Organizing the bacterial annotation space with amino acid sequence embeddings.

ABSTRACT:

Background

Due to the ever-expanding gap between the number of proteins being discovered and their functional characterization, protein function inference remains a fundamental challenge in computational biology. Currently, known protein annotations are organized in human-curated ontologies, however, all possible protein functions may not be organized accurately. Meanwhile, recent advancements in natural language processing and machine learning have developed models which embed amino acid sequences as vectors in n-dimensional space. So far, these embeddings have primarily been used to classify protein sequences using manually constructed protein classification schemes.

Results

In this work, we describe the use of amino acid sequence embeddings as a systematic framework for s

SUBMITTER: Grigson SR 

PROVIDER: S-EPMC9502642 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature