Biology subjects

Antczak, M.

Publications and source records attributed to Antczak, M..

9 recordsLinked to original sources

bioRxiv2026

RNAbridge: a database of extended and non-canonical helices

RNA function is dictated by 3D architecture. Although 2D structural models based on canonical Watson-Crick base pairs are widely used, they often fail to capture the non-canonical interactions, tertiary contacts, and coaxial stacking important for biological activity. We have developed RNAbridge, a comprehensive database and web application that systematically identifies, quantifies, and visualizes extended non-canonical helices and multi-way junctions. Using a geometry- and stacking-based pipeline, we analyzed the Protein Data Bank and compiled a catalog of 135,541 structural motifs. RNAbridge includes a user-friendly interface with interactive filters, synchronized 2D and 3D visualizations, and detailed structural data, including helical bend angles and stacking paths. By connecting simplified 2D topologies with complex 3D structures, RNAbridge serves as a valuable resource for structural biologists and lays the foundation for future machine learning applications in RNA structure prediction. The database, available at https://rnabridge.cs.put.poznan.pl/, is automatically updated once a week.

bioinformatics↗

bioRxiv2025

RNApdbee 3.0: A unified web server for comprehensive RNA secondary structure annotation from 3D coordinates

RNApdbee 3.0 (publicly available at https://rnapdbee.cs.put.poznan.pl/) offers an advanced pipeline for comprehensive RNA structural annotation, integrating 2D and 3D data to build detailed nucleotide interaction networks. It classifies base pairs as canonical or noncanonical using the Leontis-Westhof and Saenger schemes and identifies stacking, base-ribose, base-phosphate, and base-triple interactions. The tool handles incomplete or modified residues, marking missing nucleotides and distinguishing noncanonical base pairs for accurate and effective visualization. Results are provided in standard formats - namely, extended dot-bracket notation, BPSEQ, and CT - and in detailed graphical visualizations. RNApdbee decomposes 2D structures into stems, loops, and single strands and offers flexible pseudoknot encoding. Its unified framework addresses inconsistencies across structural data formats by standardizing all inputs to PDBx/mmCIF and integrating seven widely used annotation tools. Finally, RNApdbee ensures reliable, format-independent, and comprehensive RNA structural annotation and interpretation.

bioinformatics↗

bioRxiv2025

RNAtive to recognize native-like structure in a set of RNA 3D models

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSMotivationC_ST_ABSMost widely used methods for evaluating RNA 3D structure models require experimental reference structures, which restricts their use for novel RNAs. They also often overlook recurrent structural features shared across multiple predictions of the same sequence. Although consensus approaches have proven effective in RNA sequence analysis and evolutionary studies, no existing tool applies these principles to evaluate ensembles of 3D models. This gap hampers the identification of native-like folds in computational predictions, particularly as AI-driven methods become increasingly prevalent. ResultsThis paper presents RNAtive, the first computational tool to apply consensus-derived secondary structures for reference-free evaluation of RNA 3D models. RNAtive aggregates recurrent base-pairing and stacking interactions across ensembles of predicted 3D structures to construct a consensus secondary structure. It introduces a novel conditionally weighted consensus mode that treats interaction networks as fuzzy sets and uniquely allows integration of user-defined 2D structural constraints, enabling evaluation guided by experimental data. Input RNA models are ranked using two adapted binary-classification-based scores. Benchmarking against CASP15 competition data shows that models consistent with the consensus exhibit native-like structural features. The RNAtive web server offers an intuitive platform for comparing and prioritizing RNA 3D predictions, providing a scalable solution to address the variability inherent in deep learning and fragment-assembly methods. By bridging consensus principles with 3D structural analysis, RNAtive advances the exploration of RNA conformational landscapes and has potential applications in fields like therapeutic RNA design. AvailabilityRNAtive is a freely accessible web server with a modern, user-friendly interface, available for scientific, educational, and commercial use at https://rnative.cs.put.poznan.pl/. Contacttomasz.zok@cs.put.poznan.pl

bioinformatics↗

bioRxiv2024

Covalent inhibition of the SARS-CoV-2 NiRAN domain via an active-site cysteine

The kinase-like NiRAN domain of nsp12 in SARS-CoV-2 catalyzes the formation of the 5 RNA cap structure. This activity is required for viral replication, offering a new target for the development of antivirals. Here, we develop a high-throughput assay to screen for small molecule inhibitors targeting the SARS-CoV-2 NiRAN domain. We identified NCI-2, a compound with a reactive chloromethyl group that covalently binds to an active site cysteine (Cys53) in the NiRAN domain, inhibiting its activity. NCI-2 can enter cells, bind to, and inactivate ectopically expressed nsp12. A cryo-EM reconstruction of the SARS-CoV-2 replication-transcription complex (RTC) bound to NCI-2 offers a detailed structural blueprint for rational drug design. Although NCI-2 showed limited potency against SARS-CoV-2 replication in cells, our work lays the groundwork for developing more potent and selective inhibitors targeting the NiRAN domain. This approach presents a promising therapeutic strategy for effectively combating COVID-19 and potentially mitigating future coronavirus outbreaks.

biochemistry↗

bioRxiv2024

Knotted artifacts in predicted 3D RNA structures

Unlike proteins, RNAs deposited in the Protein Data Bank do not contain topological knots. Recently, admittedly, the first trefoil knot and some lasso-type conformations have been found in experimental RNA structures, but these are still exceptional cases. Meanwhile, algorithms predicting 3D RNA models have happened to form knotted structures not so rarely. Interestingly, machine learning-based predictors seem to be more prone to generate knotted RNA folds than traditional methods. A similar situation is observed for the entanglements of structural elements. In this paper, we analyze all models submitted to the CASP15 competition in the 3D RNA structure prediction category. We show what types of topological knots and structure element entanglements appear in the submitted models and highlight what methods are behind the generation of such conformations. We also study the structural aspect of susceptibility to entanglement. We suggest that predictors take care of an evaluation of RNA models to avoid publishing structures with artifacts, such as unusual entanglements, that result from hallucinations of predictive algorithms. Author summaryO_LI3D RNA structure prediction contests such as CASP and RNA-Puzzles lack measures for topology-wise evaluation of predicted models. Thus, predictors happen to submit potentially inappropriate conformations, for example, containing entanglements that are prediction artifacts. C_LIO_LIAutomated identification of entanglements in 3D RNA structures is computationally hard. Distinguishing correct from incorrectly entangled conformations is not trivial and often requires expert knowledge. C_LIO_LIWe analyzed 3D RNA models submitted to CASP15 and found that all entanglements in these models are artifacts. C_LIO_LICompared to non-ML, machine learning-based methods are more prone to generating entanglements that are not present in natural RNAs. C_LIO_LITo increase the reliability of 3D RNA structure prediction, it is necessary to reject abnormally entangled structures in the modeling stage. C_LI

bioinformatics↗

bioRxiv2024

Resistance patterns in drug-adapted cancer cell lines reflect complex evolution in clinical tumors

BackgroundHere, we introduce a novel set of triple-negative breast cancer (TNBC) cell lines consisting of MDA-MB-468, HCC38, and HCC1806 and their sublines adapted to cisplatin, doxorubicin, eribulin, paclitaxel, gemcitabine, or 5-fluorouracil. MethodsThe cell lines were characterized by whole exome sequencing and the determination of drug-response profiles. Moreover, genes harbouring resistance-associated mutations were investigated using TCGA data for potential clinical relevance. ResultSequencing combined with TCGA-derived patient data resulted in the identification of 682 biomarker candidates in the pan-cancer analysis. Thirty-five genes were considered the most promising candidates because they harboured resistance-associated variants in at least two resistant sublines, and their expression correlated with TNBC patient survival. Exome sequencing and response profiles to cytotoxic drugs and DNA damage response inhibitors identified revealed remarkably little overlap between the resistant sublines, suggesting that each resistance formation process follows a unique route. All of the drug-resistant TNBC sublines remained sensitive or even displayed collateral sensitivity to a range of tested compounds. Cross-resistance levels were lowest for the CHK2 inhibitor CCT241533, the PLK1 inhibitor SBE13, and the RAD51 recombinase inhibitor B02, suggesting that CHK2, PLK1, and RAD51 are potential drug targets for therapy-refractory TNBC. ConclusionsWe present novel preclinical models of acquired drug resistance in TNBC and many novel candidate biomarkers for further investigation. The finding that each cancer cell line adaptation process follows an unpredictable route reflects recent findings on cancer cell evolution in patients, supporting the relevance of drug-adapted cancer cell lines as preclinical models of acquired resistance.

cancer biology↗

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

Evaluation of the stereochemical quality of predicted RNA 3D models in the RNA-Puzzles submissions

In silico prediction is a well-established approach to derive a general shape of an RNA molecule based on its sequence or secondary structure. This paper reports an analysis of the stereochemical quality of the RNA three-dimensional models predicted using dedicated computer programs. The stereochemistry of 1,052 RNA 3D structures, including 1,030 models predicted by fully automated and human-guided approaches within 22 RNA-Puzzles challenges and reference structures, is analysed. The evaluation is based on standards of RNA stereochemistry that the Protein Data Bank requires from deposited experimental structures. Deviations from standard bond lengths and angles, planarity or chirality are quantified. A reduction in the number of such deviations should help in the improvement of RNA 3D structure modelling approaches.

bioinformatics↗

bioRxiv2018

Investigating the unknown functions in the minimal bacterial genome reveals many transporter proteins

The recent identification of the minimal bacterial genome revealed that nearly one third (149) of the 473 encoded genes were of unknown function, demonstrating our limited understanding of the essential functions of life. Application of state of the art in silico methods for functional annotation demonstrated that these proteins of unknown function lack orthologs, known protein domains, and templates to model their structure. Combination of the results from different complementary approaches enabled functions to be assigned to 94 of the 149 proteins, although often with general terms such as transporter or DNA binding. 22 likely transporter proteins were identified indicating the importance of nutrient uptake into and waste disposal out of the minimal bacterial cell, where many metabolic enzymes have been removed. These results advance our understanding of the minimal bacterial genome and therefore aid synthetic biology and its application to biotechnology.

synthetic biology↗