Avian Influenza Viruses (AIV), specifically H5N1, are highly adaptive and mutate continuously throughout their life-cycle. The accumulation of constant mutations causes antigenic drift, leading to the spread of epidemics which result in billions of dollars in socioeconomic losses each year. Consequently, the containment of AIV epidemics is of vital importance. Computational approaches to the study of epidemiology, such as phylodynamic simulations, enhance in vivo analysis by examining the impact of ecological parameters and evolutionary traits, as well as forecasting the rise of future variants. We propose an improvement on existing phylodynamic simulation models through the introduction of: ❶ actual Hemagglutinin (HA) protein sequences, ❷ simulating mutations, ❸ and implementing an amino-acid level antigenic analysis algorithm to model natural selection pressure. In contrast to prior approaches that use abstract antigenic models, our method uses and yields actual HA strains enabling robust validation and direct application of results to inform vaccine design. We assess the validity of our method against the currentWorld Health Organization (WHO) H5N1 nomenclature phylogram for 3 countries. Our calibration and validation experiments use > 10,000 simulations with 1,000s of different parameter settings requiring over 2,500 hours of computing time. Our results show that our calibrated models yield the expected evolutionary characteristics but with a compromise of ∼10× longer simulation times.