Biology subjects

Bujnicki, J. M.

Publications and source records attributed to Bujnicki, J. M..

At least 19 recordsLinked to original sources

bioRxiv2026

Argonaute 1 associates with nucleolar snoRNP machinery and regulates pre-rRNA levels and cell growth

Argonaute proteins are best known as cytoplasmic effectors of small-RNA-mediated gene silencing, although their compartment-specific functions remain incompletely understood. Here, we identify a previously unrecognized nucleolar role for Drosophila Argonaute 1 (AGO1). We observed stress-induced redistribution of AGO1 into cytoplasmic and nuclear ribonucleoprotein assemblies, whereas a distinct AGO1 pool consistently localized to Fibrillarin-positive nucleoli. Co-immunoprecipitation further showed that AGO1 associates with Fibrillarin-containing complexes in vivo. Re-analysis of AGO1-associated small-RNA sequencing data revealed snoRNAs, particularly C/D box snoRNAs, among AGO1-bound RNA species and increased snoRNA representation following AGO1 overexpression. Structural modelling demonstrated that simultaneous molecular interaction of AGO1 and the Fibrillarin-containing snoRNP machinery with the same snoRNA is stereochemically feasible, providing a structural framework for their potential association within a nucleolar snoRNP assembly. Functionally, AGO1 depletion reduced pre-rRNA levels and decreased both nucleolar and cell size in the follicular epithelium. Depletion of Dicer-1 did not reproduce these growth defects, arguing against a general consequence of impaired miRNA biogenesis. Together, these findings identify AGO1 as a nucleolar regulator that associates with snoRNP machinery and links small-RNA biology to pre-rRNA homeostasis and cell growth.

cell biology↗

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

RNAcentral in 2026: Genes and literature integration

RNAcentral was founded in 2014 to serve as a comprehensive database of non-coding RNA sequences. It began by providing a single unified interface to more specialised resources, and now contains 45 million sequences. It has grown beyond providing a single interface to many specialised resources and now provides several services and analyses. These include secondary structure prediction with R2DT, sequence search, and analysis with Rfam. Since its last publication in 2021, RNAcentral has developed two major features. First, literature integration with the development of LitScan and LitSumm. LitScan automatically identifies and links relevant publications to RNA entries, while LitSumm uses natural language processing to generate functional summaries from the literature. Together, these tools address the critical challenge of connecting sequence data with scattered functional knowledge across thousands of publications. Secondly, RNAcentral has created gene level entries. Gene level entries represent a large structural change to RNAcentral. While RNAcentral previously organized data exclusively at the sequence level, we now group related transcripts into gene-centric views. This allows researchers to explore all isoforms, splice variants, and related sequences for a gene in a unified interface, better reflecting biological organization and facilitating comparative analyses. RNAcentral is freely available at: https://rnacentral.org.

bioinformatics↗

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

The three YTHDF paralogs and VIRMA are the major tumor drivers among the m6A core genes in a pan-cancer analysis

N6-methyladenosine (m6A) is the most abundant internal modification in mRNAs. Despite accumulating evidence for the profound impact of m6A on cancer biology, there are conflicting reports that alterations in genes encoding the m6A machinery proteins can either promote or suppress cancer, even in the same tumor type. Using data from The Cancer Genome Atlas, we performed a pan-cancer investigation of 15 m6A core factors in nearly 10,000 samples from 31 tumor types to reveal underlying cross-tumor patterns. Altered expression, largely driven by copy number variations at the chromosome arm level, results in the most common mode of dysregulation of these factors. YTHDF1, YTHDF2, YTHDF3, and VIRMA are the most frequently altered factors and the only ones to be uniquely altered when tumors are grouped according to the expression pattern of the m6A factors. These genes are also the only ones with coherent, pan-cancer predictive power for progression-free survival. On the contrary, METTL3, the most intensively studied m6A factor as a cancer target, shows much lower levels of alteration and no predictive power for patient survival. Therefore, we propose the non-enzymatic YTHDF and VIRMA genes as preferred subjects to dissect the role of m6A in cancer and as priority cancer targets. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC="FIGDIR/small/598899v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1e39eeborg.highwire.dtl.DTLVardef@d6628dorg.highwire.dtl.DTLVardef@e048a6org.highwire.dtl.DTLVardef@c19812_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer 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

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↗

bioRxiv2023

DesiRNA: structure-based design of RNA sequences with a Monte Carlo approach

RNA sequences underpin the formation of complex and diverse structures, subsequently governing their respective functional properties. Despite the pivotal role RNA sequences play in cellular mechanisms, creating optimized sequences that can predictably fold into desired structures remains a significant challenge. We have developed DesiRNA, a versatile Python-based software tool for RNA sequence design. This program considers a comprehensive array of constraints, ranging from secondary structures (including pseudoknots) and GC content, to the distribution of dinucleotides emulating natural RNAs. Additionally, it factors in the presence or absence of specific sequence motifs and prevents or promotes oligomerization, thereby ensuring a robust and flexible design process. DesiRNA utilizes the Monte Carlo algorithm for the selection and acceptance of mutation sites. In tests on the EteRNA benchmark, DesiRNA displayed high accuracy and computational efficiency, outperforming most existing RNA design programs.

bioinformatics↗

bioRxiv2023

Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA

Ribonucleic acids (RNA) play crucial roles in living organisms as they are involved in key processes necessary for proper cell functioning. Some RNA molecules, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, while others, e.g., bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA--small molecule interactions. We recently developed fingeRNAt - a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions, such as hydrogen and halogen bonds, ionic, Pi, inorganic ion-and water-mediated, lipophilic interactions, and encodes them as computational-friendly Structural Interaction Fingerprint (SIFt). Here we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA targets. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We discuss the aid offered by Explainable Artificial Intelligence in the analysis of the binding prediction models, elucidating the decision-making process, and deciphering molecular recognition processes. Key PointsO_LIStructural Interaction fingerprints (SIFts), combined with machine learning, were successfully used to develop activity models for ligands binding to RNA. C_LIO_LISIFt-based models outperformed the classic, general-purpose scoring functions in virtual screening. C_LIO_LIExplainable Artificial Intelligence allowed us to understand the decision-making process and decipher molecular recognition processes in the analysis of RNA--ligand binding activity models. C_LIO_LIWe provide a benchmark dataset based on ligands with known or putative binding activity toward six RNA targets. It can be readily used by the scientific community to test new algorithms of virtual screening on RNA--ligand complexes. C_LI

bioinformatics↗

bioRxiv2022

Structural basis of sRNA RsmZ regulation of Pseudomonas aeruginosa virulence

Pseudomonas aeruginosa is an opportunistic human pathogen that poses threats to hospitalized immunocompromised patients 1. A non-coding small RNA (sRNA) from the repressor of secondary metabolites (Rsm) system, RsmZ, sequesters the global repressor protein RsmA to regulate downstream gene expressions that reprogram virulence repertoires associated with acute and chronic P. aeruginosa infections 2,3. Molecular insights into the full-length RsmZ architecture remain elusive, leading to the lack of understanding of RsmZ binding to RsmA and subsequent modulations of gene expressions. Here we use cryo-electron microscopy (cryo-EM) to resolve structures of the full-length RsmZ in complexes with RsmA, in which five stem-loops (SLs) and one single-stranded junction carrying the GGA binding sites in RsmZ form three pairs of clamps, each binding to a RsmA homodimer. Disruptions of the base-pairings in all stems of RsmZ significantly reduced the binding affinity to RsmA by 17-fold, which resulted in enhanced RsmA downregulation of gene expressions and phenotypes associated to both acute and chronic virulence of P. aeruginosa. Double mutations that rescued these stems of RsmZ restored the binding to RsmA by more than 5-fold, and recovered the corresponding phenotypes. Our results reveal the molecular mechanism of RsmZ regulation of P. aeruginosa virulence and suggest RsmZ as a potential target for the development of new antimicrobial agents.

microbiology↗

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↗

bioRxiv2022

DEGRONOPEDIA - a web server for proteome-wide inspection of degrons

The ubiquitin-proteasome system is a proteolytic pathway that removes damaged and unwanted proteins. Their selective turnover is initiated by ubiquitin (Ub) attachment, mainly by Ub ligases that recognize substrates through their short linear motifs termed degrons. A degradation-targeting degron comprises a nearby Ub-modified residue and an intrinsically disordered region (IDR) involved in interaction with the proteasome. Degron-signaling has been studied over the last decades, yet there are no resources for systematic screening of degron sites to facilitate studies on their biological significance, such as targeted protein degradation approaches. To bridge this gap, we developed DEGRONOPEDIA, a web server that allows exploration of degron motifs in the proteomes of seven model organisms and maps these data to Lys, Cys, Thr, and Ser residues that can undergo ubiquitination and to IDRs proximal to them, both in sequence and structure. The server also reports the post-translational modifications and pathogenic mutations within the degron and its flanking regions, as these can modulate the degrons accessibility. Degrons often occur at the amino or carboxyl end of a protein substrate, acting as initiators of the N-/C-degron pathway, respectively. Therefore, since they may appear following the protease cleavage, DEGRONOPEDIA simulate sequence nicking based on experimental data and theoretical predictions and screen for emerging degron motifs. Moreover, we implemented machine learning to predict the stability of the N-/C-termini, facilitating the identification of substrates of the N-/C-degron pathways. We are confident that our tool will stimulate research on degron-signaling providing output information in a ready-to-validate context. DEGRONOPEDIA can be freely accessed at degronopedia.com.

bioinformatics↗

bioRxiv2021

fingeRNAt - a novel tool for high-throughput analysis of nucleic acid-ligand interactions

Computational methods play a pivotal role in drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the last decades, almost all the attention in medicinal chemistry has been directed to protein-ligand binding, and computational tools have been created with this target in mind. With novel discoveries of functional RNAs and their possible applications, RNAs have gained considerable attention as potential drug targets. However, the availability of bioinformatics tools for nucleic acids is limited. Here, we introduce fingeRNAt - a software tool for detecting non-covalent interactions formed in complexes of nucleic acids with ligands. The program detects nine types of interactions: (i) hydrogen and (ii) halogen bonds, (iii) cation-anion, (iv) pi-cation, (v) pi-anion, (vi) pi-stacking, (vii) inorganic ion-mediated, (viii) water-mediated, and (ix) lipophilic interactions. However, the scope of detected interactions can be easily expanded using a simple plugin system. In addition, detected interactions can be visualized using the associated PyMOL plugin, which facilitates the analysis of medium-throughput molecular complexes. Interactions are also encoded and stored as a bioinformatics-friendly Structural Interaction Fingerprint (SIFt) - a binary string where the respective bit in the fingerprint is set to 1 if a particular interaction is present and to 0 otherwise. This output format, in turn, enables high-throughput analysis of interaction data using data analysis techniques. We present applications of fingeRNAt-generated interaction fingerprints for visual and computational analysis of RNA-ligand complexes, including analysis of interactions formed in experimentally determined RNA-small molecule ligand complexes deposited in the Protein Data Bank. We propose interaction-based similarity based on fingerprints as an alternative measure to RMSD to recapitulate complexes with similar interactions but different folding. We present an application of molecular fingerprints for the clustering of molecular complexes. This approach can be used to group ligands that form similar binding networks and thus have similar biological properties. AUTHOR SUMMARYWe present a novel bioinformatic tool, fingeRNAt, aiming to support scientists in the analysis of complexes of nucleic acids with various types of ligands. The software automatically detects non-covalent interactions and presents them in a form that is understandable to both humans and computers. Such data can help decipher the nature of interactions between nucleic acids and ligands and determine the main factors responsible for forming such complexes in nature. fingeRNAt finds application in multiple studies, both structure- and drug discovery-oriented. Here, we analyzed the experimentally solved structures of RNA complexes with small molecules to determine which binding features are most prevalent, i.e., most common interactions or their hot spots. The results of this analysis may help elucidate the mechanisms of binding and design new active molecules. Moreover, we propose to use the data generated by our software as a new metric for the quantitative comparison of two molecule complexes. We have shown that it is more reliable than the currently used methods in certain "difficult" cases. We have shown that the results of our program can be used for high-throughput analysis of molecular complexes and the search for active molecules. We are confident that fingeRNAt will be a valuable tool for exploring the complex world of interactions of nucleic acids with ligands.

bioinformatics↗

bioRxiv2021

Structure prediction of the druggable fragments in SARS-CoV-2 untranslated regions

The outbreak of the COVID-19 pandemic has led to intensive studies of both the structure and replication mechanism of SARS-CoV-2. In spite of some secondary structure experiments being carried out, the 3D structure of the key function regions of the viral RNA has not yet been well understood. At the beginning of COVID-19 breakout, RNA-Puzzles community attempted to envisage the three-dimensional structure of 5'- and 3'-Un-Translated Regions (UTRs) of the SARS-CoV-2 genome. Here, we report the results of this prediction challenge, presenting the methodologies developed by six participating groups and discussing 100 RNA 3D models (60 models of 5'-UTR and 40 of 3'-UTR) predicted through applying both human experts and automated server approaches. We describe the original protocol for the reference-free comparative analysis of RNA 3D structures designed especially for this challenge. We elaborate on the deduced consensus structure and the reliability of the predicted structural motifs. All the computationally simulated models, as well as the development and the testing of computational tools dedicated to 3D structure analysis, are available for further study.

bioinformatics↗

bioRxiv2021

Molecular insights into RNA recognition and gene regulation by the TRIM-NHL protein Mei-P26

The TRIM-NHL protein Meiotic P26 (Mei-P26) acts as a regulator of cell fate in Drosophila. Its activity is critical for ovarian germline stem cell maintenance, differentiation of oocytes and spermatogenesis. Mei-P26 functions as a post-transcriptional regulator of gene expression, however, the molecular details of how its NHL domain selectively recognizes and regulates its mRNA targets have remained elusive. Here, we present the crystal structure of the Mei-P26 NHL domain at 1.6 [A] resolution and identify key amino acids that confer substrate specificity and distinguish Mei-P26 from closely related TRIM-NHL proteins. Furthermore, we identify mRNA targets of Mei-P26 in cultured Drosophila cells and show that Mei-P26 can act as either a repressor or activator of gene expression on different RNA targets. Our work reveals the molecular basis of RNA recognition by Mei-P26 and the fundamental functional differences between otherwise very similar TRIM-NHL proteins.

biochemistry↗