{"day":"01","type":"journal_article","file":[{"success":1,"file_id":"14714","date_created":"2023-12-27T08:40:43Z","file_size":1342319,"date_updated":"2023-12-27T08:40:43Z","content_type":"application/pdf","access_level":"open_access","creator":"kschuh","file_name":"2023_NatureComm_Curatolo.pdf","checksum":"fd9e9d527c2691f03fbc24031a75a3b3","relation":"main_file"}],"date_updated":"2024-01-02T11:36:46Z","tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"month":"12","article_processing_charge":"Yes","publication_status":"published","date_published":"2023-12-01T00:00:00Z","abstract":[{"text":"The self-assembly of complex structures from a set of non-identical building blocks is a hallmark of soft matter and biological systems, including protein complexes, colloidal clusters, and DNA-based assemblies. Predicting the dependence of the equilibrium assembly yield on the concentrations and interaction energies of building blocks is highly challenging, owing to the difficulty of computing the entropic contributions to the free energy of the many structures that compete with the ground state configuration. While these calculations yield well known results for spherically symmetric building blocks, they do not hold when the building blocks have internal rotational degrees of freedom. Here we present an approach for solving this problem that works with arbitrary building blocks, including proteins with known structure and complex colloidal building blocks. Our algorithm combines classical statistical mechanics with recently developed computational tools for automatic differentiation. Automatic differentiation allows efficient evaluation of equilibrium averages over configurations that would otherwise be intractable. We demonstrate the validity of our framework by comparison to molecular dynamics simulations of simple examples, and apply it to calculate the yield curves for known protein complexes and for the assembly of colloidal shells.","lang":"eng"}],"department":[{"_id":"CaGo"}],"date_created":"2023-12-24T23:00:53Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"Published Version","oa":1,"scopus_import":"1","publisher":"Springer Nature","status":"public","quality_controlled":"1","title":"A computational toolbox for the assembly yield of complex and heterogeneous structures","publication":"Nature Communications","has_accepted_license":"1","author":[{"full_name":"Curatolo, Agnese I.","first_name":"Agnese I.","last_name":"Curatolo"},{"last_name":"Kimchi","first_name":"Ofer","full_name":"Kimchi, Ofer"},{"last_name":"Goodrich","first_name":"Carl Peter","full_name":"Goodrich, Carl Peter","id":"EB352CD2-F68A-11E9-89C5-A432E6697425","orcid":"0000-0002-1307-5074"},{"full_name":"Krueger, Ryan K.","first_name":"Ryan K.","last_name":"Krueger"},{"full_name":"Brenner, Michael P.","last_name":"Brenner","first_name":"Michael P."}],"doi":"10.1038/s41467-023-43168-4","publication_identifier":{"eissn":["20411723"]},"citation":{"ieee":"A. I. Curatolo, O. Kimchi, C. P. Goodrich, R. K. Krueger, and M. P. Brenner, “A computational toolbox for the assembly yield of complex and heterogeneous structures,” <i>Nature Communications</i>, vol. 14. Springer Nature, 2023.","mla":"Curatolo, Agnese I., et al. “A Computational Toolbox for the Assembly Yield of Complex and Heterogeneous Structures.” <i>Nature Communications</i>, vol. 14, 8328, Springer Nature, 2023, doi:<a href=\"https://doi.org/10.1038/s41467-023-43168-4\">10.1038/s41467-023-43168-4</a>.","short":"A.I. Curatolo, O. Kimchi, C.P. Goodrich, R.K. Krueger, M.P. Brenner, Nature Communications 14 (2023).","chicago":"Curatolo, Agnese I., Ofer Kimchi, Carl Peter Goodrich, Ryan K. Krueger, and Michael P. Brenner. “A Computational Toolbox for the Assembly Yield of Complex and Heterogeneous Structures.” <i>Nature Communications</i>. Springer Nature, 2023. <a href=\"https://doi.org/10.1038/s41467-023-43168-4\">https://doi.org/10.1038/s41467-023-43168-4</a>.","ista":"Curatolo AI, Kimchi O, Goodrich CP, Krueger RK, Brenner MP. 2023. A computational toolbox for the assembly yield of complex and heterogeneous structures. Nature Communications. 14, 8328.","apa":"Curatolo, A. I., Kimchi, O., Goodrich, C. P., Krueger, R. K., &#38; Brenner, M. P. (2023). A computational toolbox for the assembly yield of complex and heterogeneous structures. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-023-43168-4\">https://doi.org/10.1038/s41467-023-43168-4</a>","ama":"Curatolo AI, Kimchi O, Goodrich CP, Krueger RK, Brenner MP. A computational toolbox for the assembly yield of complex and heterogeneous structures. <i>Nature Communications</i>. 2023;14. doi:<a href=\"https://doi.org/10.1038/s41467-023-43168-4\">10.1038/s41467-023-43168-4</a>"},"article_type":"original","article_number":"8328","_id":"14710","language":[{"iso":"eng"}],"volume":14,"intvolume":"        14","year":"2023","file_date_updated":"2023-12-27T08:40:43Z","acknowledgement":"We thank Lucy Colwell for suggesting that we use covariance based methods to predict contacts and Yang Hsia, Scott Boyken, Zibo Chen, and David Baker for collaborations on designed protein complexes. We also thank Ned Wingreen for suggesting the alternative derivation of (11). This research was supported by the Office of Naval Research through ONR N00014-17-1-3029, the Simons Foundation the NSF-Simons Center for Mathematical and Statistical Analysis of Biology at Harvard (award number #1764269), the Peter B. Lewis ’55 Lewis-Sigler Institute/Genomics Fund through the Lewis-Sigler Institute of Integrative Genomics at Princeton University, and the National Science Foundation through the Center for the Physics of Biological Function (PHY-1734030).","ddc":["530"]}