PPubMed26 Jul 2024Cited 21×

AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors

Nana Ding, Zenan Yuan, Zheng Ma, Yefei Wu, Lianghong Yin, Carmen Cuadrado

Abstract

'Carmen Cuadrado'] The rational design, activity prediction, and adaptive application of biological elements (bio-elements) are crucial research fields in synthetic biology. Currently, a major challenge in the field is efficiently designing desired bio-elements and accurately predicting their activity using vast datasets. The advancement of artificial intelligence (AI) technology has enabled machine learning and deep learning algorithms to excel in uncovering patterns in bio-element data and predicting their performance. This review explores the application of AI algorithms in the rational design of bio-elements, activity prediction, and the regulation of transcription-factor-based biosensor response performance using AI-designed elements. We discuss the advantages, adaptability, and biological challenges addressed by the AI algorithms in various applications, highlighting their powerful potential in analyzing biological data. Furthermore, we propose innovative solutions to the challenges faced by AI algorithms in the field and suggest future research directions. By consolidating current research and demonstrating the practical applications and future potential of AI in synthetic biology, this review provides valuable insights for advancing both academic research and practical applications in biotechnology.

A figure from AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors — fig. from the paper

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AI models were used to design promoters and enhancers and predict their activity, enabling a malonyl‑CoA biosensor with a 6.38‑fold dynamic range.

What’s new

i.Developed AI‑driven promoter design using GAN, DeepSEED, and MCSC with CNN/XGBoost activity prediction
ii.Created iEnhancer‑DCLA deep learning framework for enhancer activity prediction achieving 0.83 accuracy
iii.Built a forward‑reverse engineering platform combining CNN and BAGAN‑GP to fine‑tune transcription‑factor biosensor dynamic range, producing a malonyl‑CoA sensor with a 6.38 dynamic range
iv.Demonstrated cross‑species applicability by designing Drosophila enhancers and comparing them with human GAN‑generated enhancers

How they did it

The study employed generative adversarial networks, convolutional neural networks, XGBoost, and other machine‑learning models to design promoters and enhancers from DNA sequences and to predict their activity. Predicted activities guided the construction of biosensors, and a forward‑reverse platform used CNN for prediction and BAGAN‑GP for rational design of ribosome‑binding sites to achieve desired dynamic ranges.

What they found

Malonyl‑CoA biosensor achieved a maximum dynamic range of 6.38
iEnhancer‑DCLA model reached 0.83 accuracy in enhancer activity prediction

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