MHTAPred-SS: A Highly Targeted Autoencoder-Driven Deep Multi-Task Learning Framework for Accurate Protein Secondary Structure Prediction.

ABSTRACT: Accurate protein secondary structure prediction (PSSP) plays a crucial role in biopharmaceutics and disease diagnosis. Current prediction methods are mainly based on multiple sequence alignment (MSA) encoding and collaborative operations of diverse networks. However, existing encoding approaches lead to poor feature space utilization, and encoding quality decreases with fewer homologous proteins. Moreover, the performance of simple stacked networks is greatly limited by feature extraction capabilities and learning strategies. To this end, we propose MHTAPred-SS, a novel PSSP framework based on the fusion of six features, including the embedding feature derived from a pre-trained protein language model. First, we propose a highly targeted autoencoder (HTA) as the driver to encode sequences

SUBMITTER: Feng R 

PROVIDER: S-EPMC11677681 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature