Efficient Prediction of Peptide Self-assembly through Sequential and
  Graphical Encoding
Zihan Liu Jiaqi Wang Yun Luo Shuang Zhao Wenbin Li Stan Z. Li
In recent years, there has been an explosion of research on the application
of deep learning to the prediction of various peptide properties, due to the
significant development and market potential of peptides. Molecular dynamics
has enabled the efficient collection of large peptide datasets, providing
reliable training data for deep learning. However, the lack of systematic
analysis of the peptide encoding, which is essential for AI-assisted
peptide-related tasks, makes it an urgent problem to be solved for the
improvement of prediction accuracy. To address this issue, we first collect a
high-quality, colossal simulation dataset of peptide self-assembly containing
over 62,000 samples generated by coarse-grained molecular dynamics (CGMD).
Then, we systematically investigate the effect of peptide encoding of amino
acids into sequences and molecular graphs using state-of-the-art sequential
(i.e., RNN, LSTM, and Transformer) and structural deep learning models (i.e.,
GCN, GAT, and GraphSAGE), on the accuracy of peptide self-assembly prediction,
an essential physiochemical process prior to any peptide-related applications.
Extensive benchmarking studies have proven Transformer to be the most powerful
sequence-encoding-based deep learning model, pushing the limit of peptide
self-assembly prediction to decapeptides. In summary, this work provides a
comprehensive benchmark analysis of peptide encoding with advanced deep
learning models, serving as a guide for a wide range of peptide-related
predictions such as isoelectric points, hydration free energy, etc.