Although AF2 was not trained to model protein dynamics, in some cases, it can provide information about the individual states in dynamic equilibrium. For example, comparison of AF2 and NMR models for CASP14 target T1027, Gaussia luciferase, against NMR data suggested that the AF2 prediction model corresponds to just one of the multiple conformations in the NMR sample . Subsequently, the proclivity of AF2 to model one of multiple conformational states was also reported for a collection of ~100 apo / holo protein structure pairs . Recently, methods have emerged to extend AF2 and other machine learning networks to model alternative protein conformations explicitly. These extensions involve leveraging ECs that distinguish multiple conformations , employing multiple templates with diverse conformations , using shallow multiple-sequence alignments , or by perturbing the neural network weights to generate conformational diversity. SPEACH_AF utilizes in silico mutations as input to AF2 to model conformational switching in soluble and membrane proteins .