Are there any practical differences between a Turing machine with a PRNG and a probabilistic Turing machine?
As long as decision problems (or optimization problems) are concerned, probabilistic Turing machines are equivalent to non-probabilistic Turing machines, since you can just simulate all the possible coin tosses (there are some subtleties here since the probabilistic Turing machine could halt only with probability 1, but let's ignore them).

However, there could be a difference in efficiency. Using truly random coin tosses could allow you to solve a specific problem faster. However, it is conjectured that truly random coin tosses can't bring down the level of difficulty from non-polynomial to polynomial, that is, if you have a probabilistic polynomial time algorithm for a problem, then there exists a deterministic polynomial time algorithm for the problem. This conjecture is known as the P=BPP conjecture. The idea is that you can simulate the truly random coin tosses with a sufficiently sophisticated PRNG. The simulation might be costly in running time, but it incurs only a polynomial overhead, and from this point of view that's a cost we're willing to pay.

Practically speaking, PRNGs are good enough, although in some rare cases you need to be slightly careful and use something beyond the fastest known PRNG. Only in cryptography does it sometimes make sense to use truly random coin tosses, since you can get absolute security guarantees this way (one-time pads), though in practice cryptographic PRNGs are used and they seem secure enough.