PTF-Vāc: An explainable and generative deep co-learning encoder-decoder system for ab initio discovery of plant...

Discovery of transcription factor (TF) binding sites (TFBSs) and their motifs in plants poses significant challenges due to high cross-species variability. The interactions between TFs and their binding sites are highly specific and context dependent. Most existing TFBS-finding tools are not sufficiently accurate to discover these binding sites in plants. They fail to capture cross-species variability, interdependence between TF structures and corresponding TFBSs, and the context specificity of binding. Because they are coupled to a predefined TF-specific model/matrix, they are strongly influenced by the volume and quality of data provided to build the motifs. All these software applications make the assumption that the user input is specific to a particular TF, limiting their use for practical applications such as genomic annotation of newly sequenced species. Here, we report PTF-Vāc, an explainable deep-learning encoder-decoder generative system. PTF-Vāc is based on PTFSpot, a universal model of deep co-learning on variability in binding sites and TF structure, making it completely free from the bottlenecks described above. PTF-Vāc decouples the process of TFBS discovery from the prior step of motif finding and the requirement for TF-specific motif models. Because it is guided by a universal model for TF-DNA interactions, it can discover binding motifs irrespective of data volume and species and without reference to TF-specific models. In a comprehensive benchmarking study across an extremely high volume of experimental data, PTF-Vāc outperformed most advanced motif-finding deep-learning algorithms. PTF-Vāc thus opens a completely new chapter in ab initio TFBS discovery through generative AI.