Predicting Protein Secondary Structure with Neural Machine Translation

Overview

We present analysis of a novel tool for protein secondary structure prediction using the recently-investigated Neural Machine Translation frame-work. The tool provides a fast and accurate folding prediction based on primary structure with sub second prediction time even for batched inputs. We hypothesize that Neural Machine Translation can improve upon current predictive accuracy by better encoding complex relationships between nearby but non-adjacent amino acids. We overview our modifications to the framework in order to improve accuracy on protein sequences. We report 65.9% Q3 accuracy and analyze the strengths and weaknesses of our predictive model.

Competition history

• AJAS 2019 Category not listed

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