Dataset Information

Unraveling self-assembly pathways of the 468-kDa proteolytic machine TET2.

ABSTRACT: The spontaneous formation of biological higher-order structures from smaller building blocks, called self-assembly, is a fundamental attribute of life. Although the protein self-assembly is a time-dependent process that occurs at the molecular level, its current understanding originates either from static structures of trapped intermediates or from modeling. Nuclear magnetic resonance (NMR) spectroscopy has the unique ability to monitor structural changes in real time; however, its size limitation and time-resolution constraints remain a challenge when studying the self-assembly of large biological particles. We report the application of methyl-specific isotopic labeling combined with relaxation-optimized NMR spectroscopy to overcome both size- and time-scale limitations. We report for the first time the self-assembly process of a half-megadalton protein complex that was monitored at the structural level, including the characterization of intermediate states, using a mutagenesis-free strategy. NMR was used to obtain individual kinetics data on the different transient intermediates and the formation of final native particle. In addition, complementary time-resolved electron microscopy and native mass spectrometry were used to characterize the low-resolution structures of oligomerization intermediates.

SUBMITTER: Macek P

PROVIDER: S-EPMC5384809 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Unraveling self-assembly pathways of the 468-kDa proteolytic machine TET2.

Macek Pavel P Kerfah Rime R Boeri Erba Elisabetta E Crublet Elodie E Moriscot Christine C Schoehn Guy G Amero Carlos C Boisbouvier Jerome J

Science advances 20170407 4

The spontaneous formation of biological higher-order structures from smaller building blocks, called self-assembly, is a fundamental attribute of life. Although the protein self-assembly is a time-dependent process that occurs at the molecular level, its current understanding originates either from static structures of trapped intermediates or from modeling. Nuclear magnetic resonance (NMR) spectroscopy has the unique ability to monitor structural changes in real time; however, its size limitati ...[more]

PMID: 28435872

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Project description:During self-assembly of macromolecules ranging from ribosomes to viral capsids, the formation of long-lived intermediates or kinetic traps can dramatically reduce yield of the functional products. Understanding biological mechanisms for avoiding traps and efficiently assembling is essential for designing synthetic assembly systems, but learning optimal solutions requires numerical searches in high-dimensional parameter spaces. Here, we exploit powerful automatic differentiation algorithms commonly employed by deep learning frameworks to optimize physical models of reversible self-assembly, discovering diverse solutions in the space of rate constants for 3-7 subunit complexes. We define two biologically-inspired protocols that prevent kinetic trapping through either internal design of subunit binding kinetics or external design of subunit titration in time. Our third protocol acts to recycle intermediates, mimicking energy-consuming enzymes. Preventative solutions via interface design are the most efficient and scale better with more subunits, but external control via titration or recycling are effective even for poorly evolved binding kinetics. Whilst all protocols can produce good solutions, diverse subunits always helps; these complexes access more efficient solutions when following external control protocols, and are simpler to design for internal control, as molecular interfaces do not need modification during assembly given sufficient variation in dimerization rates. Our results identify universal scaling in the cost of kinetic trapping, and provide multiple strategies for eliminating trapping and maximizing assembly yield across large parameter spaces.SignificanceMacromolecular complexes are frequently composed of diverse subunits. While evolution may favor repeated subunits and symmetry, we show how diversity in subunits generates an expansive parameter space that naturally improves the 'expressivity' of self-assembly, much like a deeper neural network. By using automatic differentiation algorithms commonly used in deep learning, we searched these parameter spaces to identify classes of kinetic protocols that mimic biological solutions for productive self-assembly. Our results reveal how high-yield complexes that easily become kinetically trapped in incomplete intermediates can instead be steered by internal design of rate constants or external and active control of subunits to efficiently assemble, exploiting nonequilibrium control of these ubiquitous dynamical systems.

Dataset Information

Unraveling self-assembly pathways of the 468-kDa proteolytic machine TET2.

Publications

Unraveling self-assembly pathways of the 468-kDa proteolytic machine TET2.

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