Neural Network One-hot Feature concatenation

I'm trying to add features to a model with two one hot encoded features. The features are defined like this.

vocabulary = ACGU
mapping_characters = list(vocabulary)
integer_mapping = {x: i for i,x in enumerate(list(vocabulary))}
n1 = [integer_mapping[word] for word in df[1][i]]

Afterwards I'd like to add an additional one dimentional feature. If I use a concatenate layer, this means the model I'm using will go from (N, L, 4) dimensions per sample to (N, L, 5) dimensions, with the one-hot feature taking up a depth of 4.

If I handle the feature concatenation in this way, will the one-hot feature be dimensionally more influential in the model than the other feature? If so, is there a good tensorflow function to fix this? Thank you for any help provided.