This paper studies a \textit{partial functional partially linear single-index model} that consists of a functional linear component as well as a linear single-index component. This model generalizes many well-known existing models and is suitable for more complicated data structures. However, its estimation inherits the difficulties and complexities from both components and makes it a challenging problem, which calls for new methodology. We propose a novel profile B-spline method to estimate the parameters by approximating the unknown nonparametric link function in the single-index component part with B-spline, while the linear slope function in the functional component part is estimated by the functional principal component basis. The consistency and asymptotic normality of the parametric estimators are derived, and the global convergence of the proposed estimator of the linear slope function is also established. More excitingly, the latter convergence is optimal in the minimax sense. A two-stage procedure is implemented to estimate the nonparametric link function, and the resulting estimator possesses the optimal global rate of convergence. Furthermore, the convergence rate of the mean squared prediction error for a predictor is also obtained. Empirical properties of the proposed procedures are studied through Monte Carlo simulations. A real data example is also analyzed to illustrate the power and flexibility of the proposed methodology.