Increasing the accuracy of single sequence prediction methods using a deep semi-supervised learning framework.

ABSTRACT:

Motivation

Over the past 50 years, our ability to model protein sequences with evolutionary information has progressed in leaps and bounds. However, even with the latest deep learning methods, the modelling of a critically important class of proteins, single orphan sequences, remains unsolved.

Results

By taking a bioinformatics approach to semi-supervised machine learning, we develop Profile Augmentation of Single Sequences (PASS), a simple but powerful framework for building accurate single-sequence methods. To demonstrate the effectiveness of PASS we apply it to the mature field of secondary structure prediction. In doing so we develop S4PRED, the successor to the open-source PSIPRED-Single method, which achieves an unprecedented Q3 score of 75.3% on the standard CB513 tes

SUBMITTER: Moffat L 

PROVIDER: S-EPMC8570780 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature