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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

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MMRT: MultiMut Recursive Tree for predicting functional effects of high-order protein variants from low-order

Bryce Forrest¹, Houssemeddine Derbel¹, Zhongming Zhao²

¹Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, 4505 S Maryland Pkwy, Las Vegas, NV 89154, USA.

Computational and Structural Biotechnology Journal

|March 12, 2025

Summary

Predicting high-order protein variants is challenging. A new deep learning model, MultiMut Recursive Tree (MMRT), accurately predicts functional effects of these complex variants by leveraging low-order variant data.

Keywords:

Deep learning Functional effects High-order protein variants Low-order variants

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Published on: July 14, 2015

Area of Science:

Computational biology
Protein engineering
Genomics

Background:

Protein function and stability are dictated by their sequences.
Low-order variants (single, double, triple) are well-studied, but high-order variants remain challenging to analyze.
Understanding high-order variants is crucial for disease pathogenesis, protein engineering, and precision medicine.

Purpose of the Study:

To develop a novel deep learning model for predicting the functional effects of high-order protein variants.
To address the limitations in studying complex, multi-positional protein mutations.

Main Methods:

Introduction of the MultiMut Recursive Tree (MMRT) deep learning model.
MMRT integrates deep learning with a recursive tree framework.
Leveraging information from low-order variants to predict high-order variant effects.

Main Results:

MMRT was evaluated on a dataset of 685,593 high-order variants.
Achieved a mean Spearman's correlation coefficient of 0.55.
Outperformed existing state-of-the-art methods: ESM, DeepSequence, and ECNet.

Conclusions:

MMRT provides more accurate predictions of high-order protein variant functional effects.
The model shows significant potential for aiding variant interpretation in human disease studies.
Facilitates advancements in protein engineering and precision medicine.