Biology subjects

Baker, D.

Publications and source records attributed to Baker, D..

At least 127 records · Page 7Linked to original sources

bioRxiv2024

De Novo Design of Integrin α5β1 Modulating Proteins for Regenerative Medicine

Integrin 5{beta}1 is crucial for cell attachment and migration in development and tissue regeneration, and 5{beta}1 binding proteins could have considerable utility in regenerative medicine and next-generation therapeutics. We use computational protein design to create de novo 5{beta}1-specific modulating miniprotein binders, called NeoNectins, that bind to and stabilize the open state of 5{beta}1. When immobilized onto titanium surfaces and throughout 3D hydrogels, the NeoNectins outperform native fibronectin and RGD peptide in enhancing cell attachment and spreading, and NeoNectin-grafted titanium implants outperformed fibronectin and RGD-grafted implants in animal models in promoting tissue integration and bone growth. NeoNectins should be broadly applicable for tissue engineering and biomedicine. One-Sentence SummaryA de novo-designed fibronectin substitute, NeoNectin, is specific for integrin 5{beta}1 and can be incorporated into biomaterials for regenerative medicine.

bioengineering↗

bioRxiv2024

Controlling semiconductor growth with structured de novo protein interfaces

Protein design now enables the precise arrangement of atoms on the nanometer length scales of inorganic crystal nuclei, opening up the possibility of templating the growth of metal oxides including semiconductors. We designed proteins presenting regularly repeating interfaces containing functional groups that organize ions and water molecules, and characterized their ability to bind to and template metal oxides. Two interfaces promoted the growth of hematite under conditions that otherwise resulted in the formation of magnetite. Three interfaces promoted ZnO nucleation under conditions where traditional ZnO-binding peptides and control proteins were ineffective. Designed cyclic assemblies with these ZnO nucleating interfaces lining interior cavities promoted ZnO growth within the cavity. CryoEM analysis of a designed octahedral nanocage revealed atomic density likely corresponding to the growing ZnO directly adjacent to the designed nucleation promoting interfaces. These findings demonstrate that designed proteins can direct the formation of metal oxides not observed in biological systems, opening the door to protein-semiconductor hybrid materials. One Sentence SummaryWe describe the design of structured protein interfaces that bind to, promote, and localize the growth of zinc oxide and hematite, inorganic materials which are not found in biological systems.

bioengineering↗

bioRxiv2024

Design of Repeat Alpha-Beta Proteins with Capping Helices

Proteins composed of a single structural unit tandemly repeated multiple times carry out a wide range of functions in biology. There has hence been considerable interest in designing such repeat proteins; previous approaches have employed strict constraints on secondary structure types and relative geometries, and most characterized designs either mimic a known natural topology, adhere closely to a parametric helical bundle architecture, or exploit very short repetitive sequences. Here, we describe Rosetta-based and deep learning hallucination methods for generating novel repeat protein architectures featuring mixed alpha-helix and beta-strand topologies, and 25 new highly stable alpha-beta proteins designed using these methods. We find that incorporation of terminal caps which prevent beta strand mediated intermolecular interactions increases the solubility and monomericity of individual designs as well as overall design success rate.

bioengineering↗

bioRxiv2024

Multispectral live-cell imaging with uncompromised spatiotemporal resolution

Multispectral imaging is an established method to extend the number of colours usable in fluorescence imaging beyond the typical limit of three or four, but standard approaches are poorly suited to live-cell imaging. We introduce an approach for multispectral imaging in live cells, comprising an iterative spectral unmixing algorithm and eight channel camera-based image acquisition hardware. This enables the accurate unmixing of low signal-to-noise ratio datasets, typical of live-cell imaging, captured at video rates. We use this approach on a commercial spinning disk confocal microscope and a home-built oblique plane light sheet microscope to image 1-7 spectrally distinct fluorophore species simultaneously, using both fluorescent protein fusions and small-molecule dyes. We further use de novo designed protein-binding proteins (minibinders), labelled with organic fluorophores, and use these in combination with our multispectral imaging approach to study the endosomal trafficking of cell-surface receptors at endogenous levels.

biophysics↗

bioRxiv2024

Accurate single domain scaffolding of three non-overlapping protein epitopes using deep learning

De novo protein design has seen major success in scaffolding single functional motifs, however, in nature most proteins present multiple functional sites. Here we describe an approach to simultaneously scaffold multiple functional sites in a single domain protein using deep learning. We designed small single domain immunogens, under 130 residues, that simultaneously present three distinct and irregular motifs from respiratory syncytial virus. These motifs together comprise nearly half of the designed proteins, and hence the overall folds are quite unusual with little global similarity to proteins in the PDB. Despite this, X-ray crystal structures confirm the accuracy of presentation of each of the motifs, and the multi-epitope design yields improved cross-reactive titers and neutralizing response compared to a single-epitope immunogen. The successful presentation of three distinct binding surfaces in a small single domain protein highlights the power of generative deep learning methods to solve complex protein design problems.

bioengineering↗

bioRxiv2024

Design of High Affinity Binders to Convex Protein Target Sites

While there has been progress in the de novo design of small globular miniproteins (50-65 residues) to bind to primarily concave regions of a target protein surface, computational design of minibinders to convex binding sites remains an outstanding challenge due to low level of overall shape complementarity. Here, we describe a general approach to generate computationally designed proteins which bind to convex target sites that employ geometrically matching concave scaffolds. We used this approach to design proteins binding to TGF{beta}RII, CTLA-4 and PD-L1 which following experimental optimization have low nanomolar to picomolar affinities and potent biological activity. Co-crystal structures of the TGF{beta}RII and CTLA-4 binders in complex with the receptors are in close agreement with the design models. Our approach provides a general route to generating very high affinity binders to convex protein target sites.

biochemistry↗

bioRxiv2024

Genome-wide profiling of highly similar paralogous genes using HiFi sequencing

Variant calling is hindered in segmental duplications by sequence homology. We developed Paraphase, a HiFi-based informatics method that resolves highly similar genes by phasing all haplotypes of a gene family. We applied Paraphase to 160 long (>10 kb) segmental duplication regions across the human genome with high (>99%) sequence similarity, encoding 316 genes. Analysis across five ancestral populations revealed highly variable copy numbers of these regions. We identified 23 families with exceptionally low within-family diversity, where extensive gene conversion and unequal-crossing over have resulted in highly similar gene copies. Furthermore, our analysis of 36 trios identified 7 de novo SNVs and 4 de novo gene conversion events, 2 of which are non-allelic. Finally, we summarized extensive genetic diversity in 9 medically relevant genes previously considered challenging to genotype. Paraphase provides a framework for resolving gene paralogs, enabling accurate testing in medically relevant genes and population-wide studies of previously inaccessible genes.

genomics↗

bioRxiv2024

Computational stabilization of a non-heme iron enzyme enables efficient evolution of new function

Directed evolution has emerged as a powerful tool for engineering new biocatalysts. However, introducing new catalytic residues can be destabilizing, and it is generally beneficial to start with a stable enzyme parent. Here we show that the deep learning-based tool ProteinMPNN can be used to redesign Fe(II)/KG superfamily enzymes for greater stability, solubility, and expression while retaining both native activity and industrially-relevant non-native functions. For the Fe(II)/KG enzyme tP4H, we performed site-saturation mutagenesis with both the wild-type and stabilized design variant and screened for activity increases in a non-native C-H hydroxylation reaction. We observed substantially larger increases in non-native activity for variants obtained from the stabilized scaffold compared to those from the wild-type enzyme. ProteinMPNN is user-friendly and widely-accessible, and straightforward structural criteria were sufficient to obtain stabilized, catalytically-functional variants of the Fe(II)/KG enzymes tP4H and GriE. Our work suggests that stabilization by computational sequence redesign could be routinely implemented as a first step in directed evolution campaigns for novel biocatalysts.

biochemistry↗

bioRxiv2024

Essential and virulence-related protein interactions of pathogens revealed through deep learning

Identification of bacterial protein-protein interactions and predicting the structures of the complexes could aid in the understanding of pathogenicity mechanisms and developing treatments for infectious diseases. Here, we developed a deep learning-based pipeline that leverages residue-residue coevolution and protein structure prediction to systematically identify and structurally characterize protein-protein interactions at the proteome-wide scale. Using this pipeline, we searched through 78 million pairs of proteins across 19 human bacterial pathogens and identified 1923 confidently predicted complexes involving essential genes and 256 involving virulence factors. Many of these complexes were not previously known; we experimentally tested 12 such predictions, and half of them were validated. The predicted interactions span core metabolic and virulence pathways ranging from post-transcriptional modification to acid neutralization to outer membrane machinery and should contribute to our understanding of the biology of these important pathogens and the design of drugs to combat them.

biochemistry↗

bioRxiv2024

Atomically accurate de novo design of single-domain antibodies

Despite the central role that antibodies play in modern medicine, there is currently no method to design novel antibodies that bind a specific epitope entirely in silico. Instead, antibody discovery currently relies on animal immunization or random library screening approaches. Here, we demonstrate that combining computational protein design using a fine-tuned RFdiffusion network alongside yeast display screening enables the generation of antibody variable heavy chains (VHHs) and single chain variable fragments (scFvs) that bind user-specified epitopes with atomic-level precision. To verify this, we experimentally characterized VHH binders to four disease-relevant epitopes using multiple orthogonal biophysical methods, including cryo-EM, which confirmed the proper Ig fold and binding pose of designed VHHs targeting influenza hemagglutinin and Clostridium difficile toxin B (TcdB). For the influenza-targeting VHH, high-resolution structural data further confirmed the accuracy of CDR loop conformations. While initial computational designs exhibit modest affinity, affinity maturation using OrthoRep enables production of single-digit nanomolar binders that maintain the intended epitope selectivity. We further demonstrate the de novo design of single-chain variable fragments (scFvs), creating binders to TcdB and a Phox2b peptide-MHC complex by combining designed heavy and light chain CDRs. Cryo-EM structural data confirmed the proper Ig fold and binding pose for two distinct TcdB scFvs, with high-resolution data for one design additionally verifying the atomically accurate conformations of all six CDR loops. Our approach establishes a framework for the rational computational design, screening, isolation, and characterization of fully de novo antibodies with atomic-level precision in both structure and epitope targeting.

bioengineering↗

bioRxiv2024

Proofreading and single-molecule sensitivity in T-cellreceptor signaling by condensate nucleation

T-cells display the remarkable ability to detect single foreign peptides displayed on target cells, while ignoring highly abundant self peptides. This selectivity has been explained by kinetic proofreading in the T-cell receptor (TCR) signaling pathway, which prevents responses to short-lived binding events regardless of their abundance. However, the biochemical mechanisms that drive kinetic proofreading have remained unclear. Here, using computational modeling, we show that these key signaling properties of the TCR pathway can emerge from the dynamics of LAT phosphorylation, diffusion, and condensation following TCR-pMHC binding. In this model, time delays in LAT condensate nucleation underlie kinetic proofreading, enabling selective signaling responses to high-affinity pMHC ligands. The cooperativity in the nucleation and growth of LAT condensates also provides a mechanism to amplify weak signals from single foreign peptides and for condensates to grow with increasing antigen numbers. In contrast to other models, condensate-nucleation proofreading predicts a dependence of signal strength on pMHC spacing at fixed number, a prediction we validated experimentally using a protein scaffold to present pMHCs at defined intervals. Our results suggest that nucleation-condensation proofreading underlies the remarkable antigen detection capabilities of the TCR signaling pathway. SignificanceTo fight infections and cancer, T-cells must selectively recognize low levels of foreign peptides from pathogens or cancer cells, but the mechanisms that enable these properties have remained unclear.Using mathematical modeling and experiments, we find that T-cells can selectively detect single foreign peptides through a clustering process where a key protein downstream of the T-cell receptor forms condensates containing hundreds of signaling proteins. This condensate nucleation process can explain experimentally observed features of T-cell signaling, including our finding that the size of signaling clusters depends on the distance between foreign peptides. Our work reveals a key role for the condensation of signaling molecules in setting the spatial and temporal thresholds that control the sensitivity and selectivity of T-cells.

biophysics↗

bioRxiv2023

Atomic context-conditioned protein sequence design using LigandMPNN

Protein sequence design in the context of small molecules, nucleotides, and metals is critical to enzyme and small molecule binder and sensor design, but current state-of-the-art deep learning-based sequence design methods are unable to model non-protein atoms and molecules. Here, we describe a deep learning-based protein sequence design method called LigandMPNN that explicitly models all non-protein components of biomolecular systems. LigandMPNN significantly outperforms Rosetta and ProteinMPNN on native backbone sequence recovery for residues interacting with small molecules (63.3% vs. 50.4% & 50.5%), nucleotides (50.5% vs. 35.2% & 34.0%), and metals (77.5% vs. 36.0% & 40.6%). LigandMPNN generates not only sequences but also sidechain conformations to allow detailed evaluation of binding interactions. Experimental characterization demonstrates that LigandMPNN can generate small molecule and DNA-binding proteins with high affinity and specificity. One-sentence summaryWe present a deep learning-based protein sequence design method that allows explicit modeling of small molecule, nucleotide, metal, and other atomic contexts.

biochemistry↗

bioRxiv2023

De novo design of diverse small molecule binders and sensors using Shape Complementary Pseudocycles

A general method for designing proteins to bind and sense any small molecule of interest would be widely useful. Due to the small number of atoms to interact with, binding to small molecules with high affinity requires highly shape complementary pockets, and transducing binding events into signals is challenging. Here we describe an integrated deep learning and energy based approach for designing high shape complementarity binders to small molecules that are poised for downstream sensing applications. We employ deep learning generated psuedocycles with repeating structural units surrounding central pockets; depending on the geometry of the structural unit and repeat number, these pockets span wide ranges of sizes and shapes. For a small molecule target of interest, we extensively sample high shape complementarity pseudocycles to generate large numbers of customized potential binding pockets; the ligand binding poses and the interacting interfaces are then optimized for high affinity binding. We computationally design binders to four diverse molecules, including for the first time polar flexible molecules such as methotrexate and thyroxine, which are expressed at high levels and have nanomolar affinities straight out of the computer. Co-crystal structures are nearly identical to the design models. Taking advantage of the modular repeating structure of pseudocycles and central location of the binding pockets, we constructed low noise nanopore sensors and chemically induced dimerization systems by splitting the binders into domains which assemble into the original pseudocycle pocket upon target molecule addition. One Sentence SummaryWe use a pseuodocycle-based shape complementarity optimizing approach to design nanomolar binders to diverse ligands, including the flexible and polar methotrexate and thyroxine, that can be directly converted into ligand-gated nanopores and chemically induced dimerization systems.

biochemistry↗

bioRxiv2023

Sculpting conducting nanopore size and shape through de novo protein design

Transmembrane {beta}-barrels (TMBs) are widely used for single molecule DNA and RNA sequencing and have considerable potential for a broad range of sensing and sequencing applications. Current engineering approaches for nanopore sensors are limited to naturally occurring channels such as CsgG, which have evolved to carry out functions very different from sensing, and hence provide sub-optimal starting points. In contrast, de novo protein design can in principle create an unlimited number of new nanopores with any desired properties. Here we describe a general approach to the design of transmembrane {beta}-barrel pores with different diameter and pore geometry. NMR and crystallographic characterization shows that the designs are stably folded with structures close to the design models. We report the first examples of de novo designed TMBs with 10, 12 and 14 stranded {beta}-barrels. The designs have distinct conductances that correlate with their pore diameter, ranging from 110 pS ([~]0.5 nm pore diameter) to 430 pS ([~]1.1 nm pore diameter), and can be converted into sensitive small-molecule sensors with high signal to noise ratio. The capability to generate on demand {beta}-barrel pores of defined geometry opens up fundamentally new opportunities for custom engineering of sequencing and sensing technologies. One sentence summaryDe novo design enables the generation of stable and quite transmembrane beta-barrel nanopores with tailored sizes, shapes and properties.

synthetic biology↗

bioRxiv2023

De novo design of allosterically switchable protein assemblies

Allosteric modulation of protein function, wherein the binding of an effector to a protein triggers conformational changes at distant functional sites, plays a central role in the control of metabolism and cell signaling1-3. There has been considerable interest in designing allosteric systems, both to gain insight into the mechanisms underlying such "action at a distance" modulation and to create synthetic proteins whose functions can be regulated by effectors4-7. However, emulating the subtle conformational changes distributed across many residues, characteristic of natural allosteric proteins, is a significant challenge8,9. Here, inspired by the classic Monod-Changeux-Wyman model of cooperativity10, we investigate the de novo design of allostery through rigid-body coupling of designed effector-switchable hinge modules11 to protein interfaces12 that direct the formation of alternative oligomeric states. We find that this approach can be used to generate a wide variety of allosterically switchable systems, including cyclic rings that incorporate or eject subunits in response to effector binding and dihedral cages that undergo effector-induced disassembly. Size-exclusion chromatography, mass photometry13, and electron microscopy reveal that these designed allosteric protein assemblies closely resemble the design models in both the presence and absence of effectors and can have ligand-binding cooperativity comparable to classic natural systems such as hemoglobin14. Our results indicate that allostery can arise from global coupling of the energetics of protein substructures without optimized sidechain-sidechain allosteric communication pathways and provide a roadmap for generating allosterically triggerable delivery systems, protein nanomachines, and cellular feedback control circuitry.

biochemistry↗

bioRxiv2023

Small-molecule binding and sensing with a designed protein family

Despite transformative advances in protein design with deep learning, the design of small-molecule-binding proteins and sensors for arbitrary ligands remains a grand challenge. Here we combine deep learning and physics-based methods to generate a family of proteins with diverse and designable pocket geometries, which we employ to computationally design binders for six chemically and structurally distinct small-molecule targets. Biophysical characterization of the designed binders revealed nanomolar to low micromolar binding affinities and atomic-level design accuracy. The bound ligands are exposed at one edge of the binding pocket, enabling the de novo design of chemically induced dimerization (CID) systems; we take advantage of this to create a biosensor with nanomolar sensitivity for cortisol. Our approach provides a general method to design proteins that bind and sense small molecules for a wide range of analytical, environmental, and biomedical applications.

biochemistry↗

bioRxiv2023

Local structural flexibility drives oligomorphism in computationally designed protein assemblies

Many naturally occurring protein assemblies have dynamic structures that allow them to perform specialized functions. For example, clathrin coats adopt a wide variety of architectures to adapt to vesicular cargos of various sizes. Although computational methods for designing novel self-assembling proteins have advanced substantially over the past decade, most existing methods focus on designing static structures with high accuracy. Here we characterize the structures of three distinct computationally designed protein assemblies that each form multiple unanticipated architectures, and identify flexibility in specific regions of the subunits of each assembly as the source of structural diversity. Cryo-EM single-particle reconstructions and native mass spectrometry showed that only two distinct architectures were observed in two of the three cases, while we obtained six cryo-EM reconstructions that likely represent a subset of the architectures present in solution in the third case. Structural modeling and molecular dynamics simulations indicated that the surprising observation of a defined range of architectures, instead of non-specific aggregation, can be explained by constrained flexibility within the building blocks. Our results suggest that deliberate use of structural flexibility as a design principle will allow exploration of previously inaccessible structural and functional space in designed protein assemblies.

bioengineering↗

bioRxiv2023

Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom

Although AlphaFold2 (AF2) and RoseTTAFold (RF) have transformed structural biology by enabling high-accuracy protein structure modeling, they are unable to model covalent modifications or interactions with small molecules and other non-protein molecules that can play key roles in biological function. Here, we describe RoseTTAFold All-Atom (RFAA), a deep network capable of modeling full biological assemblies containing proteins, nucleic acids, small molecules, metals, and covalent modifications given the sequences of the polymers and the atomic bonded geometry of the small molecules and covalent modifications. Following training on structures of full biological assemblies in the Protein Data Bank (PDB), RFAA has comparable protein structure prediction accuracy to AF2, excellent performance in CAMEO for flexible backbone small molecule docking, and reasonable prediction accuracy for protein covalent modifications and assemblies of proteins with multiple nucleic acid chains and small molecules which, to our knowledge, no existing method can model simultaneously. By fine-tuning on diffusive denoising tasks, we develop RFdiffusion All-Atom (RFdiffusionAA), which generates binding pockets by directly building protein structures around small molecules and other non-protein molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we design and experimentally validate proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and optically active bilin molecules with potential for expanding the range of wavelengths captured by photosynthesis. We anticipate that RFAA and RFdiffusionAA will be widely useful for modeling and designing complex biomolecular systems.

biochemistry↗