Molecular modeling of a T-cell receptor bound to a major histocompatibility complex molecule: implications for T-cell recognition.

The main functions of the T-cell receptor (TCR) involve its specific interaction with short and linear antigenic peptides bound to the major histocompatibility complex (MHC) molecules. In the absence of a 3D structure for TCR and for the TCR/peptide/MHC complex, several attempts to characterize the structural components of the TCR/peptide/MHC interaction have been made. However, this subject is still troublesome. In this paper a computer-based 3D model for a TCR/peptide/MHC complex (5C.C7/moth cytochrome c [MCC] peptide 93-103/I-Ek) was obtained. The complex surface shows a high complementarity between the 5C.C7 structure and the peptide/I-Ek molecule. The mapping of residues involved in the TCR/peptide/MHC interaction shows close agreement with mutational experiments (Jorgensen JL, Reay PA, Ehrich EW, Davis MM, 1992b, Annu Rev Immunol 10:835-873). Moreover, the results are consistent with a recent variability analysis of TCR sequences using three variability indexes (Almagro JC, Zenteno-Cuevas R, Vargas-Madrazo E, Lara-Ochoa F, 1995b, Int J Pept Protein Res 45:180-186). Accordingly, the 3D model of the 5C.C7/MCC peptide 93-103/I-Ek complex provides a framework to generate testable hypotheses about TCR recognition. Thus, starting from this model, the role played by each loop that forms the peptide/MHC binding site of the TCR is discussed.     

Structure-based discovery of a new affinity ligand to pancreatic alpha-amylase

A ligand useful for affinity capture of porcine pancreatic alpha-amylase was found by virtual screening of the commercially available compound data base MDL Available Chemicals Directory. Hits from the virtual screening were investigated for binding by nuclear magnetic resonance (NMR) and surface plasmon resonance. Selected compounds were tested for inhibition of the enzyme using a NMR-based assay. One of the binders found was covalently coupled to a chromatographic resin and a column, packed with this resin, could retain alpha-amylase, which subsequently was eluted by introduction of the known inhibitor acarbose to the elution buffer.     

Structure-based prediction of binding peptides to MHC class I molecules: application to a broad range of MHC alleles

Specific binding of antigenic peptides to major histocompatibility complex (MHC) class I molecules is a prerequisite for their recognition by cytotoxic T-cells. Prediction of MHC-binding peptides must therefore be incorporated in any predictive algorithm attempting to identify immunodominant T-cell epitopes, based on the amino acid sequence of the protein antigen. Development of predictive algorithms based on experimental binding data requires experimental testing of a very large number of peptides. A complementary approach relies on the structural conservation observed in crystallographically solved peptide-MHC complexes. By this approach, the peptide structure in the MHC groove is used as a template upon which peptide candidates are threaded, and their compatibility to bind is evaluated by statistical pairwise potentials. Our original algorithm based on this approach used the pairwise potential table of Miyazawa and Jernigan (Miyazawa S, Jernigan RL, 1996, J Mol Biol 256:623-644) and succeeded to correctly identify good binders only for MHC molecules with hydrophobic binding pockets, probably because of the high emphasis of hydrophobic interactions in this table. A recently developed pairwise potential table by Betancourt and Thirumalai (Betancourt MR, Thirumalai D, 1999, Protein Sci 8:361-369) that is based on the Miyazawa and Jernigan table describes the hydrophilic interactions more appropriately. In this paper, we demonstrate how the use of this table, together with a new definition of MHC contact residues by which only residues that contribute exclusively to sequence specific binding are included, allows the development of an improved algorithm that can be applied to a wide range of MHC class I alleles.     

Superantigen natural affinity maturation revealed by the crystal structure of staphylococcal enterotoxin G and its binding to T-cell receptor Vbeta8.2

The illnesses associated with bacterial superantigens (SAgs) such as food poisoning and toxic shock syndrome, as well as the emerging threat of purpura fulminans and community-associated methicillin-resistant S. aureus producer of SAgs, emphasize the importance of a better characterization of SAg binding to their natural ligands, which would allow the development of drugs or biological reagents able to neutralize their action. SAgs are toxins that bind major histocompatibility complex class II molecules (MHC-II) and T-cell receptors (TCR), in a nonconventional manner, inducing T-cell activation that leads to production of cytokines such as tumor necrosis factor and interleukin-2, which may result in acute toxic shock. Previously, we cloned and expressed a new natural variant of staphylococcal enterotoxin G (SEG) and evaluated its ability to stimulate in vivo murine T-cell subpopulations. We found an early, strong, and widespread stimulation of mouse Vbeta8.2 T-cells when compared with other SAgs member of the SEB subfamily. In search for the reason of the strong mitogenic potency, we determined the SEG crystal structure by X-ray crystallography to 2.2 A resolution and analyzed SEG binding to mVbeta8.2 and MHC-II. Calorimetry and SPR analysis showed that SEG has an affinity for mVbeta8.2 40 to 100-fold higher than that reported for other members of SEB subfamily, and the highest reported for a wild type SAg-TCR couple. We also found that mutations introduced in mVbeta8.2 to produce a high affinity mutant for other members of the SEB subfamily do not greatly affect binding to SEG. Crystallographic analysis and docking into mVbeta8.2 in complex with SEB, SEC3, and SPEA showed that the deletions and substitution of key amino acids remodeled the putative surface of the mVbeta8.2 binding site without affecting the binding to MHC-II. This results in a SAg with improved binding to its natural ligands, which may confer a possible evolutionary advantage for bacterial strains expressing SEG.     

Structure-based rational design of a Toll-like receptor 4 (TLR4) decoy receptor with high binding affinity for a target protein

Repeat proteins are increasingly attracting much attention as alternative scaffolds to immunoglobulin antibodies due to their unique structural features. Nonetheless, engineering interaction interface and understanding molecular basis for affinity maturation of repeat proteins still remain a challenge. Here, we present a structure-based rational design of a repeat protein with high binding affinity for a target protein. As a model repeat protein, a Toll-like receptor4 (TLR4) decoy receptor composed of leucine-rich repeat (LRR) modules was used, and its interaction interface was rationally engineered to increase the binding affinity for myeloid differentiation protein 2 (MD2). Based on the complex crystal structure of the decoy receptor with MD2, we first designed single amino acid substitutions in the decoy receptor, and obtained three variants showing a binding affinity (K(D)) one-order of magnitude higher than the wild-type decoy receptor. The interacting modes and contributions of individual residues were elucidated by analyzing the crystal structures of the single variants. To further increase the binding affinity, single positive mutations were combined, and two double mutants were shown to have about 3000- and 565-fold higher binding affinities than the wild-type decoy receptor. Molecular dynamics simulations and energetic analysis indicate that an additive effect by two mutations occurring at nearby modules was the major contributor to the remarkable increase in the binding affinities.     

Physics‐based enzyme design: Predicting binding affinity and catalytic activity

Computational enzyme design is an emerging field that has yielded promising success stories, but where numerous challenges remain. Accurate methods to rapidly evaluate possible enzyme design variants could provide significant value when combined with experimental efforts by reducing the number of variants needed to be synthesized and speeding the time to reach the desired endpoint of the design. To that end, extending our computational methods to model the fundamental physical–chemical principles that regulate activity in a protocol that is automated and accessible to a broad population of enzyme design researchers is essential. Here, we apply a physics‐based implicit solvent MM‐GBSA scoring approach to enzyme design and benchmark the computational predictions against experimentally determined activities. Specifically, we evaluate the ability of MM‐GBSA to predict changes in affinity for a steroid binder protein, catalytic turnover for a Kemp eliminase, and catalytic activity for α‐Gliadin peptidase variants. Using the enzyme design framework developed here, we accurately rank the most experimentally active enzyme variants, suggesting that this approach could provide enrichment of active variants in real‐world enzyme design applications. Proteins 2014; 82:3397–3409. © 2014 Wiley Periodicals, Inc.     

Using homology modeling to interrogate binding affinity in neutralization of ricin toxin by a family of single domain antibodies

In this report we investigated, within a group of closely related single domain camelid antibodies (V_HHs), the relationship between binding affinity and neutralizing activity as it pertains to ricin, a fast‐acting toxin and biothreat agent. The V1C7‐like V_HHs (V1C7, V2B9, V2E8, and V5C1) are similar in amino acid sequence, but differ in their binding affinities and toxin‐neutralizing activities. Using the X‐ray crystal structure of V1C7 in complex with ricin's enzymatic subunit (RTA) as a template, Rosetta‐based homology modeling coupled with energetic decomposition led us to predict that a single pairwise interaction between Arg29 on V5C1 and Glu67 on RTA was responsible for the difference in ricin toxin binding affinity between V1C7, a weak neutralizer, and V5C1, a moderate neutralizer. This prediction was borne out experimentally: substitution of Arg for Gly at position 29 enhanced V1C7's binding affinity for ricin, whereas the reverse (ie, Gly for Arg at position 29) diminished V5C1's binding affinity by >10 fold. As expected, the V5C1_R29G mutant was largely devoid of toxin‐neutralizing activity (TNA). However, the TNA of the V1C7_G29R mutant was not correspondingly improved, indicating that in the V1C7 family binding affinity alone does not account for differences in antibody function. V1C7 and V5C1, as well as their respective point mutants, recognized indistinguishable epitopes on RTA, at least at the level of sensitivity afforded by hydrogen‐deuterium mass spectrometry. The results of this study have implications for engineering therapeutic antibodies because they demonstrate that even subtle differences in epitope specificity can account for important differences in antibody function.     

Benchmarking a computational design method for the incorporation of metal ion‐binding sites at symmetric protein interfaces

The design of novel metal‐ion binding sites along symmetric axes in protein oligomers could provide new avenues for metalloenzyme design, construction of protein‐based nanomaterials and novel ion transport systems. Here, we describe a computational design method, symmetric protein recursive ion‐cofactor sampling (SyPRIS), for locating constellations of backbone positions within oligomeric protein structures that are capable of supporting desired symmetrically coordinated metal ion(s) chelated by sidechains (chelant model). Using SyPRIS on a curated benchmark set of protein structures with symmetric metal binding sites, we found high recovery of native metal coordinating rotamers: in 65 of the 67 (97.0%) cases, native rotamers featured in the best scoring model while in the remaining cases native rotamers were found within the top three scoring models. In a second test, chelant models were crossmatched against protein structures with identical cyclic symmetry. In addition to recovering all native placements, 10.4% (8939/86013) of the non‐native placements, had acceptable geometric compatibility scores. Discrimination between native and non‐native metal site placements was further enhanced upon constrained energy minimization using the Rosetta energy function. Upon sequence design of the surrounding first‐shell residues, we found further stabilization of native placements and a small but significant (1.7%) number of non‐native placement‐based sites with favorable Rosetta energies, indicating their designability in existing protein interfaces. The generality of the SyPRIS approach allows design of novel symmetric metal sites including with non‐natural amino acid sidechains, and should enable the predictive incorporation of a variety of metal‐containing cofactors at symmetric protein interfaces.     

Computational design of calmodulin mutants with up to 900-fold increase in binding specificity

Calmodulin (CaM) is a ubiquitous second messenger protein that regulates a variety of structurally and functionally diverse targets in response to changes in Ca²⁺ concentration. CaM-dependent protein kinase II (CaMKII) and calcineurin (CaN) are the prominent CaM targets that play an opposing role in many cellular functions including synaptic regulation. Since CaMKII and CaN compete for the available Ca²⁺/CaM, the differential affinity of these enzymes for CaM is crucial for achieving a balance in Ca²⁺ signaling. We used the computational protein design approach to modify CaM binding specificity for these two targets. Starting from the X-ray structure of CaM in complex with the CaM-binding domain of CaMKII, we optimized CaM interactions with CaMKII by introducing mutations into the CaM sequence. CaM optimization was performed with a protein design program, ORBIT, using a modified energy function that emphasized intermolecular interactions in the sequence selection procedure. Several CaM variants were experimentally constructed and tested for binding to the CaMKII and CaN peptides using the surface plasmon resonance technique. Most of our CaM mutants demonstrated small increase in affinity for the CaMKII peptide and substantial decrease in affinity for the CaN peptide compared to that of wild-type CaM. Our best CaM design exhibited an about 900-fold increase in binding specificity towards the CaMKII peptide, becoming the highest specificity switch achieved in any protein-protein interface through the computational protein design approach. Our results show that computational redesign of protein-protein interfaces becomes a reliable method for altering protein binding affinity and specificity.     

S100 to receptor for advanced glycation end-products binding assay: Looking for inhibitors

Secreted by tumor and stromal cells, S100 proteins exert their biological functions via the interaction with surface receptors. The most described receptor is the receptor for advanced glycation end-products (RAGE), thereby participating in the S100-dependent cell migration, invasion, tumor growth, angiogenesis and metastasis. Several approaches have been described for determining this interaction. Here we describe an easy, specific and highly reproducible ELISA-based method, by optimizing several parameters such as the binding and blocking buffer, interaction time and concentrations, directed to screen chemical and biological inhibitors of this interaction for S100A4, S100A7 and S100P proteins. The efficiency of the protocol was validated by using well described neutralizing agents of the RAGE receptor and of the S100A4 activity. The methodology described here will allow future works with other members of the S100 protein family and their receptors.     

Structure of a TCR with high affinity for self-antigen reveals basis for escape from negative selection

The failure to eliminate self-reactive T cells during negative selection is a prerequisite for autoimmunity. To escape deletion, autoreactive T-cell receptors (TCRs) may form unstable complexes with self-peptide-MHC by adopting suboptimal binding topologies compared with anti-microbial TCRs. Alternatively, escape can occur by weak binding between self-peptides and MHC. We determined the structure of a human autoimmune TCR (MS2-3C8) bound to a self-peptide from myelin basic protein (MBP) and the multiple sclerosis-associated MHC molecule HLA-DR4. MBP is loosely accommodated in the HLA-DR4-binding groove, accounting for its low affinity. Conversely, MS2-3C8 binds MBP-DR4 as tightly as the most avid anti-microbial TCRs. MS2-3C8 engages self-antigen via a docking mode that resembles the optimal topology of anti-foreign TCRs, but is distinct from that of other autoreactive TCRs. Combined with a unique CDR3β conformation, this docking mode compensates for the weak binding of MBP to HLA-DR4 by maximizing interactions between MS2-3C8 and MBP. Thus, the MS2-3C8-MBP-DR4 complex reveals the basis for an alternative strategy whereby autoreactive T cells escape negative selection, yet retain the ability to initiate autoimmunity.     

Structure-based rational design of staurosporine-based fluorescent probe with broad-ranging kinase affinity for kinase panel application

Selectivity profiling of compounds is important for kinase drug discovery. To this end, we aimed to develop a broad-range protein kinase assay by synthesizing a novel staurosporine-derived fluorescent probe based on staurosporine and kinase-binding related structural information. Upon structural analysis of staurosporine with kinases, a 4′-methylamine moiety of staurosporine was found to be located on the solvent side of the kinases, to which several linker units can be conjugated by either alkylation or acylation. However, such conjugation was suggested to reduce the binding affinities of the modified compound for several kinases, owing to the elimination of hydrogen bond donor moiety of NH-group from 4′-methylamine and/or steric hindrance by acyl moiety. Based on this structural information, we designed and synthesized a novel staurosporine-based probe without methyl group in order to retain the hydrogen bond donor, similar to unmodified staurosporine. The broad range of the kinase binding assay demonstrated that our novel fluorescent probe is an excellent tool for developing broad-ranging kinase binding assay.     

Consensus design of a NOD receptor leucine rich repeat domain with binding affinity for a muramyl dipeptide,a bacterial cell wall fragment

Repeat proteins have recently emerged as especially well‐suited alternative binding scaffolds due to their modular architecture and biophysical properties. Here we present the design of a scaffold based on the consensus sequence of the leucine rich repeat (LRR) domain of the NOD family of cytoplasmic innate immune system receptors. Consensus sequence design has emerged as a protein design tool to create de novo proteins that capture sequence‐structure relationships and interactions present in nature. The multiple sequence alignment of 311 individual LRRs, which are the putative ligand‐recognition domain in NOD proteins, resulted in a consensus sequence protein containing two internal and N‐ and C‐capping repeats named CLRR2. CLRR2 protein is a stable, monomeric, and cysteine free scaffold that without any affinity maturation displays micromolar binding to muramyl dipeptide, a bacterial cell wall fragment. To our knowledge, this is the first report of direct interaction of a NOD LRR with a physiologically relevant ligand.     

Structure-based design of novel human Pin1 inhibitors (III): Optimizing affinity beyond the phosphate recognition pocket

The design of potent Pin1 inhibitors has been challenging because its active site specifically recognizes a phospho-protein epitope. The de novo design of phosphate-based Pin1 inhibitors focusing on the phosphate recognition pocket and the successful replacement of the phosphate group with a carboxylate have been previously reported. The potency of the carboxylate series is now further improved through structure-based optimization of ligand–protein interactions in the proline binding site which exploits the H-bond interactions necessary for Pin1 catalytic function. Further optimization using a focused library approach led to the discovery of low nanomolar non-phosphate small molecular Pin1 inhibitors. Structural modifications designed to improve cell permeability resulted in Pin1 inhibitors with low micromolar anti-proliferative activities against cancer cells.     

Fast gap‐free enumeration of conformations and sequences for protein design

Despite significant successes in structure‐based computational protein design in recent years, protein design algorithms must be improved to increase the biological accuracy of new designs. Protein design algorithms search through an exponential number of protein conformations, protein ensembles, and amino acid sequences in an attempt to find globally optimal structures with a desired biological function. To improve the biological accuracy of protein designs, it is necessary to increase both the amount of protein flexibility allowed during the search and the overall size of the design, while guaranteeing that the lowest‐energy structures and sequences are found. DEE/A*‐based algorithms are the most prevalent provable algorithms in the field of protein design and can provably enumerate a gap‐free list of low‐energy protein conformations, which is necessary for ensemble‐based algorithms that predict protein binding. We present two classes of algorithmic improvements to the A* algorithm that greatly increase the efficiency of A*. First, we analyze the effect of ordering the expansion of mutable residue positions within the A* tree and present a dynamic residue ordering that reduces the number of A* nodes that must be visited during the search. Second, we propose new methods to improve the conformational bounds used to estimate the energies of partial conformations during the A* search. The residue ordering techniques and improved bounds can be combined for additional increases in A* efficiency. Our enhancements enable all A*‐based methods to more fully search protein conformation space, which will ultimately improve the accuracy of complex biomedically relevant designs. Proteins 2015; 83:1859–1877. © 2015 Wiley Periodicals, Inc.     

AB‐Bind: Antibody binding mutational database for computational affinity predictions

Antibodies (Abs) are a crucial component of the immune system and are often used as diagnostic and therapeutic agents. The need for high‐affinity and high‐specificity antibodies in research and medicine is driving the development of computational tools for accelerating antibody design and discovery. We report a diverse set of antibody binding data with accompanying structures that can be used to evaluate methods for modeling antibody interactions. Our Antibody‐Bind (AB‐Bind) database includes 1101 mutants with experimentally determined changes in binding free energies (ΔΔG) across 32 complexes. Using the AB‐Bind data set, we evaluated the performance of protein scoring potentials in their ability to predict changes in binding free energies upon mutagenesis. Numerical correlations between computed and observed ΔΔG values were low (r = 0.16–0.45), but the potentials exhibited predictive power for classifying variants as improved vs weakened binders. Performance was evaluated using the area under the curve (AUC) for receiver operator characteristic (ROC) curves; the highest AUC values for 527 mutants with |ΔΔG| > 1.0 kcal/mol were 0.81, 0.87, and 0.88 using STATIUM, FoldX, and Discovery Studio scoring potentials, respectively. Some methods could also enrich for variants with improved binding affinity; FoldX and Discovery Studio were able to correctly rank 42% and 30%, respectively, of the 80 most improved binders (those with ΔΔG < −1.0 kcal/mol) in the top 5% of the database. This modest predictive performance has value but demonstrates the continuing need to develop and improve protein energy functions for affinity prediction.