Accurate Prediction of Transcriptional Activity of Single Missense Variants in HIV Tat with Deep Learning

Abstract

Tat is an essential gene for increasing the transcription of all HIV genes, and affects HIV replication, HIV exit from latency, and AIDS progression. The Tat gene frequently mutates in vivo and produces variants with diverse activities, contributing to HIV viral heterogeneity as well as drug-resistant clones. Thus, identifying the transcriptional activities of Tat variants will help to better understand AIDS pathology and treatment. We recently reported the missense mutation landscape of all single amino acid Tat variants. In these experiments, a fraction of double missense alleles exhibited intragenic epistasis. However, it is too time-consuming and costly to determine the effect of the variants for all double mutant alleles through experiments. Therefore, we propose a combined GigaAssay/deep learning approach. As a first step to determine activity landscapes for complex variants, we evaluated a deep learning framework using previously reported GigaAssay experiments to predict how transcription activity is affected by Tat variants with single missense substitutions. Our approach achieved a 0.94 Pearson correlation coefficient when comparing the predicted to experimental activities. This hybrid approach can be extensible to more complex Tat alleles for a better understanding of the genetic control of HIV genome transcription.

Accurate prediction of functional effect of single amino acid variants with deep learning

“…Here, we propose a deep learning method, named Rep2Mut-V2, to use protein sequences as the sole input to accurately predict 27 types of measurements of mutational effects of protein variants. Rep2Mut-V2 is an improvement of our previous model Rep2Mut [40] which was designed to predict the transcriptional activity of HIV Tat protein (GigaAssay [3] ). In an assessment of 38 protein datasets, Rep2Mut-V2 demonstrated superior performance when compared to six existing methods.…”