Precision Thermostability Predictions: Leveraging Machine Learning for Examining Laccases and Their Associated Genes.

ABSTRACT: Laccases, multi-copper oxidases, play pivotal roles in the oxidation of a variety of substrates, impacting numerous biological functions and industrial processes. However, their industrial adoption has been limited by challenges in thermostability. This study employed advanced computational models, including random forest (RF) regressors and convolutional neural networks (CNNs), to predict and enhance the thermostability of laccases. Initially, the RF model estimated melting temperatures with a training mean squared error (MSE) of 13.98, and while it demonstrated high training accuracy (93.01%), the test and validation MSEs of 48.81 and 58.42, respectively, indicated areas for model optimization. The CNN model further refined these predictions, achieving lower training and validation MSEs,

SUBMITTER: Tiwari A 

PROVIDER: S-EPMC11641568 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature