Probabilistic thermal stability prediction through sparsity promoting transformer representation

TL;DR

This paper enhances protein property prediction by combining sparsity-promoting transformer models with a probabilistic framework, achieving more accurate and robust melting temperature predictions for drug design.

Contribution

It introduces a sparsity-promoting approach for transformer models and advocates for probabilistic modeling in ML-driven drug design tasks.

Findings

Mean absolute error of 0.23°C in Tm prediction

Sparsity improves robustness and accuracy

Probabilistic framing emphasizes uncertainty quantification

Abstract

Pre-trained protein language models have demonstrated significant applicability in different protein engineering task. A general usage of these pre-trained transformer models latent representation is to use a mean pool across residue positions to reduce the feature dimensions to further downstream tasks such as predicting bio-physics properties or other functional behaviours. In this paper we provide a two-fold contribution to machine learning (ML) driven drug design. Firstly, we demonstrate the power of sparsity by promoting penalization of pre-trained transformer models to secure more robust and accurate melting temperature (Tm) prediction of single-chain variable fragments with a mean absolute error of 0.23C. Secondly, we demonstrate the power of framing our prediction problem in a probabilistic framework. Specifically, we advocate for the need of adopting probabilistic frameworks especially in the context of ML driven drug design.