Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection
Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection
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DOI：
10.1101/2022.03.12.484094
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发表时间：
2022-03
影响因子：
4.3
通讯作者：
A. Di Gioacchino;Jonah Procyk;Marco Molari;J. Schreck;Yu Zhou;Y. Liu;R. Monasson;S. Cocco;P. Šulc
中科院分区：
文献类型：
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作者：
A. Di Gioacchino;Jonah Procyk;Marco Molari;J. Schreck;Yu Zhou;Y. Liu;R. Monasson;S. Cocco;P. Šulc
选择方案，如SELEX，通过多轮选择分子与目标分子结合的能力，是用于诊断和治疗目的获得结合剂的流行方法。随着高通量实验数据的不断增加，机器学习技术在分子数据集分析中的应用越来越广泛。在这里，我们证明了限制Boltzmann机器(RBM)，一个两层神经网络结构，可以成功地根据SELEX实验中的凝血酶适配子序列集成进行训练，并用于评估通过实验方案获得的序列的适合度。作为一个直接的结果，我们展示了训练的RBM可以被用来分类以及产生新的分子。为了证实我们的发现，我们从实验上验证了从RBM生成的序列。
Selection protocols such as SELEX, where molecules are selected over multiple rounds for their ability to bind to a target molecule of interest, are popular methods for obtaining binders for diagnostic and therapeutic purposes. With the increasing amount of such high-throughput experimental data available, machine learning techniques have become increasingly popular for molecular datasets analysis. Here, we show that Restricted Boltzmann Machines (RBMs), a two-layer neural network architecture, can successfully be trained on sequence ensembles from SELEX experiments for thrombin aptamers, and used to estimate the fitness of the sequences obtained through the experimental protocol. As a direct consequence, we show that trained RBMs can be exploited to classify as well as generate novel molecules. To confirm our findings, we experimentally verify the generated sequences from RBM.