Andrea Graziadei

Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning: AlphaLink

Andrea Graziadei1,3*, Kolja Stahl2*, Therese Dau3, Oliver Brock2,5, Juri Rappsilber3,6

1 Fondazione Human Technopole, 20157 Milan, Italy
2 Technische Universität Berlin, Robotics and Biology Laboratory, 10623 Berlin, Germany
3 Fritz Lipmann Institute, Leibniz Institute on Aging, 07745 Jena, Germany
4 Technische Universität Berlin, Chair of Bioanalytics, 10623 Berlin, Germany
5 Si-M/”Der Simulierte Mensch”, a Science Framework of Technische Universität Berlin and Charité – Universitätsmedizin Berlin, Berlin, Germany.
6 Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3BF, UK
*These authors contributed equally.

While AlphaFold2 can predict accurate protein structures from the primary sequence, challenges remain for proteins that undergo conformational changes or for which few homologous sequences are known. Here, we introduce AlphaLink, a modified version of the AlphaFold2 algorithm that incorporates experimental distance restraint information from crosslinking mass spectrometry into its network architecture. By employing sparse experimental contacts as anchor points, AlphaLink improves on the performance of AlphaFold2 in predicting challenging targets. We confirm this experimentally by using the noncanonical amino acid Photo-Leucine to obtain information on residue-residue contacts inside cells by crosslinking mass spectrometry. The program can predict distinct conformations of proteins based on the distance restraints provided, demonstrating the value of experimental data in driving protein structure prediction. The noise-tolerant framework for integrating data in protein structure prediction presented here opens a path to accurate characterization of protein structures from in-cell data.

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