G4STAB: A multi-input deep learning model to predict G-quadruplex thermodynamic stability based on sequence and salt concentration.

IF 5.4
Donn Liew, Akesha Dinuli Dharmatilleke, Edwin See, Ee Hou Yong
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Abstract

Motivation: G-quadruplexes (G4s) are non-canonical nucleic acid structures formed in guanine-rich regions that modulate gene regulation and genomic stability. The thermodynamic stability of G4s directly influences their biological functions and potential as therapeutic targets. However, current quantitative frameworks for predicting G4 stability rely on predetermined structural features, limiting their effectiveness for diverse G4 topologies, and fail to account for environmental factors such as ion concentration and pH that significantly modulate G4 stability in cellular contexts.

Results: We present G4STAB, a multi-input deep learning neural network that accurately predicts DNA G4 melting temperatures based on sequence features, salt concentration, and pH. Trained on 2,382 diverse DNA G4 sequences, our model achieves high accuracy (R2 = 0.8) without relying on predetermined G4 structural features. G4STAB successfully captures established G4 stability determinants and proposes previously unobserved sequence-stability relationships. Analysis of 391,502 experimentally validated G4s reveals that cancer-like ionic environments alter G4 stability profiles, with a 13.5-fold increase in number of structures exhibiting physiological melting temperatures (36-42°C). These findings suggest systematic genomic patterns in G4 stability responses across chromosomes and gene types.

Availability and implementation: G4STAB is available at https://github.com/donn-liew/G4STAB; G4STAB web database interface is available at https://donn-liew.github.io/g4stab-web-database/.

Supplementary information: Supplementary data are available at Bioinformatics online.

G4STAB:基于序列和盐浓度预测g -四联体热力学稳定性的多输入深度学习模型。
动机:g -四联体(G4s)是在鸟嘌呤富集区形成的非规范核酸结构,可调节基因调控和基因组稳定性。G4s的热力学稳定性直接影响其生物学功能和作为治疗靶点的潜力。然而,目前预测G4稳定性的定量框架依赖于预定的结构特征,限制了它们对不同G4拓扑结构的有效性,并且无法考虑在细胞环境中显著调节G4稳定性的离子浓度和pH等环境因素。结果:我们提出了G4STAB,这是一个多输入深度学习神经网络,可以根据序列特征、盐浓度和ph准确预测DNA G4熔化温度。我们的模型在2,382个不同的DNA G4序列上进行了训练,在不依赖于预先确定的G4结构特征的情况下获得了很高的准确性(R2 = 0.8)。G4STAB成功捕获了已建立的G4稳定性决定因素,并提出了以前未观察到的序列稳定性关系。对391,502个实验验证的G4的分析表明,类癌症离子环境改变了G4的稳定性,显示生理熔融温度(36-42°C)的结构数量增加了13.5倍。这些发现提示了G4稳定性反应在染色体和基因类型上的系统基因组模式。可用性和实现:G4STAB可从https://github.com/donn-liew/G4STAB获得;G4STAB网络数据库接口可在https://donn-liew.github.io/g4stab-web-database/.Supplementary信息上获得:补充数据可在Bioinformatics在线上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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