DeepSol: a deep learning framework for sequence-based protein solubility prediction.

ABSTRACT:

Motivation

Protein solubility plays a vital role in pharmaceutical research and production yield. For a given protein, the extent of its solubility can represent the quality of its function, and is ultimately defined by its sequence. Thus, it is imperative to develop novel, highly accurate in silico sequence-based protein solubility predictors. In this work we propose, DeepSol, a novel Deep Learning-based protein solubility predictor. The backbone of our framework is a convolutional neural network that exploits k-mer structure and additional sequence and structural features extracted from the protein sequence.

Results

DeepSol outperformed all known sequence-based state-of-the-art solubility prediction methods and attained an accuracy of 0.77 and Matthew's correlation coeffic

SUBMITTER: Khurana S 

PROVIDER: S-EPMC6355112 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature