R's nnet package only supports fully connected neural networks with one hidden layer. This is the most primitive type of network. I doubt it will work well for promotors finding. In addition, such network won't give you useful interpretations.
If you want to explore neural networks, you should use a one-dimension convolution layer as is described in this paper. This layer effectively represents a positional weight matrix (PWM). You can know which motifs are used. With backtracking, you can also identify the coordinates of motifs. To deploy such a model, you need to be fairly familiar with deep learning and to learn one deep learning framework. For framework, you may start with keras. Once you get used to keras, you can implement your full promotor finder in less than 100 lines of python code. Alternatively, you may try dragonn. It is supposed to simplify deploying models for DNA sequences. I have no experience with it, though.
For promotor finding, it is also worth trying traditional methods. It is interesting that few/no neuralNet-based publications have evaluated traditional methods, probably because many ML people know little about classical motif finding. These methods could work well, too.