RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow Matching

• URL: http://arxiv.org/abs/2405.18768v2
• Date: Sun, 9 Jun 2024 16:13:02 GMT
• Title: RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow Matching
• Authors: Divya Nori, Wengong Jin,
• Abstract summary: RNAFlow is a flow matching model for protein-conditioned RNA sequence-structure design. Its denoising network integrates an RNA inverse folding model and a pre-trained RosettaFold2NA network for generation of RNA sequences and structures.
• Score: 7.600990806121113
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The growing significance of RNA engineering in diverse biological applications has spurred interest in developing AI methods for structure-based RNA design. While diffusion models have excelled in protein design, adapting them for RNA presents new challenges due to RNA's conformational flexibility and the computational cost of fine-tuning large structure prediction models. To this end, we propose RNAFlow, a flow matching model for protein-conditioned RNA sequence-structure design. Its denoising network integrates an RNA inverse folding model and a pre-trained RosettaFold2NA network for generation of RNA sequences and structures. The integration of inverse folding in the structure denoising process allows us to simplify training by fixing the structure prediction network. We further enhance the inverse folding model by conditioning it on inferred conformational ensembles to model dynamic RNA conformations. Evaluation on protein-conditioned RNA structure and sequence generation tasks demonstrates RNAFlow's advantage over existing RNA design methods.

Related papers

• Comprehensive benchmarking of large language models for RNA secondary structure prediction [0.0]
  RNA-LLM uses large datasets of RNA sequences to learn, in a self-supervised way, how to represent each RNA base with a semantically rich numerical vector. Among them, predicting the secondary structure is a fundamental task for uncovering RNA functional mechanisms. We present a comprehensive experimental analysis of several pre-trained RNA-LLM, comparing them for the RNA secondary structure prediction task in a unified deep learning framework.
  arXiv  Detail & Related papers  (2024-10-21T17:12:06Z)
• Latent Diffusion Models for Controllable RNA Sequence Generation [33.38594748558547]
  RNA is a key intermediary between DNA and protein, exhibiting high sequence diversity and complex three-dimensional structures. We develop a latent diffusion model for generating and optimizing discrete RNA sequences of variable lengths. Empirical results confirm that RNAdiffusion generates non-coding RNAs that align with natural distributions across various biological metrics.
  arXiv  Detail & Related papers  (2024-09-15T19:04:50Z)
• RNACG: A Universal RNA Sequence Conditional Generation model based on Flow-Matching [0.0]
  We develop a universal RNA sequence generation model based on flow matching, namely RNACG. RNACG can accommodate various conditional inputs and is portable, enabling users to customize the encoding network for conditional inputs. RNACG exhibits extensive applicability in sequence generation and property prediction tasks.
  arXiv  Detail & Related papers  (2024-07-29T09:46:46Z)
• BEACON: Benchmark for Comprehensive RNA Tasks and Language Models [60.02663015002029]
  We introduce the first comprehensive RNA benchmark BEACON (textbfBEnchmtextbfArk for textbfCOmprehensive RtextbfNA Task and Language Models). First, BEACON comprises 13 distinct tasks derived from extensive previous work covering structural analysis, functional studies, and engineering applications. Second, we examine a range of models, including traditional approaches like CNNs, as well as advanced RNA foundation models based on language models, offering valuable insights into the task-specific performances of these models. Third, we investigate the vital RNA language model components
  arXiv  Detail & Related papers  (2024-06-14T19:39:19Z)
• Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation [55.93511121486321]
  We introduce FoldFlow-2, a novel sequence-conditioned flow matching model for protein structure generation. We train FoldFlow-2 at scale on a new dataset that is an order of magnitude larger than PDB datasets of prior works. We empirically observe that FoldFlow-2 outperforms previous state-of-the-art protein structure-based generative models.
  arXiv  Detail & Related papers  (2024-05-30T17:53:50Z)
• RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design [65.41144149958208]
  This study aims to systematically construct a data-driven RNA design pipeline. We crafted a benchmark dataset and designed a comprehensive structural modeling approach to represent the complex RNA tertiary structure. We incorporated extracted secondary structures with base pairs as prior knowledge to facilitate the RNA design process.
  arXiv  Detail & Related papers  (2023-01-25T17:19:49Z)
• E2Efold-3D: End-to-End Deep Learning Method for accurate de novo RNA 3D Structure Prediction [46.38735421190187]
  We develop the first end-to-end deep learning approach, E2Efold-3D, to accurately perform the textitde novo RNA structure prediction. Several novel components are proposed to overcome the data scarcity, such as a fully-differentiable end-to-end pipeline, secondary structure-assisted self-distillation, and parameter-efficient backbone formulation.
  arXiv  Detail & Related papers  (2022-07-04T17:15:35Z)
• Improving RNA Secondary Structure Design using Deep Reinforcement Learning [69.63971634605797]
  We propose a new benchmark of applying reinforcement learning to RNA sequence design, in which the objective function is defined to be the free energy in the sequence's secondary structure. We show results of the ablation analysis that we do for these algorithms, as well as graphs indicating the algorithm's performance across batches.
  arXiv  Detail & Related papers  (2021-11-05T02:54:06Z)
• Classification of Long Noncoding RNA Elements Using Deep Convolutional Neural Networks and Siamese Networks [17.8181080354116]
  This thesis proposes a new methodemploying deep convolutional neural networks (CNNs) to classifyncRNA sequences. As a result, clas-sifying RNA sequences is converted to an image classificationproblem that can be efficiently solved by CNN-basedclassification models.
  arXiv  Detail & Related papers  (2021-02-10T17:26:38Z)
• Neural representation and generation for RNA secondary structures [14.583976833366384]
  Our work is concerned with the generation and targeted design of RNA, a type of genetic macromolecule. The design of large scale and complex biological structures spurs dedicated graph-based deep generative modeling techniques. We propose a flexible framework to jointly embed and generate different RNA structural modalities.
  arXiv  Detail & Related papers  (2021-02-01T15:49:25Z)
• RNA Secondary Structure Prediction By Learning Unrolled Algorithms [70.09461537906319]
  In this paper, we propose an end-to-end deep learning model, called E2Efold, for RNA secondary structure prediction. The key idea of E2Efold is to directly predict the RNA base-pairing matrix, and use an unrolled algorithm for constrained programming as the template for deep architectures to enforce constraints. With comprehensive experiments on benchmark datasets, we demonstrate the superior performance of E2Efold.
  arXiv  Detail & Related papers  (2020-02-13T23:21:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.