Computational prediction of RNA tertiary structures using machine learning methods

• URL: http://arxiv.org/abs/2009.01440v1
• Date: Thu, 3 Sep 2020 04:01:43 GMT
• Title: Computational prediction of RNA tertiary structures using machine learning methods
• Authors: Bin Huang, Yuanyang Du, Shuai Zhang, Wenfei Li, Jun Wang, Jian Zhang
• Abstract summary: Computational prediction approaches can help to understand RNA structures and their stabilizing factors. Although their usage in protein-related fields has a long history, the use of machine learning methods in predicting RNA tertiary structures is new and rare.
• Score: 14.35527588241679
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: RNAs play crucial and versatile roles in biological processes. Computational prediction approaches can help to understand RNA structures and their stabilizing factors, thus providing information on their functions, and facilitating the design of new RNAs. Machine learning (ML) techniques have made tremendous progress in many fields in the past few years. Although their usage in protein-related fields has a long history, the use of ML methods in predicting RNA tertiary structures is new and rare. Here, we review the recent advances of using ML methods on RNA structure predictions and discuss the advantages and limitation, the difficulties and potentials of these approaches when applied in the field.

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)
• Predicting Distance matrix with large language models [1.8855270809505869]
  RNA structure prediction remains a significant challenge due to data limitations. Traditional methods such as nuclear magnetic resonance spectroscopy, Xray crystallography, and electron microscopy are expensive and time consuming. Distance maps provide a simplified representation of spatial constraints between nucleotides, capturing essential relationships without requiring a full 3D model.
  arXiv  Detail & Related papers  (2024-09-24T10:28:55Z)
• 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)
• Rethinking Performance Measures of RNA Secondary Structure Problems [42.25267871026153]
  Deep learning methods have surpassed traditional algorithms by predicting complex features like pseudoknots and multi-interacting base pairs. We propose the Weisfeiler-Lehman graph kernel (WL) as an alternative metric.
  arXiv  Detail & Related papers  (2023-12-04T08:46:24Z)
• Knowledge from Large-Scale Protein Contact Prediction Models Can Be Transferred to the Data-Scarce RNA Contact Prediction Task [40.051834115537474]
  We find that a protein-coevolution Transformer-based deep neural network can be transferred to the RNA contact prediction task. Experiments confirm that RNA contact prediction through transfer learning is greatly improved. Our findings indicate that the learned structural patterns of proteins can be transferred to RNAs, opening up potential new avenues for research.
  arXiv  Detail & Related papers  (2023-02-13T06:00:56Z)
• 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)
• Machine learning for plant microRNA prediction: A systematic review [0.0]
  MicroRNAs (miRNAs) are endogenous small non-coding RNAs that play an important role in gene regulation. computational and machine learning-based approaches have been adopted to predict microRNAs. This systematic review focuses on the machine learning methods developed for identification in plants.
  arXiv  Detail & Related papers  (2021-06-29T08:22:57Z)
• Review of Machine-Learning Methods for RNA Secondary Structure Prediction [21.3539253580504]
  We provide a comprehensive overview of RNA secondary structure prediction methods based on machine-learning technologies. The current pending issues in the field of RNA secondary structure prediction and future trends are also discussed.
  arXiv  Detail & Related papers  (2020-09-01T03:17:15Z)
• 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.