Prediction of membrane protein secondary structure elements, topology, and fold can be done using the BCL::MP-Fold algorithm. Given a sequence, this algorithm assembles transmembrane secondary structural elements using a Monte Carlos Metropolis approach and evaluation of these models is done with a knowledge-based potential. Experimental data, including electron paramagnetic resonance and nuclear magnetic resonance, can also be included in the evaluation of models. Other machine learning approaches to membrane protein folding in our lab includes the application of AlphaFold2 network to predict conformational landscapes of transporters and receptors.
 Weiner, B. E.; Woetzel, N.; Karakaş, M.; Alexander, N.; Meiler, J. BCL::MP-Fold: Folding Membrane Proteins through Assembly of Transmembrane Helices. Structure 2013, 21 (7), 1107–1117. https://doi.org/10.1016/j.str.2013.04.022.
 Fischer, A. W.; Alexander, N. S.; Woetzel, N.; Karakas, M.; Weiner, B. E.; Meiler, J. BCL::MP-Fold: Membrane Protein Structure Prediction Guided by EPR Restraints: EPR-Guided Membrane Protein Fold Prediction. Proteins Struct. Funct. Bioinforma. 2015, 83 (11), 1947–1962. https://doi.org/10.1002/prot.24801.
 Alamo, D. del; Sala, D.; Mchaourab, H. S.; Meiler, J. Sampling the Conformational Landscapes of Transporters and Receptors with AlphaFold2; preprint; Biophysics, 2021. https://doi.org/10.1101/2021.11.22.469536.