Enzyme Stability Prediction using BERT and CNN-A Deep Learning Approach for Enhanced Biocatalysis

Year : 2024 | Volume : | : | Page : –
By

Nikki Rani

Mehak Khurana

  1. Student Department of Computer Science and Engineering, The NorthCap University, Gurugram Haryana India
  2. Associate Professor Department of Computer Science and Engineering, The NorthCap University, Gurugram Haryana India

Abstract

An important factor in determining the efficacy of industrial enzymes used in various biotechnological applications is their stability. The goal of this study is to develop a predictive model for industrial enzyme stability, which is essential to the efficiency of these enzymes in biotechnological applications. The research takes a comprehensive strategy to comprehend the parameters affecting enzyme stability by combining statistical analysis, deep learning algorithms (BERT and CNN), and molecular dynamics simulations. Numerous different enzymes and information about their stability are included in the dataset. The model looks at the effects of environmental factors including temperature, pH, and salt concentration in order to pinpoint important factors that affect enzyme stability. With a mean squared error (MSE) of 0.007 and a cross-validation score of 0.4108, the BERT model should be used with caution due to the possibility of overfitting. However, performance was enhanced by freezing specific transformer layers and adding mutant embeddings. For ddG(free energy upon mutation) values in the test dataset, the CNN model produced predictions based on three different operation types. The molecule-level interactions influencing enzyme stability are revealed by the results of molecular dynamics simulations. This study aims to create a robust predictive model that can help in the design and optimisation of stable and effective industrial enzymes for biotechnological applications by incorporating several analytical ideas. The results have important ramifications for biotechnology since they offer useful tools for improving enzyme stability and effectiveness, which advances a variety of industrial processes.

Keywords: Biotechnology, CNN, BERT, Free energy change(ddg), Enzymes

How to cite this article: Nikki Rani, Mehak Khurana. Enzyme Stability Prediction using BERT and CNN-A Deep Learning Approach for Enhanced Biocatalysis. Research & Reviews : A Journal of Life Sciences. 2024; ():-.
How to cite this URL: Nikki Rani, Mehak Khurana. Enzyme Stability Prediction using BERT and CNN-A Deep Learning Approach for Enhanced Biocatalysis. Research & Reviews : A Journal of Life Sciences. 2024; ():-. Available from: https://journals.stmjournals.com/rrjols/article=2024/view=146543


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Ahead of Print Subscription Original Research
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Received April 18, 2024
Accepted May 2, 2024
Published May 18, 2024
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