Modeling aspects of the language of life through transfer-learning protein sequences.

ABSTRACT:

Background

Predicting protein function and structure from sequence is one important challenge for computational biology. For 26 years, most state-of-the-art approaches combined machine learning and evolutionary information. However, for some applications retrieving related proteins is becoming too time-consuming. Additionally, evolutionary information is less powerful for small families, e.g. for proteins from the Dark Proteome. Both these problems are addressed by the new methodology introduced here.

Results

We introduced a novel way to represent protein sequences as continuous vectors (embeddings) by using the language model ELMo taken from natural language processing. By modeling protein sequences, ELMo effectively captured the biophysical properties of the language of li

SUBMITTER: Heinzinger M 

PROVIDER: S-EPMC6918593 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature