Accurate Prediction of Functional Effect of Single Amino Acid Variants With Deep Learning

Authors

Language

English

Publication Date

1-1-2023

Journal

Computational and Structural Biotechnology Journal

DOI

10.1016/j.csbj.2023.11.017

PMID

38074467

PMCID

PMC10709104

PubMedCentral® Posted Date

11-10-2023

PubMedCentral® Full Text Version

Post-print

Abstract

The assessment of functional effect of amino acid variants is a critical biological problem in proteomics for clinical medicine and protein engineering. Although natively occurring variants offer insights into deleterious variants, high-throughput deep mutational experiments enable comprehensive investigation of amino acid variants for a given protein. However, these mutational experiments are too expensive to dissect millions of variants on thousands of proteins. Thus, computational approaches have been proposed, but they heavily rely on hand-crafted evolutionary conservation, limiting their accuracy. Recent advancement in transformers provides a promising solution to precisely estimate the functional effects of protein variants on high-throughput experimental data. Here, we introduce a novel deep learning model, namely Rep2Mut-V2, which leverages learned representation from transformer models. Rep2Mut-V2 significantly enhances the prediction accuracy for 27 types of measurements of functional effects of protein variants. In the evaluation of 38 protein datasets with 118,933 single amino acid variants, Rep2Mut-V2 achieved an average Spearman's correlation coefficient of 0.7. This surpasses the performance of six state-of-the-art methods, including the recently released methods ESM, DeepSequence and EVE. Even with limited training data, Rep2Mut-V2 outperforms ESM and DeepSequence, showing its potential to extend high-throughput experimental analysis for more protein variants to reduce experimental cost. In conclusion, Rep2Mut-V2 provides accurate predictions of the functional effects of single amino acid variants of protein coding sequences. This tool can significantly aid in the interpretation of variants in human disease studies.

Keywords

Functional effect, Deep learning, Single amino acid variant, Precise estimation, High-throughput experiments

Published Open-Access

yes

Recommended Citation

, "Accurate Prediction of Functional Effect of Single Amino Acid Variants With Deep Learning" (2023). Faculty, Staff and Student Publications. 597.
https://digitalcommons.library.tmc.edu/uthshis_docs/597