Improved protein model quality assessment by integrating sequential and pairwise features using deep learning.

ABSTRACT:

Motivation

Accurately estimating protein model quality in the absence of experimental structure is not only important for model evaluation and selection but also useful for model refinement. Progress has been steadily made by introducing new features and algorithms (especially deep neural networks), but the accuracy of quality assessment (QA) is still not very satisfactory, especially local QA on hard protein targets.

Results

We propose a new single-model-based QA method ResNetQA for both local and global quality assessment. Our method predicts model quality by integrating sequential and pairwise features using a deep neural network composed of both 1D and 2D convolutional residual neural networks (ResNet). The 2D ResNet module extracts useful information from pairwise featu

SUBMITTER: Jing X 

PROVIDER: S-EPMC8016469 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature