A simple way to do this is to subtract the "centering value" of the coefficient times its associated variable from the left-hand side. To go with your example,

Y=β1X1+β2X2+β3X3+β4X4+e
Assume the coefficient values should be centered at (5,1,-1,-5) respectively. Then:

Y−5X1−X2+X3+5X4=(β1−5)X1+(β2−1)X2+(β3+1)X3+(β4+5)X4+e
and, redefining terms, you have:

Y∗=β∗1X1+β∗2X2+β∗3X3+β∗4X4+e
A standard ridge regression would shrink the β∗i towards 0, which is equivalent to shrinking the original βi towards the specified centering values. to see this, consider a fully-shrunk β∗4=0, then β4+5=0 and therefore β4=−5. Shrinkage accomplished!

Ridge regression sets a normal prior centered at zero for each parameter. To penalize coefficients towards different values, just center the priors around your target instead of around 0. I think you can accomplish this using the bayesGLM function of the arm package with the following parameters:

prior.mean : set this to vector containing the values you want to use as the centers for your priors (the values you want to penalize "towards")
prior.df : set this to Inf to get normal priors. Possibly not necessary. There should be a way to control your prior variance, but it's not clear to me how this would be accomplished from looking at the documentation.
http://cran.r-project.org/web/packages/arm/arm.pdf