Biology subjects

Kang, A.

Publications and source records attributed to Kang, A..

At least 55 records · Page 3Linked to original sources

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

Targeting high-risk multiple myeloma genotypes with optimized anti-CD70 CAR-T cells

Despite the success of BCMA-targeting CAR-Ts in multiple myeloma, patients with high-risk cytogenetic features still relapse most quickly and are in urgent need of additional therapeutic options. Here, we identify CD70, widely recognized as a favorable immunotherapy target in other cancers, as a specifically upregulated cell surface antigen in high risk myeloma tumors. We use a structure-guided design to define a CD27-based anti-CD70 CAR-T design that outperforms all tested scFv-based CARs, leading to >80-fold improved CAR-T expansion in vivo. Epigenetic analysis via machine learning predicts key transcription factors and transcriptional networks driving CD70 upregulation in high risk myeloma. Dual-targeting CAR-Ts against either CD70 or BCMA demonstrate a potential strategy to avoid antigen escape-mediated resistance. Together, these findings support the promise of targeting CD70 with optimized CAR-Ts in myeloma as well as future clinical translation of this approach. One sentence summaryStructure-optimized CD27-based CAR-T cells targeting CD70 are a promising therapeutic option for high-risk multiple myeloma patients who are most likely to relapse on current BCMA-targeting cellular therapies.

cancer biology↗

bioRxiv2023

Brg1 regulates fibroblast-myofibroblast transition to promote renal fibrosis

Excessive fibrogenesis serves to disrupt the anatomical and functional integrity of the kidneys contributing to renal failure. Renal fibroblast is the major precursor to myofibroblast, the effector cell type of renal fibrosis. How fibroblast-myofibroblast transition (FMyT) is regulated in the kidneys remains incompletely understood. In the present study we investigated the role of Brahma related gene 1 (Brg1), a chromatin remodeling protein, in renal fibrosis focusing on mechanistic insights and translational potential. We report that Brg1 was up-regulated during FMyT both in vitro and in vivo. Brg1 deletion in fibroblasts partially blocked TGF-{beta} induced FMyT in vitro and attenuated renal fibrosis in three different animal models. Importantly, conditional Brg1 knockout in Postn+ mature myofibroblasts mitigated renal fibrosis induced by unilateral ureteral obstruction (UUO) or ischemia-reperfusion (IR) in mice. Transcriptomic analysis uncovered Prune2 as a potential target for Brg1. Brg1 interacted with E2F1 to activate Prune2 transcription during FMyT. Concordantly, Prune2 knockdown suppressed TGF-{beta} induced FMyT in vitro and dampened renal fibrosis in mice. Mechanistically, Prune2 likely contributed to FMyT by augmenting phosphorylation and activity of the pro-fibrogenic transcription factor PU.1. Finally, small-molecule Brg1 inhibitor PFI-3 exhibited strong antifibrotic potency in established models of renal fibrosis. In conclusion, our data provide compelling evidence that BRG1 is a pivotal regulator of as well as a promising therapeutic target for renal fibrosis.

pharmacology and toxicology↗

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

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

Epigenome erosion in Alzheimer's disease brain cells and induced neurons

Late-onset Alzheimers disease (LOAD) is typically sporadic, correlated only to advanced age, and has no clear genetic risk factors. The sporadic nature of LOAD presents a challenge to understanding its pathogenesis and mechanisms. Here, we comprehensively investigated the epigenome of LOAD primary entorhinal cortex brain tissues via single-cell multi-omics technologies, simultaneously capturing DNA methylation and 3D chromatin conformation. We identified AD-specific DNA methylation signatures and found they interact with bivalent promoters of AD differentially expressed genes. In addition, we discovered global chromosomal epigenome erosion of 3D genome structure within and across brain cell types. Furthermore, to evaluate whether these age- and disease-dependent molecular signatures could be detected in the in vitro cellular models, we derived induced neurons (iNs) converted directly from AD patients fibroblasts and found a set of conserved methylation signatures and shared molecular processes. We developed a machine-learning algorithm to identify robust and consistent methylation signatures of LOAD in vivo primary brain tissues and in vitro fibroblast-derived iNs. The results recapitulate the age- and disease-related epigenetic features in iNs and highlight the power of epigenome and chromatin conformation for identifying molecular mechanisms of neuronal aging and generating biomarkers for LOAD. HIGHLIGHTO_LIAD-specific DNA methylation signatures are identified in entorhinal cortex brain cell types C_LIO_LIThe AD differentially expressed genes linked with differentially methylated regions via loop interactions are enriched in a bivalent chromatin state C_LIO_LIChromosomal epigenome erosion of 3D genome structures occurs in LOAD brain cell types. C_LIO_LIShared and reliable methylation signatures are observed in both in vitro cellular iN models and primary brain tissues. C_LIO_LIMachine learning models identify robust and reliable methylation loci as AD biomarkers across cell types. C_LI

genomics↗

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

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

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

Cyclic peptide structure prediction and design using AlphaFold

Deep learning networks offer considerable opportunities for accurate structure prediction and design of biomolecules. While cyclic peptides have gained significant traction as a therapeutic modality, developing deep learning methods for designing such peptides has been slow, mostly due to the small number of available structures for molecules in this size range. Here, we report approaches to modify the AlphaFold network for accurate structure prediction and design of cyclic peptides. Our results show this approach can accurately predict the structures of native cyclic peptides from a single sequence, with 36 out of 49 cases predicted with high confidence (pLDDT > 0.85) matching the native structure with root mean squared deviation (RMSD) less than 1.5 [A]. Further extending our approach, we describe computational methods for designing sequences of peptide backbones generated by other backbone sampling methods and for de novo design of new macrocyclic peptides. We extensively sampled the structural diversity of cyclic peptides between 7-13 amino acids, and identified around 10,000 unique design candidates predicted to fold into the designed structures with high confidence. X-ray crystal structures for seven sequences with diverse sizes and structures designed by our approach match very closely with the design models (root mean squared deviation < 1.0 [A]), highlighting the atomic level accuracy in our approach. The computational methods and scaffolds developed here provide the basis for custom-designing peptides for targeted therapeutic applications.

biochemistry↗

bioRxiv2023

Design of stimulus-responsive two-state hinge proteins

Proteins that switch between two structural states as a function of environmental stimuli are widespread in nature. These proteins structurally transduce biochemical information in a manner analogous to how transistors control information flow in computing devices. Engineering challenges ranging from biological computing devices to molecular motors require such two-state switches, but designing these is an unsolved problem as it requires sculpting an energy landscape with two low-energy but structurally distinct conformations that can be modulated by external inputs. Here we describe a general design approach for creating "hinge" proteins that populate one distinct state in the absence of ligand and a second designed state in the presence of ligand. X-ray crystallography, electron microscopy, and double electron-electron resonance spectroscopy demonstrate that despite the significant structural differences, the two states are designed with atomic level accuracy. The kinetics and thermodynamics of effector binding can be finely tuned by modulating the free energy differences between the two states; when this difference becomes sufficiently small, we obtain bistable proteins that populate both states in the absence of effector, but collapse to a single state upon effector addition. Like the transistor, these switches now enable the design of a wide array of molecular information processing systems.

biophysics↗

bioRxiv2023

Design of amyloidogenic peptide traps

Segments of proteins with {beta}-strand propensity can self associate to form amyloid fibrils associated with many diseases. These regions often adopt alternative structures in their folded states, or are intrinsically disordered in solution, making it difficult to generate binders or inhibitors with existing strategies. Here we describe a general approach to bind such segments in {beta}-strand and {beta}-hairpin conformations using de novo designed scaffolds that contain deep peptide binding clefts flanked by {beta}-strands that form hydrogen bonds to the peptide upon binding. The designs bind their cognate peptides in vitro with nanomolar affinities and in mammalian cells. The crystal structure of a designed protein-peptide complex is close to the design model, and NMR characterization reveals how the peptide binding cleft is protected in the apo state. We use the approach to design binders to segments of the amyloid forming proteins Transthyretin, Tau, Serum amyloid A1 and A{beta}42. The A{beta} binders block assembly of A{beta} fibrils as effectively as the most potent of the clinically tested antibodies to date.

synthetic biology↗

bioRxiv2022

De novo designed ice-binding proteins from twist-constrained helices

Attaining molecular-level control over solidification processes is a crucial aspect of materials science. To control ice formation, organisms have evolved bewildering arrays of ice-binding proteins (IBPs) but these have poorly understood structure-activity relationships. We propose that reverse engineering using de novo computational protein design can shed light on structureactivity relationships of IBPs. We hypothesized that the model alpha-helical winter flounder antifreeze protein (wfAFP) uses an unusual under-twisting of its alpha-helix to align its putative ice-binding threonine residues in exactly the same direction. We test this hypothesis by designing a series of straight three-helix bundles with an ice-binding helix projecting threonines and two supporting helices constraining the twist of the ice-binding helix. We find that ice recrystallization inhibition by the designed proteins increases with the degree of designed under-twisting, thus validating our hypothesis and opening up new avenues for the computational design of icebinding proteins. Significance StatementIce-binding proteins (IBPs) modulate ice nucleation and growth in cold-adapted organisms so that they can survive in ice-laden environments at (sub)freezing temperatures. The functional repertoire of IBPs is diverse, ranging from inhibition of recrystallization and freezing point depression to shaping of ice crystals and ice nucleation. Precisely how these activities arise from the structure and ice-binding properties of IBPs is poorly understood. We demonstrate through de novo computational protein design that constraining the twist of an ice-binding helix is a key feature determining its ice-binding activity, opening new avenues for the design of synthetic IBPs with activities tailored to the requirements of specific applications, such as cell and tissue cryopreservation.

bioengineering↗

bioRxiv2022

Accurate Computational Design of 3D Protein Crystals

Protein crystallization plays a central role in structural biology1, with broad impact2 in pharmaceutical formulation3, drug delivery4, biosensing5, and biocatalysis6,7. Despite this importance, the process of protein crystallization remains poorly understood and highly empirical8-10, with largely unpredictable crystal contacts, lattice packing arrangements, and space group preferences, and the programming of protein crystallization through precisely engineered sidechain-sidechain interactions across multiple protein-protein interfaces is an outstanding challenge. Here we develop a general computational approach to designing three-dimensional (3D) protein crystals with pre-specified lattice architectures at atomic accuracy that hierarchically constrains the overall degree of freedoms (DOFs) of the system. We use the approach to design three pairs of oligomers that can be individually purified, and upon mixing, spontaneously self-assemble into large 3D crystals (>100 m). Small-angle X-ray scattering and X-ray crystallography show these crystals are nearly identical to the computational design models, with the design target F4132 and I432 space groups and closely corresponding overall architectures and protein-protein interfaces. The crystal unit cell dimensions can be systematically redesigned while retaining space group symmetry and overall architecture, and the crystals are both extremely porous and highly stable, enabling the robust scaffolding of inorganic nanoparticle arrays. Our approach thus enables the computational design of protein crystals with high accuracy, and since both structure and assembly are encoded in the primary sequence, provides a powerful new platform for biological material engineering.

synthetic biology↗

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

Subcutaneous BCG vaccination protects against streptococcal pneumonia via regulating innate immune responses in the lung

Bacillus Calmette-Guerin (BCG) still remains the only licensed vaccine for TB and has been shown to provide nonspecific protection against unrelated pathogens. This has been attributed to the ability of BCG to modulate the innate immune system, known as trained innate immunity (TII). TII is associated with innate immune cells being in a hyper-responsive state leading to enhanced host defense against heterologous infections. Both epidemiological evidence and prospective studies demonstrate cutaneous BCG vaccine-induced TII provides enhanced innate protection against heterologous pathogens. Regardless of the extensive amounts of progress made thus far, the effect of cutaneous BCG vaccination against heterologous respiratory bacterial infections and the underlying mechanisms remain unknown. Here we show for the first time that s.c BCG vaccine-induced TII provides enhanced heterologous innate protection against pulmonary S. pneumoniae infection. We further demonstrate that this enhanced innate protection is mediated by accelerated neutrophilia in the lung and is independent of centrally trained circulating monocytes. New insight from this study will help design novel effective vaccination strategies against unrelated respiratory bacterial pathogens.

immunology↗

bioRxiv2022

Altered tau in rTg4510 mice after a single interfaced CHIMERA traumatic brain injury

Traumatic brain injury (TBI) in an established risk factor for neurodegenerative disease. In this report, we used the Closed Head Injury Model of Engineered Rotational Acceleration (CHIMERA) to study the effects of a single moderate-severe TBI in rTg4510 mice, a mouse model of tauopathy. Fifteen male rTg4510 mice were impacted at 4.0J at 4-mo of age using interfaced CHIMERA and compared to sham controls. Immediately after injury, moderate-severe TBI induced significant mortality (7/15; 47%), and a prolonged duration of loss of righting reflex. At 2-mo post-injury, surviving mice displayed significant histological evidence of microgliosis (Iba1) and axonal injury (Neurosilver). Western blotting showed that TBI mice had a reduced p-GSK-3{beta} (S9):GSK-3{beta} ratio, suggesting greater tau kinase activity. However, tauopathy in surviving TBI mice showed a divergent response, with 3/8 mice having a very low level of tau protein in brain lysates (i.e. lower than sham), and were thus analyzed separately from the other 5/8 TBI mice that maintained the expected level of total tau. Compared to sham controls, TBI mice with normal tau levels had increased p-tau (PHF1 and AT8), increased autophagolysosome accumulation (p62 and Cathepsin D) and decreased hippocampal size. These findings were not observed in TBI mice with low total tau levels. These observations suggest that TBI leads to chronic white matter injury and altered GSK-3{beta} activity. However, post-injury tauopathy and autophagolysosome accumulation diverged in surviving mice through mechanisms that remain to be defined.

neuroscience↗