Modeling the Assembly Order of Multimeric Heteroprotein Complexes

Peterson, Lenna X.; Togawa, Yoichiro; Esquivel‐Rodríguez, Juan; Terashi, Genki; Christoffer, Charles; Roy, Amitava; Shin, Woong-Hee

doi:10.1016/j.bpj.2018.11.1078

Cited by 2 publications

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“…[13][14][15] Meanwhile assembly and disassembly orders have been extensively studied in the field of structural bioinformatics (SB) during the last fifteen years 16 and can now be predicted with high accuracy based on atomic structures of individual subunits. 17 Thus identification of subcomplexes involved in assembly and disassembly processes has become technically feasible, then providing important clues to characterize intermediate states consisting of assembly and disassembly processes.…”

Section: Mass Spectrometry (Ms) Analyses Have Become a Powerful Tool mentioning

confidence: 99%

Reaction Pathway Sampling and Free Energy Analyses for Multimeric Protein Complex Disassembly with Employing Hybrid Configuration Bias Monte Carlo/Molecular Dynamics Simulation

Kurisaki

Tanaka

2020

Preprint

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Physicochemical characterization of multimeric biomacromolecule assembly and disassembly processes is a milestone to understand the mechanisms for biological phenomena at molecular level. Mass spectroscopy (MS) and structural bioinformatics (SB) approaches have become feasible to identify subcomplexes involved in assembly and disassembly, while they cannot provide atomic information sufficient for free energy calculation to characterize transition mechanism between two different sets of subcomplexes. To combine observations derived from MS and SB approaches with conventional free energy calculation protocols, we here designed a new reaction pathway sampling method with employing hybrid configuration bias Monte Carlo/Molecular Dynamics (hcbMC/MD) scheme and applied it to simulate disassembly process of serum amyloid P component (SAP) pentamer. The results we obtained are consistent with those of the earlier MS and SB studies with respect to SAP subcomplex species and the initial stage of SAP disassembly processes. Furthermore, we observed a novel dissociation event, ring-opening reaction of SAP pentamer. Employing free energy calculation combined with the hcbMC/MD reaction pathway trajectories, we moreover obtained experimentally testable observations on (1) reaction time of the ring-opening reaction and (2) importance of Asp42 and Lys117 for stable formation of SAP oligomer.TOC graphics

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Section: Mass Spectrometry (Ms) Analyses Have Become a Powerful Tool mentioning

confidence: 99%

Reaction Pathway Sampling and Free Energy Analyses for Multimeric Protein Complex Disassembly with Employing Hybrid Configuration Bias Monte Carlo/Molecular Dynamics Simulation

Kurisaki

Tanaka

2020

Preprint

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“…These include docking conformational searches, such as Fast Fourier Transform (6,7), geometric hashing (4,8), and particle swarm optimization (9), and for considering protein flexibility (10,11). Development of new methods aim to extend and surpass the capabilities of simple pairwise docking, such as multi-chain docking (12)(13)(14), peptide-protein docking (15)(16)(17), docking with disordered proteins (18), docking order prediction (19,20), and docking for cryo-EM maps (21,22). Researchers have also applied recent advances in deep learning to further boost docking performance (23)(24)(25).…”

Section: Introductionmentioning

confidence: 99%

Protein Docking Model Evaluation by Graph Neural Networks

Wang

Flannery

2020

Preprint

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Physical interactions of proteins play key roles in many important cellular processes. Therefore, it is crucial to determine the structure of protein complexes to understand molecular mechanisms of interactions. To complement experimental approaches, which usually take a considerable amount of time and resources, various computational methods have been developed to predict the structures of protein complexes. In computational modeling, one of the challenges is to identify near-native structures from a large pool of generated models. Here, we developed a deep learning-based approach named Graph Neural Network-based DOcking decoy eValuation scorE (GNN-DOVE). To evaluate a protein docking model, GNN-DOVE extracts the interface area and represents it as a graph. The chemical properties of atoms and the inter-atom distances are used as features of nodes and edges in the graph. GNN-DOVE was trained and validated on docking models in the Dockground database. GNN-DOVE performed better than existing methods including DOVE, which is our previous development that uses convolutional neural network on voxelized structure models.

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Modeling the Assembly Order of Multimeric Heteroprotein Complexes

Cited by 2 publications

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Reaction Pathway Sampling and Free Energy Analyses for Multimeric Protein Complex Disassembly with Employing Hybrid Configuration Bias Monte Carlo/Molecular Dynamics Simulation

Reaction Pathway Sampling and Free Energy Analyses for Multimeric Protein Complex Disassembly with Employing Hybrid Configuration Bias Monte Carlo/Molecular Dynamics Simulation

Protein Docking Model Evaluation by Graph Neural Networks

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