Towards mechanistic models of mutational effects: Deep learning on Alzheimer's Aβ peptide.

ABSTRACT: Deep Mutational Scanning (DMS) has enabled multiplexed measurement of mutational effects on protein properties, including kinematics and self-organization, with unprecedented resolution. However, potential bottlenecks of DMS characterization include experimental design, data quality, and depth of mutational coverage. Here, we apply deep learning to comprehensively model the mutational effect of the Alzheimer's Disease associated peptide Aβ₄₂ on aggregation-related biochemical traits from DMS measurements. Among tested neural network architectures, Convolutional Neural Networks and Recurrent Neural Networks are found to be the most cost-effective models with high performance even under insufficiently-sampled DMS studies. While sequence features are essential for satisfactory pred

SUBMITTER: Wang B 

PROVIDER: S-EPMC10114515 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature