Biology subjects

Baker, D.

Publications and source records attributed to Baker, D..

At least 181 records · Page 10Linked to original sources

bioRxiv2022

Fast and versatile sequence-independent protein docking for nanomaterials design using RPXDock

Computationally designed multi-subunit assemblies have shown considerable promise for a variety of applications, including a new generation of potent vaccines. One of the major routes to such materials is rigid body sequence-independent docking of cyclic oligomers into architectures with point group or lattice symmetries. Current methods for docking and designing such assemblies are tailored to specific classes of symmetry and are difficult to modify for novel applications. Here we describe RPXDock, a fast, flexible, and modular software package for sequence-independent rigid-body protein docking across a wide range of symmetric architectures that is easily customizable for further development. RPXDock uses an efficient hierarchical search and a residue-pair transform (RPX) scoring method to rapidly search through multidimensional docking space. We describe the structure of the software, provide practical guidelines for its use, and describe the available functionalities including a variety of score functions and filtering tools that can be used to guide and refine docking results towards desired configurations. Author SummaryProtein design methodologies are now able to generate, through a stepwise approach, a wide variety of self-assembling protein structures that begin to rival the structural complexity of naturally occurring protein nanomachines. Efficient methods for docking oligomeric protein building blocks in user-defined target symmetries are central to these techniques. We developed RPXDock as a fast and versatile method to systematically dock pre-existing proteins together into a multitude of asymmetrical and symmetrical architectures. RPXdock is also readily extendable to future applications through the addition of new symmetries, score functions, and filtering criteria.

bioengineering↗

bioRxiv2022

Precisely patterned nanofibers made from extendable protein multiplexes

Molecular systems with coincident cyclic and superhelical symmetry axes have considerable advantages for materials design as they can be readily lengthened or shortened by changing the length of the constituent monomers. Among proteins, alpha helical coiled coils have such symmetric extendable architectures, but are limited by the relatively fixed geometry and flexibility of the helical protomers. Here, we describe a systematic approach to generating modular and rigid repeat protein oligomers with coincident C2 to C8 and superhelical symmetry axes that can be readily extended by repeat propagation. From these building blocks, we demonstrate that a wide range of unbounded fibers can be systematically designed by introducing hydrophilic surface patches that force staggering of the monomers; the geometry of such fibers can be precisely tuned by varying the number of repeat units in the monomer and the placement of the hydrophilic patches.

bioengineering↗

bioRxiv2022

Top-down design of protein nanomaterials with reinforcement learning

The multisubunit protein assemblies that play critical roles in biology are the result of evolutionary selection for function of the entire assembly, and hence the subunits in structures such as icosahedral viral capsids often fit together with remarkable shape complementarity1,2. In contrast, the large multisubunit assemblies that have been created by de novo protein design, notably the icosahedral nanocages used in a new generation of potent vaccines3-7, have been built by first designing symmetric oligomers with cyclic symmetry and then assembling these into nanocages while keeping the internal structure fixed8-14, which results in more porous structures with less extensive shape matching between the components. Such hierarchical "bottom-up" design approaches have the advantage that one interface can be designed and validated in the context of the cyclic oligomer building block15,16, but the disadvantage that the structural and functional features of the assemblies are limited by the properties of the predesigned building blocks. To overcome this limitation, we set out to develop a "top-down" reinforcement learning based approach to protein nanomaterial design in which both the structures of the subunits and the interactions between them are built up coordinately in the context of the entire assembly. We developed a Monte Carlo tree search (MCTS) method17,18 which assembles protein monomer structures in the context of an overall architecture guided by a loss function which enables specification of any desired overall structural properties such as shape and porosity. We demonstrate the power of the approach by designing hyperstable icosahedral assemblies more compact than any previously observed protein icosahedral structure (designed or naturally occurring), that have very low porosity and are robust to fusion and display of proteins as complex as influenza hemagglutinin. CryoEM structures of two designs are very close to the computational design models. Our top-down reinforcement learning approach should enable the design of a wide variety of complex protein nanomaterials by direct optimization of overall system properties.

synthetic biology↗

bioRxiv2022

Genetic Disruption of System xc- Mediated Glutamate Release from Astrocytes Increases Negative-Outcome Behaviors While Preserving Basic Brain Function in Rat

The impact of CNS disorders is exacerbated by the difficulty in developing safe, effective glutamatergic therapeutics. Synaptic glutamate transmission is vital for neural physiology throughout the brain, which contributes to the vast therapeutic potential and safety risk of glutamatergic therapeutics. Here, we created a genetically modified rat (MSxc) to survey the range of brain functions impacted by the loss of glutamate release from astrocytes involving system xc- (Sxc). Eliminating Sxc activity was not lethal and did not alter growth patterns, activity states, novel object recognition or performance of other simple tasks. In contrast, MSxc rats differed from WT in Pavlovian Conditioned Approach and cocaine self-administration/reinstatement paradigms. Both WT and MSxc rats readily learned that a cue predicted food delivery during Pavlovian Conditioned Approach training. However, WT rats were more likely to approach the food tray (i.e., goal tracking) whereas MSxc rats were more likely to approach the food-predicted cue (i.e., sign tracking) even when this behavior was punished. In the self-administration/reinstatement paradigm, MSxc rats had higher levels of cocaine-primed drug seeking in the absence of altered extinction or cocaine self-administration. These data demonstrate that Sxc-mediated glutamate release from astrocytes regulates non-reinforced and negative-outcome behaviors without altering simple learning or other forms of basic brain function.

neuroscience↗

bioRxiv2022

Accurate prediction of nucleic acid and protein-nucleic acid complexes using RoseTTAFoldNA

Protein-nucleic acid complexes play critical roles in biology. Despite considerable recent advances in protein structure prediction, the prediction of the structures of protein-nucleic acid complexes without homology to known complexes is a largely unsolved problem. Here we extend the RoseTTAFold end-to-end deep learning approach to modeling of nucleic acid and protein-nucleic acid complexes. We develop a single trained network, RoseTTAFoldNA, that rapidly produces 3D structure models with confidence estimates for protein-DNA and protein-RNA complexes, and for RNA tertiary structures. In all three cases, confident predictions have considerably higher accuracy than current state of the art methods. RoseTTAFoldNA should be broadly useful for modeling the structure of naturally occurring protein-nucleic acid complexes, and for designing sequence specific RNA and DNA binding proteins.

bioinformatics↗

bioRxiv2022

Hallucination of closed repeat proteins containing central pockets

In pseudocyclic proteins such as TIM barrels, {beta} barrels, and some helical transmembrane channels, a single subunit is repeated in a cyclic pattern, giving rise to a central cavity which can serve as a pocket for ligand binding or enzymatic activity. Inspired by these proteins, we devised a deep learning-based approach to broadly exploring the space of closed repeat proteins starting from only a specification of the repeat number and length. Biophysical data for 38 structurally diverse pseudocyclic designs produced in E. coli are consistent with the design models, and two crystal structures we were able to obtain are very close to the designed structures. Docking studies suggest the diversity of folds and central pockets provide effective starting points for designing small molecule binders or enzymes.

molecular biology↗

bioRxiv2022

Human iPSC Derived Enamel Organoid Guided by Single-Cell Atlas of Human Tooth Development

Tooth enamel secreted by ameloblasts is the hardest material in the human body, acting as a shield protecting the teeth. However, the enamel is gradually damaged or partially lost in over 90% of adults and cannot be regenerated due to a lack of ameloblasts in erupted teeth. Here we use sci-RNA-seq to establish a spatiotemporal single-cell atlas for the developing human tooth and identify regulatory mechanisms controlling the differentiation process of human ameloblasts. We reveal key signaling pathways involved between the support cells and ameloblasts during fetal development and recapitulate those findings in a novel human ameloblast in vitro differentiation from iPSCs. We furthermore develop a mineralizing enamel organ-like 3D organoid system. These studies pave the way for future regenerative dentistry and therapies toward genetic diseases affecting enamel formation.

developmental biology↗

bioRxiv2022

Improving the secretion of designed protein assemblies through negative design of cryptic transmembrane domains

Computationally designed protein nanoparticles have recently emerged as a promising platform for the development of new vaccines and biologics. For many applications, secretion of designed nanoparticles from eukaryotic cells would be advantageous, but in practice they often secrete poorly. Here we show that designed hydrophobic interfaces that drive nanoparticle assembly are often predicted to form cryptic transmembrane domains, suggesting that interaction with the membrane insertion machinery could limit efficient secretion. We develop a general computational protocol, the Degreaser, to design away cryptic transmembrane domains without sacrificing protein stability. Retroactive application of the Degreaser to previously designed nanoparticle components and nanoparticles considerably improves secretion, and modular integration of the Degreaser into design pipelines results in new nanoparticles that secrete as robustly as naturally occurring protein assemblies. Both the Degreaser protocol and the novel nanoparticles we describe may be broadly useful in biotechnological applications.

biochemistry↗

bioRxiv2022

Peptide binding specificity prediction using fine-tuned protein structure prediction networks

Peptide binding proteins play key roles in biology, and predicting their binding specificity is a long-standing challenge. While considerable protein structural information is available, the most successful current methods use sequence information alone, in part because it has been a challenge to model the subtle structural changes accompanying sequence substitutions. Protein structure prediction networks such as AlphaFold model sequence-structure relationships very accurately, and we reasoned that if it were possible to specifically train such networks on binding data, more generalizable models could be created. We show that placing a classifier on top of the AlphaFold network and fine-tuning the combined network parameters for both classification and structure prediction accuracy leads to a model with strong generalizable performance on a wide range of Class I and Class II peptide-MHC interactions that approaches the overall performance of the state-of-the-art NetMHCpan sequence-based method. The peptide-MHC optimized model shows excellent performance in distinguishing binding and non-binding peptides to SH3 and PDZ domains. This ability to generalize well beyond the training set far exceeds that of sequence only models, and should be particularly powerful for systems where less experimental data is available. Significance statementPeptide binding proteins carry out a variety of biological functions in cells and predicting their binding specificity could significantly improve our understanding of molecular pathways. Deep neural networks have achieved high structure prediction accuracy, but are not trained to predict binding specificity. Here we describe an approach to extending such networks to jointly predict protein structure and binding specificity. We incorporate AlphaFold into this approach, and fine-tune its parameters on peptide-MHC Class I and II structural and binding data. The fine-tuned model approaches state-of-the-art classification accuracy on peptide-MHC specificity prediction and generalizes to other peptide-binding systems such as the PDZ and SH3 domains.

bioinformatics↗

bioRxiv2022

Riboflavin Depletion Promotes Longevity and Metabolic Hormesis in Caenorhabditis elegans

Riboflavin is an essential cofactor in many enzymatic processes and in the production of flavin adenine dinucleotide (FAD). Here we report that the partial depletion of riboflavin through knockdown of the C. elegans riboflavin transporter 1 (rft-1) promotes metabolic health by reducing intracellular flavin concentrations. Knockdown of rft-1 significantly increases lifespan in a manner dependent on FOXO/daf-16, AMP-activated protein kinase (AMPK)/aak-2, the mitochondrial unfolded protein response, and mTOR complex 2 (mTORC2). Riboflavin depletion promotes altered energetic and redox states and increases adiposity, independent of lifespan genetic dependencies. Riboflavin depleted animals also exhibit activation of caloric restriction reporters without a reduction in TORC1 signaling. Our findings indicate that riboflavin depletion activates an integrated, hormetic response that promotes lifespan and healthspan in C. elegans.

genetics↗

bioRxiv2022

Improving de novo Protein Binder Design with Deep Learning

We explore the improvement of energy-based protein binder design using deep learning. We find that using AlphaFold2 or RoseTTAFold to assess the probability that a designed sequence adopts the designed monomer structure, and the probability that this structure binds the target as designed, increases design success rates nearly 10-fold. We find further that sequence design using ProteinMPNN rather than Rosetta considerably increases computational efficiency.

bioengineering↗

bioRxiv2022

Hallucinating protein assemblies

Deep learning generative approaches provide an opportunity to broadly explore protein structure space beyond the sequences and structures of natural proteins. Here we use deep network hallucination to generate a wide range of symmetric protein homo-oligomers given only a specification of the number of protomers and the protomer length. Crystal structures of 7 designs are very close to the computational models (median RMSD: 0.6 [A]), as are 3 cryoEM structures of giant rings with up to 1550 residues, C33 symmetry, and 10 nanometer in diameter; all differ considerably from previously solved structures. Our results highlight the rich diversity of new protein structures that can be created using deep learning, and pave the way for the design of increasingly complex nanomachines and biomaterials. One-sentence summaryDeep network-based protein design enables the generation of cyclic homo-oligomers across the nanoscopic scale.

biophysics↗

bioRxiv2022

Robust deep learning based protein sequence design using ProteinMPNN

While deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here we describe a deep learning based protein sequence design method, ProteinMPNN, with outstanding performance in both in silico and experimental tests. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4%, compared to 32.9% for Rosetta. Incorporation of noise during training improves sequence recovery on protein structure models, and produces sequences which more robustly encode their structures as assessed using structure prediction algorithms. We demonstrate the broad utility and high accuracy of ProteinMPNN using X-ray crystallography, cryoEM and functional studies by rescuing previously failed designs, made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target binding proteins. One-sentence summaryA deep learning based protein sequence design method is described that is widely applicable to current design challenges and shows outstanding performance in both in silico and experimental tests.

biophysics↗

bioRxiv2022

Hydrophobic mismatch drives self-organization of designer proteins into synthetic membranes

The extent to which membrane biophysical properties, such as hydrophobic thickness, can drive membrane protein organization remains unknown. Inspired by this question, we used de novo protein design, molecular dynamic simulations, and cell-free systems to elucidate how membrane-protein hydrophobic mismatch affects protein integration and organization in synthetic lipid membranes. We found that membranes must deform to accommodate membrane-protein hydrophobic mismatch, which reduces the expression and co-translational insertion of membrane proteins into synthetic membranes. We used this principle to sort proteins both between and within membranes, thereby achieving one-pot assembly of vesicles with distinct functions and controlled split-protein assembly, respectively. Our results shed light on protein organization in biological membranes and provide a framework to self-organizing membrane-based materials with new functions.

synthetic biology↗

bioRxiv2022

Increasing computational protein design literacy through cohort-based learning for undergraduate students

Undergraduate research experiences can improve student success in graduate education and STEM careers. During the COVID-19 pandemic, undergraduate researchers at our institution and many others lost their work-study research positions due to interruption of in-person research activities. This imposed a financial burden on the students and eliminated an important learning opportunity. To address these challenges, we created a paid, fully-remote, cohort-based research curriculum in computational protein design. Our curriculum used existing protein design methods as a platform to first educate and train undergraduate students and then to test research hypotheses. In the first phase, students learned computational methods to assess the stability of designed protein assemblies. In the second phase, students used a larger dataset to identify factors that could improve the accuracy of current protein design algorithms. This cohort-based program created valuable new research opportunities for undergraduates at our institute and enhanced the undergraduates feeling of connection with the lab. Students learned transferable and useful skills such as literature review, programming basics, data analysis, hypothesis testing, and scientific communication. Our program provides a model of structured computational research training opportunities for undergraduate researchers in any field for organizations looking to expand educational access. Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

scientific communication and education↗

bioRxiv2022

A new model of Notch signaling: Control of Notch receptor cis-inhibition via Notch ligand dimers

All tissue development and replenishment relies upon the breaking of symmetries leading to the morphological and operational differentiation of progenitor cells into more specialized cells. One of the main engines driving this process is the Notch signal transduction pathway, a ubiquitous signalling system found in the vast majority of metazoan cell types characterized to date. Broadly speaking, Notch receptor activity is governed by a balance between two processes: 1) intercellular Notch transactivation triggered via interactions between receptors and ligands expressed in neighbouring cells; 2) intracellular cis inhibition caused by ligands binding to receptors within the same cell. Additionally, recent reports have also unveiled evidence of cis activation. Whilst context-dependent Notch receptor clustering has been hypothesized, to date, Notch signalling has been assumed to involve an interplay between receptor and ligand monomers. In this study, we demonstrate biochemically, through a mutational analysis of DLL4, both in vitro and in tissue culture cells, that Notch ligands can efficiently self-associate. We found that the membrane proximal EGF-like repeat of DLL4 was necessary and sufficient to promote oligomerization/dimerization. Mechanistically, our experimental evidence supports the view that DLL4 ligand dimerization is specifically required for cis-inhibition of Notch receptor activity. To further substantiate these findings, we have adapted and extended existing ordinary differential equation-based models of Notch signalling to take account of the ligand dimerization-dependent cis-inhibition reported here. Our new model faithfully recapitulates our experimental data and improves predictions based upon published data. Collectively, our work favours a model in which net output following Notch receptor/ligand binding results from ligand monomer-driven Notch receptor transactivation (and cis activation) counterposed by ligand dimer-mediated cis-inhibition. Author summaryThe growth and maintenance of tissues is a fundamental characteristic of metazoan life, controlled by a highly conserved core of cell signal transduction networks. One such pathway, the Notch signalling system, plays a unique role in these phenomena by orchestrating the generation of the phenotypic and genetic asymmetries which underlie tissue development and remodeling. At the molecular level, it achieves this via two specific types of receptor/ligand interaction: intercellular binding of receptors and ligands expressed in neighbouring cells, which triggers receptor activation (transactivation); intracellular receptor/ligand binding within the same cell which blocks receptor activation (cis inhibition). Together, these counterposed mechanisms determine the strength, the direction and the specificity of Notch signalling output. Whilst, the basic mechanisms of receptor transactivation have been delineated in some detail, the precise nature of cis inhibition has remained enigmatic. Through a combination of experimental approaches and computational modelling, in this study, we present a new model of Notch signalling in which ligand monomers promote Notch receptor transactivation, whereas cis inhibition is induced optimally via ligand dimers. This is the first model to include a concrete molecular distinction, in terms of ligand configuration, between the main branches of Notch signalling. Our model faithfully recapitulates both our presented experimental results as well as the recently published work of others, and provides a novel perspective for understanding Notch-regulated biological processes such as embryo development and angiogenesis.

cell biology↗

bioRxiv2022

The {varphi}PA3 Phage Nucleus is Enclosed by a Self-Assembling, 2D Crystalline Lattice

A growing number of jumbo bacteriophages, with genomes exceeding 200 kb, have been found to establish a Phage Nucleus--a micron-scale, proteinaceous structure encompassing the replicating phage DNA. Bacteriophage and host proteins associated with replication and transcription are concentrated inside the Phage Nucleus while nucleotide synthesis, translation, and numerous other host and exogenous proteins are effectively excluded, including CRISPR-Cas and restriction endonuclease host defense systems. Here, we show that fragments of the Phage Nucleus isolated from {phi}PA3 infected Pseudomonas aeruginosa cells form a square lattice and demonstrate that the recombinantly purified primary Phage Nuclear Enclosure (PhuN) protein spontaneously assembles into sheets also constructed from a square lattice which we resolve to 3.8 [A] by cryo-EM. Our structure reveals that the flexible termini and large loops mediate adaptable inter-tetramer contacts that drive shell assembly into a C2-symmetric lattice. While the interfaces between subunits are mostly well packed, two of the interfaces are open, forming clear channels that likely have important functional implications for the transport of proteins, mRNA, and small molecules.

biochemistry↗

bioRxiv2022

Induction of cortical Par complex polarity by designed proteins causes cytoskeletal symmetry breaking in unpolarized mammalian cells

Polarized cells rely on a polarized cytoskeleton for polarized trafficking, oriented migration and spindle orientation during asymmetric cell division. While cytoskeleton remodeling machineries have been extensively characterized at the molecular level, how polarity signaling at the cortex controls remodeling of the cytoskeleton in the cytosol remains elusive. In particular, how the Par complex, the conserved mastermind of polarity during asymmetric cell division, gets assembled and functions is not understood at the molecular level. Here, we dissected the logic of the Par complex pathway by capitalizing on designed proteins able to induce spontaneous symmetry breaking of the cortex in populations of naive, unpolarized cells. We found that the primary kinetic barrier to Par complex assembly is the relief of Par6 autoinhibition, and that inducing Par complex cortical polarity was sufficient to induce two key hallmarks of asymmetric cell division in unpolarized cells: spindle orientation and central spindle asymmetry. These two outputs of the Par complex are separately controlled: spindle orientation is determined by Par3 and does not require the kinase activity of aPKC, while central spindle asymmetry solely depends on an asymmetric activity of aPKC at the cortex. Our work shows how polarity information flows between the cortex and the cytosol despite its diffusive nature, and paves the way towards induction of asymmetric cell division in cultured cells.

cell biology↗