Biology subjects

Whitehead, T. A.

Publications and source records attributed to Whitehead, T. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Structural basis of chaperone mechanisms in cells and the evolutionary emergence of the protein world

How chaperones mediate protein folding in the crowded cell environment remains poorly understood. To gain insight, we developed CHAP-SEQ to examine how chaperones affect protein folding in cells at high throughput and amino acid resolution. Performing CHAP-SEQ using three chaperone proteins and one chaperone RNA reveals distinct modes of folding assistance. Chaperone proteins act preferentially on hydrophobic core residues, whereas chaperone RNA primarily targets structural or dynamic signatures. Furthermore, while the chaperone RNA has little preference for clients baseline foldability, the chaperone proteins favor clients with greater intrinsic foldability. These differences are consistent with an evolutionary hypothesis in which greater chaperone complexity played a role in the formation of stable hydrophobic cores, suggesting a potential link between chaperone function and the evolution of protein folding.

biochemistry↗

bioRxiv2026

Protein engineering of a genetically encoded biosensor for wastewater detection of profen NSAIDs

Non-steroidal anti-inflammatory drugs (NSAIDs) are pervasive environmental contaminants due to their frequent and widespread use, multiple paths of release into surface and ground water supply, diversity of the chemical class, and toxicity to aquatic and other non-target species. In particular, the 2-arylpropionic acid ("profen") class of NSAIDs poses significant risks to aquatic ecosystems due to incomplete removal during wastewater treatment. Current monitoring precludes high frequency testing at point sources. Here we present the engineering and application of a genetically encodable, protein-based biosensor for the detection of the NSAIDs ketoprofen and pranoprofen in wastewater effluent. We repurposed the plant hormone receptor PYR1 to bind selectively to profens using computational protein design, deep mutational scanning, and yeast 2 hybrid and yeast surface display screening. The resulting sensor, PYRNSAID, has a nanomolar limit of detection for ketoprofen and panoprofen, and {micro}M sensitivity to the NSAIDs ibuprofen, fenoprofen, tolmetin and diclofenac. We also demonstrated dose responsive-activity of our sensor in simulated wastewater matrices containing the common wastewater contaminants sulfamethoxazole, caffeine, acetaminophen, and 2,4, dichlorophenol using a split Nanoluc luminescence assay. PYRNSAID is the first step towards a scalable, cost-effective alternative for real-time monitoring of pharmaceutical pollution.

bioengineering↗

bioRxiv2025

Design of post-translational, ligand-controlled inverters and switches

Rational control over diverse, ligand-responsive output networks is a foundational challenge in synthetic biology, particularly for systems based on post-translational signaling. Here, we present the design and engineering of minimal, modular protein architectures for chemically responsive molecular inverters and digital switches. Our inverter design modifies the plant-derived PYR1-HAB1 chemically inducible dimerization module by incorporating a constitutive activator, which is then competitively displaced by the ligand-bound PYR1 receptor, converting the native dimerization-on mechanism into a signal-off inverter. We establish key design features and demonstrate predictable tuning of the inverters transfer function, including its maximum output, half-maximal inhibitory concentration, and minimum output, solely by adjusting protein stoichiometry. The architecture is modular, enabling plug-and-play response to diverse, user-defined drug-like small molecules. We show that an inverter biosensor for an environmental contaminant functions in engineered living cells with a low nanomolar sensitivity. Additionally, we convert the PYR1-HAB1 sensor into a digital switch by adding an engineered molecular titrant to the system. Overall, this work provides a generalizable, minimal, and tunable protein scaffold for programming complex, post-translational signaling logic, significantly expanding the toolkit for sophisticated biological circuit design.

synthetic biology↗

bioRxiv2025

Computational redesign of a thermostable T7 RNA polymerase

T7 RNA polymerase (T7 RNAP) is a foundational enzyme for biotechnology, but its utility for many potential applications is limited by low thermal stability of 43-44{degrees}C. While stabilized variants exist, the most stable commercial version has a proprietary sequence. In this work we developed a highly stable T7 RNAP using structure-based computational design. We combined mutations from previous stabilized variants (M5, M8, V7abcd) with new mutations identified by PROSS. These mutations were filtered using data-driven heuristics to preserve function. Our final design, T7T+, contains 30 point mutations from the original T7 RNAP and demonstrates a functional stability (T50) of 54.9{degrees}C in a thermal challenge assay, which is 2.4{degrees}C higher than the most stable, published open-source variant to date. Circular dichroism spectroscopy showed an apparent melting temperature of 53.8{degrees}C. T7T+ retains 59% of wild-type activity at 37{degrees}C. 16 of the 18 tested protein designs had higher stability against thermal challenge compared with the genetic background, attesting to the high success rates of existing non deep learning computational methods for the design of stable, functional proteins. A plasmid encoding T7T+ has been deposited in AddGene and is freely available for non-commercial use.

biochemistry↗

bioRxiv2025

Graph attention with structural features improves the generalizability of identifying functional sequences at a protein interface

Accurate prediction of the set of sequences compatible with a protein-protein interface is an unsolved problem in biology. While supervised sequence-based models trained directly on experimental data can predict variant effects, they often fail to generalize to significantly diverged sequences. We hypothesized that incorporating information from deep learning models of proteins (e.g., ESM, ProteinMPNN) could enhance generalizability. To test this hypothesis, we designed and experimentally screened several deep mutational libraries of the SARS-CoV-2 Spike Receptor Binding Domain (RBD) for binding to the ACE2 receptor. Our large dataset encompasses over 43,000 sequence variants, exhibiting up to 26 substitutions away from the parental RBD sequence, thus exploring a significantly expanded sequence space compared to previous studies. Baseline supervised learning with one-hot encoded sequences achieved high accuracy within training sets but poor performance on unseen libraries. Integrating pre-trained protein model embeddings (ESM2) as a feature showed modest improvement in generalization. To further enhance predictive power, we developed a graph attention network architecture that combines representations of local residue environments using protein structure graphs with long-range inter-residue correlations captured by protein language model (PLM) embeddings (GAN-PLM). By explicitly modeling residue environments, interface geometry, and sequence dependencies, our graph attention model outperformed purely sequence-based models, achieving substantially higher balanced accuracies when predicting functional ACE2-binding variants across the diverse sequence space spanned by our independent libraries. This demonstrates the potential of structure- and sequence-based features into deep learning frameworks to achieve accurate and generalizable predictions of protein interface function, with broad implications for understanding and engineering protein interactions relevant to emerging infectious diseases and therapeutic protein design.

biochemistry↗

bioRxiv2025

Computational design of dynamic biosensors for emerging synthetic opioids

Nitazenes are an emergent class of synthetic opioids that rival or exceed fentanyl in their potency. These compounds have been detected internationally in illicit drugs and are the cause of increasing numbers of hospitalizations and overdoses. New analogs are consistently released, making detection challenging - new ways of testing a wide range of nitazenes and their metabolic products are urgently needed. Here, we developed a computational protocol to redesign the plant abscisic acid receptor PYR1 to bind diverse nitazenes and maintain its dynamic transduction mechanism. The best design has a low nanomolar limit of detection in vitro against nitazene and menitazene. Deep mutational scanning yielded sensors able to recognize a range of clinically relevant nitazenes and the common metabolic byproduct in complex biological matrices with limited cross-specificity against unrelated opioids. Application of protein design tools on privileged receptors like PYR1 may yield general sensors for a wide range of applications in vitro and in vivo.

biochemistry↗

bioRxiv2025

Unusually Broad-spectrum small-molecule sensing using a single protein scaffold

Small-molecule sensing in plants is dominated by chemical-induced dimerization modules. In the abscisic acid (ABA) system, allosteric receptors recruit phosphatase effectors and achieve nM in vivo responses from {micro}M receptor-ligand interactions. This sensitivity amplification could enable ABA receptors to serve as generic scaffolds for designing small-molecule sensors. To test this, we screened collections of mutant ABA-receptors against 2,726 drugs and other ligands and identified 569 sensors for 6.7% of these ligands. The mutational patterns indicate strong selection for ligand-specific binding pockets. We used these data to develop a sensor design pipeline and isolated sensors for multiple plant natural products, 2,4,6-trinitrotoluene (TNT), and "forever" per- and polyfluoroalkyl substances (PFAS). Thus, the ABA sensor system enables design and isolation of small-molecule sensors with broad chemical scope and antibody-like simplicity.

synthetic biology↗

bioRxiv2024

PYR1 biosensor-driven genome-wide CRISPR screens for improved monoterpenoid production in Kluyveromyces marxianus

Monoterpenoids are valued for their roles as flavors, fragrances, insecticides, and energy-dense fuels. Microbial biosynthesis offers sustainable biosynthesis routes for these important molecules, but production levels remain limited. Here, we introduce a biosensor-driven microbial engineering strategy to enhance monoterpenoid production, specifically targeting geraniol. Using mutagenized libraries of the PYR1 receptor--a versatile biosensor from plant ABA signaling pathways with a malleable binding pocket--we screened 24 monoterpenoids and identified PYR1 variants responsive to eight, including geraniol. A low background, highly selective geraniol-sensitive PYR1 variant was expressed in the thermotolerant yeast Kluyveromyces marxianus as a growth-based biosensor circuit, allowing for rapid strain engineering. By coupling the geraniol-sensitive PYR1 sensor with a genome-wide CRISPR-Cas9 mutagenesis approach, we identified six gene knockouts that enhance geraniol production, achieving up to a 2-fold increase in titer. This study demonstrates the power of the PYR1 biosensor platform to enable rapid strain engineering and the identification of mutants that improve titer of a desired metabolite.

synthetic biology↗

bioRxiv2024

in vitro analysis of a competitive inhibition model for T7 RNA polymerase biosensors

T7 RNA polymerase (T7 RNAP) biosensors, in which T7 RNAP transcribes some reporter gene or signal in response to external stimuli, have wide applications in synthetic biology and metabolic engineering. We adapted a biochemical reaction network model and used an in vitro transcription assay to determine network parameters for different T7 RNAP constructs. Under conditions where template DNA is limiting, the EC50 values of native and engineered T7 RNAPs ranged from 33 nM (29 -37 95% c.i.) to 570 nM (258 -714 95% c.i.) (wild-type T7 RNAP). The measured EC50 values were largely insensitive to free magnesium, pH, or other buffer conditions. Many biosensor configurations use a split RNAP construct, where the C-terminal (CT7) and N-terminal T7 (NT7) are fused to proximity induced dimerization modules. We used proteolysis and ion exchange chromatography to prepare a CT7 (80 kDa) product. The impact of free CT7 on T7 RNAP transcriptional activity was well described by a competitive inhibition model, with an inhibitory constant KI = 24 nM (22-26 95% c.i.) of the sensor. These model parameters will be useful for forward modeling and design of T7 RNAP-based genetic circuits. HighlightsO_LIEC50 values for natural and engineered T7 RNAPs measured in vitro C_LIO_LIC-terminal T7 RNAP is a competitive inhibitor of full-length T7 RNAP C_LIO_LIThe inhibition constant of C-terminal T7 RNAP is lower than the EC50 of T7 RNAP C_LI

bioengineering↗

bioRxiv2024

Dynamic regulation of engineered T7 RNA polymerases by endogenous metabolites

For many enzymes, activity is regulated post-translationally by endogenous metabolites. Designing liganded control of essential activities like transcription would advance predictive control of biological processes, a fundamental goal of synthetic biology. Here we demonstrate that full-length, single subunit T7-derived RNA polymerases (T7 RNAP) can be controlled by physiologically relevant concentrations of indoles. We used rational design and directed evolution to identify T7 RNAP variants with minimal transcriptional activity in the absence of indole, and a 29-fold increase in activity with an EC50 of 344 {micro}M. Indoles control T7-dependent gene expression exogenously, endogenously, and inter-cellularly. We also demonstrate indole-dependent bacteriophage viability and propagation in trans. Specificity of different indoles, T7 promoter specificities, and portability to different bacteria are shown. Our ligand activated RNA polymerases (LARPs) represent a new chemically inducible platform immediately deployable for novel synthetic biology applications, including for modulation of synthetic co-cultures.

bioengineering↗

bioRxiv2024

Rationalizing diverse binding mechanisms to the same protein fold: in-sights for ligand recognition and biosensor design

The engineering of novel protein-ligand binding interactions, particularly for complex drug-like molecules, is an unsolved problem which could enable many practical applications of protein biosensors. In this work, we analyzed two engineer ed biosensors, derived from the plant hormone sensor PYR1, to recognize either the agrochemical mandipropamid or the synthetic cannabinoid WIN55,212-2. Using a combination of quantitative deep mutational scanning experiments and molecular dynamics simulations, we demonstrated that mutations at common positions can promote protein-ligand shape complementarity and revealed prominent differences in the electrostatic networks needed to complement diverse ligands. MD simulations indicate that both PYR1 protein-ligand complexes bind a single conformer of their target ligand that is close to the lowest free energy conformer. Computational design using a fixed conformer and rigid body orientation led to new WIN55,212-2 sensors with nanomolar limits of detection. This work reveals mechanisms by which the versatile PYR1 biosensor scaffold can bind diverse ligands. This work also provides computational methods to sample realistic ligand conformers and rigid body alignments that simplify the computational design of biosensors for novel ligands of interest.

bioengineering↗

bioRxiv2024

GMMA can stabilize proteins across different functional constraints

Stabilizing proteins without otherwise hampering their function is a central task in protein engineering and design. PYR1 is a plant hormone receptor that has been engineered to bind diverse small molecule ligands. We sought a set of generalized mutations that would provide stability without affecting functionality for PYR1 variants with diverse ligand-binding capabilities. To do this we used a global multi-mutant analysis (GMMA) approach, which can identify substitutions that have stabilizing effects and do not lower function. GMMA has the added benefit of finding substitutions that are stabilizing in different sequence contexts and we hypothesized that applying GMMA to PYR1 with different functionalities would identify this set of generalized mutations. Indeed, conducting FACS and deep sequencing of libraries for PYR1 variants with two different functionalities and applying a GMMA analysis identified 5 substitutions that, when inserted into four PYR1 variants that each bind a unique ligand, provided 2-6{degrees}C in increased thermal stability and no decrease in functionality.

biochemistry↗

bioRxiv2024

An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries

Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of ten different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.

biochemistry↗

bioRxiv2023

Benchmarking cassette-based deep mutagenesis by Golden Gate assembly

Protocols for the construction of large, deeply mutagenized protein encoding libraries via Golden Gate assembly of synthetic DNA cassettes employ disparate, system specific methodology. Here we benchmark a broadly applicable Golden Gate method for building user-defined libraries. We demonstrate that a 25 l reaction, using 40 fmol of input DNA, can generate a library on the order of 1x106 members and that reaction volume or input DNA concentration can be scaled up with no losses in transformation efficiency. Such libraries can be constructed from dsDNA cassettes generated either by degenerate oligonucleotides or oligo pools. We demonstrate its real-world effectiveness by building custom, user-defined libraries on the order of 104 to 107 unique protein encoding variants for two orthogonal protein engineering systems. We include a detailed protocol and provide several general-use destination vectors.

bioengineering↗

bioRxiv2022

A method for generating user-defined circular single-stranded DNA from plasmid DNA using Golden Gate intramolecular ligation

Construction of user-defined long circular single stranded DNA (cssDNA) and linear single stranded DNA (lssDNA) is important for various biotechnological applications. Many current methods for synthesis of these ssDNA molecules do not scale to multi-kilobase constructs. Here we present a robust methodology for generating user-defined cssDNA employing Golden Gate assembly, a nickase, and exonuclease degradation. Our technique is demonstrated for three plasmids with insert sizes ranging from 2.1-3.4 kb, requires no specialized equipment, and can be accomplished in five hours with a yield of 33-43 % of the theoretical. To produce lssDNA, we evaluated different CRISPR-Cas9 cleavage conditions and report a 52 {+/-} 8% cleavage efficiency of cssDNA. Our method presented here can make ssDNA readily available to biotechnology researchers.

bioengineering↗

bioRxiv2022

Engineering SARS-CoV-2 neutralizing antibodies for increased potency and reduced viral escape

The rapid spread of SARS-CoV-2 variants poses a constant threat of escape from monoclonal antibody and vaccine countermeasures. Mutations in the ACE2 receptor binding site on the surface S protein have been shown to disrupt antibody binding and prevent viral neutralization. Here, we use a directed evolution-based approach to engineer three neutralizing antibodies for enhanced binding to S protein. The engineered antibodies showed increased in vitro functional activity in terms of neutralization potency and/or breadth of neutralization against viral variants. Deep mutational scanning revealed that higher binding affinity reduced the total number of viral escape mutations. Studies in the Syrian hamster model showed two examples where the affinity matured antibody provided superior protection compared to the parental antibody. These data suggest that monoclonal antibodies for anti-viral indications could benefit from in vitro affinity maturation to reduce viral escape pathways and appropriate affinity maturation in vaccine immunization could help resist viral variation.

bioengineering↗

bioRxiv2021

Stabilization of the SARS-CoV-2 Receptor Binding Domain by Protein Core Redesign and Deep Mutational Scanning

Stabilizing antigenic proteins as vaccine immunogens or diagnostic reagents is a stringent case of protein engineering and design as the exterior surface must maintain recognition by receptor(s) and antigen--specific antibodies at multiple distinct epitopes. This is a challenge, as stability-enhancing mutations must be focused on the protein core, whereas successful computational stabilization algorithms typically select mutations at solvent-facing positions. In this study we report the stabilization of SARS-CoV-2 Wuhan Hu-1 Spike receptor binding domain (S RBD) using a combination of deep mutational scanning and computational design, including the FuncLib algorithm. Our most successful design encodes I358F, Y365W, T430I, and I513L RBD mutations, maintains recognition by the receptor ACE2 and a panel of different anti-RBD monoclonal antibodies, is between 1-2{degrees}C more thermally stable than the original RBD using a thermal shift assay, and is less proteolytically sensitive to chymotrypsin and thermolysin than the original RBD. Our approach could be applied to the computational stabilization of a wide range of proteins without requiring detailed knowledge of active sites or binding epitopes, particularly powerful for cases when there are multiple or unknown binding sites.

biochemistry↗

bioRxiv2021

Regulatory approved monoclonal antibodies contain framework mutations predicted from human antibody repertoires

Monoclonal antibodies (mAbs) are an important class of therapeutics used to treat cancer, inflammation, and infectious diseases. Identifying highly developable mAb sequences in silico could greatly reduce the time and cost required for therapeutic mAb development. Here, we present position-specific scoring matrices (PSSMs) for antibody framework mutations developed using natural human antibody repertoire sequences. Our analysis shows that natural human antibody repertoire-based PSSMs are consistent across individuals and demonstrate high correlations between related germlines. We show that mutations in existing therapeutic antibodies can be accurately predicted solely from natural human antibody sequence data. mAbs developed using humanized mice had more human-like FR mutations than mAbs originally developed by hybridoma technology. A quantitative assessment of entire framework regions of therapeutic antibodies revealed that there may be potential for improving the properties of existing therapeutic antibodies by incorporating additional mutations of high frequency in natural human antibody repertoires. In addition, high frequency mutations in natural human antibody repertoires were predicted in silico to reduce immunogenicity in therapeutic mAbs due to the removal of T cell epitopes. Several therapeutic mAbs were identified to have common, universally high-scoring framework mutations, and molecular dynamics simulations revealed the mechanistic basis for the evolutionary selection of these mutations. Our results suggest that natural human antibody repertoires may be useful as predictive tools to guide mAb development in the future.

immunology↗