See edit and comments, this response might not be applicable to the question:
When performing regression you would tend to want your regressors to be of similar type, or at the very least range. Assuming you use log return for price changes I would recommend using the untransformed interest rate. The reason for this is that they are the same type of entity, rate of returns.
$R_t = \ln\frac{P_t}{P_{t-1}}$
$R_{t+1} = \theta_0 + \theta_1R_{t} + \theta_2I_{t} + \epsilon$
You can of course use more fancy transformations, but this would be the natural starting point. Personally I use an evolutionary algorithm to evolve the regressor transformations.
Don't worry about the interest rate being always positive. If this matters at all it will be pushed in to the intercept weight.
Edit:
Given that the interest rate and data resolution you are looking at displays tendencies to be non-stationary I would retract my recommendation above. However, this does make me wonder if you have enough data since I would intuitively expect interest rates to not trend in the long run.
In your shoes I might have attempted to try evolutionary symbolic regression to transform the interest rate data, as discussed in the comment section. When doing this you could try to use your ADF test results as a fitness measure. The resulting transformation function can be used prior to your linear regression model. Remember to split in to test and training datasets in order to detect overfitting.