An enhanced variant effect predictor based on a deep generative model and the Born-Again Networks

Ha Young Kim,Woosung Jeon,Dongsup Kim

doi:10.1038/s41598-021-98693-3

Abstract

The development of an accurate and reliable variant effect prediction tool is important for research in human genetic diseases. A large number of predictors have been developed towards this goal, yet many of these predictors suffer from the problem of data circularity. Here we present MTBAN (Mutation effect predictor using the Temporal convolutional network and the Born-Again Networks), a method for predicting the deleteriousness of variants. We apply a form of knowledge distillation technique known as the Born-Again Networks (BAN) to a previously developed deep autoregressive generative model, mutationTCN, to achieve an improved performance in variant effect prediction. As the model is fully unsupervised and trained only on the evolutionarily related sequences of a protein, it does not suffer from the problem of data circularity which is common across supervised predictors. When evaluated on a test dataset consisting of deleterious and benign human protein variants, MTBAN shows an outstanding predictive ability compared to other well-known variant effect predictors. We also offer a user-friendly web server to predict variant effects using MTBAN, freely accessible at http://mtban.kaist.ac.kr. To our knowledge, MTBAN is the first variant effect prediction tool based on a deep generative model that provides a user-friendly web server for the prediction of deleteriousness of variants.

Highlights

The development of an accurate and reliable variant effect prediction tool is important for research in human genetic diseases
We introduce MTBAN (Mutation effect predictor using the Temporal convolutional network and the Born-Again Networks), an enhanced method to predict the deleteriousness of single amino acid variants
As demonstrated in our previous w ork[4], the deep autoregressive generative model is a powerful tool for learning the distribution underlying the evolutionarily related sequences of a protein and predicting the effects of variations in a sequence

Summary

Introduction

The development of an accurate and reliable variant effect prediction tool is important for research in human genetic diseases. We apply a form of knowledge distillation technique known as the Born-Again Networks (BAN) to a previously developed deep autoregressive generative model, mutationTCN, to achieve an improved performance in variant effect prediction. When evaluated on a test dataset consisting of deleterious and benign human protein variants, MTBAN shows an outstanding predictive ability compared to other well-known variant effect predictors. Development of a reliable computational tool to predict the effects of sequence variants would aid in the treatment of many human genetic diseases To achieve this goal, many predictors have been developed based on different approaches. Many predictors have been developed based on different approaches Among these methods, supervised methods learn from labelled variant data consisting of known deleterious and benign variants, and many of them show good predictive ability. We offer a freely accessible web server for using MTBAN for variant effect prediction

Methods

Results

Conclusion