Copy models for protein sequence compression

Protein sequence compression has been a topic of research for more than two decades. Despite the obvious advantages due to storage space and transmission time reduction, better compression algorithms also help answering the question: How much information is on a certain amount of data? In this paper, we provide a step forward regarding answering this question for protein sequence data, using combinations of copy models and finite-context models, assisted by a multilayer perceptron. Compared to the state-of-the-art protein sequence compressor, AC2, the proposed approach attains better compression using one tenth of the time and less memory.