Modeling the assembly order of multimeric heteroprotein complexes

By Lenna X. Peterson, Y. Togawa, Juan Esquivel-Rodríguez, Genki Terashi, Charles W Christoffer, Amitava Roy, Woong-Hee Shin, Daisuke Kihara. 2018

29 citations recorded by the source corpus.

Protein-protein interactions are the cornerstone of numerous biological processes. Although an increasing number of protein complex structures have been determined using experimental methods, relatively fewer studies have been performed to determine the assembly order of complexes. In addition to the insights into the molecular mechanisms of biological function provided by the structure of a complex, knowing the assembly order is important for understanding the process of complex formation. Assembly order is also practically useful for constructing subcomplexes as a step toward solving the entire complex experimentally, designing artificial protein complexes, and developing drugs that interrupt a critical step in the complex assembly. There are several experimental methods for determining the assembly order of complexes; however, these techniques are resource-intensive. Here, we present a computational method that predicts the assembly order of protein complexes by building the complex structure. The method, named Path-LzerD, uses a multimeric protein docking algorithm that assembles a protein complex structure from individual subunit structures and predicts assembly order by observing the simulated assembly process of the complex. Benchmarked on a dataset of complexes with experimental evidence of assembly order, Path-LZerD was successful in predicting the assembly pathway for the majority of the cases. Moreover, when compared with a simple approach that infers the assembly path from the buried surface area of subunits in the native complex, Path-LZerD has the strong advantage that it can be used for cases where the complex structure is not known. The path prediction accuracy decreased when starting from unbound monomers, particularly for larger complexes of five or more subunits, for which only a part of the assembly path was correctly identified. As the first method of its kind, Path-LZerD opens a new area of computational protein structure modeling and will be an indispensable approach for studying protein complexes.

Subfields: Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

In Molecular Biology

Citations in the corpus

SWISS-MODEL: homology modelling of protein structures and complexes (2018, positive): “When the 3D structures of the binding partners are available, or can be reliably modelled, docking methods can be used to obtain a three-dimensional model of the complex based on geometric and physicochemical complementarity of the interacting molecules (8–11).”
Protein Docking Model Evaluation by Graph Neural Networks (2020, methodology): “Development of new methods aim to extend and surpass the capabilities of simple pairwise docking, such as multi-chain docking ( 12-14 ), peptide-protein docking ( 15-17 ), docking with disordered proteins ( 18 ), docking order prediction ( 19, 20 ), and docking for cryo-EM maps ( 21, 22 ).”