SCARSE: Small-sample Cross-validation-Anchored Regression Solution for low resource peptide Engineering

Abstract

Overview In this Zenodo upload, we introduce both the code for SCARSE (scarse.zip) and the reproducability code (SCARSE-repro.zip). How SCARSE works SCARSE is designed for peptide property prediction in low-data regimes by combining protein language model embeddings with classical machine learning methods. The workflow consists of the following steps: Input data A CSV file containing peptide sequences and one or more target variables. The sequence column (seq_col) should contain amino acid sequences. The target column(s) (score_col) contain regression values. Sequence embedding Sequences are converted into numerical representations using the ESM-2 protein language model. Model Gaussian Process Regression is used as the downstream model, chosen for its robustness in small-sample settings. Hyperparameter optimization Models are tuned using cross-validation and Optuna-based optimization. The number of folds and optimization trials can be controlled by the user. Optional wall-clock limits on the Optuna search are available (trial_timeout for a single trial, study_timeout for the whole search); both default to no limit. Training output Cross-validation performance metrics are returned. A trained model environment is stored internally and reused for prediction. Prediction New sequences are embedded using the same pipeline. The trained model generates predictions for each target variable. Notes and best practices Call order mattersYou must run scarse.train() before calling scarse.pred(), as the trained model is stored internally. Data quality is important Ensure no missing or empty sequences Use consistent formatting (standard amino acid codes(ACDEFGHIKLMNPQRSTVWY)) Small datasets are supportedSCARSE have been evaluated for datasets as small as ~20 samples, but performance generally improves with more data. Tested for Python version Python version == 3.12.10 Setup pip install scarse Usage For training a regression model: import scarse scarse.train(data_path="../app/train.csv", seq_col="sequence", score_col=["score"]) Optional Optuna time limits: import scarse # Cap a single trial at 600 s and the whole search at 3600 s. # Both default to None (no limit). scarse.train(data_path="../app/train.csv", seq_col="sequence", score_col=["score"], trial_timeout=600, study_timeout=3600) For predicting after model have been trained: df_pred = scarse.pred(data_path="../app/test.csv", seq_col="sequence") Tutorials See the file named tutorial.ipynb