ESP

ESP predicts small-molecule substrates of enzymes using machine learning to identify enzyme–substrate pairs for enzymology and metabolic studies.

Key Features:

• High Predictive Accuracy: Achieves over 91% accuracy on independent and diverse test datasets.
• General Applicability: Predicts substrates across a wide range of enzymes rather than being limited to specific enzyme families.
• Modified Transformer Enzyme Representation: Employs a modified transformer model to represent enzymes and capture complex patterns in enzyme–substrate interactions.
• Data Augmentation via Negative Sampling: Addresses scarcity of negative examples by randomly sampling small molecules and assigning them as non-substrates to augment the training set.
• Model Integration: Leverages both traditional machine learning and deep learning models for prediction.

Scientific Applications:

• Basic Research: Generates hypotheses about enzyme substrate specificity and guides experimental investigation of enzymatic functions and metabolic pathways.
• Drug Discovery and Metabolic Engineering: Suggests candidate substrates to aid identification of drug targets and to support optimization of engineered metabolic processes.

Methodology:

Training integrates traditional and deep learning models with a modified transformer-based enzyme representation on a dataset comprising known substrates and augmented non-substrate examples generated by random sampling of small molecules.