Brian Kuhlman

Epitope mapping provides critical insight into antibody-antigen interactions. Epitope mapping of autoantibodies from patients with autoimmune diseases can help elucidate disease immunogenesis and guide the development of antigen-specific therapies. Similarly, epitope mapping of commercial antibodies targeting known autoantigens enables the use of those antibodies to test specific hypotheses. Anti-Neutrophil Cytoplasmic Autoantibody (ANCA) vasculitis results from the formation of autoantibodies to multiple autoantigens, including myeloperoxidase (MPO), proteinase-3 (PR3), plasminogen (PLG), and peroxidasin (PXDN). To perform high-resolution epitope mapping of commercial antibodies to these autoantigens, we developed a novel yeast surface display library based on a series of >5000 overlapping peptides derived from their protein sequences. Using both FACS and magnetic bead isolation of …

Methods for producing Fabs and IgG bi-specific antibodies comprising expressing nucleic acids encoding designed residues in the CH1/CL interface are provided. Also provided are Fabs and IgG bi-specific antibodies produced according to the provided methods as well as nucleic acids, vectors and host cells encoding the same.

Amino acid mutations that lower a protein’s thermodynamic stability are implicated in numerous diseases, and engineered proteins with enhanced stability can be important in research and medicine. Computational methods for predicting how mutations perturb protein stability are, therefore, of great interest. Despite recent advancements in protein design using deep learning, in silico prediction of stability changes has remained challenging, in part due to a lack of large, high-quality training datasets for model development. Here, we describe ThermoMPNN, a deep neural network trained to predict stability changes for protein point mutations given an initial structure. In doing so, we demonstrate the utility of a recently released megascale stability dataset for training a robust stability model. We also employ transfer learning to leverage a second, larger dataset by using learned features extracted from ProteinMPNN, a …

The four dengue virus (DV) serotypes are mosquito-borne flaviviruses responsible for dengue fever. Live-attenuated DV vaccines that are in human trials have had mixed results with efficacy for just one or two serotypes and an increased dengue risk in some vaccinated populations. DV recombinant envelope (E) proteins as subunit vaccines have had limited success, likely due to DV wild type E protein (WT rE) is mostly monomeric at physiological temperature. Hence, it loses the quaternary structure epitopes recognized by neutralizing human antibodies (Ab) on the native dimer. We previously showed that with targeted amino acid mutations, we can form DV thermostable dimers (SD rE) displaying native quaternary epitopes targeted by strongly neutralizing Ab. Here we report on studies to test if Ab response can be directed to these quaternary epitopes by using SD rE as vaccine antigens. Since fusion loop (FL) Ab …

Protein side chain packing (PSCP) is a fundamental problem in the field of protein engineering, as high-confidence and low-energy conformations of amino acid side chains are crucial for understanding (and designing) protein folding, protein-protein interactions, and protein-ligand interactions. Traditional PSCP methods (such as the Rosetta Packer) often rely on a library of discrete side chain conformations, or rotamers, and a forcefield to guide the structure to low-energy conformations. Recently, deep learning (DL) based methods (such as DLPacker, AttnPacker, and DiffPack) have demonstrated state-of-the-art predictions and speed in the PSCP task. Building off the success of geometric graph neural networks for protein modeling, we present the Protein Invariant Point Packer (PIPPack) which effectively processes local structural and sequence information to produce realistic, idealized side chain coordinates …

Promiscuous enzymes that modify peptides and proteins are powerful tools for labeling biomolecules; however, directing these modifications to desired substrates can be challenging. Here, we use computational interface design to install a substrate recognition domain adjacent to the active site of a promiscuous enzyme, catechol O-methyltransferase. This design approach effectively decouples substrate recognition from the site of catalysis and promotes modification of peptides recognized by the recruitment domain. We determined the crystal structure of this novel multidomain enzyme, SH3-588, which shows that it closely matches our design. SH3-588 methylates directed peptides with catalytic efficiencies exceeding the wild-type enzyme by over 1,000-fold, whereas peptides lacking the directing recognition sequence do not display enhanced efficiencies. In competition experiments, the designer enzyme …

There has been considerable progress in the development of computational methods for designing protein–protein interactions, but engineering high-affinity binders without extensive screening and maturation remains challenging. Here, we test a protein design pipeline that uses iterative rounds of deep learning (DL)-based structure prediction (AlphaFold2) and sequence optimization (ProteinMPNN) to design autoinhibitory domains (AiDs) for a PD-L1 antagonist. With the goal of creating an anticancer agent that is inactive until reaching the tumor environment, we sought to create autoinhibited (or masked) forms of the PD-L1 antagonist that can be unmasked by tumor-enriched proteases. Twenty-three de novo designed AiDs, varying in length and topology, were fused to the antagonist with a protease-sensitive linker, and binding to PD-L1 was measured with and without protease treatment. Nine of the fusion …

Immune checkpoint inhibitors that bind to the cell surface receptor PD‐L1 are effective anti‐cancer agents but suffer from immune‐related adverse events as PD‐L1 is expressed on both healthy and cancer cells. To mitigate toxicity, researchers are testing prodrugs that have low affinity for checkpoint targets until activated with proteases enriched in the tumor microenvironment. Here, we engineer a prodrug form of a PD‐L1 inhibitor. The inhibitor is a soluble PD‐1 mimetic that was previously engineered to have high affinity for PD‐L1. In the basal state, the binding surface of the PD‐1 mimetic is masked by fusing it to a soluble variant of its natural ligand, PD‐L1. Proteolytic cleavage of the linker that connects the mask to the inhibitor activates the molecule. To optimize the mask so that it effectively blocks binding to PD‐L1 but releases upon cleavage, we tested a set of mutants with varied affinity for the inhibitor. The …

In our manuscript entitled “Catalysis by a De Novo Zinc-Mediated Protein Interface: Implications for Natural Enzyme Evolution and Rational Enzyme Engineering”, we reported that the engineered protein MID1-zinc hydrolyzes 4-nitrophenyl acetate and 4-nitrophenyl phosphate. Since publishing this result, we have collaborated with Donald Hilvert’s group at ETH Zürich to optimize the enzyme and uncovered a discrepancy with our prior results. As part of the collaboration, MID1-zinc was produced using solid-phase peptide synthesis and the enzyme assays were repeated with 4-nitrophenyl acetate and 4-nitrophenyl phosphate. As we observed previously, MID1-zinc catalyzes the hydrolysis of 4-nitrophenyl acetate, but unexpectedly, no activity was observed with 4-nitrophenyl phosphate. In our original study, MID1-zinc was recombinantly produced in bacteria. We now believe that the activity that we reported for 4 …

The de novo design of functional proteins requires specification of tertiary structure and incorporation of molecular binding sites. Here, we develop an inside-out design strategy in the molecular modeling program Rosetta that begins with amino acid side chains from one or two α-helices making well-defined contacts with a ligand. A full-sized protein is then built around the ligand by adding additional helices that promote the formation of a protein core and allow additional contacts with the ligand. The protocol was tested by designing 12 zinc-binding proteins, each with 4–5 helices. Four of the designs were folded and bound to zinc with equilibrium dissociation constants varying between 95 nM and 1.1 μM. The design with the tightest affinity for zinc, N12, adopts a unique conformation in the folded state as assessed with nuclear magnetic resonance (NMR) and the design model closely matches (backbone root-mean …

Many protein therapeutics are competitive inhibitors that function by binding to endogenous proteins and preventing them from interacting with native partners. One effective strategy for engineering competitive inhibitors is to graft structural motifs from a native partner into a host protein. Here, we develop and experimentally test a computational protocol for embedding binding motifs in de novo designed proteins. The protocol uses an “inside‐out” approach: Starting with a structural model of the binding motif docked against the target protein, the de novo protein is built by growing new structural elements off the termini of the binding motif. During backbone assembly, a score function favors backbones that introduce new tertiary contacts within the designed protein and do not introduce clashes with the target binding partner. Final sequences are designed and optimized using the molecular modeling program Rosetta …

Full wwPDB X-ray Structure Validation Report i Page 1 Full wwPDB X-ray Structure Validation Report i O Feb 27, 2014 – 02:08 PM GMT PDB ID : 3MI0 Title : Crystal Structure of Mycobacterium Tuberculosis Proteasome at 2.2 A Authors : Li, D.; Li, H. Deposited on : 2010-04-09 Resolution : 2.20 Å(reported) This is a full wwPDB validation report for a publicly released PDB entry. We welcome your comments at validation@mail.wwpdb.org A user guide is available at http://wwpdb.org/ValidationPDFNotes.html The following versions of software and data (see references) were used in the production of this report: MolProbity : 4.02b-467 Mogul : 1.15 2013 Xtriage (Phenix) : dev-1323 EDS : stable22639 Percentile statistics : 21963 Refmac : 5.8.0049 CCP4 : 6.3.0 (Settle) Ideal geometry (proteins) : Engh & Huber (2001) Ideal geometry (DNA, RNA) : Parkinson et. al. (1996) Validation Pipeline (wwPDB-VP) : stable22683 Page …

There has been considerable progress in the development of computational methods for designing protein-protein interactions, but engineering high-affinity binders without extensive screening and maturation remains challenging. Here, we test a protein design pipeline that uses iterative rounds of deep learning (DL)-based structure prediction (AlphaFold2) and sequence optimization (ProteinMPNN) to design autoinhibitory domains (AiDs) for a PD-L1 antagonist. Inspired by recent advances in therapeutic design, we sought to create autoinhibited (or masked) forms of the antagonist that can be conditionally activated by proteases. Twenty-three de novo designed AiDs, varying in length and topology, were fused to the antagonist with a protease sensitive linker, and binding to PD-L1 was tested with and without protease treatment. Nine of the fusion proteins demonstrated conditional binding to PD-L1 and the top …

Correction to “The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design” Page 1 Correction to “The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design” Rebecca F. Alford, Andrew Leaver-Fay, Jeliazko R. Jeliazkov, Matthew J. O’Meara, Frank P. DiMaio, Hahnbeom Park, Maxim V. Shapovalov, P. Douglas Renfrew, Vikram K. Mulligan, Kalli Kappel, Jason W. Labonte, Michael S. Pacella, Richard Bonneau, Philip Bradley, Roland L. Dunbrack, Jr., Rhiju Das, David Baker, Brian Kuhlman, Tanja Kortemme, and Jeffrey J. Gray* J. Chem. Theory Comput. 2017, 13 (6), 3031−3048. DOI: 10.1021/acs.jctc.7b00125 Cite This: J. Chem. Theory Comput. 2022, 18, 4594−4594 Read Online ACCESS Metrics & More Article Recommendations In the initially published version of this article, the potential for the disulfide dihedral CαCβSS was incorrect in Figure 4E, with a sign error in A_{2,…

Many proteins have low thermodynamic stability, which can lead to low expression yields and limit functionality in research, industrial and clinical settings. This article introduces two, web‐based tools that use the high‐resolution structure of a protein along with the Rosetta molecular modeling program to predict stabilizing mutations. The protocols were recently applied to three genetically and structurally distinct proteins and successfully predicted mutations that improved thermal stability and/or protein yield. In all three cases, combining the stabilizing mutations raised the protein unfolding temperatures by more than 20°C. The first protocol evaluates point mutations and can generate a site saturation mutagenesis heatmap. The second identifies mutation clusters around user‐defined positions. Both applications only require a protein structure and are particularly valuable when a deep multiple sequence alignment is …

Using the molecular modeling program Rosetta, we designed a de novo protein, called SEWN0.1, which binds the heterotrimeric G protein Gαq. The design is helical, well‐folded, and primarily monomeric in solution at a concentration of 10 μM. However, when we solved the crystal structure of SEWN0.1 at 1.9 Å, we observed a dimer in a conformation incompatible with binding Gαq. Unintentionally, we had designed a protein that adopts alternate conformations depending on its oligomeric state. Recently, there has been tremendous progress in the field of protein structure prediction as new methods in artificial intelligence have been used to predict structures with high accuracy. We were curious if the structure prediction method AlphaFold could predict the structure of SEWN0.1 and if the prediction depended on oligomeric state. When AlphaFold was used to predict the structure of monomeric SEWN0.1, it produced …

Correction to “The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design” Page 1 Correction to “The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design” Rebecca F. Alford, Andrew Leaver-Fay, Jeliazko R. Jeliazkov, Matthew J. O’Meara, Frank P. DiMaio, Hahnbeom Park, Maxim V. Shapovalov, P. Douglas Renfrew, Vikram K. Mulligan, Kalli Kappel, Jason W. Labonte, Michael S. Pacella, Richard Bonneau, Philip Bradley, Roland L. Dunbrack, Jr., Rhiju Das, David Baker, Brian Kuhlman, Tanja Kortemme, and Jeffrey J. Gray* J. Chem. Theory Comput. 2017, 13 (6), 3031−3048. DOI: 10.1021/acs.jctc.7b00125 Cite This: J. Chem. Theory Comput. 2022, 18, 4594−4594 Read Online ACCESS Metrics & More Article Recommendations In the initially published version of this article, the potential for the disulfide dihedral CαCβSS was incorrect in Figure 4E, with a sign error in A_{2,…

Provided herein are proteins comprising T-cell receptor (TCR) constant domains with one or more stabilization mutations, nucleic acids encoding such proteins, and methods of making and using such proteins.

Engineered, light-sensitive protein switches are used to interrogate a broad variety of biological processes. These switches are typically constructed by genetically fusing naturally occurring light-responsive protein domains with functional domains from other proteins. Protein activity can be controlled using a variety of mechanisms including light-induced colocalization, caging, and allosteric regulation. Protein design efforts have focused on reducing background signaling, maximizing the change in activity upon light stimulation, and perturbing the kinetics of switching. It is common to combine structure-based modeling with experimental screening to identify ideal fusion points between domains and discover point mutations that optimize switching. Here, we introduce commonly used light-sensitive domains and summarize recent progress in using them to regulate protein activity.

The present invention provides compositions and methods of use comprising a stabilized recombinant E glycoprotein comprising a flavivirus E glycoprotein backbone, which comprises amino acid substitutions that stabilize the E glycoprotein in dimer conformation under physiological conditions.