The following slightly different limit is probably what is intended: $$\lim_{n \to \infty} \sum_{k=n}^{5n} \binom {k-1}{n-1} \left(\frac15\right)^n \left(\frac45\right)^{k-n}.$$
It is equal to $\frac12$.
To see this, interpret the sum as a probability. Take a biased coin that lands heads with probability $\frac15$, and flip it until it lands heads $n$ times. The summand $\binom {k-1}{n-1} \left(\frac15\right)^n \left(\frac45\right)^{k-n}$ is the probability that it will take exactly $k$ coinflips for the coin to land heads $n$ times: for this to happen, the last of $k$ coinflips must be heads, so we choose which $n-1$ of the first $k-1$ coinflips were heads, multiply by the probability of $n$ heads, and multiply by the probability of $n-k$ tails. Therefore the overall sum is the probability that it will take at most $5n$ coinflips for the coin to land heads $n$ times.
The number of coinflips this takes can be written as $X_1 + X_2 + \dots + X_n$ where $X_i \sim \text{Geometric}(\frac15)$ is the number of coinflips it takes to go from $i-1$ to $i$ heads. Each $X_i$ has expected value $5$ and variance $20$, so the sum has expected value $5n$ and variance $20n$.
By the central limit theorem, the quantity
$$
Z_n := \frac{(X_1 + X_2 + \dots + X_n) - 5n}{\sqrt{20n}}
$$
converges in distribution to a standard normal random variable. In particular, $\lim_{n \to \infty} \Pr[Z_n \le 0] = \frac12$, since a standard normal random variable is equally likely to be positive or negative.
But the event that $Z_n \le 0$ is exactly the event that $X_1 + X_2 + \dots + X_n \le 5n$, which is the event whose probability is given by the sum we started with, so the original limit is also $\frac12$.