Given two sets of observations $x_i$, $y_i$ that accept the linear regression model $y_i = \beta_{0} + \beta_{1}x_i + e_i$.
Define the transformation $\tilde{y} = \log_{100}{(y+|a|)}$, where $a$ is defined to make sure the $log$ is well defined. How will it change the coefficients $\beta_0$, $\beta_1$?
Unlike for linear transformation e.g., $aY_i + b$, you won't have an elegant relationship between the old the new estimators. Just recall that for the non-transformed $y$ you have $$ \hat{\beta}_1 = \frac{\sum(x_i - \bar{x})(y_i-\bar{y})}{\sum(x_i-\bar{x})^2}, \quad \hat{\beta}_0 = \bar{y} - \hat{\beta}_1\bar{x}, $$ hence for the new estimators just replace everywhere $y_i$ with $\log(y_i + |a|)$, and due to the fact that log is non-linear, I'm afraid that there is nothing else that you can do.