A Pipeline for Data-Driven Learning of Topological Features with Applications to Protein Stability Prediction

TL;DR

This paper introduces a data-driven approach to learn interpretable topological features from biomolecular data, demonstrating their effectiveness in predicting protein stability and complementing expert-designed features.

Contribution

It presents a novel pipeline for extracting topological features and shows their utility in protein stability prediction, outperforming traditional biophysical features in some cases.

Findings

Topological features achieved 92%-99% of SME model performance

Combining topological and SME features improved accuracy

High correlations found between topological and SME features

Abstract

In this paper, we propose a data-driven method to learn interpretable topological features of biomolecular data and demonstrate the efficacy of parsimonious models trained on topological features in predicting the stability of synthetic mini proteins. We compare models that leverage automatically-learned structural features against models trained on a large set of biophysical features determined by subject-matter experts (SME). Our models, based only on topological features of the protein structures, achieved 92%-99% of the performance of SME-based models in terms of the average precision score. By interrogating model performance and feature importance metrics, we extract numerous insights that uncover high correlations between topological features and SME features. We further showcase how combining topological features and SME features can lead to improved model performance over either feature set used in isolation, suggesting that, in some settings, topological features may provide new discriminating information not captured in existing SME features that are useful for protein stability prediction.