Biology subjects

Runge, F.

Publications and source records attributed to Runge, F..

7 recordsLinked to original sources

bioRxiv2025

STRAND: Structure Refinement of RNA-Protein Complexes via Diffusion

RNA-protein interactions play crucial roles in cellular processes, from gene regulation to viral replication. While recent advances in structure prediction have revolutionized our ability to model macromolecular complexes, achieving accurate predictions of RNA-protein binding poses remains challenging. In this work, we present STRAND, a diffusion-based model for monomeric RNA-protein complex refinement that builds upon the success of DiffDock-PP in protein-protein docking. Unlike traditional docking, we develop STRAND as a modular extension to existing RNA-Protein complex prediction tools to improve their backbone predictions. We study the effect of different transformations by training models to learn either translation, rotation, torsion, or combinations of these during the diffusion process and initialize the backward process with a complex prediction at test time. Our experiments with AlphaFold 3 and ProRNA3D-single reveal that STRAND can improve the backbones of a large fraction of RNA-protein complex predictions.

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bioRxiv2024

RNA-Protein Interaction Classification via Sequence Embeddings

RNA-protein interactions (RPI) are ubiquitous in cellular organisms and essential for gene regulation. In particular, protein interactions with non-coding RNAs (ncRNAs) play a critical role in these processes. Experimental analysis of RPIs is time-consuming and expensive, and existing computational methods rely on small and limited datasets. This work introduces RNAInterAct, a comprehensive RPI dataset, alongside RPIembeddor, a novel transformer-based model designed for classifying ncRNA-protein interactions. By leveraging two foundation models for sequence embedding, we incorporate essential structural and functional insights into our task. We demonstrate RPIembeddors strong performance and generalization capability compared to state-of-the-art methods across different datasets and analyze the impact of the proposed embedding strategy on the performance in an ablation study.

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bioRxiv2024

KinPFN: Bayesian Approximation of RNA FoldingKinetics using Prior-Data Fitted Networks

AO_SCPLOWBSTRACTC_SCPLOWRNA is a dynamic biomolecule crucial for cellular regulation, with its function largely determined by its folding into complex structures, while misfolding can lead to multifaceted biological sequelae. During the folding process, RNA traverses through a series of intermediate structural states, with each transition occurring at variable rates that collectively influence the time required to reach the functional form. Understanding these folding kinetics is vital for predicting RNA behavior and optimizing applications in synthetic biology and drug discovery. While in silico kinetic RNA folding simulators are often computationally intensive and time-consuming, accurate approximations of the folding times can already be very informative to assess the efficiency of the folding process. In this work, we present KinPFN, a novel approach that leverages prior-data fitted networks to directly model the posterior predictive distribution of RNA folding times. By training on synthetic data representing arbitrary prior folding times, KinPFN efficiently approximates the cumulative distribution function of RNA folding times in a single forward pass, given only a few initial folding time examples. Our method offers a modular extension to existing RNA kinetics algorithms, promising significant computational speed-ups orders of magnitude faster, while achieving comparable results. We showcase the effectiveness of KinPFN through extensive evaluations and real-world case studies, demonstrating its potential for RNA folding kinetics analysis, its practical relevance, and generalization to other biological data.

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bioRxiv2024

Towards Generative RNA Design With Tertiary Interactions

AO_SCPLOWBSTRACTC_SCPLOWThe function of an RNA molecule depends on its structure and a strong structure-to-function relationship is already achieved on the secondary structure level of RNA. Therefore, the secondary structure based design of RNAs is one of the major challenges in computational biology. A common approach to RNA design is inverse RNA folding. However, existing RNA design methods cannot invert all folding algorithms because they cannot represent all types of base interactions. In this work, we propose RNAinformer, a novel generative transformer based approach to the inverse RNA folding problem. Leveraging axial-attention, we directly model the secondary structure input represented as an adjacency matrix in a 2D latent space, which allows us to invert all existing secondary structure prediction algorithms. Consequently, RNAinformer is the first model capable of designing RNAs from secondary structures with all base interactions, including non-canonical base pairs and tertiary interactions like pseudoknots and base multiplets. We demonstrate RNAinformers state-of-the-art performance across different RNA design benchmarks and showcase its novelty by inverting different RNA secondary structure prediction algorithms.

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bioRxiv2024

RNAformer: A Simple Yet Effective Deep Learning Model for RNA Secondary Structure Prediction

AO_SCPLOWBSTRACTC_SCPLOWPredicting RNA secondary structure is essential for understanding RNA function and developing RNA-based therapeutics. Despite recent advances in deep learning for structural biology, its application to RNA secondary structure prediction remains contentious. A primary concern is the control of homology between training and test data. Moreover, deep learning approaches often incorporate complex multi-model systems, ensemble strategies, or require external data. Here, we present the RNAformer, a scalable axial-attention-based deep learning model designed to predict secondary structure directly from a single RNA sequence without additional requirements. We demonstrate the benefits of this lean architecture by learning an accurate biophysical RNA folding model using synthetic data. Trained on experimental data, our model overcomes previously reported caveats in deep learning approaches with a novel homology-aware data pipeline. The RNAformer achieves state-of-the-art performance on RNA secondary structure prediction, out-performing both traditional non-learning-based methods and existing deep learning approaches, while carefully considering sequence and structure similarities.

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bioRxiv2024

RnaBench: A Comprehensive Library for In Silico RNA Modelling

RNA is a crucial regulator in living organisms and malfunctions can lead to severe diseases. To explore RNA-based therapeutics and applications, computational structure prediction and design approaches play a vital role. Among these approaches, deep learning (DL) algorithms show great promise. However, the adoption of DL methods in the RNA community is limited due to various challenges. DL practitioners often underestimate data homologies, causing skepticism in the field. Additionally, the absence of standardized benchmarks hampers result comparison, while tackling low-level tasks requires significant effort. Moreover, assessing performance and visualizing results prove to be non-trivial and task-dependent. To address these obstacles, we introduce RnaBench (RnB), an open-source RNA library designed specifically for the development of deep learning algorithms that mitigate the challenges during data generation, evaluation, and visualization. It provides meticulously curated homology-aware RNA datasets and standardized RNA benchmarks, including a pioneering RNA design benchmark suite featuring a novel real-world RNA design problem. Furthermore, RnB offers baseline algorithms, both existing and novel performance measures, as well as data utilities and a comprehensive visualization module, all accessible through a user-friendly interface. By leveraging RnB, DL practitioners can rapidly develop innovative algorithms, potentially revolutionizing the field of computational RNA research.

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bioRxiv2023

Partial RNA Design

RNA design is a key technique to achieve new functionality in fields like synthetic biology or biotechnology. Computational tools could help to find such RNA sequences but they are often limited in their formulation of the search space. In this work, we propose partial RNA design, a novel RNA design paradigm that addresses the limitations of current RNA design formulations. Partial RNA design describes the problem of designing RNAs from arbitrary RNA sequences and structure motifs with multiple design goals. By separating the design space from the objectives, our formulation enables the design of RNAs with variable lengths and desired properties, while still allowing precise control over sequence and structure constraints at individual positions. Based on this formulation, we introduce a new algorithm, libLEARNA, capable of efficiently solving different constraint RNA design tasks. A comprehensive analysis of various problems, including a realistic riboswitch design task, reveals the outstanding performance of libLEARNA and its robustness.

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