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

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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 used are representative and generalizable to the unseen amino acids. Herein, we develop amino acid features using molecular dynamics (MD) simulation results. Specifically, for a given amino acid, we perform MD simulation of its dipeptide to create features based on its backbone (ϕ, ψ) distributions and its electrostatic potentials. We demonstrate that these new features enable our ML models to more accurately predict the structural ensembles of cyclic peptides containing amino acids not present in the original training data set. For example, we build ML models to predict cyclic pentapeptide structures, with the training data set containing a library of 15 amino acids and the test data set containing the same 15-amino-acid library or an extended 50-amino-acid library. When using popular features such as Morgan fingerprints and MACCS keys to represent amino acids, the ML models achieve R2 = 0.963 for structural predictions of test cyclic pentapeptides containing the same 15-amino-acid library. However, these models' performances decrease significantly to R2 = 0.430 and R2 = 0.508, respectively, when tasked to predict the structures of cyclic pentapeptides containing a library of 50 amino acids. On the other hand, the model using our backbone (ϕ, ψ) features outperforms those using Morgan fingerprints and MACCS keys, with R2 = 0.700. Overall, instead of having to collect more training data, our new features enable predictions of peptide sequences containing amino acids not originally present in the training data set at the mere cost of performing new dipeptide simulations for the new amino acids.