A Closed Form Model for Molecular Ratchet-Type Chemically Induced Dimerization Modules.

Chemical-induced dimerization (CID) modules enable users to implement ligand-controlled cellular and biochemical functions for a number of problems in basic and applied biology. A special class of CID modules occur naturally in plants and involve a hormone receptor that binds a hormone, triggering a conformational change in the receptor that enables recognition by a second binding protein. Two recent reports show that such hormone receptors can be engineered to sense dozens of structurally diverse compounds. As a closed form model for molecular ratchets would be of immense utility in forward engineering of biological systems, here we have developed a closed form model for these distinct CID modules. These modules, which we call molecular ratchets, are distinct from more common CID modules called molecular glues in that they engage in saturable binding kinetics and are characterized well by a Hill equation. A defining characteristic of molecular ratchets is that the sensitivity of the response can be tuned by increasing the molar ratio of the hormone receptor to the binding protein. Thus, the same molecular ratchet can have a pico- or micromolar EC50 depending on the concentration of the different receptor and binding proteins. Closed form models are derived for a base elementary reaction rate model, for ligand-independent complexation of the receptor and binding protein, and for homodimerization of the hormone receptor. Useful governing equations for a variety of in vitro and in vivo applications are derived, including enzyme-linked immunosorbent assay-like microplate assays, transcriptional activation in prokaryotes and eukaryotes, and ligand-induced split protein complementation.

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 multikilobase 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 to 3.4 kb, requires no specialized equipment, and can be accomplished in 5 h with a yield of 33%-43% of the theoretical. To produce lssDNA, we evaluated different CRISPR-Cas9 cleavage conditions and reported a 52 ± 8% cleavage efficiency of cssDNA. Thus, our current method does not compete with existing protocols for lssDNA generation. Nevertheless, our protocol can make long, user-defined cssDNA readily available to biotechnology researchers.

Impact of In Vivo Protein Folding Probability on Local Fitness Landscapes.

It is incompletely understood how biophysical properties like protein stability impact molecular evolution and epistasis. Epistasis is defined as specific when a mutation exclusively influences the phenotypic effect of another mutation, often at physically interacting residues. In contrast, nonspecific epistasis results when a mutation is influenced by a large number of nonlocal mutations. As most mutations are pleiotropic, the in vivo folding probability-governed by basal protein stability-is thought to determine activity-enhancing mutational tolerance, implying that nonspecific epistasis is dominant. However, evidence exists for both specific and nonspecific epistasis as the prevalent factor, with limited comprehensive data sets to support either claim. Here, we use deep mutational scanning to probe how in vivo enzyme folding probability impacts local fitness landscapes. We computationally designed two different variants of the amidase AmiE with statistically indistinguishable catalytic efficiencies but lower probabilities of folding in vivo compared with wild-type. Local fitness landscapes show slight alterations among variants, with essentially the same global distribution of fitness effects. However, specific epistasis was predominant for the subset of mutations exhibiting positive sign epistasis. These mutations mapped to spatially distinct locations on AmiE near the initial mutation or proximal to the active site. Intriguingly, the majority of specific epistatic mutations were codon dependent, with different synonymous codons resulting in fitness sign reversals. Together, these results offer a nuanced view of how protein folding probability impacts local fitness landscapes and suggest that transcriptional-translational effects are as important as stability in determining evolutionary outcomes.

Criticality and Adaptivity in Enzymatic Networks.

The contrast between the stochasticity of biochemical networks and the regularity of cellular behavior suggests that biological networks generate robust behavior from noisy constituents. Identifying the mechanisms that confer this ability on biological networks is essential to understanding cells. Here we show that queueing for a limited shared resource in broad classes of enzymatic networks in certain conditions leads to a critical state characterized by strong and long-ranged correlations between molecular species. An enzymatic network reaches this critical state when the input flux of its substrate is balanced by the maximum processing capacity of the network. We then consider enzymatic networks with adaptation, when the limiting resource (enzyme or cofactor) is produced in proportion to the demand for it. We show that the critical state becomes an attractor for these networks, which points toward the onset of self-organized criticality. We suggest that the adaptive queueing motif that leads to significant correlations between multiple species may be widespread in biological systems.

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.

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 nine 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.

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 using a combination of deep mutational scanning and computational design, including the FuncLib algorithm. Our most successful design encodes I358F, Y365W, T430I, and I513L receptor binding domain mutations, maintains recognition by the receptor ACE2 and a panel of different anti-receptor binding domain monoclonal antibodies, is between 1 and 2°C more thermally stable than the original receptor binding domain using a thermal shift assay, and is less proteolytically sensitive to chymotrypsin and thermolysin than the original receptor binding domain. 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. We envision that this strategy may be particularly powerful for cases when there are multiple or unknown binding sites.

Rapid biosensor development using plant hormone receptors as reprogrammable scaffolds.

A general method to generate biosensors for user-defined molecules could provide detection tools for a wide range of biological applications. Here, we describe an approach for the rapid engineering of biosensors using PYR1 (Pyrabactin Resistance 1), a plant abscisic acid (ABA) receptor with a malleable ligand-binding pocket and a requirement for ligand-induced heterodimerization, which facilitates the construction of sense-response functions. We applied this platform to evolve 21 sensors with nanomolar to micromolar sensitivities for a range of small molecules, including structurally diverse natural and synthetic cannabinoids and several organophosphates. X-ray crystallography analysis revealed the mechanistic basis for new ligand recognition by an evolved cannabinoid receptor. We demonstrate that PYR1-derived receptors are readily ported to various ligand-responsive outputs, including enzyme-linked immunosorbent assay (ELISA)-like assays, luminescence by protein-fragment complementation and transcriptional circuits, all with picomolar to nanomolar sensitivity. PYR1 provides a scaffold for rapidly evolving new biosensors for diverse sense-response applications.

Identification of SARS-CoV-2 S RBD escape mutants using yeast screening and deep mutational scanning.

Here, we describe a protocol to identify escape mutants on the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) receptor-binding domain (RBD) using a yeast screen combined with deep mutational scanning. Over 90% of all potential single S RBD escape mutants can be identified for monoclonal antibodies that directly compete with angiotensin-converting enzyme 2 for binding. Six to 10 antibodies can be assessed in parallel. This approach has been shown to determine escape mutants that are consistent with more laborious SARS-CoV-2 pseudoneutralization assays. For complete details on the use and execution of this protocol, please refer to Francino-Urdaniz et al. (2021).

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°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.

One-shot identification of SARS-CoV-2 S RBD escape mutants using yeast screening.

The potential emergence of SARS-CoV-2 Spike (S) escape mutants is a threat to reduce the efficacy of existing vaccines and neutralizing antibody (nAb) therapies. An understanding of the antibody/S escape mutations landscape is urgently needed to preemptively address this threat. Here we describe a rapid method to identify escape mutants for nAbs targeting the S receptor binding site. We identified escape mutants for five nAbs, including three from the public germline class VH3-53 elicited by natural COVID-19 infection. Escape mutations predominantly mapped to the periphery of the ACE2 recognition site on the RBD with K417, D420, Y421, F486, and Q493 as notable hotspots. We provide libraries, methods, and software as an openly available community resource to accelerate new therapeutic strategies against SARS-CoV-2.

One-shot identification of SARS-CoV-2 S RBD escape mutants using yeast screening.

The potential emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) escape mutants is a threat to the efficacy of existing vaccines and neutralizing antibody (nAb) therapies. An understanding of the antibody/S escape mutation landscape is urgently needed to preemptively address this threat. Here we describe a rapid method to identify escape mutants for nAbs targeting the S receptor binding site. We identified escape mutants for five nAbs, including three from the public germline class VH3-53 elicited by natural coronavirus disease 2019 (COVID-19) infection. Escape mutations predominantly mapped to the periphery of the angiotensin-converting enzyme 2 (ACE2) recognition site on the RBD with K417, D420, Y421, F486, and Q493 as notable hotspots. We provide libraries, methods, and software as an openly available community resource to accelerate new therapeutic strategies against SARS-CoV-2.

Paired heavy and light chain signatures contribute to potent SARS-CoV-2 neutralization in public antibody responses.

Understanding protective mechanisms of antibody recognition can inform vaccine and therapeutic strategies against SARS-CoV-2. We discovered a new antibody, 910-30, that targets the SARS-CoV-2 ACE2 receptor binding site as a member of a public antibody response encoded by IGHV3-53/IGHV3-66 genes. We performed sequence and structural analyses to explore how antibody features correlate with SARS-CoV-2 neutralization. Cryo-EM structures of 910-30 bound to the SARS-CoV-2 spike trimer revealed its binding interactions and ability to disassemble spike. Despite heavy chain sequence similarity, biophysical analyses of IGHV3-53/3-66 antibodies highlighted the importance of native heavy:light pairings for ACE2 binding competition and for SARS-CoV-2 neutralization. We defined paired heavy:light sequence signatures and determined antibody precursor prevalence to be ~1 in 44,000 human B cells, consistent with public antibody identification in several convalescent COVID-19 patients. These data reveal key structural and functional neutralization features in the IGHV3-53/3-66 public antibody class to accelerate antibody-based medical interventions against SARS-CoV-2. HIGHLIGHTS: A molecular study of IGHV3-53/3-66 public antibody responses reveals critical heavy and light chain features for potent neutralizationCryo-EM analyses detail the structure of a novel public antibody class member, antibody 910-30, in complex with SARS-CoV-2 spike trimerCryo-EM data reveal that 910-30 can both bind assembled trimer and can disassemble the SARS-CoV-2 spikeSequence-structure-function signatures defined for IGHV3-53/3-66 class antibodies including both heavy and light chainsIGHV3-53/3-66 class precursors have a prevalence of 1:44,000 B cells in healthy human antibody repertoires.

Paired heavy- and light-chain signatures contribute to potent SARS-CoV-2 neutralization in public antibody responses.

Understanding mechanisms of protective antibody recognition can inform vaccine and therapeutic strategies against SARS-CoV-2. We report a monoclonal antibody, 910-30, targeting the SARS-CoV-2 receptor-binding site for ACE2 as a member of a public antibody response encoded by IGHV3-53/IGHV3-66 genes. Sequence and structural analyses of 910-30 and related antibodies explore how class recognition features correlate with SARS-CoV-2 neutralization. Cryo-EM structures of 910-30 bound to the SARS-CoV-2 spike trimer reveal binding interactions and its ability to disassemble spike. Despite heavy-chain sequence similarity, biophysical analyses of IGHV3-53/3-66-encoded antibodies highlight the importance of native heavy:light pairings for ACE2-binding competition and SARS-CoV-2 neutralization. We develop paired heavy:light class sequence signatures and determine antibody precursor prevalence to be ∼1 in 44,000 human B cells, consistent with public antibody identification in several convalescent COVID-19 patients. These class signatures reveal genetic, structural, and functional immune features that are helpful in accelerating antibody-based medical interventions for SARS-CoV-2.

A Method for User-defined Mutagenesis by Integrating Oligo Pool Synthesis Technology with Nicking Mutagenesis.

Saturation mutagenesis is a fundamental enabling technology for protein engineering and epitope mapping. Nicking mutagenesis (NM) allows the user to rapidly construct libraries of all possible single mutations in a target protein sequence from plasmid DNA in a one-pot procedure. Briefly, one strand of the plasmid DNA is degraded using a nicking restriction endonuclease and exonuclease treatment. Mutagenic primers encoding the desired mutations are annealed to the resulting circular single-stranded DNA, extended with high-fidelity polymerase, and ligated into covalently closed circular DNA by Taq DNA ligase. The heteroduplex DNA is resolved by selective degradation of the template strand. The complementary strand is synthesized and ligated, resulting in a library of mutated covalently closed circular plasmids. It was later shown that because very little primer is used in the procedure, resuspended oligo pools, which normally require amplification before use, can be used directly in the mutagenesis procedure. Because oligo pools can contain tens of thousands of unique oligos, this enables the construction of libraries of tens of thousands of user-defined mutations in a single-pot mutagenesis reaction, which significantly improves the utility of NM as described below. Use of oligo pools afford an economically advantageous approach to mutagenic experiments. First, oligo pool synthesis is much less expensive per nucleotide synthesized than conventional synthesis. Second, a mixed pool may be generated and used for mutagenesis of multiple different genes. To use the same oligo-pool for mutagenesis of a variety of genes, the user must only quantify the fraction of the oligo-pool specific to her mutagenic experiment and adjust the volume and effective concentration of the oligo-pool for use in nicking mutagenesis.

User-defined single pot mutagenesis using unamplified oligo pools.

User-defined mutagenic libraries are fundamental for applied protein engineering workflows. Here we show that unamplified oligo pools can be used to prepare site saturation mutagenesis libraries from plasmid DNA with near-complete coverage of desired mutations and few off-target mutations. We find that oligo pools yield higher quality libraries when compared to individually synthesized degenerate oligos. We also show that multiple libraries can be multiplexed into a single oligo pool, making preparation of multiple libraries less expensive and more convenient. We provide software for automatic oligo pool design that can generate mutagenic oligos for saturating or focused libraries.

Planning for and preventing infant abductions in the healthcare environment.

A plan relating to the four phases of an infant abduction allows the hospital to prepare most effectively to prevent abductions as well as responding to such an event and recovering from it.

Cell polarity-driven instability generates self-organized, fractal patterning of cell layers.

As a model system to study physical interactions in multicellular systems, we used layers of Escherichia coli cells, which exhibit little or no intrinsic coordination of growth. This system effectively isolates the effects of cell shape, growth, and division on spatial self-organization. Tracking the development of fluorescence-labeled cellular domains, we observed the emergence of striking fractal patterns with jagged, self-similar shapes. We then used a large-scale, cellular biophysical model to show that local instabilities due to polar cell-shape, repeatedly propagated by uniaxial growth and division, are responsible for generating this fractal geometry. Confirming this result, a mutant of E. coli with spherical shape forms smooth, nonfractal cellular domains. These results demonstrate that even populations of relatively simple bacterial cells can possess emergent properties due to purely physical interactions. Therefore, accurate physico-genetic models of cell growth will be essential for the design and understanding of genetically programmed multicellular systems.

Computational modeling of synthetic microbial biofilms.

Microbial biofilms are complex, self-organized communities of bacteria, which employ physiological cooperation and spatial organization to increase both their metabolic efficiency and their resistance to changes in their local environment. These properties make biofilms an attractive target for engineering, particularly for the production of chemicals such as pharmaceutical ingredients or biofuels, with the potential to significantly improve yields and lower maintenance costs. Biofilms are also a major cause of persistent infection, and a better understanding of their organization could lead to new strategies for their disruption. Despite this potential, the design of synthetic biofilms remains a major challenge, due to the complex interplay between transcriptional regulation, intercellular signaling, and cell biophysics. Computational modeling could help to address this challenge by predicting the behavior of synthetic biofilms prior to their construction; however, multiscale modeling has so far not been achieved for realistic cell numbers. This paper presents a computational method for modeling synthetic microbial biofilms, which combines three-dimensional biophysical models of individual cells with models of genetic regulation and intercellular signaling. The method is implemented as a software tool (CellModeller), which uses parallel Graphics Processing Unit architectures to scale to more than 30,000 cells, typical of a 100 µm diameter colony, in 30 min of computation time.