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Carson Adams

Bio: Carson Adams is an academic researcher from University of Washington. The author has contributed to research in topics: Network architecture & Protein subunit. The author has an hindex of 2, co-authored 5 publications receiving 202 citations.

Papers
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Journal ArticleDOI
20 Aug 2021-Science
TL;DR: In this article, a three-track network is proposed to combine information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level.
Abstract: DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography and cryo-electron microscopy structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.

1,907 citations

Posted ContentDOI
15 Jun 2021-bioRxiv
TL;DR: In this paper, a 3-track network is proposed to combine information at the 1D sequence level, the 2D distance map level, and the 3D coordinate level for protein structure prediction.
Abstract: DeepMind presented remarkably accurate protein structure predictions at the CASP14 conference. We explored network architectures incorporating related ideas and obtained the best performance with a 3-track network in which information at the 1D sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The 3-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables rapid solution of challenging X-ray crystallography and cryo-EM structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate models of protein-protein complexes from sequence information alone, short circuiting traditional approaches which require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research. One-Sentence Summary Accurate protein structure modeling enables rapid solution of structure determination problems and provides insights into biological function.

42 citations

Journal ArticleDOI
TL;DR: In this paper, the authors report the structure of a heterodimeric PI3Kγ complex, p110γ-p101, which is a unique assembly of catalytic and regulatory subunits.
Abstract: The class IB phosphoinositide 3-kinase (PI3K), PI3Kγ, is a master regulator of immune cell function and a promising drug target for both cancer and inflammatory diseases. Critical to PI3Kγ function is the association of the p110γ catalytic subunit to either a p101 or p84 regulatory subunit, which mediates activation by G protein-coupled receptors. Here, we report the cryo-electron microscopy structure of a heterodimeric PI3Kγ complex, p110γ-p101. This structure reveals a unique assembly of catalytic and regulatory subunits that is distinct from other class I PI3K complexes. p101 mediates activation through its Gβγ-binding domain, recruiting the heterodimer to the membrane and allowing for engagement of a secondary Gβγ-binding site in p110γ. Mutations at the p110γ-p101 and p110γ-adaptor binding domain interfaces enhanced Gβγ activation. A nanobody that specifically binds to the p101-Gβγ interface blocks activation, providing a novel tool to study and target p110γ-p101-specific signaling events in vivo.

17 citations

Posted ContentDOI
06 Aug 2021-bioRxiv
TL;DR: In this paper, the authors report a structure of yeast seipin based on cryo-electron microscopy and structural modeling data and suggest a model for LD formation, in which a closed Seipin cage enables TG phase separation and subsequently switches to an open conformation to allow LD growth and budding.
Abstract: SUMMARY Lipid droplets (LDs) form in the endoplasmic reticulum by phase separation of neutral lipids. This process is facilitated by the seipin protein complex, which consists of a ring of seipin monomers, with yet unclear function. Here, we report a structure of yeast seipin based on cryo-electron microscopy and structural modeling data. Seipin forms a decameric, cage-like structure with the lumenal domains forming a stable ring at the cage floor and transmembrane segments forming the cage sides and top. The transmembrane segments interact with adjacent monomers in two distinct, alternating conformations. These conformations result from changes in switch regions, located between the lumenal domains and the transmembrane segments, that are required for seipin function. Our data suggest a model for LD formation in which a closed seipin cage enables TG phase separation and subsequently switches to an open conformation to allow LD growth and budding.

2 citations

Posted ContentDOI
01 Jun 2021-bioRxiv
TL;DR: In this paper, the authors reported the structure of a heterodimeric PI3K-gamma complex, p110{gamma}-p101, which reveals a unique assembly of catalytic and regulatory subunits.
Abstract: The class IB phosphoinositide 3-kinase (PI3K), PI3K{gamma}, is a master regulator of immune cell function, and a promising drug target for both cancer and inflammatory diseases. Critical to PI3K{gamma} function is the association of the p110{gamma} catalytic subunit to either a p101 or p84 regulatory subunit, which mediates activation by G-protein coupled receptors (GPCRs). Here, we report the cryo-EM structure of a heterodimeric PI3K{gamma} complex, p110{gamma}-p101. This structure reveals a unique assembly of catalytic and regulatory subunits that is distinct from other class I PI3K complexes. p101 mediates activation through its G{beta}{gamma} binding domain, recruiting the heterodimer to the membrane and allowing for engagement of a secondary G{beta}{gamma} binding site in p110{gamma}. Multiple oncogenic mutations mapped to these novel interfaces and enhanced G{beta}{gamma} activation. A nanobody that specifically binds to the p101-G{beta}{gamma} interface blocks activation providing a novel tool to study and target p110{gamma}-p101-specific signaling events in vivo.

Cited by
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Journal ArticleDOI
TL;DR: The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an openly accessible, extensive database of high-accuracy protein-structure predictions.
Abstract: The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set.

2,008 citations

Journal ArticleDOI
TL;DR: ColabFold as discussed by the authors combines the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold for protein folding and achieves 40-60fold faster search and optimized model utilization.
Abstract: ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold's 40-60-fold faster search and optimized model utilization enables prediction of close to 1,000 structures per day on a server with one graphics processing unit. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at https://github.com/sokrypton/ColabFold and its novel environmental databases are available at https://colabfold.mmseqs.com .

1,553 citations

Posted ContentDOI
04 Oct 2021-bioRxiv
TL;DR: In this article, an AlphaFold model trained specifically for multimeric inputs of known stoichiometry was proposed, which significantly increases the accuracy of predicted multimimeric interfaces over input-adapted single-chain AlphaFolds.
Abstract: While the vast majority of well-structured single protein chains can now be predicted to high accuracy due to the recent AlphaFold [1] model, the prediction of multi-chain protein complexes remains a challenge in many cases. In this work, we demonstrate that an AlphaFold model trained specifically for multimeric inputs of known stoichiometry, which we call AlphaFold-Multimer, significantly increases accuracy of predicted multimeric interfaces over input-adapted single-chain AlphaFold while maintaining high intra-chain accuracy. On a benchmark dataset of 17 heterodimer proteins without templates (introduced in [2]) we achieve at least medium accuracy (DockQ [3] [≥] 0.49) on 14 targets and high accuracy (DockQ [≥] 0.8) on 6 targets, compared to 9 targets of at least medium accuracy and 4 of high accuracy for the previous state of the art system (an AlphaFold-based system from [2]). We also predict structures for a large dataset of 4,433 recent protein complexes, from which we score all non-redundant interfaces with low template identity. For heteromeric interfaces we successfully predict the interface (DockQ [≥] 0.23) in 67% of cases, and produce high accuracy predictions (DockQ [≥] 0.8) in 23% of cases, an improvement of +25 and +11 percentage points over the flexible linker modification of AlphaFold [4] respectively. For homomeric interfaces we successfully predict the interface in 69% of cases, and produce high accuracy predictions in 34% of cases, an improvement of +5 percentage points in both instances.

1,023 citations

Journal ArticleDOI
TL;DR: Targeted protein degradation with proteolysis-targeting chimeras (PROTACs) has the potential to tackle disease-causing proteins that have historically been highly challenging to target with conventional small molecules as mentioned in this paper .
Abstract: Targeted protein degradation (TPD) is an emerging therapeutic modality with the potential to tackle disease-causing proteins that have historically been highly challenging to target with conventional small molecules. In the 20 years since the concept of a proteolysis-targeting chimera (PROTAC) molecule harnessing the ubiquitin–proteasome system to degrade a target protein was reported, TPD has moved from academia to industry, where numerous companies have disclosed programmes in preclinical and early clinical development. With clinical proof-of-concept for PROTAC molecules against two well-established cancer targets provided in 2020, the field is poised to pursue targets that were previously considered ‘undruggable’. In this Review, we summarize the first two decades of PROTAC discovery and assess the current landscape, with a focus on industry activity. We then discuss key areas for the future of TPD, including establishing the target classes for which TPD is most suitable, expanding the use of ubiquitin ligases to enable precision medicine and extending the modality beyond oncology. Targeted protein degradation with proteolysis-targeting chimeras (PROTACs) has the potential to tackle disease-causing proteins that have historically been highly challenging to target with conventional small molecules. This article summarizes the first two decades of PROTAC discovery and discusses key areas for the future of this therapeutic modality, including establishing the target classes for which it is most suitable and extending its application beyond oncology.

527 citations

Journal ArticleDOI
TL;DR: For example, AlphaFold2 as discussed by the authors generates peptide-protein complex models without requiring multiple sequence alignment information for the peptide partner, and can handle binding-induced conformational changes of the receptor.
Abstract: Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been developed for the in silico folding of protein monomers, AlphaFold2 also enables quick and accurate modeling of peptide-protein interactions. Our simple implementation of AlphaFold2 generates peptide-protein complex models without requiring multiple sequence alignment information for the peptide partner, and can handle binding-induced conformational changes of the receptor. We explore what AlphaFold2 has memorized and learned, and describe specific examples that highlight differences compared to state-of-the-art peptide docking protocol PIPER-FlexPepDock. These results show that AlphaFold2 holds great promise for providing structural insight into a wide range of peptide-protein complexes, serving as a starting point for the detailed characterization and manipulation of these interactions.

390 citations