Molecular Dynamics (MD)-Derived Features for Canonical and Noncanonical Amino Acids.

ABSTRACT: Machine learning (ML) models have become increasingly popular for predicting and designing structures and properties of peptides and proteins. These ML models typically use peptides and proteins containing only canonical amino acids as the training data. Consequently, these models struggle to make accurate predictions for peptides and proteins containing new amino acids that are absent in the training data set (e.g., noncanonical amino acids). One approach to improve the accuracy of the models is to collect more training data with the desired amino acids. However, this strategy is suboptimal as new data may not be easily attainable, and additional time is required to retrain the ML models. Alternatively, the extendibility of the ML models can be improved if the amino acid features u

SUBMITTER: Hui T 

PROVIDER: S-EPMC11863381 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature