Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease.

Past studies have shown that incubation of human serum samples on high density peptide arrays followed by measurement of total antibody bound to each peptide sequence allows detection and discrimination of humoral immune responses to a variety of infectious diseases. This is true even though these arrays consist of peptides with near-random amino acid sequences that were not designed to mimic biological antigens. This "immunosignature" approach, is based on a statistical evaluation of the binding pattern for each sample but it ignores the information contained in the amino acid sequences that the antibodies are binding to. Here, similar array-based antibody profiles are instead used to train a neural network to model the sequence dependence of molecular recognition involved in the immune response of each sample. The binding profiles used resulted from incubating serum from 5 infectious disease cohorts (Hepatitis B and C, Dengue Fever, West Nile Virus and Chagas disease) and an uninfected cohort with 122,926 peptide sequences on an array. These sequences were selected quasi-randomly to represent an even but sparse sample of the entire possible combinatorial sequence space (~1012). This very sparse sampling of combinatorial sequence space was sufficient to capture a statistically accurate representation of the humoral immune response across the entire space. Processing array data using the neural network not only captures the disease-specific sequence-binding information but aggregates binding information with respect to sequence, removing sequence-independent noise and improving the accuracy of array-based classification of disease compared with the raw binding data. Because the neural network model is trained on all samples simultaneously, a highly condensed representation of the differential information between samples resides in the output layer of the model, and the column vectors from this layer can be used to represent each sample for classification or unsupervised clustering applications.

High-Throughput Screening Identifies Synthetic Peptides with Antibacterial Activity against Mycobacterium abscessus and Serum Stability.

The rise in antibiotic resistance in bacteria has spawned new technological approaches for identifying novel antimicrobials with narrow specificity. Current antibiotic treatment regimens and antituberculosis drugs are not effective in treating Mycobacterium abscessus. Meanwhile, antimicrobial peptides are gaining prominence as alternative antimicrobials due to their specificity toward anionic bacterial membranes, rapid action, and limited development of resistance. To rapidly identify antimicrobial peptide candidates, our group has developed a high-density peptide microarray consisting of 125,000 random synthetic peptides screened for interaction with the mycobacterial cell surface of M. abscessus morphotypes. From the array screening, peptides positive for interaction were synthesized and their antimicrobial activity was validated. Overall, six peptides inhibited the M. abscessus smooth morphotype (IC50 = 1.7 µM for all peptides) and had reduced activity against the M. abscessus rough morphotype (IC50 range: 13-82 µM). Peptides ASU2056 and ASU2060 had minimum inhibitory concentration values of 32 and 8 µM, respectively, against the M. abscessus smooth morphotype. Additionally, ASU2060 (8 µM) was active against Escherichia coli, including multidrug-resistant E. coli clinical isolates, Pseudomonas aeruginosa, and methicillin-resistant Staphylococcus aureus. ASU2056 and ASU2060 exhibited no significant hemolytic activity at biologically relevant concentrations, further supporting these peptides as promising therapeutic candidates. Moreover, ASU2060 retained antibacterial activity after preincubation in human serum for 24 h. With antimicrobial resistance on the rise, methods such as those presented here will streamline the peptide discovery process for targeted antimicrobial peptides.

Uncovering temporospatial sensitive TBI targeting strategies via in vivo phage display.

The heterogeneous pathophysiology of traumatic brain injury (TBI) is a barrier to advancing diagnostics and therapeutics, including targeted drug delivery. We used a unique discovery pipeline to identify novel targeting motifs that recognize specific temporal phases of TBI pathology. This pipeline combined in vivo biopanning with domain antibody (dAb) phage display, next-generation sequencing analysis, and peptide synthesis. We identified targeting motifs based on the complementarity-determining region 3 structure of dAbs for acute (1 day post-injury) and subacute (7 days post-injury) post-injury time points in a preclinical TBI model (controlled cortical impact). Bioreactivity and temporal sensitivity of the targeting motifs were validated via immunohistochemistry. Immunoprecipitation-mass spectrometry indicated that the acute TBI targeting motif recognized targets associated with metabolic and mitochondrial dysfunction, whereas the subacute TBI motif was largely associated with neurodegenerative processes. This pipeline successfully discovered temporally specific TBI targeting motif/epitope pairs that will serve as the foundation for the next-generation targeted TBI therapeutics and diagnostics.

Comprehensive Prediction of Molecular Recognition in a Combinatorial Chemical Space Using Machine Learning.

In combinatorial chemical approaches, optimizing the composition and arrangement of building blocks toward a particular function has been done using a number of methods, including high throughput molecular screening, molecular evolution, and computational prescreening. Here, a different approach is considered that uses sparse measurements of library molecules as the input to a machine learning algorithm which generates a comprehensive, quantitative relationship between covalent molecular structure and function that can then be used to predict the function of any molecule in the possible combinatorial space. To test the feasibility of the approach, a defined combinatorial chemical space consisting of ∼1012 possible linear combinations of 16 different amino acids was used. The binding of a very sparse, but nearly random, sampling of this amino acid sequence space to 9 different protein targets is measured and used to generate a general relationship between peptide sequence and binding for each target. Surprisingly, measuring as little as a few hundred to a few thousand of the ∼1012 possible molecules provides sufficient training to be highly predictive of the binding of the remaining molecules in the combinatorial space. Furthermore, measuring only amino acid sequences that bind weakly to a target allows the accurate prediction of which sequences will bind 10-100 times more strongly. Thus, the molecular recognition information contained in a tiny fraction of molecules in this combinatorial space is sufficient to characterize any set of molecules randomly selected from the entire space, a fact that potentially has significant implications for the design of new chemical function using combinatorial chemical libraries.

Comparison of personal and shared frameshift neoantigen vaccines in a mouse mammary cancer model.

BACKGROUND: It is widely hoped that personal cancer vaccines will extend the number of patients benefiting from checkpoint and other immunotherapies. However, it is clear creating such vaccines will be challenging. It requires obtaining and sequencing tumor DNA/RNA, predicting potentially immunogenic neoepitopes and manufacturing a one-use vaccine. This process takes time and considerable cost. Importantly, most mutations will not produce an immunogenic peptide and many patient's tumors do not contain enough DNA mutations to make a vaccine. We have discovered that frameshift peptides (FSP) created from errors in the production of RNA rather than from DNA mutations are potentially a rich source of neoantigens for cancer vaccines. These errors are predictable, enabling the production of a FSP microarray. Previously we found that these microarrays can identify both personal and shared neoantigens. Here, we compared the performance of personal cancer vaccines (PCVs) with that of a shared antigen vaccine, termed Frameshift Antigen Shared Therapeutic (FAST) vaccine, using the 4 T1 breast cancer model. Sera from 4 T1-tumor bearing mice were assayed on the peptide microarray containing 200 Fs neoantigens, for the PCV, the top 10 candidates were select and personal vaccines constructed and administrated to the respective mice. For the FAST, we selected the top 10 candidates with higher prevalence among all the mice challenged. Seven to 12 days challenged mice were immunized, combined or not with immune checkpoint inhibitor (ICI) (αPD-L1 and αCTLA-4). Primary and secondary tumor clearance and growth were evaluated as well as cellular and humoral immune response against the vaccine targets by IFN-γ ELISPOT and ELISA. Lastly, we analyzed the immune response of the FAST-vaccinated mice by flow cytometry in comparison to the control group. RESULTS: We found that PCVs and FAST vaccines both reduced primary tumor incidence and growth as well as lung metastases when delivered as monotherapies or in combination with ICI. Additionally, the FAST vaccine induces a robust and effective T-cell response. CONCLUSIONS: These results suggest that FSPs produced from RNA-based errors are potent neoantigens that could enable production of off-the-shelf shared antigen vaccines for solid tumors with efficacy comparable to that of PCVs.

Production of high-complexity frameshift neoantigen peptide microarrays.

Parallel measurement of large numbers of antigen-antibody interactions are increasingly enabled by peptide microarray technologies. Our group has developed an in situ synthesized peptide microarray of >400 000 frameshift neoantigens using mask-based photolithographic peptide synthesis, to profile patient specific neoantigen reactive antibodies in a single assay. The system produces 208 replicate mircoarrays per wafer and is capable of producing multiple wafers per synthetic lot to routinely synthesize over 300 million peptides simultaneously. In this report, we demonstrate the feasibility of the system for detecting peripheral-blood antibody binding to frameshift neoantigens across multiple synthetic lots.

High-resolution cryo-EM structures of outbreak strain human norovirus shells reveal size variations.

Noroviruses are a leading cause of foodborne illnesses worldwide. Although GII.4 strains have been responsible for most norovirus outbreaks, the assembled virus shell structures have been available in detail for only a single strain (GI.1). We present high-resolution (2.6- to 4.1-Å) cryoelectron microscopy (cryo-EM) structures of GII.4, GII.2, GI.7, and GI.1 human norovirus outbreak strain virus-like particles (VLPs). Although norovirus VLPs have been thought to exist in a single-sized assembly, our structures reveal polymorphism between and within genogroups, with small, medium, and large particle sizes observed. Using asymmetric reconstruction, we were able to resolve a Zn2+ metal ion adjacent to the coreceptor binding site, which affected the structural stability of the shell. Our structures serve as valuable templates for facilitating vaccine formulations.

Low-cost detection of norovirus using paper-based cell-free systems and synbody-based viral enrichment.

Noroviruses are a primary cause of gastroenteritis and foodborne illness with cases that affect millions of people worldwide each year. Inexpensive tests for norovirus that do not require sophisticated laboratory equipment are important tools for ensuring that patients receive timely treatment and for containing outbreaks. Herein, we demonstrate a low-cost colorimetric assay that detects norovirus from clinical samples by combining paper-based cell-free transcription-translation systems, isothermal amplification and virus enrichment by synbodies. Using isothermal amplification and cell-free RNA sensing with toehold switches, we demonstrate that the assay enables detection of norovirus GII.4 Sydney from stool down to concentrations of 270 aM in reactions that can be directly read by eye. Furthermore, norovirus-binding synbodies and magnetic beads are used to concentrate the virus and provide a 1000-fold increase in assay sensitivity extending its detection limit to 270 zM. These results demonstrate the utility of paper-based cell-free diagnostic systems for identification of foodborne pathogens and provide a versatile diagnostic assay that can be applied to the concentration, amplification and detection of a broad range of infectious agents.

Non-natural amino acid peptide microarrays to discover Ebola virus glycoprotein ligands.

We demonstrate a platform to screen a virus pseudotyped with Ebola virus glycoprotein (GP) against a library of peptides that contain non-natural amino acids to develop GP affinity ligands. This system could be used for rapid development of peptide-based antivirals for other emerging or neglected tropical infectious diseases.

A Simple Platform for the Rapid Development of Antimicrobials.

Recent infectious outbreaks highlight the need for platform technologies that can be quickly deployed to develop therapeutics needed to contain the outbreak. We present a simple concept for rapid development of new antimicrobials. The goal was to produce in as little as one week thousands of doses of an intervention for a new pathogen. We tested the feasibility of a system based on antimicrobial synbodies. The system involves creating an array of 100 peptides that have been selected for broad capability to bind and/or kill viruses and bacteria. The peptides are pre-screened for low cell toxicity prior to large scale synthesis. Any pathogen is then assayed on the chip to find peptides that bind or kill it. Peptides are combined in pairs as synbodies and further screened for activity and toxicity. The lead synbody can be quickly produced in large scale, with completion of the entire process in one week.

Synthetic Antibacterial Peptide Exhibits Synergy with Oxacillin against MRSA.

One proposed solution to the crisis of antimicrobial resistant (AMR) infections is the development of molecules that potentiate the activity of antibiotics for AMR bacteria, such as methicillin-resistant Staphylococcus aureus (MRSA). Rather than develop broad spectrum compounds, we developed a peptide that could potentiate the activity of a narrow spectrum antibiotic, oxacillin. In this way, the combination treatment could narrowly target the resistant pathogen and limit impact on host flora. We developed a peptide, ASU014, composed of a S. aureus binding peptide and a S. aureus inhibitory peptide conjugated to a branched peptide scaffold, which has modest activity against S. aureus but exhibits synergy with oxacillin for MRSA both in vitro and in a MRSA skin infection model. The low concentration of ASU014 and sub-MIC concentration of oxacillin necessary for activity suggest that this molecule is a candidate for future medicinal chemistry optimization.

Cross-Reactive Synbody Affinity Ligands for Capturing Diverse Noroviruses.

Noroviruses are the most common cause of acute gastroenteritis in the developed world. Noroviruses are a diverse group of nonenveloped RNA viruses that are continuously evolving. This leads to the rise of immunologically distinct strains of the same genotype on a frequent basis. This diversity presents a unique challenge for detection and tracking of new strains, with the continuous need for new norovirus affinity ligands. Our group developed a family of bivalent synbody affinity ligands using a virus-like particle (VLP) from the 2006 GII.4 Minerva strain of norovirus. We produced more than 20 synbodies with low nanomolar dissociation constants (KD < 10 nM) for GII.4 VLP. We measured binding affinity for four synbodies against VLPs from multiple GI and GII genotypes and found that the synbodies were broadly cross-reactive with affinities that ranged from 0.5 to 8 nM. We tested the ability of these synbodies to capture norovirus from dilute solutions and found that one synbody could capture GII.4 from a 200 000-fold dilution from a norovirus positive stool sample. When these synbodies were tested for the ability to capture of multiple genotypes, we found that four different genotypes were recognized. These data demonstrate that the synbody approach can generate multiple affinity ligands for future use in norovirus detection and possible therapeutic development.

Whole-Virus Screening to Develop Synbodies for the Influenza Virus.

There is an ongoing need for affinity agents for emerging viruses and new strains of current human viruses. We therefore developed a robust and modular system for engineering high-affinity synbody ligands for the influenza A/Puerto Rico/8/1934 H1N1 virus as a model system. Whole-virus screening against a peptide microarray was used to identify binding peptides. Candidate peptides were linked to bis-maleimide peptide scaffolds to produce a library of candidate influenza-binding synbodies. From this library, a candidate synbody, ASU1060, was selected and affinity-improved via positional substitution using d-amino acids to produce a new synbody, ASU1061, that bound H1N1 in an ELISA assay with a KD of <1 nM, comparable to that of a monoclonal antibody for neuraminidase (NA). We prepared a modified version of ASU1061 that contained an additional C-terminal peptide to simulate conjugation of the synbody to a carrier protein, called ASU1063, and found that H1N1 binding was unchanged. Subsequent work identified the synbody target as nucleoprotein (NP), a highly conserved protein in influenza, with a KD of <1 nM for ASU1063. This suggests that virus-binding synbodies can be conjugated to carrier proteins or other moieties that could improve the therapeutic profile of the resulting synbody. This method is a rapid process that offers a means of developing new affinity ligands to influenza and other viruses.

Conjugation Approach To Produce a Staphylococcus aureus Synbody with Activity in Serum.

Synbodies show promise as a new class of synthetic antibiotics. Here, we explore improvements in their activity and production through conjugation chemistry. Maleimide conjugation is a widely used conjugation strategy due to its high yield, selectivity, and low cost. We used this strategy to conjugate two antibacterial peptides to produce a bivalent antibacterial peptide, called a synbody that has bactericidal activity against methicillin resistant Staphylococcus aureus (MRSA). The synbody was prepared by conjugation of a partially d-amino acid substituted synthetic antibacterial peptide to a bis-maleimide scaffold. The synbody slowly degrades in serum, but also undergoes exchange reactions with other serum proteins, such as albumin. Therefore, we hydrolyzed the thiosuccinimide ring using a mild hydrolysis protocol to produce a new synbody with similar bactericidal activity. The synbody was now resistant to exchange reactions and maintained bactericidal activity in serum for 2 h. This work demonstrates that low-cost maleimide coupling can be used to produce antibacterial peptide conjugates with activity in serum.

A technology for developing synbodies with antibacterial activity.

The rise in antibiotic resistance has led to an increased research focus on discovery of new antibacterial candidates. While broad-spectrum antibiotics are widely pursued, there is evidence that resistance arises in part from the wide spread use of these antibiotics. Our group has developed a system to produce protein affinity agents, called synbodies, which have high affinity and specificity for their target. In this report, we describe the adaptation of this system to produce new antibacterial candidates towards a target bacterium. The system functions by screening target bacteria against an array of 10,000 random sequence peptides and, using a combination of membrane labeling and intracellular dyes, we identified peptides with target specific binding or killing functions. Binding and lytic peptides were identified in this manner and in vitro tests confirmed the activity of the lead peptides. A peptide with antibacterial activity was linked to a peptide specifically binding Staphylococcus aureus to create a synbody with increased antibacterial activity. Subsequent tests showed that this peptide could block S. aureus induced killing of HEK293 cells in a co-culture experiment. These results demonstrate the feasibility of using the synbody system to discover new antibacterial candidate agents.

Engineering a synthetic ligand for tumor necrosis factor-alpha.

One approach to prepare protein binding ligands is to join two low-affinity ligands that bind different sites on the target protein to create a high-affinity bivalent ligand. This typically requires some knowledge of the ligand binding site and requires exquisite orientation of the ligands in order to achieve maximum binding affinity. Here, we explored the limit of affinity improvement possible with no a priori knowledge of peptide binding site and with minimal effort spent in linking the lead peptides. We compared the affinity enhancement from linking two peptides with low affinity for tumor necrosis factor-α (TNFA) to the affinity enhancement from linking affinity improved versions of these peptides using several different scaffolds. We found that we achieved the highest affinity gain not by the precise positioning of the peptides, but rather by using affinity improved versions of the lead peptides to produce synbodies with apparent K(D)'s of 9 to 48 nM. Kinetic analysis showed that the binding kinetics of the synbody are strongly influenced by the kinetics of the starting peptide. This suggests that careful selection of peptides based on their kinetic profile prior to linking will influence the kinetics of the final binding agent.

Thermodynamic additivity of sequence variations: an algorithm for creating high affinity peptides without large libraries or structural information.

BACKGROUND: There is a significant need for affinity reagents with high target affinity/specificity that can be developed rapidly and inexpensively. Existing affinity reagent development approaches, including protein mutagenesis, directed evolution, and fragment-based design utilize large libraries and/or require structural information thereby adding time and expense. Until now, no systematic approach to affinity reagent development existed that could produce nanomolar affinity from small chemically synthesized peptide libraries without the aid of structural information. METHODOLOGY/PRINCIPAL FINDINGS: Based on the principle of additivity, we have developed an algorithm for generating high affinity peptide ligands. In this algorithm, point-variations in a lead sequence are screened and combined in a systematic manner to achieve additive binding energies. To demonstrate this approach, low-affinity lead peptides for multiple protein targets were identified from sparse random sequence space and optimized to high affinity in just two chemical steps. In one example, a TNF-α binding peptide with K(d) = 90 nM and high target specificity was generated. The changes in binding energy associated with each variation were generally additive upon combining variations, validating the basis of the algorithm. Interestingly, cooperativity between point-variations was not observed, and in a few specific cases, combinations were less than energetically additive. CONCLUSIONS/SIGNIFICANCE: By using this additivity algorithm, peptide ligands with high affinity for protein targets were generated. With this algorithm, one of the highest affinity TNF-α binding peptides reported to date was produced. Most importantly, high affinity was achieved from small, chemically-synthesized libraries without the need for structural information at any time during the process. This is significantly different than protein mutagenesis, directed evolution, or fragment-based design approaches, which rely on large libraries and/or structural guidance. With this algorithm, high affinity/specificity peptide ligands can be developed rapidly, inexpensively, and in an entirely chemical manner.

Discovery of high-affinity protein binding ligands--backwards.

BACKGROUND: There is a pressing need for high-affinity protein binding ligands for all proteins in the human and other proteomes. Numerous groups are working to develop protein binding ligands but most approaches develop ligands using the same strategy in which a large library of structured ligands is screened against a protein target to identify a high-affinity ligand for the target. While this methodology generates high-affinity ligands for the target, it is generally an iterative process that can be difficult to adapt for the generation of ligands for large numbers of proteins. METHODOLOGY/PRINCIPAL FINDINGS: We have developed a class of peptide-based protein ligands, called synbodies, which allow this process to be run backwards--i.e. make a synbody and then screen it against a library of proteins to discover the target. By screening a synbody against an array of 8,000 human proteins, we can identify which protein in the library binds the synbody with high affinity. We used this method to develop a high-affinity synbody that specifically binds AKT1 with a K(d)<5 nM. It was found that the peptides that compose the synbody bind AKT1 with low micromolar affinity, implying that the affinity and specificity is a product of the bivalent interaction of the synbody with AKT1. We developed a synbody for another protein, ABL1 using the same method. CONCLUSIONS/SIGNIFICANCE: This method delivered a high-affinity ligand for a target protein in a single discovery step. This is in contrast to other techniques that require subsequent rounds of mutational improvement to yield nanomolar ligands. As this technique is easily scalable, we believe that it could be possible to develop ligands to all the proteins in any proteome using this approach.

High-throughput screening in two dimensions: binding intensity and off-rate on a peptide microarray.

We report a high-throughput two-dimensional microarray-based screen, incorporating both target binding intensity and off-rate, which can be used to analyze thousands of compounds in a single binding assay. Relative binding intensities and time-resolved dissociation are measured for labeled tumor necrosis factor alpha (TNF-alpha) bound to a peptide microarray. The time-resolved dissociation is fitted to a one-component exponential decay model, from which relative dissociation rates are determined for all peptides with binding intensities above background. We show that most peptides with the slowest off-rates on the microarray also have the slowest off-rates when measured by surface plasmon resonance (SPR).