Ataee Shabnam, Brochet Xavier, Peña-Reyes Carlos Andrés
Institute of Information and Communication Technology (IICT), School of Management and Engineering Vaud (HEIG-VD), Yverdon-les-Bains, Switzerland.
HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland.
Front Bioinform. 2022 Jul 13;2:932319. doi: 10.3389/fbinf.2022.932319. eCollection 2022.
Bacteriophages are gaining increasing interest as antimicrobial tools, largely due to the emergence of multi-antibiotic-resistant bacteria. Although their huge diversity and virulence make them particularly attractive for targeting a wide range of bacterial pathogens, it is difficult to select suitable phages due to their high specificity which limits their host range. In addition, other challenges remain such as structural fragility under certain environmental conditions, immunogenicity of phage therapy, or development of bacterial resistance. The use of genetically engineered phages may reduce characteristics that hinder prophylactic and therapeutic applications of phages. Nowadays, there is no systematic method to modify a given phage genome conferring its sought characteristics. We explore the use of artificial intelligence for this purpose as it has the potential to both guide and accelerate genome modification to generate phage variants with unique properties that overcome the limitations of natural phages. We propose an original architecture composed of two deep learning-driven components: a phage-bacterium interaction predictor and a phage genome-sequence generator. The former is a multi-branch 1-D convolutional neural network (1D-CNN) that analyses phage and bacterial genomes to predict interactions. The latter is a recurrent neural network, more particularly a long short-term memory (LSTM), that performs genomic modifications to a phage to offer substantial host range improvement. For this component, we developed two different architectures composed of one or two stacked LSTM layers with 256 neurons each. These generators are used to modify, more precisely to rewrite, the genome sequence of 42 selected phages, while the predictor is used to estimate the host range of the modified bacteriophages across 46 strains of . The proposed generators, trained with an average accuracy of 96.1%, are able to improve the host range for an average of 18 phages among the 42 under study, increasing both their average host range, by 73.0 and 103.7%, and the maximum host ranges from 21 to 24 and 29, respectively. These promising results showed that the use of deep learning methodologies allows genetic modification of phages to extend, for instance, their host range, confirming the potential of these approaches to guide bacteriophage engineering.
噬菌体作为抗菌工具正越来越受到关注，这主要是由于多重耐药细菌的出现。尽管它们的巨大多样性和毒性使它们对于靶向多种细菌病原体特别有吸引力，但由于其高度特异性限制了宿主范围，因此很难选择合适的噬菌体。此外，还存在其他挑战，如在某些环境条件下结构脆弱、噬菌体疗法的免疫原性或细菌耐药性的发展。使用基因工程噬菌体可能会减少阻碍噬菌体预防和治疗应用的特性。如今，没有系统的方法来修饰给定的噬菌体基因组以赋予其所需的特性。我们探索为此目的使用人工智能，因为它有潜力指导和加速基因组修饰，以产生具有独特特性的噬菌体变体，克服天然噬菌体的局限性。我们提出了一种由两个深度学习驱动的组件组成的原创架构：噬菌体-细菌相互作用预测器和噬菌体基因组序列生成器。前者是一个多分支一维卷积神经网络（1D-CNN），它分析噬菌体和细菌基因组以预测相互作用。后者是一个循环神经网络，更具体地说是一个长短期记忆（LSTM）网络，它对噬菌体进行基因组修饰以大幅改善宿主范围。对于这个组件，我们开发了两种不同的架构，分别由一个或两个堆叠的LSTM层组成，每层有256个神经元。这些生成器用于修改，更确切地说是重写42种选定噬菌体的基因组序列，而预测器用于估计修饰后的噬菌体在46种菌株上的宿主范围。所提出的生成器平均准确率为96.1%，能够在所研究的42种噬菌体中平均为18种噬菌体改善宿主范围，平均宿主范围分别增加73.0%和103.7%，最大宿主范围分别从21增加到24和29。这些有希望的结果表明，使用深度学习方法可以对噬菌体进行基因修饰，例如扩展其宿主范围，证实了这些方法指导噬菌体工程的潜力。
