MMRT: MultiMut Recursive Tree for Predicting Functional Effects of High-Order Protein Variants From Low-Order Variants

Authors

Bryce Forrest
Houssemeddine Derbel
Zhongming Zhao
Qian Liu

Language

English

Publication Date

1-1-2025

Journal

Computational and Structural Biotechnology Journal

DOI

10.1016/j.csbj.2025.02.012

PMID

40070521

PMCID

PMC11894328

PubMedCentral® Posted Date

2-18-2025

PubMedCentral® Full Text Version

Post-print

Abstract

Protein sequences primarily determine their stability and functions. Mutations may occur at one, two, or three positions at the same time (low-order variants) or at multiple positions simultaneously (high-order variants), which affect protein functions. So far, low-order variants, such as single variants, double variants, and triple variants, have been well-studied through high-throughput experimental scanning techniques and computational prediction methods. However, research on high-order variants remains limited because of the difficulty of scanning an exponentially large number of potential variant combinations. Nonetheless, studying higher-order variants is crucial for understanding the pathogenesis of complex diseases, advancing protein engineering, and driving precision medicine. In this work, we introduce a novel deep learning model, namely

Keywords

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

Published Open-Access

yes

Recommended Citation

Forrest, Bryce; Derbel, Houssemeddine; Zhao, Zhongming; et al., "MMRT: MultiMut Recursive Tree for Predicting Functional Effects of High-Order Protein Variants From Low-Order Variants" (2025). Faculty, Staff and Student Publications. 622.
https://digitalcommons.library.tmc.edu/uthshis_docs/622