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Self-supervised machine learning methods for protein design improve sampling, but not the identification of high-fitness variants

M. Ertelt, R. Moretti, J. Meiler, and C. Schoeder. (June 2024)
DOI: 10.1101/2024.06.20.599843

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Methodology for rigorous modeling of protein conformational changes by Rosetta using DEER distance restraintsD. Del Alamo, K. Jagessar, J. Meiler, and H. Mchaourab. PLoS Comput. Biol., 17 (6): e1009107 (June 2021)The human antibody sequence space and structural design of the V, J regions, and CDRH3 with RosettaS. Schmitz, E. Schmitz, J. Crowe, Jr, and J. Meiler. MAbs, 14 (1): 2068212 (January 2022)Predicting the functional impact of KCNQ1 variants with artificial neural networksS. Phul, G. Kuenze, C. Vanoye, C. Sanders, A. George, Jr, and J. Meiler. PLoS Comput. Biol., 18 (4): e1010038 (April 2022)Principles of Alternating Access in LeuT-fold Transporters: Commonalities and DivergencesD. Alamo, J. Meiler, and H. Mchaourab. (May 2022)MutationExplorer: a webserver for mutation of proteins and 3D visualization of energetic impactsM. Philipp, C. Moth, N. Ristic, J. Tiemann, F. Seufert, A. Panfilova, J. Meiler, P. Hildebrand, A. Stein, D. Wiegreffe and 1 other author(s). Nucleic Acids Research, (2024)Towards generalizable predictions for G protein-coupled receptor variant expressionC. Kuntz, H. Woods, A. McKee, N. Zelt, J. Mendenhall, J. Meiler, and J. Schlebach. Biophysical Journal, 121 (14): 2712--2720 (2022)Small integral membrane protein 10 like 1 enhances differentiation of adipose progenitor cellsM. Nebe, S. Kehr, S. Schmitz, J. Breitfeld, J. Lorenz, D. Le Duc, P. Stadler, J. Meiler, W. Kiess, A. Garten and 1 other author(s). Biochemical and Biophysical Research Communications, (March 2022)Machine learning application to predict binding affinity between peptide containing non-canonical amino acids and HLA0201S. Jiang, Z. Su, N. Bloodworth, Y. Liu, C. Martina, D. Harrison, and J. Meiler. (November 2024)3D Deep Learning for Biological Function Prediction from Physical FieldsV. Golkov, M. Skwark, A. Mirchev, G. Dikov, A. Geanes, J. Mendenhall, J. Meiler, and D. Cremers. 2020 International Conference on 3D Vision (3DV), (November 2020)Automated Structure Elucidation of Organic Molecules from 13C NMR Spectra Using Genetic Algorithms and Neural NetworksJ. Meiler, and M. Will. Journal of Chemical Information and Computer Sciences, 41 (6): 1535â1546 (October 2001)

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Self-supervised machine learning methods for protein design improve sampling, but not the identification of high-fitness variants

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Self-supervised machine learning methods for protein design improve sampling, but not the identification of high-fitness variants

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Self-supervised machine learning methods for protein design improve sampling, but not the identification of high-fitness variants