Improved Prediction of Anti-Angiogenic Peptides based on Machine Learning Models and Comprehensive Features from Peptide Sequences

Angiogenesis is a key process for the proliferation and metastatic spread of cancer cells. Anti-angiogenic peptides (AAPs), with the capability of inhibiting angiogenesis, are promising candidates in cancer treatment. We propose AAPL, a sequence-based predictor to identify AAPs with machine learning models of improved prediction accuracy. Each peptide sequence was transformed to a vector of 4335 numeric values according to 58 different feature types, followed by a heuristic algorithm for feature selection. Next, the hyperparameters of six machine learning models were optimized with respect to the feature subset. We considered two datasets, one with entire peptide sequences and the other with 15 amino acids from peptide N-termini. AAPL achieved Matthew’s correlation coefficients of 0.671 and 0.756 for independent tests based on the two datasets, respectively, outperforming existing predictors by a range of 5.3% to 24.6%. Further analyses show that AAPL yields higher prediction accuracy for peptides with more hydrophobic residues, and fewer hydrophilic and charged residues.