As a proof-of-concept for analyte-responsive polymers (ARPs) for biosensing, this study investigates how ligand binding changes the temperature-dependent dynamics and self-assembly of an elastin-like polymer (ELP) fused with a peptide recognition element for the small globular protein domain SH3. Using isothermal titration calorimetry, we characterized the apparent binding thermodynamics when one binding partner is fused with an ELP. Circular dichroism, dynamic light scattering, and temperature-dependent UV-vis spectroscopy were used to examine how ligand binding influences ARP conformational dynamics and phase behavior. SH3 binding was associated with an increase in transition temperature that reproduced in a complex medium and was consistent with predictions from a published model for ELP fusions. Addition of SH3 destabilized ARP assemblies, demonstrating that an ARP response can be specifically triggered by ligand binding. This work advances our understanding of how ligand binding and phase behavior are interdependent in systems involving intrinsically disordered proteins and their assemblies.

Peptides are important biomolecules, and their interactions with proteins make them useful in sensing and therapeutic applications. Computational peptide design methods can benefit from high-quality peptide-protein structures paired with thermodynamic data. The Predicted and Experimental Peptide Binding Information (PEPBI) database provides 329 predicted peptide-protein complexes, each based on an experimentally determined structure, with corresponding experimental measurements of changes in Gibbs free energy, enthalpy, and entropy. For each complex, 40 properties calculated using Rosetta’s Interface Analyzer are included. Complexes were selected for inclusion in PEPBI using eight stringent structural criteria, including peptide length (5–20 residues), structure resolution (≤2.0 Å), less than 30% sequence identity between complexes, and having a corresponding unbound protein structure in the Protein Data Bank with at least 90% sequence identity to the bound form with minimal changes in the binding pocket. PEPBI is expected to be of use for the development of computational methods for peptide design with desired binding properties to protein targets.

Antibody repurposing is the process of changing a known antibody so that it binds to a mutated antigen. One of the findings to emerge from the Coronavirus Disease 2019 (COVID-19) pandemic was that it was possible to repurpose neutralizing antibodies for Severe Acute Respiratory Syndrome, a related disease, to work for COVID-19. Thus, antibody repurposing is a possible pathway to prepare for and respond to future pandemics, as well as personalizing cancer therapies. For antibodies to be successfully repurposed, it is necessary to know both how antigen mutations disrupt their binding and how they should be mutated to recover binding, with this work describing an analysis to address the first of these topics. Every possible antigen point mutation in the interface of 246 antibody-protein complexes were analyzed using the Rosetta molecular mechanics force field. The results highlight a number of features of how antigen mutations affect antibody binding, including the effects of mutating critical hotspot residues versus other positions, how many mutations are necessary to be likely to disrupt binding, the prevalence of indirect effects of mutations on binding, and the relative importance of changing attractive versus repulsive energies. These data are expected to be useful in guiding future antibody repurposing experiments.

Developing a continuous insulin-monitoring biosensor is of great importance for both the cellular biomanufacturing industry and for treating diabetes mellitus. Such a sensor needs to be able to effectively monitor insulin across a range of temperatures and pHs and with varying concentrations of competing analytes. One of the two main components of any biosensor is the recognition element, which is responsible for interacting with the molecule of interest. Prior literature describes an insulin-binding peptide (IBP) that was reported to bind to insulin with a 3 nM affinity. Here, we used orthogonal and complementary electrochemical, computational, and thermodynamic characterization methods to evaluate IBP's appropriateness for use in a biosensor. Unfortunately, all three methods failed to produce evidence of IBP-insulin binding either on surfaces or in solution. This indicates that the binding exhibited in previous reports is likely restricted to a limited set of conditions and that IBP is not a suitable recognition element for a continuous insulin biosensor.

The last few years have seen the rapid proliferation of machine learning methods to design binding proteins. Although these methods have shown large increases in experimental success rates compared to prior approaches, the majority of their predictions fail when they are experimentally tested. It is evident that computational methods still struggle to distinguish the features of real protein binding interfaces from false predictions. Short molecular dynamics simulations of 20 antibody-protein complexes were conducted to identify features of interactions that should occur in binding interfaces. Intermolecular salt bridges, hydrogen bonds, and hydrophobic interactions were evaluated for their persistences, energies, and stabilities during the simulations. It was found that only the hydrogen bonds where both residues are stabilized in the bound complex are expected to persist and meaningfully contribute to binding between the proteins. In contrast, stabilization was not a requirement for salt bridges and hydrophobic interactions to persist. Still, interactions where both residues are stabilized in the bound complex persist significantly longer and have significantly stronger energies than other interactions. Two hundred and twenty real antibody-protein complexes and 8194 decoy complexes were used to train and test a random forest classifier using the features of expected persistent interactions identified in this study and the macromolecular features of interaction energy (IE), buried surface area (BSA), IE/BSA, and shape complementarity. It was compared to a classifier trained only on the expected persistent interaction features and another trained only on the macromolecular features. Inclusion of the expected persistent interaction features reduced the false positive rate of the classifier by two- to five-fold across a range of true positive classification rates.

Transmembrane receptors that endow mammalian cells with the ability to sense and respond to biomaterial-bound ligands will prove instrumental in bridging the fields of synthetic biology and biomaterials. Materials formed with thiol-norbornene chemistry are amenable to thiol-peptide patterning, and this study reports the rational design of synthetic receptors that reversibly activate cellular responses based on peptide-ligand recognition. This transmembrane receptor platform, termed Extracellular Peptide-ligand Dimerization Actuator (EPDA), consists of stimulatory or inhibitory receptor pairs that come together upon extracellular peptide dimer binding with corresponding monobody receptors. Intracellularly, Stimulatory EPDAs phosphorylate a substrate that merges two protein halves, whereas Inhibitory EPDAs revert split proteins back to their unmerged, inactive state via substrate dephosphorylation. To identify ligand-receptor pairs, over 2000 candidate monobodies were built in silico using PETEI, a novel computational algorithm we developed. The top 30 monobodies based on predicted peptide binding affinity were tested experimentally, and monobodies that induced the highest change in protein merging (green fluorescent protein, GFP) were incorporated in the final EPDA receptor design. In soluble form, stimulatory peptides induce intracellular GFP merging in a time- and concentration-dependent manner, and varying levels of green fluorescence were observed based on stimulatory and inhibitory peptide-ligand dosing. EPDA-programmed cells encapsulated in thiol-norbornene hydrogels patterned with stimulatory and inhibitory domains exhibited 3D activation or deactivation based on their location within peptide-patterned hydrogels. EPDA receptors can recognize a myriad of peptide-ligands bound to 3D materials, can reversibly induce cellular responses beyond fluorescence, and are widely applicable in biological research and regenerative medicine.

After approximately 60 years of work, the protein folding problem has recently seen rapid advancement thanks to the inventions of AlphaFold and RoseTTAFold, which are machine-learning algorithms capable of reliably predicting protein structures from their sequences. A key component in their success was the inclusion of pairwise interaction information between residues. As research focus shifts towards developing algorithms to design and engineer binding proteins, it is likely that knowledge of interaction features at protein interfaces can improve predictions. Here, 574 protein complexes were analyzed to identify the stability features of their pairwise interactions, revealing that interactions between pre-stabilized residues are a selected feature in protein binding interfaces. In a retrospective analysis of 475 de novo designed binding proteins with an experimental success rate of 19%, inclusion of pairwise interaction pre-stabilization parameters increased the frequency of identifying experimentally successful binders to 40%.

The inventions of AlphaFold and RoseTTAFold are revolutionizing computational protein science due to their abilities to reliably predict protein structures. Their unprecedented successes are due to the parallel consideration of several types of information, one of which is protein sequence similarity information. Sequence homology has been studied for many decades and depends on similarity matrices to define how similar or different protein sequences are to one another. A natural extension of predicting protein structures is predicting the interactions between proteins, but similarity matrices for protein-protein interactions do not exist. This study conducted a mutational analysis of 384 non-redundant antibody-protein antigen complexes to calculate antibody-protein interaction similarity matrices. Every important residue in each antibody and each antigen was mutated to each of the other 19 commonly occurring amino acids and the percentage changes in interaction energies were calculated using three force fields: CHARMM, Amber, and Rosetta. The data were used to construct six interaction similarity matrices, one for antibodies and another for antigens using each force field. The matrices exhibited both commonalities, such as mutations of aromatic and charged residues being the most detrimental, and differences, such as Rosetta predicting mutations of serines to be better tolerated than either Amber or CHARMM. A comparison to nine previously published similarity matrices for protein sequences revealed that the new interaction matrices are more similar to one another than they are to any of the previous matrices. The created similarity matrices can be used in force field specific applications to help guide decisions regarding mutations in protein-protein binding interfaces.

Despite the successes of antibodies as therapeutic binding proteins, they still face production and design challenges. Alternative binding scaffolds of smaller size have been developed to overcome these issues. A subset of these alternative scaffolds recognizes target molecules through mutations to a set of surface resides, which does not alter their backbone structures. While the computational design of antibodies for target epitopes has been explored in depth, the same has not been done for alternative scaffolds. The commonly used dock-and-mutate approach for binding proteins, including antibodies, is limited because it uses a constant sequence and structure representation of the scaffold. Docking fixed-backbone scaffolds with a varied group of surface amino acids increases the chances of identifying superior starting poses that can be improved with subsequent mutations. In this work, we have developed MutDock, a novel computational approach that simultaneously docks and mutates fixed backbone scaffolds for binding a target epitope by identifying a minimum number of hydrogen bonds. The approach is broadly divided into two steps. The first step uses pairwise distance alignment of hydrogen bond-forming areas of scaffold residues and compatible epitope atoms. This step considers both native and mutated rotamers of scaffold residues. The second step mutates clashing variable interface residues and thermodynamically unfavorable residues to create additional strong interactions. MutDock was used to dock two scaffolds, namely, Affibodies and DARPins, with ten randomly selected antigens. The energies of the docked poses were minimized and binding energies were compared with docked poses from ZDOCK and HADDOCK. The top MutDock poses consisted of higher and comparable binding energies than the top ZDOCK and HADDOCK poses, respectively. This work contributes to the discovery of novel binders based on smaller-sized, fixed-backbone protein scaffolds.

Cancer is the leading cause of death in the geriatric dog population. Currently, the use of immune checkpoint inhibitors (ICIs) such as anti-CTLA4 antibodies has markedly improved the prognosis of several cancers in their advanced stages. However, ICIs targeting CTLA4 blockade to treat canine cancer patients are yet to define. In this study, we sought to develop, characterize and assess whether chimeric heavy chain only antibodies (cHcAbs) against CTLA4 are viable therapeutic candidates for the treatment of canine cancers. Anti-CTLA4 nanobodies (Nbs) were identified from a yeast nanobody (Nb) library using magnetic-assisted cell sorting (MACS) and flow cytometry. cHcAbs were engineered by genetically fusing the DNA sequences coding for anti-CTLA4 Nbs with the Fc domain of the subclass B of canine IgG. Recombinant cHcAbs were purified from ExpiCHO-S cells. Stable cell lines expressing canine CTLA4 and FcγRI were used to elucidate the binding ability and specificity of cHcAbs. PBMCs isolated from healthy dogs were used to evaluate the ability of cHcAbs to activate canine PBMCs (cPBMCs). Novel Nbs were identified using the extracellular domain of canine CTLA4 protein to screen a fully synthetic yeast nanobody library. Purified Nbs bind specifically to natïve canine CTLA4. We report that chimeric HcAbs, which were engineered by fusing the anti-CTLA4 Nbs and Fc region of subclass B of canine IgG, were half the size of a conventional mAb and formed dimers. The chimeric HcAbs specifically binds both with canine CTLA4 and Fcγ receptors. As the binding of Nbs overlapped with the MYPPPY motif of canine CTLA4, these Nbs were expected to sterically disrupt the interaction of canine CTLA4 to B-7s. Like their human counterpart, canine CTLA4 was expressed on helper T cells and a small subset of cytotoxic T cells. Canine Tregs also constitutively expressed CTLA4, and stimulation with PMA/Ionomycin dramatically increased expression of CTLA4 on the cell surface. Stimulation of cPBMCs in the presence of agonistic anti-CD3 Ab and cHcAb6 significantly increased the expression of IFN-γ as compared to the isotype control. This study identifies a novel nanobody-based CTLA4 inhibitor for the treatment of canine cancer patients.

The detection of pathogen-specific antibodies remains a cornerstone of clinical diagnostics. Yet, many test exhibit undesirable performance or are completely lacking. Given this, we developed serum epitope repertoire analysis (SERA), a method to rapidly discover conserved, pathogen-specific antigens and their epitopes, and applied it to develop an assay for Chagas disease caused by the protozoan parasite Trypanosoma cruzi. Antibody binding peptide motifs were identified from 28 Chagas repertoires using a bacterial display random 12-mer peptide library and next-generation sequencing (NGS). Thirty-three motifs were selected and mapped to candidate Chagas antigens. In a blinded validation set (n = 72), 30/30 Chagas were positive, 30/30 non-Chagas were negative, and 1/12 Leishmania sp. was positive. After unblinding, a Leishmania cross-reactive epitope was identified and removed from the panel. The Chagas assay exhibited 100% sensitivity (30/30) and specificity (90/90) in a second blinded validation set including individuals with other parasitic infections. Amongst additional epitope repertoires with unknown Chagas serostatus, assay specificity was 99.8% (998/1000). Thus, the Chagas assay achieved a combined sensitivity and specificity equivalent or superior to diagnostic algorithms that rely on three separate tests to achieve high specificity. NGS-based serology via SERA provides an effective approach to discover antigenic epitopes and develop high performance multiplex serological assays.

Antibodies are medically and experimentally important immune system proteins. By binding to foreign molecules with high specificity and affinity, they act as flags that guide the immune response. The high-quality binding characteristics of antibodies have led to their widespread use as both experimental reagents and therapeutic molecules. Due to their importance, the interactions between antibodies and antigens has been an ongoing area of research. Here, a non-redundant database of 492 antibody – protein complexes was studied. Using the CHARMM and Amber force fields, the energies of the complexes were minimized and the interaction energies of individual residues were determined. This revealed that the percent contributions to binding energy of amino acids follows an exponential decay, with only a few residues being responsible for most of the energy.

Computational antibody engineering efforts to date have focused on improving binding affinities or biophysical characteristics. De novo design of antibodies binding specific epitopes could greatly accelerate discovery of therapeutics as compared to conventional immunization or synthetic library selection strategies. Here, we employed de novo complementarity determining region (CDR) design to engineer targeted antibody–antigen interactions using previously described in silico methods. CDRs predicted to bind the minimal FLAG peptide (Asp–Tyr–Lys–Asp) were grafted onto a single-chain variable fragment (scFv) acceptor framework. Fifty scFvs comprised of designed heavy and light or just heavy chain CDRs were synthesized and screened for peptide binding by phage ELISA. Roughly half of the designs resulted in detectable scFv expression. Four antibodies, designed entirely in silico, bound the minimal FLAG sequence with high specificity and sensitivity. When reformatted as soluble antigen-binding fragments (Fab), these clones expressed well, were predominantly monomeric and retained peptide specificity. In both formats, the antibodies bind the peptide only when present at the amino-terminus of a carrier protein and even conservative peptide amino acid substitutions resulted in a complete loss of binding. These results support in silico CDR design of antibody specificity as an emerging antibody engineering strategy.

Disease-specific antibodies can serve as highly effective biomarkers but have been identified for only a relatively small number of autoimmune diseases. A method was developed to identify disease-specific binding motifs through integration of bacterial display peptide library screening, next-generation sequencing (NGS) and computational analysis. Antibody specificity repertoires were determined by identifying bound peptide library members for each specimen using cell sorting and performing NGS. A computational algorithm, termed Identifying Motifs Using Next- generation sequencing Experiments (IMUNE), was developed and applied to discover disease- and healthy control-specific motifs. IMUNE performs comprehensive pattern searches, identifies patterns statistically enriched in the disease or control groups and clusters the patterns to generate motifs. Using celiac disease sera as a discovery set, IMUNE identified a consensus motif (QPEQPF[PS]E) with high diagnostic sensitivity and specificity in a validation sera set, in addition to novel motifs. Peptide display and sequencing (Display-Seq) coupled with IMUNE analysis may thus be useful to characterize antibody repertoires and identify disease-specific antibody epitopes and biomarkers.

BACKGROUND:Engineered antibodies with pH responsive cell surface target antigen-binding affinities that decrease at the acidic pH (5.5-5.8) within the endosomes have been found to have reduced susceptibility to degradation within the lysosomes and increased serum half-life. Such pH responsive recombinant antibodies have been developed for the treatment of cancer and cardiovascular disease. Engineered tenth type III human fibronectin (Fn3) domains are emerging as a class of target antigen-binding biopharmaceuticals that could complement or be superior to recombinant antibodies in a number of biomedical contexts. As such, there is strong motivation for demonstrating the feasibility of engineering Fn3s with pH responsive antigen binding behavior that could lead to improved Fn3 pharmacokinetics.RESULTS:A yeast surface-displayed Fn3 histidine (His) mutant library screening approach yielded epidermal growth factor receptor (EGFR)-binding Fn3 domains with EGFR binding affinities that markedly decrease at endosomal pH; the first reported case of engineering Fn3s with pH responsive antigen binding. Yeast surface-displayed His mutant Fn3s, which contain either one or two His mutations, have equilibrium binding dissociation constants (KDs) that increase up to four-fold relative to wild type when pH is decreased from 7.4 to 5.5. Assays in which Fn3-displaying yeast were incubated with soluble EGFR after ligand-free incubation in respective neutral and acidic buffers showed that His mutant Fn3 pH responsiveness is due to reversible changes in Fn3 conformation and/or EGFR binding interface properties rather than irreversible unfolding.CONCLUSIONS:We have established a generalizable method for efficiently constructing and screening Fn3 His mutant libraries that could enable both our laboratory and others to develop pH responsive Fn3s for use in a wide range of biomedical applications.

Proteins are an important class of biomolecules with applications spanning across biotechnology and medicine. In many cases, native proteins must be redesigned to improve various performance metrics by changing their amino acid sequences. Algorithms can help sharpen protein library design by focusing the library on sequences that optimize computationally accessible proxies. The Iterative Protein Redesign and Optimization (IPRO) suite of programs offers an integrated environment for (1) altering protein binding affinity and specificity, (2) grafting a binding pocket into an existing protein scaffold, (3) predicting an antibody's tertiary structure based on its sequence, (4) enhancing enzymatic activity, and (5) assessing the structure and binding energetics for a specific mutant. This manuscript provides an overview of the methods involved in IPRO, input language terminology, algorithmic details, software implementation specifics and application highlights. IPRO can be downloaded at http://maranas.che.psu.edu . © 2014 Wiley Periodicals, Inc.

Antibody-based therapeutics provides novel and efficacious treatments for a number of diseases. Traditional experimental approaches for designing therapeutic antibodies rely on raising antibodies against a target antigen in an immunized animal or directed evolution of antibodies with low affinity for the desired antigen. However, these methods remain time consuming, cannot target a specific epitope and do not lead to broad design principles informing other studies. Computational design methods can overcome some of these limitations by using biophysics models to rationally select antibody parts that maximize affinity for a target antigen epitope. This has been addressed to some extend by OptCDR for the design of complementary determining regions. Here, we extend this earlier contribution by addressing the de novo design of a model of the entire antibody variable region against a given antigen epitope while safeguarding for immunogenicity (Optimal Method for Antibody Variable region Engineering, OptMAVEn). OptMAVEn simulates in silico the in vivo steps of antibody generation and evolution, and is capable of capturing the critical structural features responsible for affinity maturation of antibodies. In addition, a humanization procedure was developed and incorporated into OptMAVEn to minimize the potential immunogenicity of the designed antibody models. As case studies, OptMAVEn was applied to design models of neutralizing antibodies targeting influenza hemagglutinin and HIV gp120. For both HA and gp120, novel computational antibody models with numerous interactions with their target epitopes were generated. The observed rates of mutations and types of amino acid changes during in silico affinity maturation are consistent with what has been observed during in vivo affinity maturation. The results demonstrate that OptMAVEn can efficiently generate diverse computational antibody models with both optimized binding affinity to antigens and reduced immunogenicity.