Biology subjects

Iwakiri, J.

Publications and source records attributed to Iwakiri, J..

10 recordsLinked to original sources

bioRxiv2026

PKProbDesign: RNA inverse folding including pseudoknots by optimizing thermodynamic folding probability

Motivation: RNA inverse folding, the design of RNA sequences that fold into specified target secondary structures, is a central problem in RNA design, with applications in functional RNA engineering, synthetic biology, and nucleic-acid therapeutics. This task becomes especially challenging for pseudoknotted target structures because pseudoknots break the nested structure assumed by standard thermodynamic folding models. Existing pseudoknot inverse-folding methods often rely on structure-predictor-based objectives. These methods do not directly optimize the probability that a sequence folds into the specified pseudoknotted target structure. Such optimization requires an evaluator that can assign a folding probability to the specified target within a pseudoknot-aware ensemble. Results: We present PKProbDesign, a sampling-based inverse-folding framework that directly optimizes a thermodynamic folding-probability objective for pseudoknotted targets. For each target, sampled sequences are scored by combining the folding probability of a pseudoknot-free scaffold with the conditional folding probability of the remaining extension component. On 254 unique density-2 PseudoBase++ targets, PKProbDesign achieved the highest pseudo-joint folding probability on 245 targets, compared with 6 for DesiRNA, 3 for MODENA, and none for antaRNA. Conclusions: PKProbDesign demonstrates that pseudoknot inverse folding can be formulated with target folding probability as its objective rather than structure-prediction agreement alone. By combining scaffold decomposition with conditional folding-probability evaluation based on CParty, the method provides a practical folding-probability-based approach to designing sequences for density-2 pseudoknotted targets. Availability: The source code of PKProbDesign is available at https://github.com/TakumiOtagaki/PKProbDesign.

bioinformatics↗

bioRxiv2026

Pathway-Centric Integration of CRISPR Fitness with Molecular Features Draws Cancer State Maps

Cancer cells display heterogeneous pathway activity that shapes therapeutic vulnerability, but mapping it remains challenging. Transcriptomic scores do not directly measure functional activity, and CRISPR knockout data alone lack molecular interpretability. We introduce StateMap, a pathway-centric framework integrating gene expression and genome-wide CRISPR knockout fitness data from the Cancer Dependency Map. For a given pathway, StateMap selects features by co-dependency and mutual information, then projects cell lines into a low-dimensional space reflecting pathway activity and molecular state. Applied to the Hippo pathway, it resolved five functional states refining the YAP-on/YAP-off dichotomy. Notably, the Hippo-strong state showed selective dependence on integrin V{beta}5; ITGAV depletion triggered Hippo-dependent cell aggregation and G1 arrest via enhanced cell-cell adhesion. Machine learning transfer to TCGA identified a matching subtype with poor prognosis, nominated NNMT as a biomarker, and predicted sensitivity to the V inhibitor Cilengitide. StateMap enables pathway-specific state mapping and discovery of state-selective therapeutic vulnerabilities.

cancer biology↗

bioRxiv2025

LinearCapR: Linear-time computation of per-nucleotide structural-context probabilities of RNA without base-pair span limits

MotivationRNA molecules adopt dynamic ensembles of secondary structures, where the local structural context of each nucleotide-such as whether it resides in a stem or a specific type of loop-strongly shapes molecular interactions and regulatory function. Structural-context probabilities therefore provide a more functionally informative view of RNA folding than the minimum free energy structures or base-pairing probabilities. However, existing tools either require O (N3) time or employ span-restricted approximations that omit long-range base-pairs, limiting their applicability to large and biologically important RNAs. ResultsWe introduce LinearCapR, enabling linear-time, span-unrestricted computation of structural-context marginalized probabilities, using beam-pruned Stochastic Context Free Grammar-based computation. LinearCapR retains global ensemble features lost by span-limited methods and yields superior predictive power on bpRNA-1m(90) dataset, especially for multiloops and exterior regions, as well as long-distance stems. LinearCapR supports analysis of long RNAs, demonstrated on the full genome of SARS-CoV-2. ConclusionsLinearCapR provides the first base-pair-span-unrestricted, linear-time framework for RNA structural-context analysis, retaining key thermodynamic ensemble features essential for functional interpretation. It enables large-scale studies of viral genomes, long non-coding RNAs, and downstream analyses such as RNA-binding protein site prediction. AvailabilityThe source code of LinearCapR is available at https://github.com/hoget157/LinearCapR.

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↗

bioRxiv2022

Remarkable improvement in detection of readthrough downstream-of-gene transcripts by semi-extractable RNA-sequencing

The mammalian cell nucleus contains dozens of membrane-less nuclear bodies that play significant roles in various aspects of gene expression. Several nuclear bodies are nucleated by specific architectural noncoding RNAs (arcRNAs) acting as structural scaffolds. We have reported that a minor population of cellular RNAs exhibits an unusual semi-extractable feature upon using the conventional procedure of RNA preparation and that needle shearing or heating of cell lysates remarkably improves extraction of dozens of RNAs. Because semi-extractable RNAs, including known arcRNAs, commonly localize in nuclear bodies, this feature may be a hallmark of arcRNAs. Using the semi-extractability of RNA, we performed genome-wide screening of semi-extractable long non-coding RNAs to identify new candidate arcRNAs for arcRNA under hyperosmotic and heat stress conditions. After screening stress-inducible and semi-extractable RNAs, hundreds of readthrough downstream-of-gene (DoG) transcripts over several hundreds of kilobases, many of which were not detected among RNAs prepared by the conventional extraction procedure, were found to be stress-inducible and semi-extractable. We further characterized some of the abundant DoGs and found that stress-inducible transient extension of the 3-UTR made DoGs semi-extractable. Furthermore, they were localized in distinct nuclear foci that were sensitive to 1,6-hexanediol. These data suggest that semi-extractable DoGs exhibit arcRNA-like features and our semi-extractable RNA-seq is a powerful tool to extensively monitor DoGs that are induced under specific physiological conditions.

molecular biology↗

bioRxiv2021

Modeling SARS-CoV-2 nsp1-5'-UTR complex via the extended ensemble simulations

Nonstructural protein 1 (nsp1) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a 180-residue protein that blocks translation of host mRNAs in SARS-CoV-2-infected cells. Although it is known that SARS-CoV-2s own RNA evades nsp1s host translation shutoff, the molecular mechanism underlying the evasion was poorly understood. We performed an extended ensemble molecular dynamics simulation to investigate the mechanism of the viral RNA evasion. Simulation results showed that the stem loop structure of the SARS-CoV-2 RNA 5-untranslated region (SL1) is recognized by both nsp1s globular region and intrinsically disordered region. The recognition presumably enables selective translation of viral RNAs. Cluster analysis of the binding mode and detailed analysis of the binding poses revealed several residues involved in the SL1 recognition mechanism. The simulation results imply that the nsp1 C-terminal helices are lifted from the 40S ribosome upon the binding of SL1 to nsp1, unblocking translation of the viral RNA.

biophysics↗

bioRxiv2019

LncRNA-dependent nuclear stress bodies promote intron retention through SR protein phosphorylation

A number of long noncoding RNAs (lncRNAs) are induced in response to specific stresses to construct membrane-less nuclear bodies; however, their function remains poorly understood. Here, we report the role of nuclear stress bodies (nSBs) formed on highly repetitive satellite III (HSATIII) lncRNAs derived from primate-specific satellite III repeats upon thermal stress exposure. A transcriptomic analysis revealed that depletion of HSATIII lncRNAs, resulting in elimination of nSBs, promoted splicing of 533 retained introns during thermal stress recovery. A HSATIII-Comprehensive identification of RNA-binding proteins by mass spectrometry (ChIRP-MS) analysis identified multiple splicing factors in nSBs, including serine and arginine-rich pre-mRNA splicing factors (SRSFs), the phosphorylation states of which affect splicing patterns. SRSFs are rapidly dephosphorylated upon thermal stress exposure. During stress recovery, CDC like kinase 1 (CLK1) was recruited to nSBs and accelerated the re-phosphorylation of SRSF9, thereby promoting target intron retention. Our findings suggest that HSATIII-dependent nSBs serve as a conditional platform for phosphorylation of SRSFs by CLK1 to promote the rapid adaptation of gene expression through intron retention following thermal stress exposure.

molecular biology↗

bioRxiv2019

RintC: fast and accuracy-aware decomposition of distributions of RNA secondary structures with extended logsumexp

The analysis of secondary structures is essential to understanding the function of RNAs. Because RNA molecules thermally fluctuate, it is necessary to analyze the probability distribution of secondary structures. Existing methods, however, are not applicable to long RNAs owing to their high computational complexity. Additionally, previous research has suffered from two numerical difficulties: overflow and significant numerical error. In this research, we reduced the computational complexity in calculating the landscape of the probability distribution of secondary structures by introducing a maximum-span constraint. In addition, we resolved numerical computation problems through two techniques: extended logsumexp and accuracy-guaranteed numerical computation. We analyzed the stability of the secondary structures of 16S ribosomal RNAs at various temperatures without overflow. The results obtained are consistent with in vivo assay results reported in previous research. Furthermore, we quantitatively assessed numerical stability using our method. These results demonstrate that the proposed method is applicable to long RNAs. Source code is available on https://github.com/eukaryo/rintc.

bioinformatics↗

bioRxiv2018

Nearest-neighbor parameter for inosine-cytosine pairs through a combined experimental and computational approach

In RNA secondary structure prediction, nearest-neighbor parameters are used to determine the stability of a given structure. We derived the nearest-neighbor parameters for RNAs containing inosine-cytosine pairs. For parameter derivation, we developed a method that combines UV adsorption measurement experiments with free-energy calculations using molecular dynamics simulations. The method provides fast drop-in parameters for modified bases. Derived parameters were compared and found to be consistent with existing parameters for canonical RNAs. A duplex with an internal inosine-cytosine pair is 0.9 kcal/mol more unstable than the same duplex with an internal guanine-cytosine pair, and is as stable as the one with an internal adenine-uracil pair (only 0.1 kcal/mol more stable) on average.

bioinformatics↗

bioRxiv2018

reactIDR: Evaluation of the statistical reproducibility of high-throughput structural analyses for a robust RNA reactivity classification

MotivationRecently, next-generation sequencing techniques have been applied for the detection of RNA secondary structures called high-throughput RNA structural (HTS) analy- sis, and dozens of different protocols were used to detect comprehensive RNA structures at single-nucleotide resolution. However, the existing computational analyses heavily depend on experimental data generation methodology, which results in many difficulties associated with statistically sound comparisons or combining the results obtained using different HTS methods.\n\nResultsHere, we introduced a statistical framework, reactIDR, which is applicable to the experimental data obtained using multiple HTS methodologies, and it classifies the nucleotides into three structural categories, stem, loop, and unmapped. reactIDR uses the irreproducible discovery rate (IDR) with a hidden Markov model (HMM) to discriminate accurately between the true and spurious signals obtained in the replicated HTS experiments. In reactIDR, IDR and HMM parameters are efficiently optimized by using an expectation-maximization algorithm. Furthermore, if known reference structures are given, a supervised learning can be applicable in a semi-supervised manner. The results of our analyses for real HTS data showed that reactIDR achieved the highest accuracy in the classification problem of stem/loop structures of rRNA using both individual and integrated HTS datasets as well as the best correspondence with the three-dimensional structure. Because reactIDR is the first method to compare HTS datasets obtained from multiple sources in a single unified model, it has a great potential to increase the accuracy of RNA secondary structure prediction at transcriptome-wide level with further experiments performed.\n\nAvailabilityreactIDR is implemented in Python. Source code is publicly available at https://github.com/carushi/reactIDRhttps://github.com/carushi/reactIDR.\n\nContactkawaguchi-rs@aist.go.jp\n\nSupplementary informationSupplementary data are available at online.

bioinformatics↗