“…(Swiezawska et al, 2015(Swiezawska et al, , 2017 and Japanese morning glory (Szmidt-Jaworska et al, 2009) and in the monocot grass model Brachypodium (Swiezawska et al, 2020), as well as in a human immune-responsive protein IRAK3 (Freihat et al, 2019), have demonstrated the robustness of this approach and its significance in biological discoveries across different systems. Furthermore, the method enables candidate selections for follow-up in vivo and in planta studies that will eventually reveal the biological roles of these functional centers such as those reported in Joudoi et al (2013), Shen et al (2019), Vaz Dias et al (2019, Angkawijaya et al (2020), Lee et al (2020), andTurek et al (2020). We foresee that emerging experimental data will inform and strengthen motif refinement efforts, and enable the development of modern machine learning techniques that incorporate multiple features ranging from the classical physicochemical properties of protein domains and protein-protein interaction (PPI) networks to GO based function predictions, to not only automate annotations for uncharacterized proteins, but also to identify hidden functional centers in complex multi-functional proteins (Rifaioglu et al, 2019;Bonetta and Valentino, 2020;Cai et al, 2020;Littmann et al, 2021).…”