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Abstract

Abstract

Predicting functional properties of mutations like the change in enzyme activity remains challenging and is not well captured by traditional pathogenicity prediction. Yet such functional predictions are crucial in areas like targeted cancer therapy where some drugs may only be administered if a mutation causes an increase in enzyme activity. Current approaches either leverage static Protein-Language Model (PLM) embeddings or complex multi-modal features (e.g., static PLM embeddings, structure, and evolutionary data) and either (1) fall short in accuracy or (2) involve complex data processing and pre-training. Standardized datasets and metrics for robust benchmarking would benefit model development but do not yet exist for functional effect prediction.

To address these challenges we develop ESM-Effect, an optimized PLM-based functional effect prediction framework through extensive ablation studies. ESM-Effect fine-tunes ESM2 PLM with an inductive bias regression head to achieve state-of-the-art performance. It surpasses the multi-modal state-of-the-art method PreMode, indicating redundancy of structural and evolutionary features, while training 6.7-times faster.

In addition, we develop a benchmarking framework with robust test datasets and strategies, and propose a novel metric for prediction accuracy termed relative Bin-Mean Error (rBME): rBME emphasizes prediction accuracy in challenging, non-clustered, and rare gain-of-function regions and correlates more intuitively with model performance than commonly used Spearman’s rho. Finally, we demonstrate partial generalization of ESM-Effect to unseen mutational regions within the same protein, illustrating its potential in precision medicine applications. Extending this generalization across different proteins remains a promising direction for future research. ESM-Effect is available at: https://github.com/moritzgls/ESM-Effect.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

* ↵* profile: moritzgls.github.io. Former: Dept. of Translational Genomics, University of Cologne

* ↵† Mildred Scheel School of Oncology, Center for Molecular Medicine Cologne, Faculty of Medicine and University Hospital Cologne

* https://github.com/moritzgls/ESM-Effect

Details

1009240
Title
ESM-Effect: An Effective and Efficient Fine-Tuning Framework towards accurate prediction of Mutation’s Functional Effect
Publication title
bioRxiv; Cold Spring Harbor
Publication year
2025
Publication date
Feb 7, 2025
Section
New Results
Publisher
Cold Spring Harbor Laboratory Press
Source
BioRxiv
Place of publication
Cold Spring Harbor
Country of publication
United States
University/institution
Cold Spring Harbor Laboratory Press
Publication subject
ISSN
2692-8205
Source type
Working Paper
Language of publication
English
Document type
Working Paper
ProQuest document ID
3165215928
Document URL
https://www.proquest.com/working-papers/esm-effect-effective-efficient-fine-tuning/docview/3165215928/se-2?accountid=208611
Copyright
© 2025. This article is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (“the License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-02-11
Database
ProQuest One Academic