Using machine learning to predict the effects and consequences of mutations in proteins |
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| Affiliation: | 1. Department of Chemistry, The University of Texas at Austin, 105 E 24TH St., Austin, 78712, Texas, USA;2. Department of Molecular Biosciences, The University of Texas at Austin, 100 East 24th St., Stop A5000, Austin, 78712, Texas, USA;3. Department of Integrative Biology, The University of Texas at Austin, 2415 Speedway, Stop C0930, Austin, 78712, Texas, USA;1. Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, 3800, Victoria, Australia;2. Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, 16802, Pennsylvania, USA;1. Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 12, 14195, Berlin, Germany;2. Department of Physics and Astronomy, Rice University, 6100 Main Street, Houston, 77005, Texas, USA;3. Department of Physics, Freie Universität Berlin, Arnimallee 12, 14195, Berlin, Germany;4. Center for Theoretical Biological Physics, Rice University, 6100 Main Street, Houston, 77005, Texas, USA;5. Microsoft Research AI4Science, Karl-Liebknecht Str. 32, Berlin, 10178, Berlin, Germany;6. Department of Chemistry, Rice University, 6100 Main Street, Houston, 77005, Texas, USA |
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| Abstract: | Machine and deep learning approaches can leverage the increasingly available massive datasets of protein sequences, structures, and mutational effects to predict variants with improved fitness. Many different approaches are being developed, but systematic benchmarking studies indicate that even though the specifics of the machine learning algorithms matter, the more important constraint comes from the data availability and quality utilized during training. In cases where little experimental data are available, unsupervised and self-supervised pre-training with generic protein datasets can still perform well after subsequent refinement via hybrid or transfer learning approaches. Overall, recent progress in this field has been staggering, and machine learning approaches will likely play a major role in future breakthroughs in protein biochemistry and engineering. |
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| Keywords: | Mutational effect Deep learning Protein engineering Transformer Convolutional neural network Hybrid learning |
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