Biology subjects

Baker, D.

Publications and source records attributed to Baker, D..

At least 109 records · Page 6Linked to original sources

bioRxiv2024

Computational design of bifaceted protein nanomaterials with tailorable properties

Recent advances in computational methods have led to considerable progress in the design of self-assembling protein nanoparticles. However, nearly all nanoparticles designed to date exhibit strict point group symmetry, with each subunit occupying an identical, symmetrically related environment. This limits the structural diversity that can be achieved and precludes anisotropic functionalization. Here, we describe a general computational strategy for designing multi-component bifaceted protein nanomaterials with two distinctly addressable sides. The method centers on docking pseudosymmetric heterooligomeric building blocks in architectures with dihedral symmetry and designing an asymmetric protein-protein interface between them. We used this approach to obtain an initial 30-subunit assembly with pseudo-D5 symmetry, and then generated an additional 15 variants in which we controllably altered the size and morphology of the bifaceted nanoparticles by designing de novo extensions to one of the subunits. Functionalization of the two distinct faces of the nanoparticles with de novo protein minibinders enabled specific colocalization of two populations of polystyrene microparticles coated with target protein receptors. The ability to accurately design anisotropic protein nanomaterials with precisely tunable structures and functions could be broadly useful in applications that require colocalizing two or more distinct target moieties.

bioengineering↗

bioRxiv2024

Improved protein binder design using -pairing targeted RFdiffusion

Despite recent advances in the computational design of protein binders, designing proteins that bind with high affinity to polar protein targets remains an outstanding problem. Here we show that RFdiffusion can be conditioned to efficiently generate protein scaffolds that form geometrically matched extended beta-sheets with target protein edge beta-strands in which polar groups on the target are nearly perfectly complemented with hydrogen bonding groups on the design. We use this approach to design binders against a set of therapeutically relevant polar targets (KIT, PDGFR[a], ALK-2, ALK-3, FCRL5, and NRP1) and find that beta-strand-targeted design yields higher affinities and success rates than unconditioned RFdiffusion. All by all binding experiments show that the designs have affinities ranging from 137 pM to mid nM for their targets and essentially no off target binding despite the sharing of beta-strand interactions, likely reflecting the precise customization of interacting beta-strand geometry and additional designed binder-target interactions. A co-crystal structure of one such design in complex with the KIT receptor is nearly identical to the computational design model confirming the accuracy of the design approach. The ability to robustly generate binders displaying high affinity and specificity to polar interaction surfaces with exposed beta-strands considerably increases the range and capabilities of computational binder design.

biochemistry↗

bioRxiv2024

Bond-centric modular design of protein assemblies

We describe a modular bond-centric approach to protein nanomaterial design inspired by the rich diversity of chemical structures that can be generated from the small number of atomic valencies and bonding interactions. We design protein building blocks with regular coordination geometries and bonding interactions that enable the assembly of a wide variety of closed and opened nanomaterials using simple geometrical principles. Experimental characterization confirms successful formation of more than twenty multi-component polyhedral protein cages, 2D arrays, and 3D protein lattices, with a high (10-50 %) success rate and electron microscopy data closely matching the corresponding design models. Because of the modularity, individual building blocks can assemble with different partners to generate distinct regular assemblies, resulting in an economy of parts and enabling the construction of reconfigurable systems.

biochemistry↗

bioRxiv2024

Tuning Insulin Receptor Signaling Using De Novo Designed Agonists

Binding of insulin to the insulin receptor (IR) induces conformational changes in the extracellular portion of the receptor that lead to activation of the intracellular kinase domain and the AKT and MAPK pathways, and downstream modulation of glucose metabolism and cell proliferation. We reasoned that designed agonists that induce different conformational changes in the receptor might induce different downstream responses, which could be useful both therapeutically and to shed light on how extracellular conformation is coupled to intracellular signaling. We used de novo protein design to first generate binders to individual IR extracellular domains, and then to fuse these together in different orientations and with different conformational flexibility. We describe a series of synthetic agonists that signal through the IR that differ from insulin and from each other in the induction of receptor autophosphorylation, MAPK activation, intracellular trafficking, and cell proliferation. We identify designs that are more potent than insulin causing much longer lasting reductions in glucose levels, and that retain signaling activity on disease-causing receptor mutants that do not respond to insulin. These results inform our understanding of how changes in receptor conformation and dynamics are transmitted to downstream signaling, and our synthetic agonists have considerable therapeutic potential for diabetes and severe insulin resistance syndromes. HighlightsO_LIComputational design yielded super agonists, partial agonists, and antagonists of IR. C_LIO_LIDe novo agonists induce a distinct IR active conformation. C_LIO_LIDesigned agonists tune IR signaling by modulating conformational dynamics of activated IR. C_LIO_LIDesigned agonists are more potent than insulin, reducing glucose levels longer and activating disease-causing IR mutants. C_LI

bioengineering↗

bioRxiv2024

Computing the Human Interactome

Protein-protein interactions (PPI) are essential for biological function. Recent advances in coevolutionary analysis and Deep Learning (DL) based protein structure prediction have enabled comprehensive PPI identification in bacterial and yeast proteomes, but these approaches have limited success to date for the more complex human proteome. Here, we overcome this challenge by 1) enhancing the coevolutionary signals with 7-fold deeper multiple sequence alignments harvested from 30 petabytes of unassembled genomic data, and 2) developing a new DL network trained on augmented datasets of domain-domain interactions from 200 million predicted protein structures. These advancements allow us to systematically screen through 200 million human protein pairs and predict 18,316 PPIs with an expected precision of 90%, among which 5,578 are novel predictions. 3D models of these predicted PPIs nearly triple the number of human PPIs with accurate structural information, providing numerous insights into protein function and mechanisms of human diseases.

biochemistry↗

bioRxiv2024

Modeling protein-small molecule conformational ensembles with ChemNet

Modeling the conformational heterogeneity of protein-small molecule interactions is important for understanding natural systems and evaluating designed systems, but remains an outstanding challenge. We reasoned that while residue level descriptions of biomolecules are efficient for de novo structure prediction, for probing heterogeneity of interactions with small molecules in the folded state an entirely atomic level description could have advantages in speed and generality. We developed a graph neural network called PLACER (Protein-Ligand Atomistic Conformational Ensemble Resolver) trained to recapitulate correct atomic positions from partially corrupted input structures from the Cambridge Structural Database and the Protein Data Bank; the nodes of the graph are the atoms in the system. PLACER accurately generates structures of diverse organic small molecules given knowledge of their atom composition and bonding, and given a description of the larger protein context, builds up structures of small molecules and protein side chains for protein-small molecule docking. Because PLACER is rapid and stochastic, ensembles of predictions can be readily generated to map conformational heterogeneity. In enzyme design efforts described here and elsewhere, we find that using PLACER to assess the accuracy and pre-organization of the designed active sites results in higher success rates and higher activities; we obtain a preorganized retroaldolase with a kcat/KM of 11000 M-1min-1, considerably higher than any pre-deep learning design for this reaction. We anticipate that PLACER will be widely useful for rapidly generating conformational ensembles of small molecule and small molecule-protein systems, and for designing higher activity preorganized enzymes.

biochemistry↗

bioRxiv2024

Nucleation limited assembly and polarized growth of a de novo-designed allosterically modulatable protein filament

The design of inducibly assembling protein nanomaterials is an outstanding challenge. Here, we describe the computational design of a protein filament formed from a monomeric subunit that binds a peptide ligand. The cryoEM structure of the micron-scale fibers is very close to the computational design model. The ligand acts as a tunable allosteric modulator: while not part of the fiber subunit-subunit interfaces, the assembly of the filament is dependent on ligand addition, with longer peptides having more extensive interaction surfaces with the monomer, promoting more rapid growth. Seeded growth and capping experiments reveal that the filaments grow primarily from one end. We show that designed nucleators that present 12 copies of the peptide ligand promote fiber assembly at concentrations where otherwise assembly occurs very slowly, likely by generating critical local concentrations of monomer in the assembly competent conformation. Following filament assembly, the peptide ligand can be exchanged with free peptide in solution fused to any functional protein of interest, opening the door to a wide variety of tunable engineered materials.

bioengineering↗

bioRxiv2024

Engineered Receptors for Soluble Cell-to-Cell Communication

Despite recent advances in mammalian synthetic biology, there remains a lack of modular synthetic receptors that can robustly respond to soluble ligands and in turn activate cellular functions. Such receptors would have extensive clinical potential to regulate the activity of engineered therapeutic cells, but to date only receptors against cell surface targets have approached clinical translation. To address this gap, we developed a receptor architecture based on synNotch, called SyNthetic Intramembrane Proteolysis Receptor (SNIPR), that has the added ability to be activated by soluble ligands, both natural and synthetic, with remarkably low baseline activity and high fold activation. SNIPRs are able to access an endocytic, pH-dependent cleavage mechanism to achieve soluble ligand sensing, in addition to employing a canonical-like pathway for detecting surface-bound ligands. We demonstrate the therapeutic capabilities of the receptor platform by localizing the activity of CAR T-cells to solid tumors where soluble disease-associated factors are expressed, bypassing the major hurdle of on-target off-tumor toxicity in bystander organs. We further applied the SNIPR platform to engineer fully synthetic signaling networks between cells orthogonal to natural signaling pathways, expanding the scope of synthetic biology. Our design framework enables cellular communication and environmental interactions, extending the capabilities of synthetic cellular networking in clinical and research contexts.

bioengineering↗

bioRxiv2024

Target-conditioned diffusion generates potent TNFR superfamily antagonists and agonists

Despite progress in designing protein binding proteins, the shape matching of designs to targets is lower than in many native protein complexes, and design efforts have failed for TNF receptor (TNFR1) and other protein targets with relatively flat and polar surfaces. We hypothesized that free diffusion from random noise could generate shape-matched binders for challenging targets, and tested this on TNFR1. We obtain designs with low picomolar affinity whose specificity can be completely switched to other family members using partial diffusion. Designs function as antagonists or as superagonists when presented at higher valency for OX40 and 4-1BB. The ability to design high-affinity and specificity antagonists and agonists for pharmacologically important targets in silico presages a new era in which binders are made by computation rather than immunization or random screening approaches.

biochemistry↗

bioRxiv2024

De novo design of Ras isoform selective binders

The proto-oncogene Ras which governs diverse intracellular pathways has four major isoforms (KRAS4A, KRAS4B, HRAS, and NRAS) with substantial sequence homology and similar in vitro biochemistry. There is considerable interest in investigating the roles of these independently as their association with different cancers vary, but there are few Ras isoform-specific binding reagents as the only significant sequence differences are in their disordered and highly charged C-termini which have been difficult to elicit antibodies against. To overcome this limitation, we use deep learning-based methods to de novo design Ras isoform-specific binders (RIBs) for all major Ras isoforms that specifically target the Ras C-terminus. The RIBs bind to their target Ras isoforms both in vitro and in cells with remarkable specificity, disrupting their membrane localization and inhibiting Ras activity, and should contribute to dissecting the distinct roles of Ras isoforms in biology and disease.

bioengineering↗

bioRxiv2024

Computational design of serine hydrolases

Enzymes that proceed through multistep reaction mechanisms often utilize complex, polar active sites positioned with sub-angstrom precision to mediate distinct chemical steps, which makes their de novo construction extremely challenging. We sought to overcome this challenge using the classic catalytic triad and oxyanion hole of serine hydrolases as a model system. We used RFdiffusion1 to generate proteins housing catalytic sites of increasing complexity and varying geometry, and a newly developed ensemble generation method called ChemNet to assess active site geometry and preorganization at each step of the reaction. Experimental characterization revealed novel serine hydrolases that catalyze ester hydrolysis with catalytic efficiencies (kcat/Km) up to 3.8 x 103 M-1 s-1, closely match the design models (C RMSDs < 1 [A]), and have folds distinct from natural serine hydrolases. In silico selection of designs based on active site preorganization across the reaction coordinate considerably increased success rates, enabling identification of new catalysts in screens of as few as 20 designs. Our de novo buildup approach provides insight into the geometric determinants of catalysis that complements what can be obtained from structural and mutational studies of native enzymes (in which catalytic group geometry and active site makeup cannot be so systematically varied), and provides a roadmap for the design of industrially relevant serine hydrolases and, more generally, for designing complex enzymes that catalyze multi-step transformations.

biochemistry↗

bioRxiv2024

De novo design of potent inhibitors of Clostridioides difficile toxin B

Clostridioides difficile is a major cause of secondary disease in hospitals. During infection, C. difficile toxin B drives disease pathology. Here we use deep learning and Rosetta-based approaches to de novo design small proteins that block the entry of TcdB into cells. These molecules have binding affinities and neutralization IC50s in the pM range and are compelling candidates for further clinical development. By directly targeting the toxin rather than the pathogen, these molecules have the advantage of immediate cessation of disease and lower selective pressure for escape compared to conventional antibiotics. As C. difficile infects the colon, the protease and pH resistance of the designed proteins opens the door to oral delivery of engineered biologics. Significance statementC. difficile infection (CDI) is a major public health concern with over half a million cases in the United States annually resulting in 30,000 deaths. Current therapies are inadequate and frequently result in cycles of recurrent infection (rCDI). Progress has been made in the development of anti-toxin mAb therapies that can reduce the rate of rCDI, but these remain unaffordable and out of reach for many patients. Using de novo protein design, we developed small protein inhibitors targeting two independent receptor binding sites on the toxin that drives pathology during CDI. These molecules are high affinity, potently neutralizing and stable in simulated intestinal fluid, making them strong candidates for the clinical development of new CDI therapies.

microbiology↗

bioRxiv2024

De novo designed inhibitor confers protection against lethal toxic shock

Paeniclostridium sordellii causes a toxic shock syndrome with a mortality rate of nearly 70%, primarily affecting postpartum and post-abortive women. This disease is driven by the production of the P. sordellii lethal toxin, TcsL, for which there are currently no effective treatments. We used a protein diffusion model, RFdiffusion, to design high affinity TcsL inhibitors. From a very small set of 48 starting designs and 48 additional sequence optimized designs, we developed a potent inhibitor with <100 pM affinity that protects mice prophylactically and therapeutically (post exposure) from lung edema and death in a stringent lethal challenge model. This inhibitor, which can be lyophilized without any loss of activity, is a promising therapeutic candidate for this rare but deadly disease, and our results highlight the ability of deep learning-based protein design to rapidly generate biologics with potential clinical utility.

microbiology↗

bioRxiv2024

A pan-variant miniprotein inhibitor protects against SARS-CoV-2 variants

The continued evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has compromised neutralizing antibody responses elicited by prior infection or vaccination and abolished the utility of most monoclonal antibody therapeutics. We previously described a computationally-designed, homotrimeric miniprotein inhibitor, designated TRI2-2, that protects mice against pre-Omicron SARS-CoV-2 variants. Here, we show that TRI2-2 exhibits pan neutralization of variants that evolved during the 4.5 years since the emergence of SARS-CoV-2 and protects mice against BQ.1.1, XBB.1.5 and BA.2.86 challenge when administered post-exposure by an intranasal route. The resistance of TRI2-2 to viral escape and its direct delivery to the upper airways rationalize a path toward clinical advancement.

biochemistry↗

bioRxiv2024

Design of a Soluble Multivalent Notch Agonist

Designed protein agonists can enhance the efficiency of endogenous signaling pathways, and provide a powerful means to control cellular functions and develop disease therapeutics. Designing a soluble cytokine-like agonist for Notch signaling, an evolutionarily conserved pathway that regulates cell fate in embryonic and adult development, is especially challenging because Notch receptor activation requires a mechanical force that is typically mediated by cell-associated transmembrane ligands at sites of cell-cell contact. Moreover, free soluble Notch ligand is signal inhibitory. Here, we exploit computationally designed protein oligomers with precise geometries and valencies to generate cytokine-like, protein only, multivalent soluble Notch agonists. These tools promote cell-cell contact, cluster Notch proteins in synapses at the cell surface, and activate Notch signaling in reporter cell lines and cells expressing endogenous receptors. We demonstrate the utility of these soluble Notch agonists in T cell differentiation from cord blood (CB) and human induced pluripotent stem cells (iPSCs), and in bioreactor production of T cells in liquid suspension. Soluble multivalent Notch agonists can be applied broadly to in vitro cellular differentiation methods to generate clinical cell products and to develop immunotherapies.

synthetic biology↗

bioRxiv2024

Parametrically guided design of beta barrels and transmembrane nanopores using deep learning

Francis Cricks global parameterization of coiled coil geometry has been widely useful for guiding design of new protein structures and functions. However, design guided by similar global parameterization of beta barrel structures has been less successful, likely due to the deviations from ideal barrel geometry required to maintain inter-strand hydrogen bonding without introducing backbone strain. Instead, beta barrels have been designed using 2D structural blueprints; while this approach has successfully generated new fluorescent proteins, transmembrane nanopores, and other structures, it requires expert knowledge and provides only indirect control over the global shape. Here we show that the simplicity and control over shape and structure provided by parametric representations can be generalized beyond coiled coils by taking advantage of the rich sequence-structure relationships implicit in RoseTTAFold based design methods. Starting from parametrically generated barrel backbones, both RFjoint inpainting and RFdiffusion readily incorporate backbone irregularities necessary for proper folding with minimal deviation from the idealized barrel geometries. We show that for beta barrels across a broad range of beta sheet parameterizations, these methods achieve high in silico and experimental success rates, with atomic accuracy confirmed by an X-ray crystal structure of a novel barrel topology, and de novo designed 12, 14, and 16 stranded transmembrane nanopores with conductances ranging from 200 to 500 pS. By combining the simplicity and control of parametric generation with the high success rates of deep learning based protein design methods, our approach makes the design of proteins where global shape confers function, such as beta barrel nanopores, more precisely specifiable and accessible. SignificanceDe novo beta barrel proteins have previously been designed using "blueprint" based methods which require expert knowledge of the rules of folding and provide only indirect control of the overall barrel shape by specifying structural features such as glycine kinks and beta bulges. The barrel shape can be directly modeled using global parametric methods, but to date such methods have not succeeded in generating folded proteins, likely due to the absence of such structural features. Here, we describe methods that combine the simplicity and control of parametric barrel specification with the high success rates of deep learning based protein design methods to successfully design new beta barrel folds of different and pre-specified sizes, including both soluble designs and transmembrane nanopores.

biochemistry↗

bioRxiv2024

Sequence-specific targeting of intrinsically disordered protein regions

Intrinsically disordered proteins and peptides play key roles in biology, but the lack of defined structures and the high variability in sequence and conformational preferences has made targeting such systems challenging. We describe a general approach for designing proteins that bind intrinsically disordered protein regions in diverse extended conformations with side chains fitting into complementary binding pockets. We used the approach to design binders for 39 highly diverse unstructured targets and obtain designs with pM to 100 nM affinities in 34 cases, testing [~]22 designs per target (including polar targets). The designs function in cells and as detection reagents, and are specific for their intended targets in all-by-all binding experiments. Our approach is a major step towards a general solution to the intrinsically disordered protein and peptide recognition problem.

biochemistry↗

bioRxiv2024

Diffusing protein binders to intrinsically disordered proteins

Proteins which bind intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) with high affinity and specificity could have considerable utility for therapeutic and diagnostic applications. However, a general methodology for targeting IDPs/IDRs has yet to be developed. Here, we show that starting only from the target sequence of the input, and freely sampling both target and binding protein conformation, RFdiffusion can generate binders to IDPs and IDRs in a wide range of conformations. We use this approach to generate binders to the IDPs Amylin, C-peptide and VP48 in a range of conformations with Kds in the 3 -100nM range. The Amylin binder inhibits amyloid fibril formation and dissociates existing fibers, and enables enrichment of amylin for mass spectrometry-based detection. For the IDRs G3bp1, common gamma chain (IL2RG) and prion, we diffused binders to beta strand conformations of the targets, obtaining 10 to 100 nM affinity. The IL2RG binder colocalizes with the receptor in cells, enabling new approaches to modulating IL2 signaling. Our approach should be widely useful for creating binders to flexible IDPs/IDRs spanning a wide range of intrinsic conformational preferences.

biochemistry↗