Architecture of autoinhibited and active BRAF–MEK1–14-3-3 complexes

Park, Eun‐Young; Rawson, Shaun; Li, Kunhua; Kim, Byeong-Won; Ficarro, Scott B.; Pino, Gonzalo Gonzalez-Del; Sharif, Humayun; Marto, Jarrod A.; Jeon, Hyesung; Eck, Michael J.

doi:10.1038/s41586-019-1660-y

Cited by 247 publications

(366 citation statements)

References 57 publications

Supporting

Mentioning

309

Contrasting

Order By: Relevance

“…130,131 These validate our assertion that the autoinhibited state is often unstable. 130,131 These validate our assertion that the autoinhibited state is often unstable.…”

Section: B-raf's Autoinhibited State Is Stabilized By 14-3-3supporting

confidence: 84%

Autoinhibition can identify rare driver mutations and advise pharmacology

2019

View full text Add to dashboard Cite

Identification of protein mutations that drive cancer is a major challenge. A primary reason is that driver mutations are principally identified by their high frequency even though they can also be rare. Driver mutations can locate at functional (binding or active) sites. We dub these orthosteric drivers. However, often they are allosteric drivers. Identification is particularly formidable for rare allosteric drivers. Autoinhibition, where a segment of the protein covers its functional site, is a common allosteric regulation mechanism. A modest shift in the equilibrium can switch the system from the autoinhibited to the active state. This can suggest why (i) mutations are likely to evolve to target it; (ii) inhibitors can straightforwardly relieve the autoinhibition but not vice versa; and why (iii) mutations that relieve the autoinhibition are likely to be drivers—even if they are rare. We explain in simple terms the linkage between allosteric driver mutations, release of autoinhibition, free energy landscapes, and targeted pharmacology in precision medicine. We review the literature and propose new concepts in identification of rare drivers in this framework.

show abstract

“…130,131 These validate our assertion that the autoinhibited state is often unstable. 130,131 These validate our assertion that the autoinhibited state is often unstable.…”

Section: B-raf's Autoinhibited State Is Stabilized By 14-3-3supporting

confidence: 84%

Autoinhibition can identify rare driver mutations and advise pharmacology

2019

View full text Add to dashboard Cite

show abstract

“…The RAF kinases are wellknown binding partners for 14-3-3 (42). 14-3-3 proteins bind and stabilize the autoinhibited form of BRAF (33,34,43), and a recent cryo-EM structure is available for the complex between 14-3-3 and autoinhibited BRAF (35). However, 14-3-3 also stabilizes the RAF dimers (25,33,34) and a recent cryo-EM structure illuminates the structure of dimerized RAF bound to 14-3-3 (30).…”

Section: -3-3 Proteins Increase the Magnitude Of Paradoxical Activamentioning

confidence: 99%

“…For example, we find that autoinhibitory conformational dynamics and their perturbation by drugs can explain the magnitudes of PA that have been observed. We extended our model to include the roles of 14-3-3 proteins in stabilizing RAF in the autoinhibitory state and also in stabilizing RAF dimers (33,34), both of which have generated recent attention due to new cryo-EM structures of these complexes (30,35). We mathematically find 14-3-3 can further potentiate PA, and that this effect increases with increasing levels of 14-3-3 expression.…”

Section: Introductionmentioning

confidence: 98%

RAF conformational autoinhibition and 14-3-3 proteins promote paradoxical activation

Mendiratta

Abbott

et al. 2019

Preprint

View full text Add to dashboard Cite

RAF kinase inhibitors can actually increase RAF kinase signaling. This process, which is commonly referred to as "paradoxical activation" (PA), is incompletely understood. RAF kinases are regulated by autoinhibitory conformational changes, and the role of these conformational changes in PA is unclear. Our mathematical investigations find that PA can result from a dynamical equilibrium between autoinhibited and non-autoinhibited forms of RAF, along with the RAF inhibitor stabilizing the non-autoinhibited form. We also investigate whether PA is influenced by 14-3-3 proteins, which can both stabilize RAF autoinhibition and RAF dimerization. Using both computational and experimental methods we demonstrate that 14-3-3 proteins potentiate PA. Third generation RAF inhibitors normally display minimal to no PA. Our mathematical modeling led us to hypothesize that increased 14-3-3 expression should also amplify PA for these agents. Subsequent experiments support our hypothesis and show that 14-3-3 overexpression increases PA in these third generation RAF inhibitors, effectively "breaking" these "paradox breakers" and pan-RAF inhibitors. We have therefore created and experimentally validated a robust mechanism for PA based solely on equilibrium dynamics of canonical interactions in RAF signaling.

show abstract

“…crYOLO [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] . For example, Pang et al 23 used crYOLO to selectively pick particles, which were attached to liposomes; Rogala et al 14 highlighted in their study about mTORC1 that crYOLO was especially useful to exclude particles on carbon; Joppe et al 24 made use of crYOLO in a streamlined pipeline for rapid structure determination of yeast fatty acid synthase; and the new filament mode was recently used by Pospich et al to examine the structural effects of toxins on actin filaments 16 .…”

Section: Impact Of Cryolomentioning

confidence: 99%

The evolution of SPHIRE-crYOLO particle picking and its application in automated cryo-EM processing workflows

Wagner

Raunser

2020

Commun Biol

View full text Add to dashboard Cite

Particle selection is a crucial step when processing electron cryo microscopy data. Several automated particle picking procedures were developed in the past but most struggle with non-ideal data sets. In our recent Communications Biology article, we presented crYOLO, a deep learning based particle picking program. It enables fast, automated particle picking at human levels of accuracy with low effort. A general model allows the use of crYOLO for selecting particles in previously unseen data sets without further training. Here we describe how crYOLO has evolved since its initial release. We have introduced filament picking, a new denoising technique, and a new graphical user interface. Moreover, we outline its usage in automated processing pipelines, which is an important advancement on the horizon of the field.The crYOLO particle picking procedure A major goal of electron cryo microscopy (cryo-EM) is to obtain high-resolution threedimensional (3D) reconstructions of proteins and protein complexes to gain novel biological insights. This process involves the selection of thousands to millions of noisy two-dimensional (2D) particle projections, a number that only keeps increasing with recent advances in hardware and software development.In our recent work in Communications Biology 1 we introduced the "crYOLO" particle picking procedure. It is based on a deep neural network and the You Only Look Once (YOLO) object detection framework 2 . This approach enables the automated picking of particles within cryo-EM micrographs with a low signal-to-noise ratio requiring minimal human supervision or intervention. CrYOLO is easy to configure and train on a specific data set. It is fast and can process up to six micrographs per second. As crYOLO sees the complete micrograph, it is able to learn the

show abstract

Architecture of autoinhibited and active BRAF–MEK1–14-3-3 complexes

Cited by 247 publications

References 57 publications

Autoinhibition can identify rare driver mutations and advise pharmacology

Autoinhibition can identify rare driver mutations and advise pharmacology

RAF conformational autoinhibition and 14-3-3 proteins promote paradoxical activation

The evolution of SPHIRE-crYOLO particle picking and its application in automated cryo-EM processing workflows

Contact Info

Product

Resources

About