Biology subjects

Baker, D.

Publications and source records attributed to Baker, D..

At least 145 records · Page 8Linked to original sources

bioRxiv2023

Single-cell signaling analysis reveals that Major Vault Protein facilitates RasG12C inhibitor resistance

Recently developed covalent inhibitors for RasG12C provide the first pharmacological tools to target mutant Ras-driven cancers. However, the rapid development of resistance to current clinical Ras G12C inhibitors is common. Presumably, a subpopulation of RasG12C-expressing cells adapt their signaling to evade these inhibitors and the mechanisms for this phenomenon are unclear due to the lack of tools that can measure signaling with single-cell resolution. Here, we utilized recently developed Ras sensors to profile the environment of active Ras and to measure the activity of endogenous Ras in order to pair structure (Ras signalosome) to function (Ras activity), respectively, at a single-cell level. With this approach, we identified a subpopulation of KRasG12C cells treated with RasG12C-GDP inhibitors underwent oncogenic signaling and metabolic changes driven by WT Ras at the golgi and mutant Ras at the mitochondria, respectively. Our Ras sensors identified Major Vault Protein (MVP) as a mediator of Ras activation at both compartments by scaffolding Ras signaling pathway components and metabolite channels. We found that recently developed RasG12C-GTP inhibitors also led to MVP-mediated WT Ras signaling at the golgi, demonstrating that this a general mechanism RasG12C inhibitor resistance. Overall, single-cell analysis of structure-function relationships enabled the discovery of a RasG12C inhibitor-resistant subpopulation driven by MVP, providing insight into the complex and heterogenous rewiring occurring during drug resistance in cancer.

cancer biology↗

bioRxiv2023

Improving protein expression, stability, and function with ProteinMPNN

Natural proteins are highly optimized for function, but are often difficult to produce at a scale suitable for biotechnological applications due to poor expression in heterologous systems, limited solubility, and sensitivity to temperature. Thus, a general method that improves the physical properties of native proteins while maintaining function could have wide utility for protein-based technologies. Here we show that the deep neural network ProteinMPNN together with evolutionary and structural information provides a route to increasing protein expression, stability, and function. For both myoglobin and tobacco etch virus (TEV) protease, we generated designs with improved expression, elevated melting temperatures, and improved function. For TEV protease, we identified multiple designs with improved catalytic activity as compared to the parent sequence and previously reported TEV variants. Our approach should be broadly useful for improving the expression, stability, and function of biotechnologically important proteins.

biochemistry↗

bioRxiv2023

Computational design of sequence-specific DNA-binding proteins

Sequence-specific DNA-binding proteins (DBPs) play critical roles in biology and biotechnology, and there has been considerable interest in the engineering of DBPs with new or altered specificities for genome editing and other applications. While there has been some success in reprogramming naturally occurring DBPs using selection methods, the computational design of new DBPs that recognize arbitrary target sites remains an outstanding challenge. We describe a computational method for the design of small DBPs that recognize specific target sequences through interactions with bases in the major groove, and employ this method in conjunction with experimental screening to generate binders for 5 distinct DNA targets. These binders exhibit specificity closely matching the computational models for the target DNA sequences at as many as 6 base positions and affinities as low as 30-100 nM. The crystal structure of a designed DBP-target site complex is in close agreement with the design model, highlighting the accuracy of the design method. The designed DBPs function in both Escherichia coli and mammalian cells to repress and activate transcription of neighboring genes. Our method is a substantial step towards a general route to small and hence readily deliverable sequence-specific DBPs for gene regulation and editing.

synthetic biology↗

bioRxiv2023

Cytogenetic resource enables mechanistic resolution of changing trends in human pluripotent stem cell aberrations linked to feeder-free culture

Since the first derivation of human pluripotent stem cells (hPSCs), the number of culture conditions has steadily increased, making hPSC culture more facile. Nonetheless, there remains the persistent issue of culture-acquired genetic changes, hampering the reproducibility of hPSC research and jeopardising their clinical use. Here, we utilised comprehensive karyotyping datasets from over 20,000 hPSC cultures sampled under different conditions to ascertain association of genetic changes with specific culture regimens. We found condition-dependent patterns of aberrations, with higher prevalence of chromosome 1q gains in recent years, associated with increased use of contemporary, feeder-free cultures. Mechanistically, we show the context-dependent selection of 1q variants is mainly driven by MDM4, a gene amplified in many cancers, located on chromosome 1q. To facilitate reproducibility of hPSC research and their safe clinical utility, we provide a unique hPSC karyotype resource for informing the risk assessment of genetic aberrations and developing strategies to suppress their occurrence.

cell biology↗

bioRxiv2023

De novo designed Hsp70 activator dissolves intracellular condensates

Protein quality control (PQC) is carried out in part by the chaperone Hsp70, in concert with adapters of the J-domain protein (JDP) family. The JDPs, also called Hsp40s, are thought to recruit Hsp70 into complexes with specific client proteins. However, the molecular principles regulating this process are not well understood. We describe the de novo design of a set of Hsp70 binding proteins that either inhibited or stimulated Hsp70s ATPase activity; a stimulating design promoted the refolding of denatured luciferase in vitro, similar to native JDPs. Targeting of this design to intracellular condensates resulted in their nearly complete dissolution. The designs inform our understanding of chaperone structure-function relationships and provide a general and modular way to target PQC systems to condensates and other cellular targets.

biochemistry↗

bioRxiv2023

De novo design of buttressed loops for sculpting protein functions

In natural proteins, structured loops play central roles in molecular recognition, signal transduction and enzyme catalysis. However, because of the intrinsic flexibility and irregularity of loop regions, organizing multiple structured loops at protein functional sites has been very difficult to achieve by de novo protein design. Here we describe a solution to this problem that generates structured loops buttressed by extensive hydrogen bonding interactions with two neighboring loops and with secondary structure elements. We use this approach to design tandem repeat proteins with buttressed loops ranging from 9 to 14 residues in length. Experimental characterization shows the designs are folded and monodisperse, highly soluble, and thermally stable. Crystal structures are in close agreement with the computational design models, with the loops structured and buttressed by their neighbors as designed. We demonstrate the functionality afforded by loop buttressing by designing and characterizing binders for extended peptides in which the loops form one side of an extended binding pocket. The ability to design multiple structured loops should contribute quite generally to efforts to design new protein functions.

biochemistry↗

bioRxiv2023

Designed Endocytosis-Triggering Proteins mediate Targeted Degradation and amplify signaling

Endocytosis and lysosomal trafficking of cell surface receptors can be triggered by interaction with endogenous ligands. Therapeutic approaches such as LYTAC1,2 and KineTAC3, have taken advantage of this to target specific proteins for degradation by fusing modified native ligands to target binding proteins. While powerful, these approaches can be limited by possible competition with the endogenous ligand(s), the requirement in some cases for chemical modification that limits genetic encodability and can complicate manufacturing, and more generally, there may not be natural ligands which stimulate endocytosis through a given receptor. Here we describe general protein design approaches for designing endocytosis triggering binding proteins (EndoTags) that overcome these challenges. We present EndoTags for the IGF-2R, ASGPR, Sortillin, and Transferrin receptors, and show that fusing these tags to proteins which bind to soluble or transmembrane protein leads to lysosomal trafficking and target degradation; as these receptors have different tissue distributions, the different EndoTags could enable targeting of degradation to different tissues. The modularity and genetic encodability of EndoTags enables AND gate control for higher specificity targeted degradation, and the localized secretion of degraders from engineered cells. The tunability and modularity of our genetically encodable EndoTags should contribute to deciphering the relationship between receptor engagement and cellular trafficking, and they have considerable therapeutic potential as targeted degradation inducers, signaling activators for endocytosis-dependent pathways, and cellular uptake inducers for targeted antibody drug and RNA conjugates.

synthetic biology↗

bioRxiv2023

Rapid and automated design of two-component protein nanomaterials using ProteinMPNN

The design of novel protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. A new generation of deep learning methods promises to simplify protein-protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning-based methods to unlock the widespread application of designed protein-protein interfaces and self-assembling protein nanomaterials in biotechnology.

biochemistry↗

bioRxiv2023

Protein Ensemble Generation through VariationalAutoencoder Latent Space Sampling

Mapping the ensemble of protein conformations that contribute to function and can be targeted by small molecule drugs remains an outstanding challenge. Here we explore the use of soft-introspective variational autoencoders for reducing the challenge of dimensionality in the protein structure ensemble generation problem. We convert high-dimensional protein structural data into a continuous, low-dimensional representation, carry out search in this space guided by a structure quality metric, then use RoseTTAFold to generate 3D structures. We use this approach to generate ensembles for the cancer relevant protein K-Ras, training the VAE on a subset of the available K-Ras crystal structures and MD simulation snapshots, and assessing the extent of sampling close to crystal structures withheld from training. We find that our latent space sampling procedure rapidly generates ensembles with high structural quality and is able to sample within 1 angstrom of held out crystal structures, with a consistency higher than MD simulation or AlphaFold2 prediction. The sampled structures sufficiently recapitulate the cryptic pockets in the held-out K-Ras structures to allow for small molecule docking.

biochemistry↗

bioRxiv2023

Salmonella Infantis, the emerging human multidrug resistant pathogen - a One Health perspective

Salmonella Infantis presents an ever-increasing threat to public health due to its spread throughout many countries and association with high levels of antimicrobial resistance (AMR). Whole genome sequences of 5,284 S. Infantis strains from 74 countries, isolated between 1989 and 2020 from a wide variety of sources including humans, animals, and food, were analysed to compare genetic phylogeny, AMR determinants and plasmid presence. The global S. Infantis population structure diverged into three clusters: a North American cluster, European cluster and a global cluster. The levels of AMR varied between the S. Infantis clusters and by isolation source; 73% of poultry isolates had multidrug resistance (MDR) compared to 35% of human isolates. This correlated with plasmid of emerging S. Infantis (pESI) presence; 71% of poultry isolates contained pESI versus 32% of human isolates. This provides important information for public health teams engaged in reducing the spread of this pathogen.

microbiology↗

bioRxiv2023

Hierarchical design of pseudosymmetric protein nanoparticles

Discrete protein assemblies ranging from hundreds of kilodaltons to hundreds of megadaltons in size are a ubiquitous feature of biological systems and perform highly specialized functions 1-3. Despite remarkable recent progress in accurately designing new self-assembling proteins, the size and complexity of these assemblies has been limited by a reliance on strict symmetry 4,5. Inspired by the pseudosymmetry observed in bacterial microcompartments and viral capsids, we developed a hierarchical computational method for designing large pseudosymmetric self-assembling protein nanomaterials. We computationally designed pseudosymmetric heterooligomeric components and used them to create discrete, cage-like protein assemblies with icosahedral symmetry containing 240, 540, and 960 subunits. At 49, 71, and 96 nm diameter, these nanoparticles are the largest bounded computationally designed protein assemblies generated to date. More broadly, by moving beyond strict symmetry, our work represents an important step towards the accurate design of arbitrary self-assembling nanoscale protein objects.

bioengineering↗

bioRxiv2023

Design of four component T=4 tetrahedral, octahedral, and icosahedral protein nanocages through programmed symmetry breaking

Four, eight or twenty C3 symmetric protein trimers can be arranged with tetrahedral (T-sym), octahedral (O-sym) or icosahedral (I-sym) point group symmetry to generate closed cage-like structures1,2. Generating more complex closed structures requires breaking perfect point group symmetry. Viruses do this in the icosahedral case using quasi-symmetry or pseudo-symmetry to access higher triangulation number architectures3-9, but nature appears not to have explored higher triangulation number tetrahedral or octahedral symmetries. Here, we describe a general design strategy for building T = 4 architectures starting from simpler T = 1 structures through pseudo-symmetrization of trimeric building blocks. Electron microscopy confirms the structures of T = 4 cages with 48 (T-sym), 96 (O-sym), and 240 (I-sym) subunits, each with four distinct chains and six different protein-protein interfaces, and diameters of 33nm, 43nm, and 75nm, respectively. Higher triangulation number viruses possess very sophisticated functionalities; our general route to higher triangulation number nanocages should similarly enable a next generation of multiple antigen displaying vaccine candidates10,11 and targeted delivery vehicles12,13.

biochemistry↗

bioRxiv2023

De novo design of highly selective miniprotein inhibitors of integrins αvβ6 and αvβ8

The RGD (Arg-Gly-Asp)-binding integrins v{beta}6 and v{beta}8 are clinically validated cancer and fibrosis targets of considerable therapeutic importance. Compounds that can discriminate between the two closely related integrin proteins and other RGD integrins, stabilize specific conformational states, and have sufficient stability enabling tissue restricted administration could have considerable therapeutic utility. Existing small molecules and antibody inhibitors do not have all of these properties, and hence there is a need for new approaches. Here we describe a method for computationally designing hyperstable RGD-containing miniproteins that are highly selective for a single RGD integrin heterodimer and conformational state, and use this strategy to design inhibitors of v{beta}6 and v{beta}8 with high selectivity. The v{beta}6 and v{beta}8 inhibitors have picomolar affinities for their targets, and >1000-fold selectivity over other RGD integrins. CryoEM structures are within 0.6-0.7[A] root-mean-square deviation (RMSD) to the computational design models; the designed v{beta}6 inhibitor and native ligand stabilize the open conformation in contrast to the therapeutic anti-v{beta}6 antibody BG00011 that stabilizes the bent-closed conformation and caused on-target toxicity in patients with lung fibrosis, and the v{beta}8 inhibitor maintains the constitutively fixed extended-closed v{beta}8 conformation. In a mouse model of bleomycin-induced lung fibrosis, the v{beta}6 inhibitor potently reduced fibrotic burden and improved overall lung mechanics when delivered via oropharyngeal administration mimicking inhalation, demonstrating the therapeutic potential of de novo designed integrin binding proteins with high selectivity.

biochemistry↗

bioRxiv2023

Blueprinting expandable nanomaterials with standardized protein building blocks

A wooden house frame consists of many different lumber pieces, but because of the regularity of these building blocks, the structure can be designed using straightforward geometrical principles. The design of multicomponent protein assemblies in comparison has been much more complex, largely due to the irregular shapes of protein structures1. Here we describe extendable linear, curved, and angled protein building blocks, as well as inter-block interactions that conform to specified geometric standards; assemblies designed using these blocks inherit their extendability and regular interaction surfaces, enabling them to be expanded or contracted by varying the number of modules, and reinforced with secondary struts. Using X-ray crystallography and electron microscopy, we validate nanomaterial designs ranging from simple polygonal and circular oligomers that can be concentrically nested, up to large polyhedral nanocages and unbounded straight "train track" assemblies with reconfigurable sizes and geometries that can be readily blueprinted. Because of the complexity of protein structures and sequence-structure relationships, it has not been previously possible to build up large protein assemblies by deliberate placement of protein backbones onto a blank 3D canvas; the simplicity and geometric regularity of our design platform now enables construction of protein nanomaterials according to "back of an envelope" architectural blueprints.

biochemistry↗

bioRxiv2023

Directing polymorph specific calcium carbonate formation with de novo protein templates

Biomolecules modulate inorganic crystallization to generate hierarchically structured biominerals1-5, but the atomic structure of the organic-inorganic interfaces that regulate mineralization remain unknown6-8. We hypothesized that heterogeneous nucleation of calcium carbonate could be achieved by a structured flat molecular template that pre-organizes calcium ions on its surface. To test this hypothesis, we designed helical repeat proteins (DHRs) displaying regularly spaced carboxylate arrays on their surfaces and found that both protein monomers and protein-Ca2+ assemblies directly nucleate nano-calcite with non-natural (110) or (202) faces while vaterite, which forms first absent the proteins, is bypassed. The nanocrystals then assemble by oriented attachment into calcite mesocrystals. We find further that nanocrystal size and polymorph can be tuned by varying the length and surface chemistry of the designed protein templates. Thus, bio-mineralization can be programmed using de novo protein design, providing a route to next-generation hybrid materials. One sentence summaryDe novo designed protein templates promote nucleation of nano-calcite and direct its growth by oriented particle attachment.

biochemistry↗

bioRxiv2023

De novo design of modular protein hydrogels with programmable intra- and extracellular viscoelasticity

Relating the macroscopic properties of protein-based materials to their underlying component microstructure is an outstanding challenge. Here, we exploit computational design to specify the size, flexibility, and valency of de novo protein building blocks, as well as the interaction dynamics between them, to investigate how molecular parameters govern the macroscopic viscoelasticity of the resultant protein hydrogels. We construct gel systems from pairs of symmetric protein homo-oligomers, each comprising 2, 5, 24, or 120 individual protein components, that are crosslinked either physically or covalently into idealized step-growth biopolymer networks. Through rheological assessment and molecular dynamics (MD) simulation, we find that the covalent linkage of multifunctional precursors yields hydrogels whose viscoelasticity depends on the crosslink length between the constituent building blocks. In contrast, reversibly crosslinking the homo-oligomeric components with a computationally designed heterodimer results in non-Newtonian biomaterials exhibiting fluid-like properties under rest and low shear, but shear-stiffening solid-like behavior at higher frequencies. Exploiting the unique genetic encodability of these materials, we demonstrate the assembly of protein networks within living mammalian cells and show via fluorescence recovery after photobleaching (FRAP) that mechanical properties can be tuned intracellularly, in correlation with matching formulations formed extracellularly. We anticipate that the ability to modularly construct and systematically program the viscoelastic properties of designer protein-based materials could have broad utility in biomedicine, with applications in tissue engineering, therapeutic delivery, and synthetic biology. SignificanceProtein-based hydrogels have many applications in cellular engineering and medicine. Most genetically encodable protein hydrogels are made from naturally harvested proteins or protein-polymer hybrid constructs. Here we describe de novo protein hydrogels and systematically investigate the impact of microscopic properties of the building blocks (e.g., supramolecular interaction, valencies, geometries, flexibility) on the resultant macroscopic gel mechanics, both intra-and extracellularly. These de novo supramolecular protein assemblies, whose properties can be tuned from solid gels to non-Newtonian fluids, provide expanded opportunities for applications in synthetic biology and medicine.

bioengineering↗

bioRxiv2023

Efficient and accurate prediction of protein structure using RoseTTAFold2

AlphaFold2 and RoseTTAFold predict protein structures with very high accuracy despite substantial architecture differences. We sought to develop an improved method combining features of both. The resulting method, RoseTTAFold2, extends the original three-track architecture of RoseTTAFold over the full network, incorporating the concepts of Frame-aligned point error, recycling during training, and the use of a distillation set from AlphaFold2. We also took from AlphaFold2 the idea of structurally coherent attention in updating pair features, but using a more computationally efficient structure-biased attention as opposed to triangle attention. The resulting model has the accuracy of AlphaFold2 on monomers, and AlphaFold2-multimer on complexes, with better computational scaling for large proteins and complexes. This excellent performance is achieved without hallmark features of AlphaFold2, invariant point attention and triangle attention, indicating that these are not essential for high accuracy prediction. Almost all recent work on protein structure prediction has re-used the basic AlphaFold2 architecture; our results show that excellent performance can be achieved with a broader class of models, opening the door for further exploration.

biochemistry↗

bioRxiv2023

Joint Generation of Protein Sequence and Structure with RoseTTAFold Sequence Space Diffusion

Protein denoising diffusion probabilistic models (DDPMs) show great promise in the de novo generation of protein backbones but are limited in their inability to guide generation of proteins with sequence specific attributes and functional properties. To overcome this limitation, we develop ProteinGenerator, a sequence space diffusion model based on RoseTTAfold that simultaneously generates protein sequences and structures. Beginning from random amino acid sequences, our model generates sequence and structure pairs by iterative denoising, guided by any desired sequence and structural protein attributes. To explore the versatility of this approach, we designed proteins enriched for specific amino acids, with internal sequence repeats, with masked bioactive peptides, with state dependent structures, and with key sequence features of specific protein families. ProteinGenerator readily generates sequence-structure pairs satisfying the input conditioning (sequence and/or structural) criteria, and experimental validation showed that the designs were monomeric by size exclusion chromatography (SEC), had the desired secondary structure content by circular dichroism (CD), and were thermostable up to 95{degrees}C. By enabling the simultaneous optimization of both sequence and structure, ProteinGenerator allows for the design of functional proteins with specific sequence and structural attributes, and paves the way for protein function optimization by active learning on sequence-activity datasets.

biochemistry↗