bioRxiv2024

ARTEMIS - a method for topology-independent superposition of RNA 3D structures and structure-based sequence alignment

Non-coding RNAs play a major role in diverse processes in living cells with their sequence and spatial structure serving as the principal determinants of their function. Superposition of RNA 3D structures is the most accurate method for comparative analysis of RNA molecules and for inferring sequence alignments. Topology-independent superposition is particularly relevant, as evidenced by structurally similar RNAs with sequence permutations such as tRNA and Y RNA. To date, state-of-the-art methods for RNA 3D structure superposition rely on intricate heuristics, and the potential for topology-independent superposition has not been exhausted. Recently, we introduced the ARTEM method for unrestrained pairwise superposition of RNA 3D modules and now we developed it further to solve the global RNA 3D structure alignment problem. Our new tool ARTEMIS significantly outperforms state-of-the-art tools in both sequentially-ordered and topology-independent RNA 3D structure superposition. Using ARTEMIS we discovered a helical packing motif to be preserved within different backbone topology contexts across various non-coding RNAs, including multiple ribozymes and riboswitches. We anticipate that ARTEMIS will be essential for elucidating the landscape of RNA 3D folds and motifs featuring sequence permutations that thus far remained unexplored due to limitations in previous computational approaches.

bioinformatics↗

bioRxiv2023

Statistical framework for calling allelic imbalance in high-throughput sequencing data

High-throughput sequencing facilitates large-scale studies of gene regulation and allows tracing the associations of individual genomic variants with changes in gene expression. Compared to classic association studies, allelic imbalance at heterozygous variants captures the functional effects of the regulatory genome variation with smaller sample sizes and higher sensitivity. Yet, the identification of allele-specific events from allelic read counts remains non-trivial due to multiple sources of technical and biological variability, which induce data-dependent biases and overdispersion. Here we present MIXALIME, a novel computational framework for calling allele-specific events in diverse omics data with a repertoire of statistical models accounting for read mapping bias and copy-number variation. We benchmark MIXALIME against existing tools and demonstrate its practical usage by constructing an atlas of allele-specific chromatin accessibility, UDACHA, from thousands of available datasets obtained from diverse cell types. Availabilityhttps://github.com/autosome-ru/MixALime, https://udacha.autosome.org

bioinformatics↗

bioRxiv2023

SQUARNA - an RNA secondary structure prediction method based on a greedy stem formation model

Non-coding RNAs play a diverse range of roles in various cellular processes, with their spatial structure being pivotal to their function. The RNAs secondary structure is a key determinant of its overall fold. Given the scarcity of experimentally determined RNA 3D structures, understanding the secondary structure is vital for discerning the molecules function. Currently, there is no universally effective solution for de novo RNA secondary structure prediction. Existing methods are becoming increasingly complex without marked improvements in accuracy, and they often overlook critical elements such as pseudoknots. In this work, we introduce SQUARNA, a novel approach to de novo RNA secondary structure prediction. This method utilizes a simple, greedy stem formation model, addressing many of the limitations inherent in previous tools. Our benchmarks demonstrate that SQUARNA matches the performance of leading methods for single sequence inputs and significantly surpasses existing tools when applied to sequence alignment inputs.

bioinformatics↗

bioRxiv2022

A comprehensive survey of long-range tertiary interactions and motifs in non-coding RNA structures

Understanding the 3D structure of RNA is key to understanding RNA function. RNA 3D structure is modular and can be seen as a composition of building blocks of various sizes called tertiary motifs. Currently, long-range motifs formed between distant loops and helical regions are largely less studied than the local motifs determined by the RNA secondary structure. We surveyed long-range tertiary interactions and motifs in a non-redundant set of non-coding RNA 3D structures. A new dataset of annotated LOng-RAnge RNA 3D modules (LORA) was built using an approach that does not rely on the automatic annotations of non-canonical interactions. An original algorithm, ARTEM, was developed for annotation-, sequence- and topology-independent superposition of two arbitrary RNA 3D modules. The proposed methods allowed us to identify and describe the most common long-range RNA tertiary motifs. Three basic interaction types were identified to be recurrent in the long-range RNA 3D modules: ribose-ribose interactions, canonical Type I and Type II A-minor interactions, and previously undescribed staple interactions. These three interaction types were found to be different building blocks of the same complex staple motifs common to non-coding RNA 3D structures.

bioinformatics↗